SBSs and the UEs are randomly distributed within a finite region according to
mutually-independent FHPPPs having densities $\phi_{\text{M}}$,
$\phi_{\text{S}}$ and $\phi_{\text{U}}$, respectively. In this way, following
the mmWave channel model in [225], the power received by each node can be
expressed as

$P_{\text{r}}=P_{\text{t}}h_{\text{t,r}}G_{\text{t,r}}{L}_{(1\text{m})}L_{\text{t,r}}\left|\left|\mathbf{x}_{\text{t}}-\mathbf{x}_{\text{r}}\right|\right|^{-1}.$
(8)

where $\mathbf{x}_{\text{t}},\mathbf{x}_{\text{r}}$ are the locations of the
transmitter and receiver, respectively. Moreover, $h_{t,r}$ denotes the small-
scale fading, $P_{\text{t}}$ is the transmit power, and $G_{t,r}$ represents
the combined antenna gain of the transmitter and the receiver of the
considered link. Also, $L_{t,r}$ and ${L}_{(1m)}$ are the path loss due to
propagation and the reference path loss at 1 meter distance, respectively. In
the simulations, we model the small-scale fading by a normalized Rayleigh
random variable. Using the 5GCM UMa close-in model [226], the path loss, in
dB, is given by

$\text{PL}=32.4+10\alpha\log_{10}\left(\frac{r}{1\textrm{
m}}\right)+20\log_{10}\left(\frac{f_{\text{c}}}{1\textrm{ GHz}}\right),$ (9)

with $r$ denoting the propagation distance between the nodes, $f_{\text{c}}$
being the carrier frequency, and $\alpha$ is the path loss exponent. Depending
on the blockage, NLoS and LoS links are affected by different path loss
exponents, and the propagation loss in the link between nodes $i$ and $j$ is
obtained by

$L_{\text{i,j}}=\begin{cases}(r/1\textrm{ m})^{\alpha_{\text{{N}}}},&\text{if
NLoS,}\\\ (r/1\textrm{ m})^{\alpha_{{{\text{L}}}}},&\text{if
NLoS.}{}\end{cases}$ (10)

For the blockage, we use the germ grain model [227, Chapter 14], which is an
accurate model for environments with large obstacles because it takes the
obstacles induced blocking correlation into account. Here, the blockages are
distributed according to an FHPPP distributed with density
$\lambda_{\text{B}}$ in the same area as the other nodes. Also, all blockings
are assumed to be walls of length $l_{\text{B}}$ and orientation $\theta$,
which is an IID uniform random variable in $[0,2\pi)$.

Modeling the beam pattern as a sectored-pattern antenna array, the antenna
gain between two nodes is obtained by [225]

$G_{i,j}(\varphi)=\begin{cases}G_{0}&\frac{-\theta_{\text{HPBW}}}{2}\leq\varphi\leq\frac{\theta_{\text{HPBW}}}{2}\\\
g(\varphi)&\text{otherwise,}\end{cases}$ (11)

with $i,j$ denoting the indices of the transmit and receive nodes, $\varphi$
representing the angle between them and $\theta_{\text{{HPBW}}}$ being the
half power beamwidth of the antenna. Also, $G_{0}$ denotes the directional
antenna’s maximum gain and $g(\varphi)$ gives the side lobe gain.

In our analysis, the inter-UE interference is neglected, motivated by the low
power of the devices and with the assumption of sufficient isolation. Also,
motivated by the high beamforming capability in the IAB-IAB backhaul links, we
ignore the interference and assume them to be noise-limited. Then, the UEs are
served by either an MBS or an SBS following open access strategy and based on
the maximum average received power rule. Also, we follow the same approach as
in [221] to determine the bandwidth allocation in the access and backhaul of
each SBS proportional to its load and the number of UEs in the access link
such that the network coverage probability is maximized.

In Figs. 18 and 19, we compare the performance of IAB and fiber-connected
networks, in terms of coverage probability, i.e., the probability of the event
that the UEs’ minimum target rate requirements are satisfied. Note that, in
practice, a number of SBSs may have access to fiber. For this reason, the
figures also present the results for the cases with a fraction of SBSs being
fiber-connected. In this case, we assume the fiber-connected SBSs to be
randomly distributed, and adapt the association, the resource allocation rule
and the achievable rates correspondingly.

Figure 18:  Density of IAB nodes providing the same coverage probability as in
fiber-backhauled networks. The parameters are set to
$(\alpha_{\text{LoS}},\alpha_{\text{NLoS}})=(2,3)$, $l_{\text{B}}=5$ m,
$f_{\text{c}}=28$ GHz, bandwidth$=1$ GHz and
${P_{\text{MBS}},P_{\text{SBS}},P_{\text{UE}}}=(40,24,0)$ dBm.

Figure 19: Service coverage probability as a function of percentage of fiber-
backhauled SBSs, $(\alpha_{\text{LoS}},\alpha_{\text{NLoS}})=(2,3)$,
$l_{\text{B}}=5$ m, $f_{\text{c}}=28$ GHz, bandwidth$=1$ GHz and
${P_{\text{MBS}},P_{\text{SBS}},P_{\text{UE}}}=(40,24,0)$ dBm.

Fig. 18 shows the required number of IAB nodes that guarantee the same
coverage probability as in the cases with (partially) fiber-connected SBSs.
Moreover, Fig. 19 demonstrates the coverage probability versus the fraction of
fiber-connected SBSs. The figure also compares the performance of the IAB
network with the cases having only MBSs. In each simulation setup, we have
followed the same approach as in [221] to optimize different parameters, such
as resource allocation and UE association, such that the coverage probability
is maximized. The details of the simulation parameters are given in the
figures’ captions.

As can be seen in the figures, IAB network increases the coverage probability,
compared to the cases with only MBSs, significantly (Fig. 19). For a broad
range of parameter settings, the IAB network can provide the same coverage
probability as in the fiber-connected network with relatively small increment
in the number of IAB nodes. For instance, consider the parameter settings of
Fig. 18 and the UEs’ target rate threshold of 100 Mbps. Then, a fully fiber-
connected network with the SBSs density $\lambda_{\text{s}}=60\text{ km}^{-2}$
corresponds to an IAB network with density $\lambda_{s}=85\text{ km}^{-2}$
resulting in coverage probability $0.81$. Also, interestingly, providing
$30\%$ of the SBSs with fiber connection reduces the required density to
$\lambda_{\text{s}}=70\text{ km}^{-2}$, i.e., only $16\%$ increment in the
number of SBSs. Then, as the network density increases, the relative
performance gap of the IAB and fiber-connected networks decrease (Fig. 18).
Finally, it is interesting to note that our results, which are based on FHPPP,
give a pessimistic performance of the IAB networks. The reason is that, in
practice, the network will be fairly well planned, resulting in even less gap
between the performance of IAB and fiber-connected networks.

Such a small increment in the number of fiber-free SBSs, i.e., IAB nodes,
leads to the following benefits:

  * •

_Network flexibility increment_ : As opposed to fiber-connected networks,
where the APs can be installed only in the places with fiber connection, the
IAB nodes can be installed in different places as long as they have fairly
good connection to their parent nodes. This increases the network flexibility
and the possibility for topology optimization remarkably.

  * •

_Network cost reduction_ : An SBS is much cheaper than fiber444In general, the
fiber cost varies vastly in different regions. However, for different areas,
fiber installation accounts to a significant fraction, in the order of $80\%,$
of the total network cost. For instance, as illustrated in [228, Table 7], in
an urban area the fiber cost is in the range of 20000 GBP/km, while an SBS in
5G is estimated to cost around 2500 GBP per unit [229]. Moreover, different
evaluations indicate that, for dense urban/suburban areas, even in the
presence of dark fiber, the IAB network deployment is an opportunity to reduce
the total cost of ownership.

  * •

_Time-to-market reduction_ : Fiber laying may take a long time, because it
requires different permissions and labor work. In such cases, IAB can
establish new radio sites quickly. Consequently, starting with IAB and,
if/when required, replacing it by fiber is expected to be a common setup.

Along with these benefits, different evaluations indicate that, unless for the
cases with suburban areas and moderate/high tree foliage, with proper antenna
height and network density the performance of the IAB network is fairly robust
to blockage, rain and tree foliage, which introduces it as a reliable system
in different environments. These are the reasons that different operators have
shown interest to implement IAB in 5G networks [230], [231], and the trend is
expected to increase in beyond 5G/6G networks.

###  IV-E Integrated Space and Terrestrial Networks

Figure 20: Illustration of the ISTN architecture and its three layers. Yellow
line: free space optics link; green line: mmWave link. Blue line: other
microwave link).

In parallel with the development of terrestrial mobile systems such as 5G,
another major international effort in wireless communications is the
development of space communications networks which enable global coverage at
any time and from anywhere, such as on the sea, over the air and space, and in
rural and remote areas. The concept of using various space platforms to
perform data acquisition, transmission and information processing has been
around for several tens of years. However, due to the limited bandwidth and
large transmission delay, existing space networks alone cannot provide the
sufficient capacity and guaranteed quality-of-service to meet the ever
increasing demand for global wireless connectivity. Seamlessly interconnecting
space networks with terrestrial networks to support truly global high-speed
wireless communications will be one of the major objectives of the 6G wireless
systems [232].

Fig. 20 shows the architecture of a typical ISTN which consists of three
layers: the spaceborne network layer, the airborne network layer, and the
conventional ground-based network layer. The spaceborne network consists of
various orbiting satellites such as the geostationary-Earth orbit (GEO)
satellites, medium-Earth orbit (MEO) and LEO satellites, and mini satellites
known as CubeSats. The airborne network consists of various aerial platforms
including stratospheric balloons, airships and aircrafts, UAVs, and HAPSs. The
traditional ground-based networks include wireless cellular networks,
satellite ground BSs, mobile satellite terminals, and many more. The
integrated network can make full use of the signal propagation characteristics
of large space coverage, low loss LoS transmission, etc. to achieve seamless
high-speed communications with global coverage.

There are a number of grand technological challenges to achieve the ISTN with
high capacity and low cost. The two predominant bottlenecks that limit the
current ISTN development are the available bandwidth for high-speed aerial
backbones and the spectral efficiency for direct air-to-ground communications
between the airborne and ground based networks. Aerial backbones uses high-
speed links to connect the major nodes in the airborne network and between the
airborne and ground based networks. They play a pivotal role in the ISTN by
handling the aggregation and distribution of various data flows such as voice,
video, Internet, and other data sources. Because it sits in-between the
spaceborne network and ground based network, the airborne network is an
indispensable and important intermediate layer in the ISTN and hence the high-
speed, flexible, and all-weather mobile aerial backbones become the important
infrastructure to support the services provided by the airborne network.
Unfortunately, existing aerial backbones mainly use microwave links with very
limited bandwidth and, hence, cannot meet the requirements of the future high
capacity ISTN. Frequency reuse can be easily achieved in ground based cellular
networks by limiting the coverage area of each cell within its cell boundary
in order to improve the spectral efficiency, but this is not true for
communications between the space and terrestrial networks. Because the
satellite is far away from the ground, the coverage of a narrow communication
beam will be very large when it reaches the ground, resulting in very low
spectral efficiency. For the given available bandwidth, low frequency reuse
will significantly affect the overall capacity of the ISTN.

The capacity of the aerial backbones is proportional to the bandwidth. The
available bandwidth is limited in lower bands to a few MHz up to a few hundred
MHz. One way to increase the bandwidth is to use a higher frequency band (such
as mmWave or THz). However, then the signal propagation is affected by
atmospheric losses, and the communication distance is greatly reduced. Another
way to increase the capacity is to utilize multiple antennas to perform
spatial multiplexing, but the size and weight limits the number of antennas
that can be deployed on a satellite.

Taking all the above ways into consideration, it is believed that using an
mmWave communication system is the best choice for realizing the high-speed RF
backbone with data rate up to 100 Gbps. Due to the over 10 GHz bandwidth in
the mmWave band (such as the 71-76 and 81-86 GHz E-band), it can meet the
spectrum requirements of the system. The increase in transmit power can be
solved with a high power amplifier combined with an antenna array. The
atmospheric attenuation is below 0.3 dB/km and the total atmospheric and
cumulus loss (assuming 50 km link distance) is estimated to be only about 10
dB. Multi-channel transmission can also be implemented using line-of-sight
multiple input multiple output technology (LoS-MIMO).

Although being superior to microwave systems, currently existing mmWave links
still cannot meet the requirements of the high capacity ISTN due to the lack
of advanced technologies. For instance, in-band full duplex has not been
adopted due to the difficulty in cancelling self-interference especially in
mmWave frequencies, thus only reaching 50% of the potential data rate.
Further, although LoS-MIMO has been considered to achieve spatial
multiplexing, the rank of the channel is limited since the propagation
distance is large and the antenna spacing is small. Finally, yet importantly,
to reach longer communication distance, sufficiently high transmit power is
necessary. Even with the most powerful 40 dBm solid-state power sources in
mmWave bands, achieving longer communication distance beyond 50 km is still
very difficult. This calls for the employment of massive, preferably conformal
and reconfigurable, antenna arrays.

Other enabling technologies to realize high-speed and low-cost ISTN include
the modelling of the mmWave aeronautical transmission channels and the dynamic
behaviours of spaceborne and airborne networks especially when LEO satellites
are involved, three-dimensional (Space-Air-Ground) networking and
optimization, and high-speed communication protocol optimization.

###  IV-F Integrated Wideband Broadcast Networks

The demands on network capabilities continue to increase, among other aspects,
in terms of capacity, availability and cost. These increasing demands could be
challenging to networks that only support unicast and, more precisely, when
there are more users that require simultaneous service than the APs are able
to separate by beamforming. When some of those users request the same data at
the same time, broadcast and multicast are suitable transport mechanisms
[233]. They are great options for large-scale delivery, since they permit the
transmission of the same content to a vast number of devices within the
covered area simultaneously, and with a predefined quality-of-service.

Future 6G delivery networks need to be as flexible as possible to respond to
the needs of service providers, network operators and users. Hence, broadcast
and multicast mechanisms could be integrated into 6G networks as a flexible
and dynamic delivery option to enable a cost-efficient and scalable delivery
of content and services in situations where the APs have limited beamforming
capabilities. More precisely, if the AP can only send wide beams, then it
should be able to broadcast information over the coverage area of that beam.

6G also represents a great opportunity for the convergence of mobile broadband
and traditional broadcast networks, usually used for TV video broadcasting
[234]. Low-Power Low-Tower cellular networks with adaptive beamforming
capabilities would benefit from the complementary coverage provided by High-
Power High-Tower broadcast networks with fixed antenna patterns [235], as
shown in Fig. 21. A technology flexible enough to efficiently distribute
content over any of these networks would allow the 6G infrastructure to better
match the needs of future consumers and make more efficient use of the
existing tower infrastructure. The convergence could be addressed from
different perspectives, i.e., the design of a single and highly efficient
radio physical layer; the use of common transport protocols across fixed and
mobile networks including broadcast, multicast and unicast services; or a
holistic approach that allows client applications running on handsets to
better understand and, therefore, adapt to the capabilities of the underlying
networks.

Figure 21: Network convergence and different topologies involved.

In Europe, it was decided to “ensure availability at least until 2030 of the
470-694 MHz (‘sub-700 MHz’) frequency band for the terrestrial provision of
broadcasting services” [236]. Some assignations are already happening in the
USA, where the 600 MHz frequency band is being assigned to mobile broadband
services [237]. The timing is very well aligned with the release of 6G, and it
is now when a full convergence could take place.

A potential solution would be the use of a 6G wideband broadcasting system,
proposed in [238, 239]. By using 6G wideband, all RF channels within a
particular frequency band could be used on all high-power high-tower
transmitter sites (i.e., frequency reuse-1). This is drastically different
from current broadcasting networks, where usually a frequency reuse-7 is used
to avoid inter-cell interference among transmitters. The entire wideband
signal requires only half the transmission power of a single traditional
digital RF channel, as shown in Fig. 22. Another advantage is that a similar
capacity could be obtained, thanks to the use of a much more robust modulation
and coding rate combination, since the whole frequency band is employed. In
terms of power, 6G wideband also permits to transmit about 17 dB less power
(around 50 times) per RF channel (considering a bandwidth of 8 MHz, typical
from digital terrestrial broadcasting channels), although using more RF
channels per station. This leads to a total transmit power saving of around
90% [238]. Thanks to this higher spectrum use, the approach allows not only
for a dramatic reduction in fundamental power and cost, but also about a
37-60% increase in capacity for the same coverage as with current services.

Figure 22: 6G wideband concept and comparison with traditional digital
broadcast (Source: [238]).

##  V Enablers at the Protocol/Algorithmic Level

The third factor in (1) is the spectral efficiency, which is closely related
to the modulation and coding, interference management, and resource management
in general. These are some of the enabling technologies covered in this
section.

###  V-A Coding, Modulation, Waveform, and Duplex

####  V-A1 Channel Coding

From 4G to 5G, the peak data rate has increased by 10-100 times, and this
trend is likely to continue with 6G. The throughput of a single decoder in a
6G device will reach hundreds of Gbps. Infrastructure links are even more
demanding since they aggregate user throughput in a given cell or virtual
cell, which is expected to increase due to spatial multiplexing. However, it
will be difficult to achieve such a high throughput, only relying on the
progress of integrated circuit manufacturing technology within ten years.
Solutions must be found on the algorithm side as well. Both FEC code design
and corresponding encoding/decoding algorithms need to be taken into account
to reduce the decoding iterations and improve the decoder’s level of
parallelism. Moreover, it is vital for the decoder to achieve reasonably high
energy efficiency. To maintain the same energy consumption as in current
devices, the energy consumption per bit needs to be reduced by 1-2 orders of
magnitude. Implementation considerations such as area efficiency (in
Gbps/mm2), energy efficiency (in Tb/J), and absolute power consumption (W)
place huge challenges on FEC code design, decoder architecture, and
implementation [240].

FEC code design implies trade-offs between communications performance and high
throughput. High communications performance typically requires complex
decoding algorithms and large number of iterations to achieve near maximum-
likelihood performance and large block lengths to approach the Shannon bound.
On the other hand, 6G communication systems require flexibility in the
codeword length and coding rate. The most commonly used coding schemes today
are turbo codes, polar codes, and LDPC. Their performance and throughput have
already been pushed towards the limit using 16 nm and 7 nm technology [240].
Trade-offs must be made between parallelization, pipelining, iterations, and
unrolling, linking with the code design and decoder architecture. Future
performance and efficiency improvements could come from CMOS scaling, but the
picture is quite complex [241]. Indeed, trade-offs must be made to cope with
issues such as power density/dark silicon, interconnect delays, variability,
and reliability. Cost considerations render the picture even more complex:
costs due to silicon area, design effort, test, yield and masks, explode at 7
nm and below.

The channel coding scheme used in 6G high-reliability scenarios must provide a
lower error floor and better “waterfall” performance than that in 5G. Short
and moderate length codes with excellent performance need to be considered.
Polar codes, due to their error correction capabilities and lack of error
floor, might be the preferred choice in 6G. However, state-of-the art CA-SCL
decoding does not scale up well with throughput due to the serial nature of
the algorithm. As a result, iterative algorithms like BP which are more
parallelizable have become a prime candidate for channel decoding in high
throughput data transmissions in 6G. However, as of now for polar codes, there
exists a significant performance gap between state-of-the art CA-SCL decoding
and BP. Hence, effort has been made towards improving the performance of
iterative algorithms. In [242], authors propose a novel variant of BP called
multi-trellis belief propagation which is based on permuted factor graphs of
the polar code. Further improvements to the algorithm are investigated in
[243, 244]. In [245], along with a discussion on implication of various
aspects of the polar code factor graph on the performance, a new variant of
the multi-trellis BP decoder is proposed based on permutation of a subgraph of
the original factor graph (Fig. 23). This enables the decoder to retain
information about variable nodes in the subgraphs, which are not permuted,
reducing the required number of iterations needed in-between the permutations.
Even though the iterative algorithms are prime candidates in high throughput
applications, progress has been made on reducing the latency and improving the
parallelizability of algorithms based on successive cancellation. In [246]
authors propose methods to calculate the information bits without traversing
the complete binary tree on sub trees with specific information bit and frozen
bit patterns resulting in latency improvements. Same idea is extended to
successive cancellation list decoding in [247]. Work in [248] proposes some
new algorithms to process new types of node patterns that appear within
multiple levels of pruned sub-trees, and it enables the processing of certain
nodes in parallel improving the throughput of the decoder. Furthermore,
modified polar code constructions can be adopted to improve the performance of
belief propagation by selecting the information bits such that the minimum
stopping set is larger [249]. In [250] similar approach is adopted to improve
the latency in SC decoding. Here, the polar code construction is formulated as
an optimization problem which minimizes the complexity under a mutual
information based performance constraint. Another potential avenue of
improving the throughput is the use of deep neural networks to approximate the
iterative decoders which is called as deep unfolding [251, 252]. These
approaches could lead to better communication performance but they will
require significant advances in understanding the behavior, robustness, and
generalization of neural networks.

Figure 23: Partially permuted factor graph for polar code of block length 8.

####  V-A2 Modulation and Waveform

Modulation is another aspect that can be revised in 6G. High-order QAM
constellations have been used to improve spectral efficiency in high SNR
situations. However, because of the non-linearity of hardware, the benefits
obtained in higher-order QAM constellations are gradually disappearing. Non-
uniform constellations have been adopted in the ATSC 3.0 standard for
terrestrial broadcasting [253]. Probabilistic shaping schemes as in [254, 255]
provide in general even better performance and are nowadays widely used for
optical fiber communication, and they can be employed for wireless backhaul
channels [256]. Significant shaping gains can already be achieved by simple
modifications of the 5G NR polar coding chain [257, 258, 259]. Similar
approaches may be used to generate transmit symbol sequences with arbitrary
probability distributions optimized for different channel models (see e.g.
[260, 261, 262] and the references therein).

Reducing the PAPR is another important technology direction in order to enable
IoT with low-cost devices, edge coverage in THz communications, industrial-IoT
applications with high reliability, etc. There will be many different types of
demanding use cases in 6G, each with its own requirements. No single waveform
solution will address the requirements of all scenarios. For example, as
discussed in section III-A1, the high-frequency scenario is faced with
challenges such as higher phase noise, larger propagation losses, and lower
power amplifier efficiency. Single-carrier waveforms might be preferable over
conventional multi-carrier waveforms to overcome these challenges [263]. For
indoor hotspots, the requirements instead involve higher data rates and the
need for flexible user scheduling. Waveforms based on OFDM or its variants
exhibiting lower out-of-band emissions [264, 265, 266, 267, 268] will remain a
good option for this scenario. 6G needs a high level of reconfigurability to
become optimized towards different use cases at different times or
frequencies.

As previously mentioned, a critical impact from RF impairments is expected for
THz systems. The main impairments include: IQ imbalance; oscillator phase
noise; and more generally non-linearities impacting amplitude and phase. The
non-linearities of analog RF front-end create new challenges in both the
modeling of circuits and the design of mitigation techniques. It motivates the
definition for new modulation shaping, inherently robust to these RF
impairments. As an example, we consider the robustness to the phase noise.
According to [269], when the corner frequency of the oscillator is small in
comparison to the system bandwidth, the phase noise can be accurately modeled
by an uncorrelated Gaussian process. Using this mathematically convenient
assumption, it is demonstrated in [270] that using a constellation defined
upon a lattice in the amplitude-phase domain is particularly relevant for
phase noise channels. More specifically, a constellation defined upon a
lattice in the amplitude-phase domain is robust to PN and leads to a low-
complexity implementation. The Polar-QAM (PQAM) constellation is introduced in
[270]. This constellation defines a set of $M$ complex points placed on
$\Gamma\in\\{1,2,4,\dots,M\\}$ concentric circles, i.e. amplitude levels. Each
of the $\Gamma$ circles contains $M/\Gamma$ signal points. Any PQAM
constellation are hence entirely defined by two parameters, the modulation
order $M$ and the modulation shape $\Gamma$.

|
---|---
(a) | (b)
Figure 24: Illustration of $16$-PQAM($\Gamma$) constellations in (a) the IQ
plane and (b) the amplitude-phase domain.

We therefore use the notation $M$-PQAM($\Gamma$). Examples of different
$16$-PQAM($\Gamma$) constellations are depicted in Fig. 24. Some particular
cases of the PQAM fall into well known modulations: a $M$-PQAM($M/2$)
describes an amplitude-shift keying while a $M$-PQAM($1$) is a phase-shift
keying.

---
Figure 25: Achievable rate in bit/symbol of the QAM and PQAM modulation
schemes with a LDPC code for two phase noise configurations, medium and
strong. PAPR of the constellation is also depicted through a color map.

The PQAM is a structured definition of an APSK constellation. Combining this
modulation scheme with a dedicated demodulation – see for instance the polar
metric detector proposed in [270] – leads to a simple and robust transmission
technique for phase noise channels. With regard to RF power-amplifiers,
directly related to the energy consumption of transmitters, the PAPR is a key
performance indicator for any communication system. In the case of the PQAM,
the PAPR is given by

$3\cdot\frac{2\Gamma-1}{2\Gamma+1}.$ (12)

It can be noticed that the PAPR is an increasing function of $\Gamma$ and does
not depend on the modulation order $M$. We see here the trade-off to be found
between the PAPR and the robustness to phase noise. Put it differently,
increasing $\Gamma$ improves the robustness to phase noise, yet it increases
the PAPR in the same time. To fully understand the trade-offs we are dealing
with, let us present the results of numerical simulations for the Gaussian
phase noise channel, with two phase noise variances, medium and strong. We
consider different settings of the PQAM combined with a state-of-the-art
channel coding scheme. The coding scheme is based on the 5G-NR LDPC. It is
implemented with an input packet size of $1500$ bytes and a coding rate
ranging from $0.3$ to $0.9$. A Packet Error Rate (PER) of $10^{-2}$ is
targeted for numerical evaluations. The phase noise variances are
$\sigma_{\phi}^{2}=10^{-2}$ rad2 for the medium phase noise case and
${\sigma_{\phi}^{2}=10^{-1}}$ rad2 for the strong phase noise one. It should
be emphasized that the ${\sigma_{\phi}^{2}=10^{-1}}$ rad2 (resp.
${\sigma_{\phi}^{2}=10^{-2}}$ rad2) corresponds to an oscillator spectral
density floor of $-100$ dBc/Hz (resp. $-110$ dBc/Hz) for a bandwidth of $1$
GHz [269]. Results are depicted in Fig. 25. We also put for comparison the
performance of a QAM modulation with both the standard Euclidean detector and
an optimized joint amplitude-phase detector, known to improve the demodulation
performance on phase noise channels [271].

In the case of medium phase noise, the proposed Polar-QAM scheme demonstrates
a minor performance loss with respect to the QAM. However, if the PAPR
constraint becomes critical, the use of a Polar-QAM constellation with a low
PAPR can be envisaged. The performance in the case of strong phase noise are
particularly interesting to analyze. First of all, we clearly observe that the
achievable information rate is severely limited when the QAM is used due to
phase noise. Conversely, the PQAM allows to reach larger spectral-
efficiencies. In the low SNR regime, it is possible to considered low PAPR
modulation, i.e. most of the information bits are carried by the phase
component. In this regime the degradation from thermal noise is dominant in
comparison to phase noise. On the contrary in the high SNR regime, performance
are limited by phase noise. It is clear from Fig. 25 that it is not possible
to reach high rate with low PAPR in this case. On the contrary to the QAM, the
Polar-QAM provides an additional degree of freedom in the choice of the
modulation scheme to find the best trade-off between the level of PAPR and the
phase noise robustness. For this reason, the Polar-QAM offers an interesting
modulation scheme for future coherent THz communication systems targeting high
spectral-efficiency.

####  V-A3 Full-Duplex

The current wireless systems (e.g., 4G, 5G, WiFi) use TDD or FDD methods, and
the so-called half-duplex, where transmission and reception are not performed
at the same time or frequency. In contrast, the full-duplex or the IBFD
technology allows a device to transmit and receive simultaneously in the same
frequency band. Full-duplex technology has the potential to double the
spectral efficiency and significantly increase the throughput of wireless
communication systems and networks. The biggest hurdle in the implementation
of IBFD technology is self-interference, i.e., the interference generated by
the transmit signal to the received signal, which can typically be over 100 dB
higher than the receiver noise floor. Three classes of cancellation techniques
are usually used to cope with self-interference: passive suppression, analog
cancellation, and digital cancellation (see e.g. [272] and the references
therein). Passive suppression involves achieving high isolation between the
transmit and receive antennas before analog or digital cancellation. Analog
cancellation in the RF domain is compulsory to avoid saturating the receiving
chain, and nonlinear digital cancellation in the baseband is needed to further
suppress self-interference, for example, down to the level of the noise floor.
The analog RF cancellation can also be performed jointly with the digital
cancellation using a common processor.

The full-duplex technique has a wide range of benefits, e.g., for relaying,
bidirectional communication, cooperative transmission in heterogeneous
networks, and cognitive radio applications. Other prospective use cases
include, for instance, SWIPT, see e.g., [273, 274, 275, 276, 277] and the
references therein. The feasibility of full-duplex transmission has been
experimentally demonstrated in small-scale wireless communications
environments, and it was also considered as an enabling technique for 5G but
not yet adopted by 3GPP [278]. However, for the full-duplex technique to be
successfully employed in 6G wireless systems, there exist challenges on all
layers, ranging from the antenna and circuit design (e.g., due to hardware
imperfection and nonlinearity, the non-ideal frequency response of the
circuits, phase noise, etc.), to the development of theoretical foundations
for wireless networks with IBFD terminals. Note that IBFD becomes particularly
challenging when MIMO is taken into account and is even more so with massive
MIMO. Nevertheless, more antennas brings also larger degrees of freedom. Some
of the antennas may be used for communication, while other may function as
support antennas to cancel the self-interference, or all antennas used for
both tasks jointly. The suitability of IBFD technology for 6G is an open
research area, where an inter-disciplinary approach will be essential to meet
the numerous challenges ahead [272].

####  V-A4 Protocol-Level Interference Management

Beamforming and sectorization have been effectively used in 4G to manage the
co-user interference. In 5G, massive MIMO has been the main drive to control
co-user interference together with highly adaptive beamforming, which helps to
serve the spatially separated users non-orthogonally by spatial multiplexing.
However, there may be situations in future communication systems when the
interference management provided by massive MIMO is insufficient. For example,
if the arrays are far from the users, then the spatial resolution of the
antenna panels may be limited [279]. In such cases, interference can be
managed alternatively at the protocol level. NOMA [280] and RS [281] schemes
may be two appropriate candidates for this purpose.

The NOMA techniques are mainly based on two domains: power and code [282].
Power-domain NOMA refers to the superposition of multiple messages using
different transmit powers so that users with higher SNRs can decode
interfering signals before decoding their own signal, while users with lower
SNRs can treat interference as noise [283]. Code-domain NOMA refers to the use
of non-orthogonal spreading codes, which provide users with higher SNRs after
despreading, at the expense of additional interference [284]. Both power-
domain and code-domain NOMA have their advantages and disadvantages in terms
of performance and implementation complexity when compared to each other. On
the other hand, RS is based on dividing the users’ messages into private and
common parts, where each user decodes its private part and all users decode
the common parts to extract the data. From the implementation point of view,
RS can be viewed as a generalization of power-domain NOMA [285].

As for the interference management, while massive MIMO treats any multi-user
interference as noise, NOMA-based superposition coding with successive
interference cancellation fully decodes and removes the multi-user
interference. On the other hand, RS partially decodes the multi-user
interference and partially treats it as noise providing a trade-off between
fully decoding the interference and fully treating it as noise. With their
modified transceiver structures, both NOMA and RS schemes may have increased
complexity due to coding and receiver processing. However, there may be
prospective 6G use cases where interference management at the protocol level
could potentially provide sufficiently large gains to outweigh the increased
implementation complexity.

Some considerations of NOMA and RS for interference management can be
summarized as follows. In massive connectivity scenarios, where many devices
transmit small packages intermittently, grant-free access using code-domain
NOMA or similar protocols could be very competitive. In mmWave or THz
communications, where the controllability of the beamforming is limited by
hardware constraints (e.g., phased arrays), NOMA and RS could enable multiple
users to share the same beam [286]. In VLC, where coherent adaptive
beamforming may be practically impossible for multiple access, NOMA and RS
could be an appropriate solution [287]. For massive connectivity and achieving
high data rates in 6G, different implementations of NOMA techniques for
cellular networks could also be considered to increase the system capacity
[288]. In addition to the implementation of NOMA and RS for interference
management purposes, NOMA-based research that covers proposing simplified
transceiver structures and hybrid multiple access schemes could help the
improvement of interference management using NOMA and RS.

On the other hand, there are several practical challenges with implementing
robust interference management at the protocol level. One involves the error
propagation effects that appear when applying superposition coding and
successive interference cancellation to finite-sized data blocks [289].
Another issue is the high complexity in jointly selecting the coding rates of
all messages, to enable successful decoding wherever needed, and conveying
this information to the users. Implementing adaptive modulation and coding is
nontrivial in fading environments with time-varying interference and
beamforming, even if it is carried out on a per-user basis. With NOMA and RS,
the selection of modulation/coding becomes coupled between the users, making
it an extremely challenging resource allocation problem. Scheduling and ARQ
retransmissions are other tasks that become more complex when the user data
transmission is coupled. Hence, protocol-level interference management schemes
need to be investigated in detail under practical conditions in order to
determine suitable implementations for 6G use cases.

####  V-A5 THz-Band Spatial Tuning

Besides combating the high propagation and absorption losses at high
frequencies (as previously highlighted in Sec. IV), achieving any form of
spatial multiplexing with ultra-massive antenna configurations is hindered by
the high spatial channel correlation, caused by having a few angular
propagation paths. Even though the research community is familiar with the
impact of spatial correlation on MIMO links, the severity of this phenomenon
at mmWave/THz frequencies and the peculiarities of THz devices result in more
severe challenges and novel opportunities, respectively. This is particularly
the case in doubly-massive MIMO configurations under LoS quasi-optical THz
propagation, where channels tend to be of very low rank. In such scenarios, it
is known that the physical separation between the antenna elements at both the
transmitter and the receiver can be tuned to retain high channel ranks [290,
291]. In particular, for a given communication range $D$, there exists an
optimal separation $\Delta$ between the antennas that guarantees a sufficient
number of eigenchannels to spatially multiplex multiple data streams. With
highly configurable THz antenna architectures, especially plasmonic antennas,
such optimal separation can be tuned in real-time [95, 292]. However, if $D$
is larger than the achievable Rayleigh distance, which is a function of the
physical array size, such tuning fails, and ill-conditioned channels become
unavoidable. Hence, the Rayleigh distance serves as an important metric to
capture THz system performance. For a number of transmit antennas $N_{t}$ and
receive antennas $M_{r}$, this distance is expressed as [293]

$D_{\mathrm{Ray}}=\mathrm{max}\\{M_{r},N_{t}\\}\Delta_{r}\Delta_{t}/\lambda,$
(13)

where $\lambda$ is the operating wavelength, and $\Delta_{r}$ and $\Delta_{t}$
are the uniform separation between antennas at the receiver and transmitter,
respectively. Note that in a hybrid array-of-subarrays configuration where
multiplexing is conducted at the level of subarrays, $\Delta_{r}$ and
$\Delta_{t}$ can denote the corresponding separations between subarrays.

The achievable Rayleigh distances under different THz LoS configurations are
illustrated in Fig. 26, as a function of antenna separations, array
dimensions, and operating frequencies. A uniform planar array-of-subarrays
configuration is assumed, and two special cases of $M_{r}=N_{t}=128\times 128$
and $M_{r}=N_{t}=2\times 2$ are simulated, assuming $\Delta_{r}=\Delta_{t}$.
For antenna separations of a few millimeters, a large number of antennas is
required to realize multiplexing-achieving distances beyond a few meters.
Although for the same $\Delta$, larger arrays and higher frequencies result in
larger Rayleigh distances, for the same footprint, larger antenna numbers
incur a quadratic Rayleigh distance reduction in $\Delta$. While tens of
mmWave-operating antennas typically occupy a few square centimeters, a huge
number of THz-operating antenna elements can be embedded in few square
millimeters. This adaptability in design, when combined with numerical
optimization, is what we call _spatial tuning_. In particular, antenna
separations in plasmonic arrays can be reduced significantly, to values much
below $\lambda/2$, while still evading the effects of mutual coupling. In such
scenarios, to maintain an optimal $\Delta$ for multiplexing, a large number of
antennas can be kept idle. Alternatively, spatial modulation [95] or
multicarrier [294] configurations can be applied. More importantly, antenna
oversampling can be further utilized to lower the spatio-temporal frequency-
domain region of support of plane waves [295], and this can be exploited for
noise shaping techniques [296]. While this discussion on channel
characteristics is illustrative, several assumptions need to be relaxed for
the exact characterization of THz channels, mainly by accounting for non-
uniform array architectures [297], spherical wave propagation [298], and
wideband channels.

Figure 26: Achievable Rayleigh distances under different THz LoS
configurations. Figure 27: BER performance of spatial multiplexing: $f$=1 THz,
$D$=1 m, $16\times 16$ subarrays, and 16-QAM.

Efficient spatial tuning requires a sufficiently large array of antennas (a
uniform graphene sheet, for example), in which antenna elements can be
contiguously placed over a three-dimensinoal structure, and subarrays can be
virtually formed and adapted. For a given target communication range, a
specific number of antenna elements is allotted in the corresponding subarrays
to achieve the required beamforming gain. Afterward, the diversity and
multiplexing gains are dictated by the possible utilizations of the subarrays,
limited by the number of available RF chains and the overall array dimension.
Note that frequency-interleaved multicarrier utilization of antenna arrays is
feasible because each antenna element can be tuned to target resonant
frequency without modifying its physical dimensions. The latter can be
attained via electrostatic bias or material doping at high frequencies,
whereas software-defined plasmonic metamaterials can alternatively be used for
sub-THz frequencies [93].

Spatial tuning can further be extended to IRS-assisted THz NLoS environments.
By controlling which element to reflect, a spatial degree of freedom is added
at the IRS level, which could enhance the multiplexing gain of the NLoS
system, but at the expense of a reduced total reflected power. Global
solutions can be derived by jointly optimizing $\Delta_{1}$, $\Delta_{2}$, and
$\Delta_{3}$, at the transmitting array, intermediate IRS, and receiving
array, respectively. Nevertheless, true optimizations allow arbitrary
precoding and combining configurations and arbitrary responses on all
reflecting elements; the latter bounds the performance of lower-complexity
spatial tuning techniques. Fig. 27 illustrates the importance of spatial
tuning in allocating antenna elements for beamforming and maintaining channel
orthogonality in a LoS scenario under perfect alignment; by allocating 1000
antenna elements per subarray, a 60 dB performance enhancement is noted and by
maintaining channel orthogonality error floors are avoided.

###  V-B Machine Learning-Aided Algorithms

Wireless technology becomes more complicated for every generation with so many
sophisticated interconnected components to design and optimize. In recent
research, there have been an increasing interest in utilizing ML techniques to
complement the traditional model-driven algorithmic design with data-driven
approaches. There are two main motivations for this [299]: modeling or
algorithmic deficiencies.

The traditional model-driven approach is optimal when the underlying models
are accurate. This is often the case in the physical layer, but not always.
When the system operates in complex propagation environments that have
distinct but unknown channel properties, there is a modeling deficiency and,
therefore, a potential benefit in tweaking the physical layer using ML.

There are also situations where the data-driven approach leads to highly
complex algorithms, both when it comes to computations, run-time, and the
acquisition of the necessary side information. This is called an algorithmic
deficiency and it can potentially be overcome by ML techniques, which can
learn how to take shortcuts in the algorithmic design. This is particularly
important when we need to have effective signal processing for latency-
critical applications and when jointly optimizing many blocks in a
communication system.

This section provides some concrete examples and potential future research
directions.

####  V-B1 End-to-End Learning

End-to-end learning of communication systems using ML allows joint
optimization of the transmitter and receiver components in a single process
instead of having the artificial block structure as in conventional
communication systems. In [300], autoencoder concept is used to learn the
system minimizing the end-to-end message reconstruction error. Extensions of
the autoencoder implementation for different system models and channel
conditions can be found in [301, 302, 303, 304]. Furthermore, different
approaches for end-to-end learning have been considered when the channel model
is unknown or difficult to model analytically [305, 306, 307]. Specifically,
[307] provides an iterative algorithm for the training of communication
systems with an unknown channel model or with non-differentiable components. A
unified multi-task deep neural network framework for NOMA is proposed in
[308], consisting of different components which utilize data-driven and
communication-domain expertise approaches to achieve end-to-end optimization.

An example scenario of end-to-end learning is discussed below. Given the task
of transmitting message $s$ out of $M$ possible messages from the transmitter
to receiver over the AWGN channel using $n$ complex channel uses with a
minimum error, the system is modelled as an autoencoder and implemented as a
feedforward neural network. A model similar to the model proposed in [300] is
implemented with slight variations [309]. The transmitter consists of multiple
dense layers followed by a normalization layer to guarantee the physical
constraints of the transmit signal. During the model training, the AWGN
channel is modelled as an additive noise layer with a fixed variance
$\beta=(2RE_{b}/N_{0})^{-1}$, where $E_{b}/N_{0}$ denotes the energy per bit
($E_{b}$) to noise power spectral density ($N_{0}$) ratio. The receiver also
consists of multiple dense layers and an output layer with softmax activation
to output the highest probable estimated message.

The model is trained in end-to-end manner on the set of all possible messages
$s\in{\mathbb{M}}$ using the categorical cross-entropy loss function. A
training set of 1,000,000 randomly generated messages with $E_{b}/N_{0}=5$ dB
is used for model training. The autoencoder learns optimum transmit symbols
after the model training and the BER performance of the learnt transmit
mechanism is evaluated with another set of 1,000,000 random messages across
the 0 dB to 8 dB $E_{b}/N_{0}$ range. Fig. 28 and Fig. 29 show the BER
performance of different autoencoder models in comparison with conventional
BPSK and QPSK modulation schemes. It can be observed that increasing the
message size $M$ improves the BER performance of the autoencoder resulting in
a better BER than the conventional BPSK and QPSK schemes.

Practical aspects considering the model training implementations, methods to
accompany unknown channel conditions and varying channel statistics in long-
term, transmitter-receiver synchronization etc. are some of the potential
future research directions.

####  V-B2 Joint Channel Estimation and Detection

Channel estimation, equalization, and signal detection are three tasks in the
physical layer that are relatively easy to carry out individually. However,
their optimal joint design is substantially more complicated, making ML a
potential shortcut. In [310], a learning-based joint channel estimation and
signal detection algorithm is proposed for OFDM systems, which can reduce the
signaling overhead and deal with nonlinear clipping. In [311], joint channel
estimation and signal detection is carried out when both the transmitter and
receiver are subject to hardware impairments with an unknown model. Online
learning-based channel estimation and equalization is proposed in [312], which
can jointly handle fading channels and nonlinear distortion.

Methods to overcome the challenge of offline model training, which causes
performance degradation due to the discrepancies between the real channels and
training channels, need to be considered. ML implementations with online
training and constructing training data to match real-world channel conditions
are potential future directions in this regard.

Figure 28: BER performance of the $R=1$ bits/channel use systems compared with
theoretical AWGN BPSK performance.

Figure 29: BER performance of the $R=2$ bits/channel use systems compared with
theoretical AWGN QPSK performance.

####  V-B3 Resource Management

Resource management problems are often combinatorial in nature, which implies
that optimal exhaustive-search based algorithms are impossible to utilize in
practice. This provides an opportunity for ML-based algorithms to outperform
existing suboptimal approaches. A generic resource allocation framework is
provided in [313], whereas a joint beamforming, power control, and
interference coordination approach is proposed in [314]. Some examples of
hybrid beamforming in mmWave MIMO systems [315, 316, 317, 318] utilize ML-
based hybrid precoder-combiner designs, resulting in close performance and
reduced complexity compared to the conventional exhaustive search-based
optimization techniques. The work in [319, 320, 321, 322] utilize different
supervised learning and reinforcement learning techniques to learn and predict
the optimum power allocation in the network dynamically.

Some potential research areas which are already being studied include power
control, beamforming in massive MIMO and in cell-free environments, predictive
scheduling and resource allocation, etc. Reinforcement learning frameworks and
transfer learning techniques to learn, adapt, and optimize for varying
conditions over time are expected to be useful for performing resource
management tasks with minimal supervision [323].

###  V-C Coded Caching

Coded caching is originally proposed by Maddah-Ali and Niesen in [324], as a
way to increase the data rate with the help of cache memories available
throughout the network. It enables a global caching gain, proportional to the
total cache size of all users in the network, to be achieved in addition to
the local caching gain at each user. This additional gain is achieved by
multicasting carefully created codewords to various user groups, so that each
codeword contains useful data for every user in the target group. Technically,
if $K$ is the user count, $M$ denotes the cache size at each user and $N$
represents the number of files in the library, using CC the required data size
to be transmitted over the broadcast link can be reduced by a factor of $1+t$
(equivalently, the data rate can be increased by a factor of $1+t$), where
$t=\frac{KM}{N}$ is called the CC gain.

Interestingly, the CC gain is not only achievable in multi-antenna
communications, but is also additive with the spatial multiplexing gain of
using multiple antennas [325, 326, 327]. In fact, caching non-overlapping file
fragments at user terminals provides multicasting opportunities, as multiple
users can be potentially served with a single multicast message. Therefore,
the additive gain of CC in multi-antenna communications can be achieved
through multicast beamforming of multiple parallel (partially overlapping) CC
codewords to larger sets of users, while removing (or suppressing) inter-
codeword interference with the help of carefully designed beamforming vectors.
The generalized multicast beamforming design enables innovative, flexible
resource allocation schemes for CC. Depending on the spatial degrees of
freedom and the available power resources, a varying number of multicast
messages can be transmitted in parallel to distinct subsets of users [328].

Figure 30: Immersive viewing scenario with coded caching.

The multi-antenna CC structure also provides more flexibility in reducing
subpacketization, defined as the number of smaller files each file should be
split into, for the CC structure to work properly. An exponentially growing
subpacketization requirement is known to be a major problem in implementing
original single- and multi-antenna CC schemes [329]. However, recently it is
shown that in multi-antenna CC, especially when the spatial multiplexing gain
is larger than the CC gain, linear or near-linear subpacketization growth is
possible through well-defined algorithms [329, 330, 331]. Overall, the nice
implementation possibility of CC in multi-antenna setups makes it a desirable
option to be implemented in future wireless networks, where MIMO techniques
are considered to be a core part.

The number of multimedia applications benefiting from CC is expected to grow
in the future. One envisioned scenario assumes an extended reality or hyper-
reality environment (e.g., educational, industrial, gaming, defense, social
networking), as depicted in Fig. 30. A large group of users is submerged in a
network-based immersive application, which runs on high-end eye-wear that
requires heavy multimedia traffic and is bound to guarantee a well-defined
quality-of-experience level for every user in the operating theatre. The users
are scattered across the area covered by the application and can move freely,
and their streamed data is unique and highly location- and time-dependent.
Notably, a large part of the rich multimedia content for rendering a certain
viewpoint is common among the users. This offers the opportunity for efficient
use of pooled memory resources through intelligent cache placement and
multicast content delivery mechanisms. In such a scenario, the possibility of
caching on the user devices and computation offloading onto the network edge
could potentially deliver high-throughput, low-latency traffic, while ensuring
its stability and reliability for a truly immersive experience. The fact that
modern mobile devices are continuing to increase their storage capacity (which
is one of the cheapest network resources) makes CC especially beneficial given
the uniqueness of this use case, where the popularity of limited and location-
dependent content becomes much higher than in any traditional network.

Alongside its strong features, there are many practical issues with CC that
must be addressed to enable its implementation in 6G. Most importantly,
various parameters affecting the performance and complexity of CC schemes need
to be identified, and possible trade-offs among them has to be clarified. For
example, one such trade-off is depicted in Fig. 31 ($L$ is the number of
antennas at the transmitter side), in which the performance of the multi-
antenna CC scheme in [326, 328] is compared with the reduced-subpacketization
scheme of [330], at various SNR levels and for fully-optimized (MMSE-type) and
zero-force beamforming strategies. In general, the MS performs better than the
RED; and optimized beamforming provides better results than zero-forcing.
However, the MS scheme requires exponentially growing subpacketization, making
it impractical for networks with even moderate number of users; and optimized
beamforming requires non-convex optimization problems to be solved through
computationally intensive methods such as successive convex approximation
[328]. On the other hand, the difference between various strategies is
dependent on the SNR regime [332]. For example, in the figure it is clear that
when the SNR is below 15 dB, the RED scheme with optimized beamforming
outperforms MS strategy with zero-forcing; while for SNR above 15 dB this
comparison is no longer valid. Moreover, the gap between optimized and zero-
force beamformers vanishes at the high-SNR regime.

The provided example only clarifies the interaction between the
subpacketization, beamforming strategy and operating SNR regime. It is shown
that as the CC operation is heavily dependent on the underlying multicasting
implementation, its performance is degraded in case some users in the network
are suffering from poor channel conditions [333]. In addition, the energy
efficiency of CC schemes is largely unaddressed in the literature. In fact, CC
schemes require a proactive cache placement phase during which the cache
memories in the network are filled with data chunks from all files in the
library. This imposes a communication overhead and necessitates excess energy
consumption for the required data transfer, which can considerably degrade the
energy efficiency of the underlying CC scheme.

Figure 31: Rate vs SNR, $K=6$, $t=2$, $L=3$.

##  VI 6G Broadband connectivity for rural/underdeveloped areas

Around half of the world population, or almost 4 billion people, still lack
broadband connectivity. Wireless connectivity of some form is the only
realistic alternative for most people in large rural areas since building a
new wired infrastructure is typically cost prohibitive. While each new
wireless generation so far has focused on giving higher data rates where the
population is most concentrated, they have offered much less benefit in rural
regions and sometimes resulted in even worse coverage than the previous
generations, which has resulted in an increased digital divide. This
unbalanced trend still holds true today. Many emerging wireless technologies
are primarily targeted at improving the wireless connectivity where it is
already relatively good; at short distances to the access network and to serve
densely packed users and devices with even higher capacity. While there may be
some improvement also in rural areas and to remote locations, the gain is
usually much lower than in urban areas.

Any technology that aims to reduce the digital divide needs to offer as
uniform performance as possible over as large area as possible. One such
technology is massive MIMO, which can improve the performance at the cell edge
considerably given an altruistic max-min power allocation. While conventional
high-gain sector antennas provide array gains in the cell center, the highly
adaptive beamforming of massive MIMO can provide array gains wherever the user
is located in the coverage area [134, Sec. 6.1]. However, this comes at the
price of a reduced sum-rate which may be economically unfeasible for an
operator, since the long-distance users will consume a large portion of the
available power for an AP in a cellular system, resulting in comparable much
lower rates for nearby users compared to uniform power allocation. Long-range
users may also have insufficient transmit power in the uplink. Cell-free
massive MIMO, however, with max-min power control provide a much higher energy
efficiency and throughput per user in rural environments compared to a single
cell cellular system. This is achieved due to the lower path-loss and that
only a few antennas need to transmit on full power in cell-free systems with
distributed antennas [334].

Long-range massive MIMO can greatly increase the range and coverage of an AP
and increase the capacity also at long distances, e.g., using very tall towers
[335]. Coherent joint transmission between adjacent APs can be utilized to
enhance the performance and diversity at cell edges, effectively creating a
long-range cell-free massive MIMO system. However, challenges exist in that
long-distance LOS dominated channels are more sensitive to shadowing and
blocking, and frequent slow fading in general, which impose practical
challenges for massive MIMO in rural areas. Unfortunate homes and places may
be in a permanent deep shadow and thus always out of coverage. The near-far
problem is often enhanced due to the nature of the wireless channels in rural
areas, with typically decreasing spatial diversity at longer distances.
Coherent joint transmissions from multiple distributed APs in a cell-free
arrangement require more backhaul/fronthaul capacity and are subject to large
delays. The synchronization of widely distributed transmitters is also highly
challenging due to large variations in propagation delays.

LEO satellite constellations can offer almost uniform coverage and high
capacity over huge rural areas, including seas and oceans, and to other remote
locations using mmWaves and THz frequencies. They can also be used in
conjunction with HAPS. Since these solutions view the coverage area high from
above, they can fill in the coverage gaps of terrestrial long-range massive
MIMO, as described above, and also offer large-capacity backhaul links for
remotely located ground-based APs. High array gains are possible by using many
antenna elements on the satellites and HAPS. Each user can either be served by
the best available satellite/HAPS, or by coherent transmission from multiple
ones. The coordination can be enabled via optical inter-satellite connections.
Coherent transmission from satellites and ground-based APs is also possible,
to effectively create a space-based cell-free network. Many challenges exist,
however, in that a large number of LEO satellites are required to make them
constantly available in rural regions, which is associated with a high
deployment cost. Interference may arise between uncoordinated satellites and
terrestrial systems using the same radio spectrum. The large pathloss per
antenna element requires large antenna arrays and adaptive beamforming, since
LEO satellites are constantly in motion. The transmit power is limited,
particularly in the uplink. Coherent transmission from multiple satellites, or
between space and the ground, requires higher fronthaul capacity and varying
delays cause synchronization issues.

IRSs can be deployed on hills and mountains to remove coverage holes in
existing networks by reflecting signals from APs or satellites towards places
where the LoS path is blocked and the natural multipath propagation is
insufficient. Fig. 10 illustrates such a case when the direct path is heavily
attenuated (Case 1) and where an IRS improves the spectral efficiency
considerably by reflecting many coherent signal paths to the user. An IRS can
be powered by solar panels or other renewable sources. The cost is low
compared to deploying and operating additional APs since no backhaul
infrastructure or connection to the power grid is needed. However, the larger
the propagation distance becomes, the larger the reflecting surface area needs
to be in order to counteract the large path-losses. Remote control of the IRS
is challenging and might only support fixed access or low mobility. An IRS
deployed in nature can be exposed to harsh climate and weather conditions and
a subject of sabotage.

##  VII Concluding remarks

TABLE VI: Summary of Challenges, Potential 6G Solutions, and Open Research Questions Challenges | Potential 6G solutions | Open research questions
---|---|---
Stable service quality in coverage area |  User-centric cell-free massive MIMO |  Scalable synchronization, control, and resource allocation
Coverage improvements |  Integration of a spaceborne layer, ultra-massive MIMO from tall towers, intelligent reflecting surfaces |  Joint control of space and ground based APs, real-time control of IRS
Extremely wide bandwidths |  Sub-THz, VLC |  Hardware development and mitigation of impairments
Reduced latency |  Faster forward error correcting schemes, wider bandwidths |  Efficient encoding and decoding algorithms
Efficient spectrum utilization |  Ultra-massive MIMO, waveform adaptation, interference cancellation |  Holographic radio, use-case based waveforms, full-duplex, rate-splitting
Efficient backhaul infrastructure |  Integrated access and backhauling |  Dynamic resource allocation framework using space and frequency domains
Smart radio environment |  Intelligent reflecting surfaces |  Channel estimation, hardware development, remote control
Energy efficiency |  Cell-free massive MIMO, suitable modulation techniques |  Novel modulation methods with limited hardware complexity
Modeling or algorithmic deficiencies in complex and dynamic scenarios |  ML-/AI-based, model-free, data-driven learning and optimization techniques |  End-to-end learning/joint optimization, unsupervised learning for radio resource management

This paper has provided a survey of the most promising candidate technologies
at the PHY and MAC layer for the realization of Tbps wireless connectivity in
future 6G wireless networks. These technologies, named enablers, have been
categorized into three separate classes, i.e. enablers at the spectrum level,
at the infrastructure level, and at the protocol/algorithmic level.

Our vision is that with 6G wireless networks there will be a paradigm shift in
the way users are supported, moving from the network-centric view where
networks are deployed to deliver extreme peak rates in special cases to a
user-centric view where consistently high rates are prioritized. Such
ubiquitous connectivity can be delivered by cell-free massive MIMO and IAB,
and complemented by IRSs. The sub-6 GHz spectrum will continue defining the
wide-area coverage and 1 Tbps can be reached in this spectrum range by making
use of infrastructure enablers for extremely high spatial multiplexing. A
significant effort is needed in sub-THz and THz bands to achieve short-range
connectivity with data rates in the vicinity of 1 Tbps. The extremely wide
bandwidths can take us far towards the goal, but it challenging to maintain a
decent spectral efficiency. Novel coding, modulation and waveforms will be
needed to support extreme data rates with manageable complexity and robustness
to the hardware impairments that will increase with the carrier frequency. In
situations where the beamforming capabilities offered by the ultra-massive
MIMO technology are insufficient to manage interference in the spatial domain,
coding methods based on rate-splitting or broadcasting can be also utilized.
The efficiency can also be improved by making use of caching techniques.
Seamless integration between satellites and terrestrial networks will ensure
that large populations outside the urban centers will be reached by high-
quality broadband connectivity.

Throughout the paper the main challenges related to the use of the described
technologies have been highlighted and discussed. For reader’s convenience,
these are briefly summarized in Table VI.

Our hope is that this paper will help accelerating the interest of the
scientific community towards these problems, so that one day not so far into
the future we will be able to score the Terabit/s goal for broadband
connectivity for everyone. It will be essential both for short-range
communication links and to handle the massive traffic from a large number
devices.

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