There are two classes of electronic devices capable of generating  radiation. The first class is high-power devices primarily based on the principle of electron tubes, capable of producing signal power ranging from 10 to 1. These high-power devices are typically necessary for specialized applications, such as satellite communications. Another class consists of semiconductor devices, constructed from various semiconductor materials, which provide a more compact and cost-effective solution. While they may have limitations in terms of transmission power, semiconductor devices are suitable for THz communications and sensing <cit.>.  III-V-semiconductors such as GaAs, GaN, and InP, are commonly used to fabricate THz components. In addition, SiGe based devices show promising performance. Compared to the aforementioned technologies, the CMOS technology has limitations such as low transmit power and high noise figure. However, recent research argues that the CMOS technology may also be suitable for THz communications and sensing applications <cit.>.

Most semiconductor-based  transceivers follow the conventional design for lower frequencies, such as the heterodyne transceiver approach, as shown in  <ref>. This entails passing a modulated baseband signal to the front-end circuitry. Then, it is up-converted to the  carrier frequency by mixing with the LO signal. The signal is amplified and sent over an antenna. At the receiver, the same steps are applied in a reverse order, and the down-converted signal is passed to the baseband circuitry for further processing. The most critical aspect lies in LO generation, as it involves synthesizing a THz signal. To achieve this, a multiplexer is employed to upconvert a high-frequency signal into the  range. However, it's important to note that each multiplexer stage introduces additional inter-modulation products, which in turn raise the noise figure and distort the generated signal. Consequently, the number of multiplexer stages becomes a limiting factor for THz signal generation.

overview of Semiconductor transmitter and receiver Technologies

Semiconductor Techno.                      Freq. []       Transmit Power []      Noise Figure []     Throughput []     Reference      Pub. Year
70 InP           300             10          9.8         20      <cit.>      2016

80 InP           300             -3          N/A               120     <cit.>          2020

130 InP          590             -2          —               —           <cit.>          2016

25 InP           850             -0.3        12.7        —           <cit.>           2017

130 SiGe         D-band                9           9           48      <cit.>        2016

130 SiGe         D-band                10          10          —           <cit.>          2020

130 SiGe         240             8.5         —               50      <cit.>              2017

130 SiGe         300             -4.1        —               —           <cit.>              2021

130 SiGe         400             -20         —               —           <cit.>                2012

28 CMOS          240             0.7         —               —           <cit.>             2022

40 CMOS          300             -5.5        —               105     <cit.>         2017

40 CMOS          300             —               27          32      <cit.>            2017

28 CMOS          390             -5.4        —               28      <cit.>        2020

Table <ref> offers a summary of the literature on the transmitter and receiver front end in terms of different semiconductor technologies. Among III-V semiconductor materials, InP components demonstrate superior results in terms of output power and noise figure. Furthermore, InP transistors can reach a maximum oscillating frequency $f_\mathrm{max}$ of up to 1.5 <cit.>. A transceiver design based on a high-electron-mobility transistor (HEMT) was reported. HEMT supports THz frequencies up to 850, with particular attention given to the frequency band around 300 <cit.>.

SiGe and CMOS are both silicon-based technologies suitable for the THz frequency band. Due to the intrinsic characteristics of silicon, the maximum oscillation frequency is limited below 1. There are studies that show HBT based on 130 SiGe process reaching $f_\mathrm{max}$ of 620 <cit.> as well as 720 <cit.>. The SiGe components fabricated for HBT exhibit advantages such as good linearity, high gain, and low noise. However, the power gain is limited, which prevents the operation above 500. Recent research focused particularly on the components and devices for THz communications and sensing over the D-band, which refers to the range of frequencies around 110170. As a result, novel transceiver design was introduced such as those presented in <cit.>. The authors of <cit.> demonstrated a complete communication link that realizes a high throughput of 200. Some researchers present their transceiver design operating around 300 <cit.>. A detailed survey on SiGe transceiver for different purposes is provided in <cit.>.

THz devices built from CMOS exhibit lower performance compared to other semiconductor technologies. CMOS FET can only reach a maximum oscillating frequency of around 450, with limited power gain. Nevertheless, CMOS technology is known for its low production costs, which is its major advantage. The transceivers operating at 240 <cit.> or 300 <cit.> were implemented. The authors of <cit.> showcased some CMOS transcivers operating in 300, which achieve high data rates such as 105 or 32. The highest reported operating frequency is 390, reaching a data throughput of 28. Further achievements of CMOS transceiver are summarized in <cit.>.

§.§.§ Photonic Devices

node[twoportshape, anchor=right, ld, t=5emWavelength-
tunable laser](ld1)
– ++ (1.5,0)
– ++ (0,-.75)
– ++ (0,-.75)
– ++ (-1.5,0)
node[twoportshape, anchor=right, ld, t=5emWavelength-
tunable laser](ld2)
(ld1.right) node[below right]$f_1$
(ld2.right) node[below right]$f_2$
– ++ (2,0)
node[twoportshape, mod, t=4emOptical
– ++ (2.5,0)
node[twoportshape, pd, t=4emUTC-PD](pd)
– ++ (1.75,0)
to[short, -o] ++ (0,.75)
(pd.right) node[below right]$f_{\si{THz}} = f_1 - f_2$

node[twoportshape, anchor = right, ld, t=5emWavelength-
tunable laser](ld1)
– ++ (2,0)
node[twoportshape, mod, t=4emOptical
– ++ (2,0)
– ++ (0,-.75)
– ++ (0,-.75)
– ++ (-4,0)
node[twoportshape, anchor=right, ld, t=5emWavelength-
tunable laser](ld2)
to[short, -o] ++ (0,.75)
(ld1.right) node[below right]$f_1$
(ld2.right) node[below right]$f_2$
– ++ (2,0)

node[twoportshape, pd, t=4emUTC-PD](pd)
– ++ (1.75,0)
(pd.right) node[below right]$f_{\si{THz}} = f_1 - f_2$

Configuration of photo-mixing transmitters with (a) Double sub-carrier modulation and (b) Single sub-carrier modulation <cit.>.

To transition from extremely high-frequency photonic radiation to a lower THz frequency, an optical frequency downconverter, commonly known as photomixing, is required. Illustrated in  <ref>, this process involves feeding two laser signals with frequencies $f_1$ and $f_2$ towards a photomixing diode, such as the UTC-PD <cit.>. Subsequently, the photodiode, akin to traditional high-frequency mixing, generates a  signal at frequency $f_{\si{\THz}} = f_1 - f_2$.
Photomixing provides high tunability and modulation bandwidth. Also, a variety of complex modulation schemes may be implemented with moderate efforts, as compared to electronic solutions.  <ref> shows two modulation approaches, modulation of both laser signals as well as the single laser signal only <cit.>.  A further advantage is the ability to deliver an optical signal over a large distance by means of an optical fiber. Thus, optical signal generation and modulation may be performed separated from the  signal generation, which may yield flexibility to the transmission system <cit.>.

Another photonic method of generating  signals is through QCL. Conventional lasers emit photons through the recombination of electron-hole pairs across the band gap. Thus, the lower bound for the frequency of the emitted photons is defined by the band gap, which limits the ability of the lasers to work below the IR band. In contrast, QCLs are unipolar <cit.>. The photons are emitted by quantum jumps of electrons between two different energy levels. These energy levels are defined by the structure of the quantum wells. This provides the possibility to tune the frequency of the laser. Due to the low energy gap between the energy levels, QCL are well suited for mid-IR applications. In recent decades, there was also a big progress on QCL development for the far-IR or  range <cit.>. QCLs are able to cover the frequency band from 15 with a peak radiation power of 1 <cit.>. However, the operating frequency for QCL is in the cryogenic range, which limits their applications.

§ BEAM-FORMING AND ALIGNMENT FOR THZ COMMUNICATIONS AND SENSING

The use of the THz band can alleviate the problem of spectrum scarcity and facilitate novel applications, such as nano-scale networks and in-device communications. However, its practical use is challenged by large propagation losses, which generally lead to  very short distances of signal transmission <cit.>. The main causes of this problem include the high spreading loss that grows quadratically with carrier frequency, the gaseous absorption due to oxygen molecules and water vapor in the atmosphere, and the adverse effects of weather conditions, as discussed in the previous section. Such a propagation loss can reach hundreds of decibels per kilometer or even higher. Additionally, this problem is further aggravated by the following two factors <cit.>:

* Strong thermal noise: Noise power is proportional to signal bandwidth with  the constant power density. Therefore, the unique advantage of massive bandwidth at the THz band imposes a side effect of strong thermal noise.

* Hardware constraint: The transmit power at the THz band is quite constrained since the output power decreases with frequency and is at the level of decibel-milli-Watts in the foreseeable future. Hence,  raising power to extend the communications distance is not feasible <cit.>.

To extend the signal transmission distance beyond a few meters, high-gain directional antennas are necessary to compensate for such a high propagation loss in THz communications and sensing. Thanks to tiny wavelengths,  massive numbers of elements can be tightly packed in a small area to generate high beamforming gains <cit.>. In this section, we will discuss the cutting-edge antenna forms, novel beamforming techniques, and necessary beam alignment schemes at the THz band <cit.>.

§.§ Ultra-Massive MIMO

Since the length of a resonant antenna is typically in the order of the wavelength at the resonance frequency, the dimension of an array with tens of elements is a few square meters and a few square centimeters at the sub-6G and mmWave bands <cit.>, respectively. Moving to the THz band, the antenna length further reduces. Hundreds of elements can be compacted in an array within a few centimeters using conventional metallic materials. However, this number is not sufficient to overcome the huge propagation loss suffered by THz signals <cit.>.

Taking advantage of  SPP waves <cit.>, the inter-element separation of an array can be further reduced to the SPP wavelength, which is much smaller than the EM wavelength. Consequently,  nanomaterials that support the propagation of SPP waves, such as graphene and metamaterials, are employed to further improve the hardware compactness.  Graphene, a one-atom-thick carbon nanomaterial with unprecedented mechanical, electrical, and optical properties, is employed to fabricate plasmonic nano-antennas with a smaller size of almost two orders of magnitude compared to metallic THz antennas <cit.>. In particular, thousands of graphene-based nano-antennas can be embedded in a few square millimeters at 1. The emergence of nano-antennas paves the way for building very large-scale arrays for THz communications. In 2016, Akyildiz and Jornet <cit.> presented the concept of UMMIMO communications, and demonstrated a $1024\times 1024$ system where both the transmitter and receiver are equipped with an array of $1024$ nano-antennas.

A massive number of elements impose challenges such as prohibitive power consumption and high hardware complexity <cit.>. It is worth rethinking the array architecture and beamforming schemes in UMMIMO systems at the THz band.
Fully digital beamforming can generate a desired beam but it leads to unaffordable energy consumption and hardware cost since each antenna in a large-scale array needs its dedicated RF chain.  This motivates the study of analog beamforming with low complexity. By employing analog phase shifters,  only a single RF chain is needed, substantially lowering hardware and power costs.  Nevertheless, the analog architecture supports only a single stream, limiting data rates and the number of users. As a compromise of these two forms, hybrid digital-analog architecture is the best choice for THz from the perspective of performance and complexity trade-off. Combining an analog-shifter network with a few RF chains, hybrid beamforming can significantly reduce hardware cost and low energy consumption, while achieving comparable performance as digital beamforming.

Although hybrid beamforming has been extensively studied for the sub-6GHz and mmWave bands <cit.>, the peculiarities of the THz band, such as channel sparsity <cit.> and beam squint <cit.>, impose many difficulties for designing an UMMIMO system. Currently, many new forms of hybrid beamforming are discussed in the literature, including AoSA to balance the power consumption and data rate, widely-spaced multi-subarray to overcome the low spatial multiplexing gain due to channel sparsity, and true-time-delay-based hybrid beamforming to address the problem of beam squint <cit.>.

§.§.§ Array of Subarrays

In a hybrid architecture, the connection between elements and RF chains has two basic forms: FC and AoSA <cit.>. In the FC hybrid beamforming, each element is fully connected to all RF chains via a signal combiner, and the signal of an RF chain radiates over all antenna elements via an individual group of phase shifters.  Any RF chain should have the capability to drive the entire large-scale antenna array, which is power-aggressive. Particularly, the use of a large number of phase shifters and combiners will exacerbate the problems of high hardware cost and power consumption. In contrast, all elements in  AoSA are divided into disjoint subsets called subarrays, and a subarray is only accessible to one specific RF chain <cit.>. AoSA conducts signal processing at a subarray level with fewer phase shifters, such that hardware cost, power consumption, and signal power loss can be dramatically reduced. In addition, beamforming and spatial multiplexing can be jointly optimized by cooperating with precoding in the baseband.

Recent literature shows the strong interest to exploit an array of subarrays. For instance,   Lin and Li  published a series of works on this topic. In <cit.>, they analyzed the ergodic capacity of an indoor single-user THz communications system and obtained an upper bound, providing guidance on the design of antenna subarray size and numbers for certain long-term data rate requirements with different distances. An adaptive beamforming scheme, which considers the impact of transmission distance,  was proposed for multi-user THz communications in <cit.>. Therein the array-of-subarray structure for multi-user sub-THz communications was considered and its spectral and energy efficiency were analyzed. They  then showcased a THz-based multi-user system for indoor usage that uses an array-of-subarrays architecture to handle hardware restrictions and channel characteristics in the THz band, which has shown a great advantage by comparing with the FC structure in both spectral and energy efficiency <cit.>. In <cit.>, Tarboush et al.  proposed an accurate stochastic simulator of statistical THz channels, named TeraMIMO, aiming at catalyzing the research of UMMIMO antenna configurations. TeraMIMO adopted the AoSA antenna structure for hybrid beamforming and accounted for spatial sparsity.

To further reduce the complexity, various alternating optimization algorithms have been proposed for the AoSA architectures <cit.>. In contrast to the FC architecture, the AoSA architecture has a restricted number of phase shifters that equals the number of antennas. However, since the RF chains and antennas are connected exhaustively, the FC architecture can achieve data rates comparable to those of the optimal fully-digital beamforming architecture. Conversely, the data rate of the AoSA architecture is significantly lower compared to that of the FC architecture. This is attributed to the partial interconnection between antennas and RF chains. Hence, we need to balance the power consumption and data rate of the THz hybrid beamforming, inspired by the challenge of designing large-scale antenna arrays in THz UMMIMO systems. To address this issue, some researchers introduced a new form of hybrid beamforming called DAoSA <cit.>, which features a flexible hardware connection. DAoSA achieves a good balance between power consumption and data rates by intelligently determining the connection between sub-arrays and RF chains.

§.§.§ Widely-Spaced Multi-Subarray

Due to the tiny wavelength, the THz channel is usually sparse, consisting of a LoS path and a few reflection paths. The transmit power concentrates on the LoS path, and the overall angular spread of THz signals is small.  For instance, a maximal angular spread of 40 has been observed for indoor environments in the THz band, compared to 120 for indoor scenarios at 60 mmWave frequencies <cit.>.  Since the number of spatial degrees of freedom is upper-bounded by the number of multipath components, the number of data streams or the potential spatial multiplexing gain is usually small, limiting the achievable bandwidth efficiency at the THz band. A widely-spaced multi-subarray hybrid beamforming architecture is proposed <cit.> to overcome the low spatial multiplexing gain due to channel sparsity. Instead of critical spacing, the inter-subarray separation is over hundreds of wavelengths, reducing the correlation between the subarrays.

The WSMS hybrid beamforming architecture is promising by exploiting intra-path multiplexing for THz UMMIMO systems <cit.>. It was discovered in <cit.> that when the distance between antennas is expanded, the planar-wave assumption becomes invalid, and it is necessary to consider the propagation of spherical waves between antennas. Previous research has examined the use of intra-path multiplexing in LoS MIMO architecture operating at microwave and mmWave frequencies, which enables multiplexing gain to be achieved using just a single LoS path <cit.>. Given that the intra-path multiplexing gain is not restricted by the number of multipath, it is a highly viable and promising solution for THz communications, which are known to exhibit significant channel sparsity <cit.>. In <cit.>, the authors demonstrated that the WSMS architecture can substantially improve the spectral efficiency of THz systems through the use of additional intra-path multiplexing gain, which sets it apart from existing hybrid beamforming that solely relies on inter-path multiplexing. As the follow-up, the authors designed an alternating optimization algorithm to maximize the sum rate <cit.> under the WSMS architecture.

§.§.§ True-Time-Delay-Based Hybrid Beamforming

Most of the current hybrid beamforming architectures rely on phase shifters, which are frequency-independent, inducing the same phase rotation at different frequency components of a signal. Under the ultra-wide bandwidth at the THz, these shifters only provide correct phase shifting for a certain frequency, whereas other frequencies suffer from phase misalignment. As a result, the formed beam is squinted with a substantial power loss, e.g., 5 reported in <cit.>. To solve the problem of beam squint at the THz band, TTD can be applied to substitute phase shifters <cit.>. The TTD is frequency-dependent, and the phase rotation adjusted by TTD is proportional to the carrier frequency and perfectly matches the ultra-wideband THz beamforming.

According to <cit.>, the TTD-based phase shifting is aligned with the requirements of wideband THz hybrid beamforming, given its proportional relationship with the carrier frequency. While ideal TTDs with infinite or high resolution are capable of precise phase adjustments, they are often associated with high power consumption and hardware complexity <cit.>. For the perspective of practical THz systems, low-resolution TTDs that strike a balance between energy efficiency and performance are more suitable, as reported in the literature such as <cit.>. In <cit.>, a novel hybrid precoding architecture named DPP was introduced to mitigate the issue of beam squint in THz communications systems. By incorporating a time delay network between digital and analog precoding, DPP generates frequency-dependent beamforming vectors. Similarly, Gao et al. <cit.> proposed a TTD-based hybrid beamforming that aims to address beam squint through virtual subarrays, as first presented in <cit.>. The proposed algorithm achieves near-optimal performance as that of full-digital precoding.

In order to address the limitation of TTD, Nguyen and Kim <cit.> proposed a hybrid beamforming scheme that takes into account the relationship between the number of antennas and the required delay for TTD. They also carried out joint optimization under limited delay to create an optimal  compensation scheme for beam squint. It is noted that most research work, as mentioned above, focused on 2D hybrid beamforming, which is primarily designed for ULA. However, ULAs may not be suitable for UMMIMO systems due to limited antenna aperture. In contrast, UPAs that can accommodate a large number of elements compactly, are more potential for deploying UMMIMO systems.
There is a lack of research on beam squint compensation in 3D hybrid beamforming using ULAs for THz broadband UMMIMO systems. Responding to this, the authors of <cit.>  proposed a 3D beamforming architecture by leveraging two-tier TTD, which is able to combat the beam squint effect from both the horizontal and vertical directions. The impact of the array structure on the beam squint has been analyzed in <cit.>.

§.§ Lens Antenna Array

Despite its high potential at the THz band, hybrid beamforming is still confined by high hardware and power costs due to the use of many analog phase shifters. Some studies <cit.> demonstrate that the power consumed by phase shifters becomes critical. In this context, a disruptive antenna technology called lens antenna <cit.> has been studied in recent years.

James Clerk Maxwell predicted the existence of EM waves  in 1873 and inferred that visible light is one type of  EM waves. To verify Maxwell's theory, early scientists who believed a radio wave is a form of invisible light, concentrated on duplicating classic optics experiments into radio. Heinrich Hertz proved the existence of  EM waves and also first demonstrated the refraction phenomena of radio waves at 450 using a prism. These experiments revealed the possibility of focusing radio waves on a narrow beam as visible lights through an optical lens. In 1894, Oliver Lodge <cit.> successfully used an optical lens to concentrate 4 radio waves. In the same year, Indian physicist Jagadish Chandra Bose <cit.> built a cylindrical sulfur lens to generate a beam in his microwave experiments over 660. In 1894, Augusto Righi at the University of Bologna focused radio waves at 12 with 32 lenses. In World War II, the race of developing radar technology fostered the emergence of modern lens antennas. Used as a radar reflector, the famous Luneberg lens <cit.> was invented in 1946, which is also attached to stealth fighters nowadays to make it detectable during training or to conceal their true  EM signature.

As refracting visible light by an optical lens, a lens antenna uses a shaped piece of radio-transparent material to bend and concentrate  EM waves <cit.>.  It usually comprises an emitter radiating radio waves and a piece of dielectric or composite material in front of the emitter as a converging lens to force the radio waves into a narrow beam. Conversely, the lens directs the incoming radio waves into the feeder in a receive antenna, converting the induced electromagnetic waves into electric currents. To generate narrow beams, the lens needs to be much larger than the wavelength of the  EM wave <cit.>. Hence, a lens antenna is more suitable for mmWave and THz communications, with tiny wavelengths. Like an optical lens, radio waves have a different speed within the lens material than in free space so the varying lens thickness delays the waves passing through it by different amounts, changing the shape of the wavefront and the direction of the waves.

On top of lens antennas, an advanced antenna structure referred to as a lens antenna array has been developed <cit.>. A lens antenna array usually consists of two major components: an  EM lens and an array with antenna elements positioned in the focal region of the lens.  EM lenses can be implemented in different ways, e.g., dielectric materials, transmission lines with variable lengths, and periodic inductive and capacitive structures. Despite its various implementation, the purpose of  EM lenses is to provide variable phase shifting for electromagnetic waves at different angles <cit.>. In other words, a lens antenna array can direct the signals emitted from different transmit antennas to different beams with sufficiently separated angles of departure. Conversely, a lens antenna array at the receiver can concentrate the incident signals from sufficiently separated directions to different receive antennas <cit.>.

Recent research work reported a few high-gain THz lens antennas, such as dielectric or metallic lens antennas <cit.>. Dielectric lens antennas have been demonstrated with high gain and wide operating bandwidth by integrating the dielectric lens with a standard rectangular waveguide feed <cit.> or a leaky-wave feed <cit.>. But their radiation efficiency needs to be improved. On the other hand, metallic lens antennas have no dielectric loss, making them suitable for THz communications and sensing. In <cit.>, a high-gain THz antenna using a metallic lens composed of metallic waveguide delay lines was reported. For wideband signal transmission, recently, the authors of <cit.> presented a fully metallic lens antenna with a wide impedance bandwidth and high gain at the D band from 110170. A ﬂared H-plane horn is used to achieve a large H-plane radiation aperture to further increase the radiation gain.

The deployment of MMIMO systems entails the challenges associated with a huge amount of antenna elements <cit.>.  EM lens arrays with a reasonable number of elements can lower the required number of antennas and corresponding RF chains while maintaining high beamforming gain. However, dielectric  EM lenses are difﬁcult to integrate with multiple antenna techniques due to their bulky size, high insertion loss, and long focal lengths to control the beam gain. The metallic lens antennas are defined as artificial composites that obtain electrical properties from their structure rather than their constituent materials. Some studies on metallic lens antennae have been done to achieve beam gain from a single antenna, like <cit.>.  Lee et al.  proposed a large-aperture metallic lens antenna designed for multi-layer MIMO transmission for 6G, demonstrating that a single large-aperture metallic lens antenna can achieve a beam gain of up to 14 compared to the case without a lens. By adopting the proposed large-aperture metallic lens antenna,  system-level simulations show that the data throughput of the user equipment is effectively increased <cit.>.

§.§ Beam Alignment: Training of Beams

The utilization of the promising THz spectrum range is hindered by significant propagation losses imposed on its frequencies. To counteract the losses, large antenna arrays, such as UMMIMO and lens antenna array discussed above, can be employed, but this leads to highly directional and narrow beams <cit.>. To ensure a satisfactory SNR at the receiver and prevent connection loss, it is critical to maintaining degree-level alignment between the transmitter and receiver beams.
Therefore, beam alignment is a critical issue that must be addressed for establishing a reliable connection. This is accomplished by aligning the beams at the transmitter and receiver to the direction of the channel paths, where CSI is critical for implementing the fine alignment <cit.>. However, traditional channel sensing methods used at lower frequencies are not feasible at THz frequencies due to the significant path losses that render pilot signals undetectable during the link establishment stage.

Significant research efforts have been made in recent years to understand the unique characteristics of the THz channel and to develop appropriate beam alignment algorithms. These efforts aim to establish beam alignment during the link establishment stage <cit.> and to maintain alignment while the beams are in motion (beam tracking). Two categories of beam alignment techniques have been identified: beam training <cit.> and beam estimation <cit.>, as shown in  <ref>. The former involves transmitting known signals and adjusting beamforming parameters to align the beams. The latter involves estimating CSI from received signals and using it to refine the beamforming parameters. This part surveys the state-of-the-art advances in beam tracking techniques, while beam estimation is discussed in the subsequent part.

Illustration of beam alignment techniques.

Beam training involves scanning the channel with directional beams from a codebook to determine the beam pair at the transmitter and receiver that results in the highest SNR of the received signal <cit.>. Beam training can be broadly classified into two categories: exhaustive and hierarchical training, which are discussed as follows:

§.§.§ Exhaustive Training

Many studies have adopted exhaustive training, which involves sequentially probing all the predefined directions in the codebook to find the optimal beam pair that maximizes the SNR at the receiver <cit.>. The approach is used in the IEEE 802.15.3c standard <cit.>. However, the method is time-consuming and not practical at THz frequencies, where beams from a large-scale antenna array tend to be very narrow, making it difficult to scan the entire space in a reasonable amount of time. Additionally, the accuracy of the training is limited by the codebook's resolution.

Responding to these limitations, Tan and Dai  <cit.> have improved the exhaustive search by exploiting the beam split or squint effect. They used delay-phase-precoding architecture to control the beam split effect and accelerate the tracking process <cit.>. By doing so, the split beams have wider angular coverage and can scan many directions in a single shot. Another approach from the RF domain used LWA at the transmitter and receiver of a THz link to estimate the AoD and AoA <cit.>. The angular radiation of an LWA is frequency-dependent, and the received spectral peak determines the AoD. The bandwidth of the received signal is proportional to the rotation angle over the AoA of the LWA receiver, which speeds up the channel scanning process but requires additional hardware at both the transmitter and receiver.

§.§.§ Hierarchical Training
From a practical point of view, many studies have adopted hierarchical training to reduce training overhead <cit.>. Hierarchical algorithms are based on multi-resolution codebooks, which contain wide beams at lower levels and narrow beams at higher levels. The search begins at the lowest level and gradually moves to higher levels to find the optimal narrow beam. In <cit.>, the authors proposed a subarray-based multi-resolution codebook, where beams at each level are generated by the contribution of all subarrays. In <cit.>, the authors proposed an accelerated hierarchical training that concurrently scans angular space with different RF chains. The authors of <cit.> proposed a multi-modal beam pattern-based training that simultaneously radiates beams targeting at different directions using a single RF chain. The equally spaced activation approach has been proposed to generate the steering vector for multiple beam radiation. However, the loss at THz frequencies may render the training algorithm ineffective. The authors in <cit.> and <cit.> adopted hierarchical training that utilizes the power-angular spectral correlation between sub-6GHz and THz frequencies.

In <cit.>, Stratidakis et al. proposed a localization-aided hierarchical beam tracking algorithm that uses location information to reduce pilot overhead. This algorithm assumes the linear motion of a user, which may not be accurate in realistic scenarios. In <cit.>, the authors proposed a unified 3D training and tracking framework based on a 3D hierarchical codebook built upon the quadruple-uniform-planar-array architecture. This proposal offers two advantages: a unified framework for training and tracking and 3D space coverage compared to 2D space coverage in most existing works. The training overhead of hierarchical algorithms is much lower compared to exhaustive ones. However, hierarchical  algorithms suffer from a high overhead of feedback messages required for coordination between the transmitter and receiver. The number of levels in multi-resolution codebooks also leads to higher training overhead, which may not enhance performance, especially in multi-hop THz links <cit.>. A new approach is proposed in <cit.>, where it is based on a multi-armed bandit algorithm and utilizes prior knowledge of channel frequency-selective fading. This algorithm is designed with a hierarchical structure to accelerate the beam alignment process.

§.§ Beam Alignment: Estimating of Beams

Beam estimation is a method of acquiring channel information with the goal of reducing training overhead when compared to beam scanning techniques. The estimation process  begins with initial training, which involves collecting channel measurement. These measurement are then processed to derive the angular information of the target channel. Prior studies have proposed a variety of algorithms, based on linear estimation <cit.>, ComS-based sparse estimation <cit.>, beamspace-based estimation  <cit.>, subspace-based estimation  <cit.>, or deep learning-based estimation <cit.>.

§.§.§ Linear Estimation

The authors of  <cit.> used an extended Kalman filter, which is a well-known example of linear estimation, to perform beam tracking for a MS. The MS sends training symbols over the uplink during each time slot, and the extended Kalman filter-based algorithm at the BS iteratively estimates the channel parameters (the path gain, AoD, and AoA) from the observed signal. The proposed algorithm achieves milli-degree level angle estimation with moderate mobility of the MS and antenna array size. However, the study assumes the BS is equipped with a fully digital beamformer, which is not practical due to the high power consumption imposed by a large number of RF chains. Additionally, the study assumes that the MS is parallel to the BS during tracking, such that AoD equals AoA, which is not realistic because the orientation of the MS can be arbitrary in real-world scenarios. Other linear methods, such as maximum likelihood and least squares, can also be applied <cit.>. However, these estimators require a large number of observations and do not exploit the sparsity feature of THz channels.

§.§.§ Compress Sensing-based Sparse Estimation

The sparsity property of THz channels can significantly reduce the computational complexity of beam estimation algorithms by transforming the problem into a sparse recovery problem. A technique referred to as ComS is considered an optimal approach to solving these problems, as discussed by the authors in <cit.>. They analyzed two ComS-based algorithms, i.e., greedy compressive sampling matching and Dantzig selector-based method, for solving convex programs. The results show that the ComS-based methods have higher accuracy compared to linear estimation based on least squares. The authors in <cit.> utilized ComS-based techniques to accelerate the training proposed in <cit.>. In this approach, the estimated angles from wide beams in the first stage are refined using an L1-norm regularized least squares method to obtain accurate estimates of AoD and AoA, reducing the scope of the narrow beam search in the second stage.

Yang et al. <cit.> proposed an orthogonal matching pursuit-based fast algorithm to estimate the AoA and AoD of a BS-MS link. This study considered the cost and power consumption of adopting an auxiliary fully-digital array for channel estimation and evaluated the effect of RF imperfections and low-resolution ADC per RF chain. The study adopts the virtual channel model, which assumes that the AoA and AoD are discretely distributed over a spatial grid utilizing the angular sparsity of the THz channel, making it a sparse recovery problem suitable for ComS-based algorithms. However, this discrete grid assumption reduces the estimation accuracy due to the grid resolution. To mitigate this limitation, the authors in <cit.> proposed an iterative reweight-based super-resolution estimation scheme, which optimizes the on-grid estimation iteratively to converge to neighboring off-grid angles. Simulation results show that the off-grid solution has an improved accuracy and spectral efficiency compared to on-grid solutions. Similarly, the authors in <cit.> proposed a gridless ComS-based algorithm to estimate the AoA for arbitrary 3D antenna arrays, eliminating the quantization error of the grid assumption.

The mentioned ComS-based estimators are generally built on the assumption of angular sparsity of THz channels, which holds in the far field but  is not valid in the near field. Therefore, the work in <cit.> considers ComS-based estimation in the near field where the angular sparsity assumption does not hold. The results show that the channel in the near field exhibits polar sparsity rather than angular sparsity, which was exploited by a ComS-based polar-domain simultaneous orthogonal matching pursuit algorithm.

§.§.§ Beamspace-based Estimation

As we know, the MIMO beamspace channel can be realized through the use of a DLA. Such arrays function as passive phase shifters that steer beams towards specific directions based on the incident point to the lens aperture <cit.>. The number of these directions is limited by the number of antenna elements, resulting in a beam-sparse channel. This artificially created sparsity reduces the pilot overhead required for channel estimation compared to conventional methods. In <cit.>, the authors adopted the DLA-based MIMO system architecture to create the MIMO beamspace channel and utilized its sparsity for fast channel tracking. A priori information-aided tracking scheme was proposed for MIMO beamspace systems, where the channel was conventionally estimated in the first three time slots and the physical direction of the MS was then derived based on a temporal variation law. The estimated physical direction was used to determine the support of the beamspace channel, which corresponds to the dominant beam directions. However, the estimation accuracy depends strongly on the localization accuracy. In <cit.>, the authors extended the work in <cit.> and proposed a cooperative localization-aided tracking algorithm with multiple BSs, each equipped with a DLA. These BSs cooperate to accurately localize the MS for improved channel tracking. While beamspace MIMO solutions significantly reduce the overhead in comparison with that of conventional estimation methods, their accuracy may be limited by the discrete directions generated by the DLA and restricted numbers of beams.

§.§.§ Subspace-based Estimation

When estimating continuously distributed AoA and AoD, another way referred to as subspace-based algorithms can be performed, with the aim of avoiding the estimation error caused by the sparse solutions or beam sparsity in beamspace solutions. In general, these algorithms collect channel measurement and identify the eigenvectors that correspond to the signal subspace. Two widely known algorithms, i.e., MUSIC (MUltiple SIgnal Classification) and ESPRIT (Estimation of Signal Parameters via Rotational Invariance Technique), belong to subspace-based estimation <cit.>.

In <cit.>, the authors adopted the MUSIC algorithm to achieve millidegree-level AoA estimation. This study utilized a hybrid AoSA architecture and collected measurement data by probing random steering vectors. The covariance matrix of the measurement data was then calculated and decomposed into signal and noise subspaces.  The AoA was estimated by searching the MUSIC pseudospectrum function for vectors orthogonal to the noise eigenvectors. The estimation was further refined by collecting new measurement based on the coarse estimated angles. In <cit.>, the ESPRIT algorithm was adopted for super-resolution channel estimation, which involved multiple steps such as spatial smoothing, forward-backward averaging techniques <cit.>, singular value decomposition (SVD), and joint diagonalization. While subspace solutions show improved performance compared to sparse solutions, their computational complexity is significantly higher.

§.§.§ Deep Learning-based Estimation

Recently, DL-based techniques have emerged as a promising alternative to replace conventional estimators. DL-based solutions are particularly effective for complex multi-user scenarios where the input and output of the channel are not directly related. In <cit.>, a branch of DL referred to as DCNN is used to estimate the AoA of a multipath channel. The measurement matrix is collected through random beamforming and combining matrices at the transmitter and receiver, respectively. The measured data is fed into the neural network. Three convolutional layers extract the spatial peculiarities of the channel, and two fully-connected layers capture the non-linear relationship between these peculiarities and AoA estimation. The results show higher estimation accuracy compared to that of the subspace-based MUSIC algorithm at high SNR.

In <cit.>, a DCNN architecture is used to estimate the near-field channel under the spherical wave propagation model. The proposed DCNN-based approach addresses the spherical wave propagation model by considering the inter-subarray phase error as an output parameter of the network.
In <cit.>, DKL combined with GPR is used to estimate the indoor THz channel in a multi-user scenario. In particular, a DNN is trained to capture the non-linear relationship between the input and output of the channel. The results show that this DNN-based solution outperformed the MMSE and least squares-based linear estimators.

Prior studies have demonstrated the superiority of DL-based solutions over the conventional solutions in complex scenarios. It also revealed that DL networks need lots of computational and storage resources,  intensive offline training, and validation. Moreover, their efficiency in low SNR scenarios requires further investigation. In order to achieve fast initial access in wireless networks, a DNN framework called DeepAI has been proposed, which maps the RSS to identify the optimal beam direction <cit.>. The authors have introduced a sequential feature selection (SFS) algorithm that selects efficient and reliable beam pairs for DeepAI's inputs in LoS mmWave channels. However, the SFS algorithm fails to improve the accuracy and performance of DeepAI in NLoS scenarios. The simulation results show that DeepAI outperforms the conventional beam-sweeping method. Another DL-based beam selection algorithm is suitable for 5G NR has been proposed by the authors in <cit.>.

§ THZ SYSTEMS AND NETWORKS TOWARD 6G AND BEYOND

The ongoing research and development of the 6G system is set to revolutionize the way that various domains and layers of a mobile network interact and communicate with each other and with authorized third parties <cit.>. As we stated numerous times throughout this paper, one of the key enablers of 6G is THz communications and sensing, which promise to deliver ultra-high data rates, ultra-low-latency connectivity, high-resolution sensing, and high-accuracy positioning in the coming decades. Nevertheless, the full potential of THz communications and sensing can only be realized through its integration with other emerging technologies.

This section explores THz networks from a systematic point of view, with an emphasis on the synergy of THz communications and sensing with a variance of potential 6G technologies, including MMIMO, UMMIMO, NOMA, RIS, non-terrestrial networks, digital twins, AIm ML. Moreover, we discuss security, localization, joint communications and sensing,  multi-connectivity, and channel awareness for THz systems and networks. By examining these synergies, we hope to shed light on the most significant research challenges and opportunities facing the development and deployment of  THz communications and sensing in 6G and beyond networks, as well as the potential benefits for future applications and services.

§.§ THz-MMIMO Systems and Networks

Compared to lower frequencies at sub-6  and mmWave, the THz bands have a much smaller signal wavelength, which leads to a tiny size of antenna (i.e. a larger number of antennas within the same surface area) and narrower beams. Both the factors are beneficial for MMIMO and grant a great potential in the THz band than at lower frequencies. For example, Akyildiz and Jornet demonstrated that an UMMIMO system operating in 1 THz with the dimension of $1024\times 1024$ is realized by the arrays that have a size of only 1^2 <cit.>.
However, the application of MMIMO and/or UMMIMO in practical 6G THz communications and sensing are challenged in various aspects. In addition to the barriers in the fabrication of nano-antenna arrays, the complexity and sparsity of THz channels are also limiting the exploitation of MMIMO in this band. Accurate channel models, PHY layer enabling technologies, as well as novel link layer design, are needed to release the full potential of THz MMIMO.

A lot of works in modeling THz MMIMO channels have been reported since the late 2010s, overwhelmingly with the ray-tracing methodology. Han et al. proposed in <cit.> a model for UMMIMO channels over a distance to 20 and in the frequency windows of 0.30.4 and 0.91. Busari et al. studied the 0.1 MMIMO channel in <cit.> a specific outdoor street-side scenario, investigating the impacts of precoding scheme, carrier frequency, bandwidth, and antenna gain on the system regarding spectral and energy efficiencies. Sheikh et al. focused on the critical features of rough surface reflection and diffuse scattering at THz frequencies, and proposed in <cit.> a 3D indoor model for 0.3 and 0.35 MMIMO channels with different surface roughness levels, considering both LoS and NLoS scenarios. In the recent work <cit.>, Tarboush et al. reported their channel model for wideband UMMIMO THz communications and a simulator based thereon.

Efforts on the physical layer deal with the problem of beamforming and combining from the perspectives of beam training and beam tracking, i.e.,  finding the best beam pattern and online adjusting it, in order to obtain the best link quality and maintain it against the time variation of the channel. As outlined by Ning et al. in their tutorial <cit.>, there are two basic principles of beamforming: the precoding/decoding that is executed in the digital domain, and the beam steering that works in the analog domain. Each of the principles, as well as their hybrid, when applied in the wideband THz systems, must be carefully designed to address two main issues: the spatial-wideband effect that different antennas receive different symbols at the same time, and the frequency-wideband effect that the beam pattern of a phase array codeword changes with the frequency of the signal (a.k.a. the beam squint or beam split). While most existing methods leverage either the digital precoding approach <cit.>, or the precoding/steering hybrid <cit.>, new research interests in the steering approach based on RIS are arising <cit.>. Whilst higher layer design has not been a major research focus of MMIMO THz systems so far, there is pioneering work on multi-access scheme reported in <cit.>.

§.§ THz-NOMA Systems and Networks

Another promising RAN technology for enabling THz-MIMO systems and networks is NOMA, which allows allocating of the same radio resources to more than one user simultaneously and invokes the so-called SIC approach on the receiver side to decode the information for different users successively <cit.>. Compared to lower frequencies, the low-rank channels in the THz bands can be much more correlated because of the limited-scattering transmission, which reduces the channel orthogonality between different users and makes NOMA a promising technique to improve the spectral efficiency <cit.>.
Serghiou et al. <cit.> believe that in LoS scenarios where spatial processing approaches fail to separate the users from each other, a combination of NOMA with UMMIMO can provide more fair user access in terms of resource allocation, and therewith achieve better spectral efficiency of the overall network. Meanwhile, with proper resource allocation algorithm, NOMA can also enhance the energy efficiency in THz communications and sensing systems <cit.>.

To evaluate the feasibility of MIMO-NOMA systems in THz bands, Sabuj et al. proposed in <cit.> a FBL channel model and therewith evaluated the system performance regarding CMTC scenarios.
In contrast to its good performance in LoS scenarios, THz-NOMA performs much more poorly when the connected devices are blocked by obstacles <cit.>. To address this issue, RIS appears as a promising solution. In <cit.>, Xu et al. proposed a smart RIS framework for THz-NOMA, which delivers significant enhancements in the system energy efficiency and the reliability of super-fast-experience users. The principle of NOMA requires users to be paired/clustered for sharing radio resources, and relies on an appropriate clustering to achieve satisfactory system performance. Shahjala et al. comparatively reviewed the user clustering techniques for MMIMO-NOMA THz systems in <cit.>, and proposed a fuzzy C-means-based clustering approach in <cit.>.

It should be noted that many popular clustering policies are tending to pair a user with good channel, called CCU, to another with poor channel, called CEU. Such policies lead to a gain in the spectral efficiency of the overall system, but a degradation at  the CEU due to power splitting. To address this issue, Ding et al. proposed in <cit.> a CNOMA scheme where the CCU always forwards the message for CEU that it obtains during the SIC, so that the performance loss at CEU is compensated. However, this design is forcing the CCU to work as a relay which drains its battery. Therefore, SWIPT is often introduced into CNOMA systems so that the CCU is able to harvest energy from the radio signal to support the relaying <cit.>. At THz frequencies, due to the high spreading loss and atmospheric absorption, the power propagation loss is more critical than that at lower frequencies, and the SWIPT-assisted CNOMA solution can be more important. Oleiwi and Al-Raweshidy analyzed the performance of SWIPT THz-NOMA in <cit.>, and correspondingly designed a channel-aware pairing mechanism <cit.>.

§.§ THz-RIS Systems and Networks

The 6G and beyond systems will be revolutionized by the tremendous potential of RIS <cit.> and THz <cit.>, the two cutting-edge enablers for the access domain of a futuristic communications and sensing network. The synergy between RIS and THz lies in the fact that RIS can be utilized to improve the performance of THz systems by providing a cost-effective solution to the propagation challenges associated with THz frequencies <cit.>. By utilizing the reconfigurability and versatility features of RIS, it is possible to address the challenges of THz wave propagation, especially the use of bypassing the blockage of THz beams <cit.>, thereby improving the overall performance of THz communications and sensing.

By controlling the phase, amplitude, and polarization, RIS can effectively steer, reflect, and amplify electromagnetic waves in THz systems and networks. Consequently, it enables a vast array of applications and use-case scenarios, including beamforming, wireless power transfer, and indoor localization, among others. In addition, by employing RIS-assisted spatial modulation, the THz-RIS systems and networks have the potential to dramatically improve their spectral efficiency. More importantly, RIS can be used to generate virtual channels that compensate for the propagation losses of THz waves so that SNR and the coverage area of THz communications and sensing could be increased.
By far, the intersection of the RIS and THz has been intensively studied in the literature. There exist a number of overview and survey papers that provide an insight into such synergies, including <cit.>. In addition to the aforementioned surveys, we discovered during our research that the intersection between these two technologies has been dramatic, including in the context of massive MIMO, millimeter wave, 3D beamforming, satellite networks, and many others. Moreover, a large number and various types of physical layer-related optimization problems have been jointly investigated.

§.§ THz-Aided Non-Terrestrial Networks

With its ambition of ubiquitous 3D coverage, 6G and beyond are envisioned to include different non-terrestrial infrastructures, such as UAV, HAP, LEO satellites, and GEO satellites, as an indispensable part of its architecture. Since the air/space channels and air/space-to-ground channels are less subject to blockages w.r.t. terrestrial channels, the LoS link availability is much higher, implying a vast potential for THz communications and sensing <cit.>. On the one hand, the tremendous amount of spectral resources offers the feasibility of efficient interconnection among these terrestrial, air, and space platforms through THz communications links. On the other hand,  non-terrestrial infrastructures enable the flexible deployment of a variety of THz sensing equipment at favorable altitudes and places.

Nevertheless, the practical deployment of THz-NTN is still facing various technical challenges, which include but are not limited to the feasibility assessment of THz frequencies for space-to-earth links, transceiver implementation, and accurate NTN platform positioning <cit.>. Regarding the characterization of THz-NTN channels, the authors of <cit.> proposed an analytical propagation model for low-altitude NTN platforms such as UAV in the frequency range 0.2753, while the authors of <cit.>  modeled the cross-link interference for LEO satellites.  The use of satellites to serve air planes on the THz band and related channel models have been analyzed in <cit.>. Concerning the THz transceiver implementation, NTN systems pose high antenna design requirements. For example, the antennas are supposed to produce multiple high-gain beams to support dynamic networking and realize long-range communications. There are various approaches towards this aim, which are well summarized in the survey by Guo et al. <cit.>. For THz CubeSat networks, the antennas are required to provide sufficient beamwidth angle to enable faster neighbor discovery, while simultaneously providing a high gain to overcome the path loss. To fulfill these requirements, Alqaraghuli et al. designed a two-stage Origami horn antenna <cit.>.

On the PHY layer, digital signal processing techniques are studied to overcome the limitations of the analog front end in THz transceivers. Tamesue et al. proposed to deploy digital predistortion in RF power amplifiers of THz-NTN systems to compensate for nonlinear distortion <cit.>. In <cit.>, Kumar and Arnon reported a DNN beamformer to replace the phase shifters in THz-MMIMO antenna arrays for wideband LEO satellite communication.
It also creates additional benefits for NTN by deploying THz communications and sensing in conjunction with other novel enabling technologies. For example, RIS can contribute to the deployment of THz in future integrated terrestrial/non-terrestrial networks by means of enhancing the beamforming <cit.>. By leveraging the ISAC technology, the DAR, which are traditionally used for weather sensing, can be granted an extra capability of communicating with LEO satellites <cit.>.

§.§ Digital Twin-Aided THz Systems and Networks

The digital-twin technology <cit.> is an emerging novel concept (which is also considered to be a key enabler of the 6G and beyond systems) in which a virtual replica of a physical system, object, process, network, or link is created through employing accurate data collected in real-time <cit.>. It enables the autonomous control, intelligent monitoring, and accurate self-optimization of physical networks, processes, and systems in a fully virtualized environment. To our knowledge, there exists synergy between the digital-twin technology and THz communications/sensing networks that can produce a combined effect on the 6G and beyond aimed at improving the overall performance in delivering data-driven services. This synergy stems from the fact that both rely on accurate and real-time data that is collected from their corresponding data nodes. On the one hand, THz communications can support the transmission of large amounts of data generated by digital-twin nodes by facilitating high-speed communications links <cit.>, while THz sensing can help the acquisition of high-accuracy environment data for digital twin. On the other hand, a digital twin can improve the overall performance of THz communications and sensing by offering a virtual testing, monitoring, decision-making, and optimization environment for the said THz systems and networks <cit.>.

To be specific, digital twins can be utilized to generate and enable virtual replicas (also known as virtual models) for manufacturing processes and systems, such as the digital twins for the machines, links, services, materials, networks, and products contained in industry. By controlling and monitoring the virtual replicas in a real-time manner, it can be feasible to detect and subsequently address any maintenance issues and bottlenecks that may arise in the said industry, including device and machine complete failures, unsuccessful service delivery attempts, and material shortages, among many others. To enable the digital twinning of the manufacturing industry, THz communications/sensing systems and networks can be deployed to acquire, transmit, and receive data (and at some points enrichment information) between the physical objects and virtual replicas of the manufacturing systems, enabling real-time control, accurate decision-making, and autonomous optimization <cit.>.

In the literature, the relationship between digital-twin technologies and THz systems has received scant attention. We uncovered three references addressing this intersection of the two technologies. First, the authors in <cit.> proposed a THz signal guidance system in which a digital twin is utilized to aim at modeling, controlling, and predicting the indoor signal propagation features and characteristics. The authors claim that their methodology achieved the “best” THz signal path from a nearby base station to the targeted user equipment using a number of certain models. Second, in reference <cit.>, the authors proposed a framework that is based on the THz communications system and aimed at implementing the digital-twin prediction for enabling extremely security-sensitive systems and objects. Finally, reference <cit.> studies the delay minimization optimization problem within the context of THz communications system and visible light communications system. In their study, the authors claim that their approach reduces up to 33.2% transmission delay in comparison with the traditional methods.

§.§ AI/ML-Aided THz Systems and Networks

THz communication/sensing systems and AI/ML <cit.> can benefit from each other synergistically. There are several facets of THz systems that can benefit from the application of AI techniques and ML algorithms in 6G and beyond networks. For example, AI/ML can be employed  for (a) signal processing to enhance the quality of THz signals and reduce noise; (b) THz channel estimation to be maintained over long distances and not be affected by atmospheric effects; and (c) the optimization of error correction codes and modulation schemes. On the one hand, the performance, effectiveness, and dependability of THz systems and networks can be improved through the utilization of AI/ML approaches. On the other hand, THz systems can offer high-speed wireless data transfer and high-accuracy sensing capabilities that can be helpful for the deployment of AI/ML services.

In addition, AI/ML technologies can be utilized to create intelligent and data-driven THz communications and sensing systems with being capable of adapting to quickly changing environmental conditions. For instance, with advanced AI/ML algorithms, a self-healing, self-optimizing, and self-regulating THz communication and sensing network can be built that can modify their parameters autonomously to maintain required performance and service levels. Moreover, AI/ML can be utilized in THz imaging and sensing application use-case scenarios, including security screening, medical diagnosis, industrial inspection, and many others. THz images can be processed using AI/ML algorithms to extract enrichment and/or useful information, resulting in more accurate and reliable results.

The synergistic relationship between AI/ML and THz systems has also been demonstrated by some recently published works. During our investigation, we found three papers <cit.>, which provide an overview of various aspects of the AI/ML applicability in THz systems and future research directions in this domain. To be specific, the authors in <cit.> provide a survey of the current state-of-the-art research in THz communication, including signal processing, front-end chip design, channel modeling, modulation schemes, and resource management. The paper also highlights the challenges and opportunities in 6G THz communications systems and discusses the potential applications of THz communications in various fields. Reference <cit.> provides a comprehensive review of the recent achievements and future challenges of ML in THz communication. More specifically, the paper summarizes the state-of-the-art research on ML-based THz imaging, sensing, and communications systems, including signal processing, feature extraction, classification, and optimization. The paper also discusses the potential applications of ML in THz technology, such as medical diagnosis, security screening, and wireless communication, and outlines the future research directions and challenges in this field. The authors of <cit.> cover the fundamentals of THz sensing, including sources of THz radiation, detection techniques, and applications. This paper presents a comprehensive survey of signal processing techniques, including time-domain and frequency-domain methods, feature extraction, and classification. It also reviews recent developments in ML-based THz sensing and highlights the challenges and future directions for signal processing and ML techniques in THz sensing.

Finally, and in addition to the above three overview papers related to THz communications systems, there exist a number of papers that study ML techniques for time-domain spectroscopy and THz imaging <cit.>, the application of AI in THz healthcare technologies <cit.>, two types of low-cost THz tags using ML-assisted algorithms <cit.>, and molecular screening for THz detection using ML-assisted techniques <cit.>.

§.§ Security in THz Systems and Networks

Resiliency against eavesdropping and other security threats has become one of the key design priorities. Security measures are accessible at every layer of a wireless network and can be integrated across layers to ensure redundancy. These measures encompass various forms, including software (e.g., encryption and authentication at the upper layers), hardware (e.g., trusted platform modules), and the physical layer (e.g., wave-front engineering, near-field antenna modulation, and polarization multiplexing) <cit.>. Physical-layer security offers notable advantages: they do not require a shared private key, demand minimal additional computing resources, and do not hinge on the assumption that the attacker possesses limited computational capacity, making it attractive, especially in THz systems.

The intersection between physical-layer security and THz communications and sensing is raised from the particular properties of THz waves, such as channel sparsity and pencil-like beams, where an eavesdropper is difficult to intercept highly directional THz signals. Additionally, the use of ultra-wide signal bandwidth and spread spectrum techniques can enhance the security of THz transmission. Spread-spectrum techniques spread a signal over a wide range of frequencies, making it hard for signal detection and improving robustness against common jamming attacks <cit.>. To the best of our knowledge, physical-layer security can be deployed in the following aspects of THz communications and sensing: channel authentication, PHY encryption, beamforming, and PHY key generation. These security techniques can improve the security of THz communications and sensing and make them suitable for a variety of 6G scenarios, such as hot spots, wireless backhaul, satellite interconnection, industrial networks, positioning, and imaging.

As of the time of this writing, a number of studies have uncovered numerous facets of physical-layer security for THz systems.  In <cit.>, a hybrid physical and multi-dimensional coded modulation scheme for THz communications systems was proposed. In <cit.>, physical-layer authentication in THz systems was presented. Following that, some works <cit.> were conducted (both based on simulation and calculation) on resiliency against eavesdropping using a directional atmosphere-limited LoS THz link. Regarding the probability of eavesdropping, the authors of <cit.> have also investigated about decreasing message detection using inherent multi-path THz systems. Moreover, a physically secure THz system operating in 310 using orbital angular momentum was presented in <cit.>.  Last but not least, it is worth noting that THz sensing and communications face unique security challenges that must be addressed. For example, THz waves can penetrate some materials, including clothing and certain types of packaging, which could potentially be exploited by malicious actors <cit.>.

§.§ Localization Services in THz Systems and Networks

Localization services and THz systems are two completely different research areas. Nevertheless, they can be integrated synergistically with being aimed at improving their capabilities and opening up new avenues for a variety of applications and services in 6G. The intersection of localization and THz systems results from the unique propagation characteristics of THz frequencies, which can be utilized for localization purposes in the access network domain. THz frequencies are particularly sensitive to rapidly occurring environmental changes, such as the presence of obstacles (a.k.a. problematic objects) or changes in the refractive index of materials in an environment. This extreme sensitivity can be utilized to create THz-based localization services and systems that can operate in a variety of environments, including indoor environments where GPS-based systems may not function optimally.

The integration of localization services into THz systems and network  has the potential to enable a variety of novel use cases and applications in the 6G era, such as intelligent factories, manufacturing, healthcare, and many others. THz-based localization services, for instance, could be used to track assets within a factory or warehouse, while THz communications could be used to enable high-speed data transfer between machines and objects in the said factory. THz-based localization services could be deployed to monitor and control patient movement within a hospital, while THz communications could be used to enable wireless video transmission for remote consultations and many other services.

The intersection between localization services and THz communications systems has been studied to some extent in the literature. A tutorial  <cit.> provides a comprehensive review of THz-band localization techniques for 6G systems. The authors discussed various aspects of THz waves, including propagation characteristics, channel modeling, and antenna design. They also explore different localization methods such as the time of arrival, angle of arrival, and hybrid techniques. The paper concludes by highlighting some potential applications of THz-band localization in 6G networks. In addition, two research articles discuss various aspects of this intersection. In <cit.>, the authors proposed a deep learning model for 3D THz indoor localization using a structured bidirectional long short-term memory network. The authors claim that their proposed method achieves better localization accuracy than state-of-the-art methods, making it a promising solution for indoor localization in THz-band communications systems. Finally, reference <cit.> proposes a new deep learning method for THz indoor localization called SIABR, utilizing a structured intra-attention bidirectional recurrent neural network to learn features from the received signal and estimate the location of the target.

§.§ Multi-Connectivity for THz Systems and Networks

Due to the high atmospheric absorption and low penetration capability, THz signals suffer from such strong propagation loss, fading, shadowing, and blockage, that they are hard to maintain with mobility even when beamforming and combining are ideally performed. To address this issue, THz systems will need MC as an essential feature so that a continuous and stable data connection between the users and the network can be ensured by means of radio link redundancy in case a single radio link fails.
The basic principle of MC is to keep multiple radio connections to different BSs simultaneously, but only use one of them at a time for signal transmission. The effectiveness of multi-connectivity in addressing the issue of blockage in THz band has been confirmed by evidence from various studies: a higher density of BSs is proven to enhance the system performance from the perspectives of link probability <cit.>, capacity <cit.>, and session completion rate <cit.>.

Especially, there are two different strategies for selecting the active radio link, namely the CMC where the closest BS with LoS link is always selected for communications and the RMC in which the active radio link is only re-selected when the current LoS link is blocked. While the CMC strategy is significantly outperforming the single-connectivity strategy, the RMC strategy brings only a marginal - sometimes even negative - gain, and is therefore discouraged despite its low signaling overhead <cit.>. It should also be noted that the application of MC has an influence on the handover mechanism since it links the status model. In <cit.>, Özkoç et al. established an analytical framework to assess the joint impact of the MC degree and the handover constraints on system performance of THz cellular networks.

An alternative approach to exploit MC is to allow multiple BS to simultaneously serve multiple mobile stations, i.e., each user may be communicating with multiple BS rather than one at a time. This is usually known as the network MIMO or DMIMO, which exploits the spatial diversity by means of intensifying the BS instead of antenna units in each array like in classical MMIMO/UMMIMO. To minimize the cross-interference among adjacent BS and maximize the throughput in DMIMO networks, the CoMP technologies shall be invoked. CoMP allows different BS to be clustered into small groups, and to coordinately optimize their user association and beamforming within each group. More specifically, there are two principles of CoMP: the JT where multiple BS transmit the same signal simultaneously to the same UE, and the CSCB where each BS sends a different signal and the signals are combined at the UE. An example of applying CoMP in the THz band is presented in <cit.>, which combines joint power allocation and quantized co-phasing schemes to maximize the aggregated data rate.

Since the late 2010s, the concepts of DMIMO and CoMP have evolved into the CFN paradigm, where all UE in an area are jointly served by numerous single-antenna BS in a CoMP manner <cit.>. Having been well studied at mmWave frequencies, the applicability of CFN in the THz band still remains
under-studied <cit.>. Pioneering work was reported in 2022 by Abbasi and Yanikomeroglu <cit.>, considering a NTN scenario. In some research works <cit.>, multi-connectivity also refers to establishing connections in different communications bands, e.g., transmitting control signaling in the sub-6GHz band while delivering data in the THz band (also deliver data when the THz band is in an outage).

§.§ Channel Awareness for THz Systems and Networks

While modern wireless data transmission technologies generally rely on the knowledge of channel state to achieve satisfactory performance, the acquisition of accurate CSI can be a critical challenge for THz systems and networks. First, the pilot symbols can be easily blocked due to the susceptibility of THz channels, leading to a low efficiency of classical channel estimation methods. Second, THz channels are selective regarding many different parameters, e.g., time, frequency, beam pattern, beam direction, polarization, etc. Therefore, it takes much effort to comprehensively measure the CSI of a THz channel, in addition to a significant overhead to encode the dimensional and sparse CSI. Similar to the pilots, the CSI reported from UE to the network can also be blocked if transmitted in the THz band itself <cit.>.

Regarding these challenges, the out-of-band channel estimation occurs as a promising solution. This involves acquiring the CSI of THz channels using the channel estimation at lower frequencies, leveraging the spatial correlation. To assess the feasibility of this approach, the authors of <cit.> and <cit.> studied the spatial similarity among THz, mmWave, and sub-6 bands based on point cloud ray-tracing simulation and field measurement. Their results support the use of out-of-band beam search strategy, not only in LoS scenarios but also even in NLoS ones, when using well-designed antenna patterns in specific frequency bands. Meanwhile, Peng et al. demonstrated the feasibility of out-of-band channel estimation beam searching with both ray-tracing simulations <cit.> and real-world experiments <cit.>.

However, the exploitation of channel similarity for THz communications and sensing is still facing technical challenges. First, the difference in the size of the antenna array within the same aperture leads to a mismatch in the beamwidth between the lower frequencies and the THz ones <cit.>, which is proven to have a strong impact on channel similarity than the frequency gap itself <cit.>. Second, the correlation matrix is difficult to estimate, considering its large size and the small dimension of antenna arrays measuring at lower frequencies <cit.>. Third, despite the feasibility of out-of-band estimation on static THz channels, the dynamics such as user mobility, scatter mobility, and blockages, are lifting the difficulty of this task to the next level <cit.>.

§ INTEGRATED THZ COMMUNICATIONS AND SENSING

The novel concepts of “network as a sensor” and “Internet of Senses” are becoming unprecedented essentials in the upcoming 6G and beyond cellular networks so as to support a multitude of emerging use cases <cit.>. The two major functionalities, i.e., communications and sensing (including positioning and imaging), will be merged, synergized, and integrated, benefiting from each other rather than competing for network resources. Future base stations are supposed to provide not only legacy communication services but also localization, sensing, and even electromagnetic imaging capabilities, acting as dual-functional ISAC transceivers <cit.>.

In general on the physical layer, ISAC has two widely adopted terminologies, i.e., RCC and DFRC <cit.>. It aims for enhanced spectral and energy efficiency, reduced hardware cost, and decreased power consumption as well as deployment and computational complexity. In the literature, such as <cit.>, THz sensing, imaging, localization, and communications are treated separately. Different from these, we in this section provide a holistic survey on the recent activities in integrated THz communications and sensing. Special attention is paid to use cases and KPI, waveform design, algorithm development, RIS-boosted ISAC, and potential challenges and solutions. The architectural overview of THz ISAC is depicted in Fig. <ref>, and the selected major contributions related to THz ISAC are summarized in Table <ref>.

An architectural overview of THz ISAC.

Summary of selected major contributions on THz-ISAC

Category   Year     Authors     Contributions

34em       2020     Wymeersch et al. <cit.>     Listed a series of application examples of RIS-based localization and mapping services.

34emUse cases, KPI, and applications     2021     Wymeersch et al. <cit.>     Provided an overview of the vision of the European 6G flagship project Hexa-X  alongside
the envisioned use cases; Discussed the technical enablers and associated research challenges.

34em      2022     Chen et al. <cit.>     Identified the
prospects, challenges, and requirements of THz localization techniques.

34em     2022     Moltchanov <cit.>     Evaluated user- and system-centric KPI of mmWave
and THz communication systems.

34em     2021     Zhang <cit.>    Set up JCAS in the mobile network context
and envisage its potential applications.

34em     2022     Akyildiz <cit.>    Pointed out 6G applications enabled by THz communications and their corresponding performance objectives.

34emWaveform design     2022     Wu <cit.>     Proposed SI-DFT-s-OFDM system for THz
ISAC with FGI scheme, which tremendously enhances the data transmission rate.

34em      2022     Wu <cit.>     Proposed DFT-s-OTFS system to improve the robustness
to Doppler effects and reduce PAPR for THz ISAC.

34em ISAC algorithms     2021     Yang <cit.>     Leveraged multi-domain cooperation to enhance the performance
of the ISAC system through active and passive sensing, multi-user, and multi-frequency-band

34em      2022     Helal <cit.>     Addressed the effectiveness of deep learning techniques by
exploring their promising sensing and localization capabilities
at the THz band.

34em     2022     Mateos-Ramos <cit.>     Studied model-driven end-to-end learning for joint single
target sensing and MISO communication.

34emRIS-boosted ISAC     2021     Jiang <cit.>     Investigated the joint optimization of
the IRS passive PSM and precoding matrix of
the radar-aided base station for the DRC system.

34em      2021     Wang <cit.>    Investigated joint constant-modulus waveform and discrete RIS phase shift design, with the aim of minimizing MUI under the CRB constraint for DoA estimation.

34em     2021      Wang <cit.>      Studied the minimization of MUI under the strict beam pattern constraint by jointly optimizing DFRC waveform and RIS phase shift matrix.

34em      2022     Wang <cit.>     Minimized the CRB of the 2D DoA estimation of the sensing target subject to the minimum communication requirement.

34emChallenges and solutions     2022     Elbir <cit.>      Studied several design challenges such as beam
split, range-dependent bandwidth, near-field beamforming, and
distinct channel model for ISAC at THz-band, and provided research opportunities in developing
novel methodologies for channel estimation, near-field beam split,
waveform design and beam misalignment.

34em      2022     Han <cit.>     Elaborated challenges from THz
channel and transceiver perspectives, as well as the difficulties of

§.§ THz-ISAC Use Cases and KPI

Localization and sensing, including determining the 2D/3D location and EM properties of objects, alongside multi-scale communications enable a multitude of emerging use cases, which may have different functional and non-functional QoS requirements in terms of accuracy, range, latency, velocity, update rate, reliability, and availability. The use case families consist of various vertical applications, e.g., massive twinning, immersive telepresence, wireless XR, cooperative robots, Tera-IoT, local trust zones, vehicular communication
and radar sensing <cit.>, and THz IAB. In general, these fall into the category of data-demanding and delay-sensitive applications. However, the QoS requirements differ from one use case to another. For instance, manufacturing and smart cities, as two sub-categories of massive twining, need different accuracy, data rates, latency, update rates, reliability, and availability  <cit.>.  A full list of selected use cases and their corresponding performance metrics can be referred to <cit.>, closely tied to these of upcoming 6G and beyond systems, for which the ultimate goal would be realizing terabit-per-second links and millimeter-level sensing/localization accuracy <cit.>.

§.§ THz-ISAC Waveform Design

THz channels usually have short delay spread and THz transceivers suffer from low PA efficiency (rapid decline on its saturated
output power). Besides, frequency selectivity becomes less severe. As a result, coherence bandwidth increases. Therefore, the single-carrier waveform is more preferred compared to the OFDM waveform <cit.>. Sensing integrated DFT-spread-orthogonal frequency-division multiplexing (SI-DFT-s-OFDM) can yield lower PAPR than OFDM while maintaining single-carrier characteristic, as reported in <cit.>. In addition, it brings a ten-fold improvement in velocity estimation of the moving target and significant enhancement on data rate, beneficial from the FGI approach, which is capable of reducing the CP overhead. In ISAC systems, communication and sensing channels may possess different properties. For instance, a sensing channel can expose a significant delay spread. Thus, different design criteria for communications and sensing waveforms should be considered in terms of cyclic prefix lengths and pilots <cit.>.

Another type of intensively studied waveform, so-called OTFS, can well handle the Doppler effect and accommodate the channel dynamics in the delay-Doppler domain. Nevertheless, it still can not meet the strict requirements of power amplifier efficiency and signal processing complexity. Similar to SI-DFT-s-OFDM, DFT-s-OTFS was proposed to address the high PAPR issue faced in the original OTFS <cit.>. In contrast to OTFS, DFT-s-OTFS can achieve strong robustness to Doppler spread compared to OFDM and DFT-s-OFDM and reach lower PAPR compared to its OTFS counterpart <cit.>. By imposing superimposed pilots in the delay-Doppler domain alongside the DFT precoding operation, THz ISAC system facilitates low-complexity iterative
channel estimation and data detection. Because of the aforementioned advantages, DFT-s-OTFS is the most promising candidate waveform for THz ISAC systems.

§.§ THz-ISAC Algorithm Development

The ISAC algorithms typically fall into three categories, i.e., the data-driven AI-based approaches, model-based approaches, and hybrid approaches (a combination of the former two). AI techniques rely on large-volume date sets for training customized NN models for sensing, localization, and signal detection, while tackling the mathematically intractable non-linearity issues from, e.g., phase noise and offset, power amplifier, and mutual coupling. On the contrary, for the model-based ISAC algorithms, the majority of them need to harness the well-justified domain knowledge and modeling, such as geometric relationship among the transceivers and the environmental objects, and take full advantage of channel sparsity in the form of rank deficiency of the channel matrix or a limited number of resolvable paths, as to obtain satisfactory performance.

Under the framework of THz ISAC, joint data detection (signal recovery) and sensing parameter estimation are conducted with multi-task NN in <cit.>. In a broad sense, the ML roles on ISAC can be classified into three categories:

* JSAC

* sensing-aided communications <cit.>

* communication-aided sensing <cit.>.

To be specific, the first category includes the following activities: JSAC waveform design, spatial beam pattern design,  inter- and self-interference cancellation, resource allocation, etc. Without any doubt, communications and sensing can be mutually beneficial for each other. In the category of sensing-aided communication, sensing information (treated as prior information), e.g., the location of the transmitter, receiver, and environmental objects, can be leveraged for enhancing the beam prediction/alignment and reducing the overhead of beam training as well as channel sounding <cit.>. In dynamic scenarios where the user is under mobility, regardless of low or high velocity, such sensing information can be utilized for predicting potential blockages and enabling smooth handovers <cit.>. Similarly, communication signals can also be exploited to boost the sensing performance during the data transmission phase. The back-scattered data signals can gradually refine/improve the sensing parameter estimation, similar to data-aided channel estimation in the literature <cit.>.

The DL algorithms alongside other counterparts, e.g., deep reinforcement learning and transfer learning, pave the way for the integrated detectors and estimators for both communications and sensing, in terms of e.g., sensing parameters estimation, interference mitigation/cancellation, beam tracking/prediction, and network resource allocation/management <cit.>. Meanwhile, it can successfully tackle the mathematically intractable non-linearity issues and hardware impairments in ISAC systems <cit.>. Furthermore, the latent features related to sensing parameters can be more readily learned and extracted by the adoption of DL algorithms. For instance, two sensing neural network
(SensingNet) models, one for range estimation and the other for velocity estimation, were proposed in <cit.>, composed of an input layer, a flatting
layer, five dense layers for feature extraction and nonlinear
mapping, and an average output layer. Meanwhile, a concatenated two-level communication neural network
(ComNet) model for data detection was developed, where the first level is leveraged for channel information acquisition and its output is further utilized in the second level. Combining both the SensingNet and ComNet models bring us a promising DL-empowered solution for THz ISAC systems. It should be noted that other DL variants can also be adopted for both sensing and communication applications.

In the DFT-s-OTFS system <cit.>, a two-stage sensing parameter estimation approach was proposed, i.e., coarse on-grid search in the first stage followed by refined off-grid search in the second stage for extracting the sensing parameters. Under the framework of ISAC, data detection and sensing parameter estimation can be performed in an iterative manner by considering the conjugate gradient method until a certain preset stopping criterion is reached. Besides, ISAC performance can be further enhanced by multi-domain cooperation through joint active and passive sensing, and multi-user and multi-frequency operations <cit.>. The tensor decomposition approach is capable of leveraging the channel sparsity and guaranteeing a unique solution for each environmental sensing parameter without any ambiguity <cit.>. Such sensed information can be then utilized to reconstruct a high-resolution indoor mapping to further boost the prediction of blockages and the availability of LoS path, and reduce the beam tracking frequency. Thus, higher spectrum efficiency in data transmission can be achieved accordingly.

The traditional model-based algorithms adopt ComS techniques, on-the-grid, off-the-grid, and the combination of the former two (e.g., in <cit.>), for extracting channel and sensing parameters by taking advantage of channel sparsity <cit.>. Model-driven end-to-end learning, falling into the category of hybrid approaches, for joint single target sensing and precoder design in MISO communication networks was studied in <cit.>. In particular, the authors jointly consider precoder design at the transmitter and target AoA estimation at the receiver by applying an AE while accounting for the hardware impairment. During the model-driven end-to-end learning, the matrix composed of steering vectors at discretized grid points is considered as trainable parameters to simplify the transmit precoder design and optimized by only maximizing the sensing-related performance metric <cit.>.

§.§ RIS-Boosted THz-ISAC

With the newly-introduced capability of manipulating the radio propagation environment, RIS is able to expand the communication coverage and enhance the sensing performance <cit.>. The potential roles that can be played by an RIS are multi-fold: scattering, reflecting, refraction, absorption, polarization, and diffraction. With all the preceding DoF, intelligent, programmable wireless propagation environments can be established for different tasks, e.g., communications, sensing, localization, and imaging. The RIS can enrich the LoS availability by establishing a virtual one when the real one is suffering from temporary blockage, which frequently occurs at THz frequencies.

The various benefits of integrating RIS into ISAC were discussed in <cit.>. The gains against the RIS-free counterpart heavily rely on the cross-correlation between the sensing and communication channels. The more the mutual coupling, the more gain can be accomplished in terms of ISAC performance. By introducing the RIS, enhanced flexibility and adaptation to channel dynamics is seen in altering the coupling level of these channels <cit.>. The importance of tight coupling of communications and localization was also emphasized in <cit.> for the purpose of harnessing the full potential of RIS. That is to say, the SLAC requires smart RIS control, co-design of communications and localization, and the flexible trade-off and reinforcement between the two functionalities.

In the rich RIS-boosted ISAC literature, various optimization problems are formulated with different objectives along with different constraints. These works can be cast into three different classes:

* sensing-centric design <cit.>

* communication-centric design <cit.>

* joint design and optimization <cit.>.

For the first category, the objective is sensing-oriented, while the communication metrics are taken as constraints. For instance, the authors of <cit.> maximized the SNR at the radar while considering a communication SNR constraint. By addressing this optimization problem, SDR along with bisection search was considered for transmit beamforming design while majority-minimization is considered for RIS design. With respect to the second category, the reference <cit.> takes interference among the communication users as the objective while treating the desired MSE of DoA estimation as a constraint. To be specific, the authors developed an alternating optimization algorithm for finding the optimal design of constant-modulus waveform and discrete RIS phase shifts for the RIS-assisted ISAC systems. For the former design, manifold optimization algorithm was considered, while for the later design, two schemes, one based on manifold optimization and the other based on successive optimization, were proposed. In terms of the last category, the weighted sum of two objectives, one for communications and the other for sensing, are usually considered <cit.>. Similar to that in <cit.>, an alternating optimization algorithm based on oblique manifold optimization alongside Riemannian steepest descent was introduced to jointly optimize the DFRC waveform and RIS phase shift matrix. All the above categories share some common constraints, e.g., individual transmit power, sum transmit power, hardware (especially for RIS, e.g., phase quantization, constant modulus of amplitude), etc. A holistic comparison among the three classes can be referred to <cit.>.

Recently, a more promising type of RIS, termed as STAR-RIS, was introduced, which is able to offer additional benefits thanks to its inherent dual-mode operation and full-dimensional coverage <cit.>. The STAR-RIS can concurrently reflect and refract the incident signals towards multiple desired MS. Because of this, the STAR-RIS can further boost the ISAC performance compared to the sole-reflection-type RIS <cit.> with extended flexibility. Either an outdoor or indoor BS is capable of providing both communications and sensing services to the users located indoors and outdoors by installing a STAR-RIS on a transparent glass window <cit.>.

§.§ Challenges and Solutions for THz-ISAC

The open problems for THz-ISAC are listed and discussed in <cit.>. For example, waveform design should be customized depending on sensing applications. Dynamic beamforming control faces great challenges since the beamwidth is narrow and highly directional <cit.>. As a consequence, the probability of beam misalignment can be inevitably high. A robust design of candidate beams for communication purposes requires a wider beamwidth. However, to enhance the sensing resolution and accuracy, narrow beams are preferred. Multiple concurrent beams comprise one fixed sub-beam for point-to-point communications and multiple time-varying sub-beams for sensing purposes can achieve a well-balanced performance between communications and sensing <cit.>. However, multiple simultaneous beams suffer from degraded beamforming gains.

Imperfections, resulting from IQ imbalance, PA nonlinear distortions, and phase noise at the local oscillator, need to be compensated for when designing robust THz-ISAC algorithms. The wide-band channel becomes highly selective with high Doppler spread, which may break the orthogonality of OFDM transmission and incur inter-carrier interference <cit.>. Besides, the near-filed propagation, where channel sparsity vanishes in the angular domain, makes the beamforming design intractable. However, a recent study <cit.> shows that sparse representations of the near-field channel from the polar domain are still available, making efficient CSI acquisition feasible with the aid of advanced compressive sensing techniques.

The beam squint effect makes the designed beam deviate from the exact one, resulting in reduced array gain and performance degradation <cit.>. The beam squint and split effect will become more obvious as the increase of carrier frequency and bandwidth, causing significant performance degradation on sensing and communication. As examined in <cit.> for a broadside target, beam split can reach as much as $4^\circ$ for 0.3 THz with 30 bandwidth while it is only $1.4^\circ$ for 60 with 2 bandwidth. This effect should be mitigated and compensated for when designing the beamforming patterns, such as DPP <cit.>. By introducing a time delay  network between the RF chains and
frequency-independent phase shifters in the hybrid precoding architecture, DPP is capable of performing frequency-dependent delay-phase controlled beamforming for reducing the array gain loss introduced by the beam split effect. The newly-introduced time delays can make beams aligned with the target physical directions across the entire bandwidth. Due to the user mobility and frequent blockage, beam misalignment occurs when LoS path is unavailable between the BS and MS. Provided that the user can be tracked and blockage can be predicted in advance, beam misalignment can be avoided. However, this requires high-precision sensing information. In line with the 5G CSI acquisition signals, the authors in <cit.> adopt SSB for blockage detection and RS for user tracking.

Until all the above-mentioned challenges are thoroughly addressed in the forthcoming years, the vision that everything will be sensed, connected, and intelligent can be fulfilled.

§ THZ TRIALS AND EXPERIMENTS

Summary of experimental and demonstration systems for THz Communications and Sensing.

Ref.     Year     Freq.[]     BW[]     Rate[]     Distance []     Contributions
<cit.>      2011     625     narrow N/A     2.5     0.2      A novel approach is reported for 2.5 signalling at a carrier frequency of 625. Duobinary baseband modulation on the transmitter side generates a signal with a sufficiently narrow spectral bandwidth to pass an upconverting frequency multiplier chain.
<cit.>      2013     237.5     35     100     20      Present for the first time, a single-input and single-output wireless communications system at 237.5 for transmitting data over 20 m at a data rate of 100 from combining terahertz photonics and electronics.
<cit.>      2015     240     32     64     850     A directive fixed wireless link operating at a center frequency of 240 achieves a data rate of 64 over a transmission distance of 850 m using QPSK and 8PSK modulation, in a single-channel approach without the use of spatial diversity concepts.
<cit.>      2017     140     N/A     5     21000     The 16QAM modulation scheme is used in baseband processing, and mixers are used for cascading frequency up and down-converting. Cascading power amplification technique is adopted with a solid-state power amplifier and a vacuum electronic device. A 21 km wireless communications testing is carried out, by means of two Cassegrain antennas with 50i gain each.
<cit.>      2019     375-500     30     120     1.42      The first experimental demonstration of 2 × 2 MIMO wireless transmission of multi-channel THz-wave signal, which realizes 6 x 20 six-channel polarization division multiplexing QPSK THz-wave signal delivery over 10 km wireline single-mode fiber-link and 142 cm wireless 2 x 2 MIMO link.
<cit.>      2020     335-365     30     600     2.8     Demonstration of a hybrid THz photonic-wireless transmission based on a THz orthogonal polarization dual-antenna scheme. Probabilistic shaped 64QAM-OFDM modulation format is used to realize a high transmission rate. A potential total system throughput of 612.65 is successfully achieved.
<cit.>      2020     300     40     115     110      A local-oscillator tone is transmitted along with the signal, and the amplitude and phase of the complex signal envelope are digitally reconstructed from the photocurrent by exploiting their Kramers–Kronig-type relation. Using Schottky-barrier diode as a nonlinear receiver element and 16-state QAM, a net data rate of 115 at a carrier frequency of 0.3 THz over a distance of 110 m is achieved.
<cit.>      2021     340     30     44.8     104     Demonstrates the capability of over 54/104 meters wireless transmission with a record-breaking net data rate of 128/44.8 at THz-band by utilizing both suitable dielectric lenses and DSP algorithms, without THz amplifier.
<cit.>      2021     231     79     240     115     The first transparent optical-THz-optical link providing record-high line-rates up to 240 and 190 over distances from 5 to 115 meters is demonstrated.
<cit.>       2022     340-510     37.7     103     3     Demonstrates a real-time fiber-THz-fiber 2 × 2 MIMO seamless integration system at 340–510 using commercial DCO modules for baseband signals processing, which realizes a record net rate of 103.125 DP-QPSK signals delivery over two spans of 20 km wireline single-mode fiber-link and 3 m wireless 2 × 2 MIMO link without using THz power amplifier.
<cit.>      2023     360-430     37.7     206     1      A novel UWB fiber-THz-fiber seamlessly converged real-time architecture, which utilizes the commercially mature digital coherent optical module to realize ultrahigh-capacity THz real-time wireless communication, is proposed.

In order to give readers an insightful view of the current status of the practical use of THz communications and sensing towards 6G and beyond, we summarize state-of-the-art THz trials and experiments worldwide in this section, where the achieved data rates at the certain THz bands with specific features are surveyed.

In the past decade, the electronic mixing technology was widely applied to generate  high-frequency THz signals by up-converting a low-frequency microwave signal, as the traditional way to realize THz transmission as listed in Table <ref> <cit.>. One of the most remarkable approaches was done by Bell Labs in 2011, where THz radiation at 625 was generated by using an all-solid-state electric mixer. It achieved a data rate of 2.5 at a distance of 0.2 under the transmission power of 1  <cit.>. In 2015, the researchers at the University of Stuttgart in Germany successfully transmitted 240 THz signals to the receiver at a distance of 850.  The trial achieved a peak data rate of 64 using QPSK and 8PSK modulation in a single-channel approach without the use of spatial diversity <cit.>. In the year 2017, a research team from the China Academy of Engineering Physics achieved ultra-long-distance THz wireless communications over up to 21 and realized single-channel transmission speed up to 5, taking advantage of two Cassegrain antennas with 50i gain each <cit.>. In the same year, the first demonstration of photonically-enabled independent side-bands D-Band wireless transmission up to 352 by means of $2\times2$ antenna polarization multiplexing was achieved <cit.>.

Because of the inherent properties of electronic devices, the parameters of high-frequency electronic devices gradually approach the theoretical limit, with relatively lower bandwidth and a limited transmission rate.
Recently, much attention was shifted to the photonics-assisted heterodyne beating technique for higher data rate and better signal quality, where the rates of THz transmission is able to reach hundreds of  or even  <cit.>. It should be pointed out that  the THz signal power generated by the photonics-assisted heterodyne beating method is usually limited to the  level because of the lower responsivity of the uni-traveling carrier photodiode (UTC-PD), resulting in the limited transmission distance. Therefore, some researchers utilized high-gain THz amplifiers or high-gain lens antennas to extend distances to 100. In early 2013, the researchers <cit.> utilized the large frequency range in the THz window between 200 and 300 to implement a SISO wireless 100 link with a carrier frequency of 237.5 over a distance of 20. Several years later, a team from Fudan University, China successfully applied $2\times2$ MIMO and wavelength division multiplexing (WDM) technologies at THz signal transmission, achieving a data rate of 120 by using QPSK modulation <cit.>. Meanwhile, some researchers at Zhejiang University in China achieved THz signal transmission of 600 using 64QAM multi-carrier modulation <cit.>. However, the distances of THz signal transmission of the above two approaches are only 1.42 and 2.8, respectively.

In the past three years, some research teams have presented prominent improvements in THz communications. The wireless transmission distances were effectively extended to more than 100 with the assistance of high-gain THz ampliﬁers or high-gain lens antenna. In 2020, a team at Karlsruhe Institute of Technology (KIT), Germany took advantage of THz amplifiers and the Kramers-Kronig method for simplifying the design of the receiver and launched an offline multi-carrier THz system. It offers a peak data rate of 115 at a carrier frequency of 300 over a distance of 110 <cit.>. One year later, the Fudan University in China successfully transmitted a 44.8 64QAM-modulated signal over a distance of 104 without using the THz amplifier but utilizing both suitable dielectric lenses and DSP algorithms <cit.>. In the same year, Yannik Horst et al. <cit.> from Switzerland demonstrated the transparent optical-THz-optical link, providing a transmission rate of 240 over a distance up to 115.

With the objective of achieving full-coverage and low-cost deployment towards future 6G mobile communications,  the priority of the hybrid optoelectronic down-conversation solution was presented <cit.> and <cit.>, where a novel fiber-THz-fiber seamlessly converged real-time architecture was successfully demonstrated. It adopts both dual-polarization photonic up-conversion for THz signal generation and hybrid optoelectronic down-conversion for THz reception, by thoroughly reusing commercial digital coherent optical modules. In the case of hybrid channel transmission with two hops consisting of a 20km-long fiber and 1m-long THz wireless link, a THz signal with a net rate of 206.25 was successfully transmitted real-timely <cit.>. It is also pointed out that zhe THz phased array techniques are key to realizing 6G THz mobile communications and sensing, which meets the needs of application scenarios, such as multiple users and beam tracking.

In addition to the excellent demonstrations and validations that have been achieved by research teams around the world, some equipment suppliers and organizations have also presented great advances in THz commercialization. The NYU WIRELESS is currently focusing on sub-THz bands at 140, 220, and higher. The radio-frequency integrated circuit (RFIC) probe stations working up to 220, and channel sounders for propagation measurement at 140 <cit.> are provided by the Keysight Technologies. Keysight has also closely corporated with Nokia Bell labs on the sub-THz testbed, which was chosen to verify the performance of transceiver modules, power amplifiers, and antennas under both linear and nonlinear conditions.  Recently, the Huawei 6G research team has developed and demonstrated THz integrated sensing and communications (THz-ISAC) prototype. Using wireless electromagnetic waves, the prototype can sense and produce images of blocked objects with millimeter-level resolution and communicates at an ultra-high rate of 240, opening up new service possibilities for 6G and beyond systems <cit.>.

§ CONCLUSIONS

In summary, the upcoming 6G and beyond cellular systems are envisioned to exploit the THz band beyond 100, which not only offers an abundant amount of spectral resources for globally ubiquitous, ultra-high-rate, super-reliable, hyper-low-latency, massive-density telecommunications services but also empowers high-resolution cognition through THz sensing, positioning, and imaging. The use of THz frequencies will bring novel applications such as tera-bits-per-second/Tbps hot spots or links, and, in addition, disruptive uses like nano-scale networks and on-chip communications.
Despite its high potential, we do not expect that the THz band can replace the sub-6GHz and mmWave bands, which have been employed as the basis of previous generations of cellular communications networks. Instead, the THz band is highly probably being used as the complementary resource to aid the success of low-frequency bands in future generations of cellular systems. Meanwhile, there is still tremendous work to be done in terms of characterizing and modeling THz channels, developing affordable, usable THz antennas and devices, designing novel algorithms for long-range THz signal transmission, proposing efficient protocols for flexible THz networking, and elaborately considering its synergy with other 6G-enabling technologies. It is hoped that this survey could be able to provide the researchers with a holistic view of all technical aspects and issues required to design and build THz communications and sensing for 6G and beyond from an application and implementation perspective. Although there is a long journey to go before the success of THz communications and sensing in 6G and beyond cellular systems, this survey might be able to speed up a bit the research endeavors.

§ LIST OF ACRONYMS

lofsectionList of acronyms

Wei Jiang (M'09-SM'19) received a Ph.D. degree in Computer Science from Beijing University of Posts and Telecommunications (BUPT) in 2008. From 2008 to 2012, he was with the 2012 Laboratory, HUAWEI Technologies. From 2012 to 2015, he was with the Institute of Digital Signal Processing, University of Duisburg-Essen, Germany. Since 2015, he has been a Senior Researcher with German Research Center for Artificial Intelligence (DFKI), the biggest European AI research institution and the birthplace of the “Industry 4.0" strategy. Meanwhile, he was a Senior Lecturer at the University of Kaiserslautern, Germany, from 2016 to 2018. He has published two monographs – 6G Key Technologies: A Comprehensive Guide (Wiley & IEEE Press, 2023) and Cellular Communication Networks and Standards: The Evolution from 1G to 6G (Springer, 2024), and contributed three book chapters in 5G and machine-learning-based communications. He has over 100 conference and journal papers, holds around 30 granted patents, and participated in a number of EU and German research projects: ABSOLUTE, 5G COHERENT, 5G SELFNET, 5G-ACIA, AI@EDGE, TACNET4.0, KICK, AI-NET-ANTELLAS, and Open6GHub. He received the best paper award in IEEE CQR 2022 and the best presentation award in IEEE CCAI 2023. He was the Guest Editor for the Special Issue on “Computational Radio Intelligence: A Key for 6G Wireless" in ZTE Communications (December 2019). He serves as an Associate Editor for IEEE Access (2019-2023), an Editor for IEEE Communications Letters and IEEE Open Journal of the Communications Society, and a Moderator for IEEE TechRxiv. He served as a member of the organizing committee or technical committee for many conferences such as IEEE ICASSP 2022, CCS 2014, PIMRC 2015/2020, GLOBECOM 2022/2023/2024, ICC 2023/2024, WCNC 2024, ICCC 2017/2021/2022/2023, HP3C 2022, HSPR 2024, and icWCSN 2022/2023. He was the founding member and vice chair of the special interest group (SIG) “Cognitive Radio in 5G” under the IEEE Technical Committee on Cognitive Networks (TCCN).

Qiuheng Zhou received his B.Sc degree in Electronics and Information Engineering from Tianjin University of Technology, China, in 2015. He obtained his M.Sc. degree in autonomous and networked driving from the University of Stuttgart, Germany, in 2020. Since April 2020, he has been working as a researcher in the intelligent networks of German Research Center for Artificial Intelligence (DFKI).  His main research interests include channel measurement, software-defined radio networks, machine learning, channel prediction, and resource allocation.

[]Jiguang He (M'20-SM'22)
received the Ph.D. degree from the University of Oulu, Finland, in 2018 on communications engineering. He is now a senior researcher at Technology Innovation Institute, Abu Dhabi, United Arab Emirates, and holds Docentship (adjunct professor) at the University of Oulu. From September 2013 to March 2015, he was with the State Key Laboratory of Terahertz and Millimeter Waves at the City University of Hong Kong, working on beam tracking over millimeter wave MIMO systems. From June 2015 to August 2021, he has been with the Centre for Wireless Communications (CWC), University of Oulu, Finland, first as a doctoral candidate, and then a postdoctoral researcher. He was an assistant professor at Macau University of Science and Technology from August 2021 to March 2022. He has participated in many international and national projects, e.g., EU FP7 RESCUE, EU H2020 ARIADNE, 6G Flagship, and received one FDCT-GDST joint research project from Macau Science and Technology Development Fund. He is an Exemplary Reviewer for IEEE Transactions on Communications as well as IEEE Communications Letters and a TPC member for various prestigious IEEE conferences. His research interests span millimeter wave MIMO communications, reconfigurable intelligent surfaces for simultaneous localization and communications (SLAC), and advanced signal processing techniques.

[]Mohammad Asif Habibi received his B.Sc. degree in Telecommunications Engineering from Kabul University, Afghanistan, in 2011. He obtained his M.Sc. degree in Systems Engineering and Informatics from the Czech University of Life Sciences, Czech Republic, in 2016. Since January 2017, he has been working as a research fellow and Ph.D. candidate at the Division of Wireless Communications and Radio Navigation, Rheinland-Pfälzische Technische Universität (previously known as Technische Universität Kaiserslautern), Germany.  From 2011 to 2014, he worked as a radio access network engineer for HUAWEI. His main research interests include network slicing, network function virtualization, resource allocation, machine learning, and radio access network architecture.

Sergiy Melnyk  received his Dipl.-Ing degree in Electronics and Information Engineering from Technische University Munich in 2012. Since April 2015, he has been working as a researcher in the Intelligent Networks Group of German Research Center for Artificial Intelligence (DFKI).  His main research interests include industrial communications systems,  software-defined radio networks, and lower-layer protocol design.

[]Mohammed El-Absi is currently working as a Senior Researcher at Digital Signal Processing Institute at University of Duisburg-Essen, Duisburg, Germany, where he received his Ph.D. degree (Summa Cum Laude) in electrical engineering in 2015. He received the M.S. degree in electrical engineering in 2008 from Jordan University of Science and Technology and the B.E. degree in electrical engineering in 2005 from Islamic University of Gaza, Gaza, Palestine. He received a Mercator fellow at the Collaborative Research Center "Mobile Material Characterization and Localization by Electromagnetic Sensing" (MARIE) in the period of 2017-2018. He received the German Academic Exchange Service Fellowship in 2006 and 2011. He is currently contributing in 6G research hub for open, efficient and secure mobile radio systems (6GEM) and Collaborative Research Center "Mobile Material Characterization and Localization by Electromagnetic Sensing" (MARIE). He is a principal investigator in the excellent terahertz research for communication, localization, material characterization, medical technology and environmental monitoring (terahertz.NRW). His research interests are in the area of communications and signal processing.

Bin Han (M'15-SM'21) received his B.E. degree in 2009 from Shanghai Jiao Tong University, M.Sc. in 2012 from the Technical University of Darmstadt, and a Ph.D. degree in 2016 from Karlsruhe Institute of Technology. Since July 2016 he has been with the Division of Wireless Communications and Radio Positioning, RPTU Kaiserslautern-Landau (formerly: Technical University of Kaiserslautern) as a Postdoctoral Researcher and Senior Lecturer. His research interests are in the broad area of wireless communications and networking, with the current focus on B5G/6G and MEC. He is the author of one book, five book chapters, and over 50 research papers. He has participated in multiple EU research projects. He is Editorial Board Member for Network, Guest Editor for Electronics, and has served as Organizing Committee Member and/or TPC Member for GLOBECOM, ICC, EuCNC, EW, and ITC. He is a voting member of the IEEE Standards Association Working Groups P2303 and P3106.

Marco Di Renzo (Fellow, IEEE) received the Laurea (cum laude) and Ph.D. degrees in electrical engineering from the University of L’Aquila, Italy, in 2003 and 2007, respectively, and the Habilitation à Diriger des Recherches (Doctor of Science) degree from University Paris-Sud (currently Paris-Saclay University), France, in 2013. Currently, he is a CNRS Research Director (Professor) and the Head of the Intelligent Physical Communications group in the Laboratory of Signals and Systems (L2S) at Paris-Saclay University – CNRS and CentraleSupelec, Paris, France. Also, he is an elected member of the L2S Board Council and a member of the L2S Management Committee, and is a Member of the Admission and Evaluation Committee of the Ph.D. School on Information and Communication Technologies, Paris-Saclay University. He is a Founding Member and the Academic Vice Chair of the Industry Specification Group (ISG) on Reconfigurable Intelligent Surfaces (RIS) within the European Telecommunications Standards Institute (ETSI), where he served as the Rapporteur for the work item on communication models, channel models, and evaluation methodologies. He is a Fellow of the IEEE, IET, and AAIA; an Academician of AIIA; an Ordinary Member of the European Academy of Sciences and Arts, an Ordinary Member of the Academia Europaea; and a Highly Cited Researcher. Also, he holds the 2023 France-Nokia Chair of Excellence in ICT, and was a Fulbright Fellow at the City University of New York (USA), a Nokia Foundation Visiting Professor (Finland), and a Royal Academy of Engineering Distinguished Visiting Fellow (UK). His recent research awards include the 2021 EURASIP Best Paper Award, the 2022 IEEE COMSOC Outstanding Paper Award, the 2022 Michel Monpetit Prize conferred by the French Academy of Sciences, the 2023 EURASIP Best Paper Award, the 2023 IEEE ICC Best Paper Award, the 2023 IEEE COMSOC Fred W. Ellersick Prize, the 2023 IEEE COMSOC Heinrich Hertz Award, the 2023 IEEE VTS James Evans Avant Garde Award, and the 2023 IEEE COMSOC Technical Recognition Award from the Signal Processing and Computing for Communications Technical Committee. He served as the Editor-in-Chief of IEEE Communications Letters during the period 2019-2023, and he is now serving on the Advisory Board. He currently serves as a Voting Member of the Fellow Evaluation Standing Committee and as the Director of Journals of the IEEE Communications Society.

Hans D. Schotten (S'93-M'97) received the Ph.D. degree from the RWTH Aachen University of Technology, Germany, in 1997. From 1999 to 2003, he worked for Ericsson. From 2003 to 2007, he worked for Qualcomm. He became manager of a R&D group, Research Coordinator for Qualcomm Europe, and Director for Technical Standards. In 2007, he accepted the offer to become a full professor at the University of Kaiserslautern. In 2012, he - in addition - became the scientific director of the German Research Center for Artificial Intelligence (DFKI) and head of the Department for Intelligent Networks. Professor Schotten served as dean of the Department of Electrical Engineering of the University of Kaiserslautern from 2013 until 2017. Since 2018, he is chairman of the German Society for Information Technology and a member of the Supervisory Board of the VDE. He is the author of more than 200 papers and participated in 30+ European and national collaborative research projects.

Fa-Long Luo (SM'95-F'16) has served as a Board Member of both the Conference Board and the Membership Board of the IEEE Signal Processing Society (SPS) as well as an IEEE Fellow Committee Member. Dr. Luo served as the Society Representative of SPS in the IEEE TAB Committee on Standards.  He was the Chairman of the Industry DSP Technology Standing Committee (IDSP-SC) and a Technical Directions Board Member of IEEE SPS as well as a founding member of the IoT SIG of SPS.  He was the founding Editor-in-Chief of the International Journal of Digital Multimedia Broadcasting.
He has 39 years of academic, industry, and research experience with an emphasis on translating adaptive signal processing techniques to practical applications and commercial products related to multimedia, wireless communications, and digital broadcasting.  Including his well-received books: “Applied Neural Networks for Signal Processing” (1997, Cambridge University Press) and “Signal Processing for 5G: Algorithms and Implementations” (2016, Wiley-IEEE), Dr. Luo has published 7 books and more than 100 technical papers in the related fields.  Dr. Luo has also contributed 115 USA patents (issued or pending) which have successfully resulted in a number of new or improved commercial products in mass production.

Tarek S. El-Bawab (Fellow, IEEE) is the Dean of the School of Engineering and Professor of Electrical and Computer Engineering at the American University of Nigeria. Before this, he was Professor and Dean of Engineering and Applied Sciences at Nile University (Egypt), Professor of Electrical and Computer Engineering at Jackson State University (USA), and Project Manager with the Network Strategy Group of Alcatel-Lucent USA (now Nokia). Earlier, he assumed research roles with Alcatel-Lucent, Colorado State University (USA), and the University of Essex (UK). Before this he led large-scale international telecommunication projects in the Middle East and Africa for 10 years.

Dr. El-Bawab research interests include telecommunications, network architectures, performance analysis, and Discipline Based Education Research (DBER). He has more than 80 scholarly journal/conference papers and patents. His book Optical Switching is one of the most comprehensive references in its subject. He is the Series Editor of Springer’s Textbooks in Telecommunication Engineering, the Editor in Chief of the IEEE Communications Magazine (2017-2021), IEEE Distinguished Lecturer (2016-2019), Eta Kappa Nu (HKN) member, and NSF Review Panelist. Tarek led the Telecommunication Engineering Education (TEE) initiative and movement (2008-2014), which resulted in recognition of network/telecommunication engineering as distinct ABET-accreditable education discipline. He is the first recipient of the IEEE Communications Society’s (ComSoc) Education Award due to this work (2015).

He has served IEEE and ComSoc in numerous capacities. He serves/served as Board Member of the IEEE Educational Activities Board, EAB (2016-2017), as Board Member of the IEEE PSPB’s Thesaurus Editorial Board (2021-2023), as Board Member of the ComSoc Board of Governors (2014-2015, 2018-2019, and 2020-2021), and Board Member of the ComSoc Educational Services Board (2012-2019). He served as the ComSoc Director of Industry Communities (2020-2021), Director for Standards Development (2018-2019) and Director of Conference Operations (2014-2015). He was elected as Chair of the Transmission, Access, and Optical Systems (TAOS) Technical Committee for two terms, and as chair in several ICC/Globecom Conferences. Dr. El-Bawab has B.Sc. in electrical engineering from Ain Shams University (Egypt), M.Sc. in solid state science from the American University in Cairo, M.Sc. in telecommunications and information systems from the University of Essex (UK), and Ph.D. in electrical engineering from Colorado State University (USA).

[]Markku Juntti (Fellow, IEEE) received his M.Sc. (EE) and Dr.Sc. (EE) degrees from University of Oulu, Oulu, Finland in 1993 and 1997, respectively.
Dr. Juntti was with University of Oulu in 1992–98. In academic year 1994–95, he was a Visiting Scholar at Rice University, Houston, Texas. In 1999–2000, he was a Senior Specialist with Nokia Networks in Oulu, Finland. Dr. Juntti has been a professor of communications engineering since 2000 at University of Oulu, Centre for Wireless Communications (CWC), where he leads the Communications Signal Processing (CSP) Research Group. He also serves as Leader of CWC – Radio Technologies (RT) Research Unit. His research interests include signal processing for wireless networks as well as communications and information theory. He is an author or co-author in almost 500 papers published in international journals and conference records as well as in books Wideband CDMA for UMTS in 2000–2010, Handbook of Signal Processing Systems in 2013 and 2018 and 5G Wireless Technologies in 2017. Dr. Juntti is also an Adjunct Professor at Department of Electrical and Computer Engineering, Rice University, Houston, Texas, USA.

Dr. Juntti is an Editor of IEEE Transactions on Wireless Communications, and served previously in a similar role in IEEE Transactions on Communications and IEEE Transactions on Vehicular Technology. He was Secretary of IEEE Communications Society Finland Chapter in 1996–97 and the Chairman for years 2000–01. He has been Secretary of the Technical Program Committee (TPC) of the 2001 IEEE International Conference on Communications (ICC), and the Chair or Co-Chair of the Technical Program Committee of several conferences including 2006 and 2021 IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), the Signal Processing for Communications Symposium of IEEE Globecom 2014, Symposium on Transceivers and Signal Processing for 5G Wireless and mm-Wave Systems of IEEE GlobalSIP 2016, ACM NanoCom 2018, and 2019 International Symposium on Wireless communications Systems (ISWCS). He has also served as the General Chair of 2011 IEEE communications Theory Workshop (CTW 2011) and 2022 IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[]Mérouane Debbah (Fellow, IEEE) is a professor at  Khalifa University of Science and Technology in Abu Dhabi and founding Director of the KU 6G Research Center. He is a frequent keynote speaker at international events in the field of telecommunication and AI. His research has been lying at the interface of fundamental mathematics, algorithms, statistics, information, and communication sciences with a special focus on random matrix theory and learning algorithms. In the Communication field, he has been at the heart of the development of small cells (4G), Massive MIMO (5G), and Large Intelligent Surfaces (6G) technologies. In the AI field, he is known for his work on Large Language Models, distributed AI systems for networks, and semantic communications. He received multiple prestigious distinctions, prizes, and best paper awards for his contributions to both fields. He is an IEEE Fellow, a WWRF Fellow, an EURASIP Fellow, an AAIA Fellow, an Institut Louis Bachelier Fellow, and a Membre émérite SEE.