# Contextual Beamforming: Exploiting Location and AI for Enhanced Wireless
Telecommunication Performance

Jaspreet Kaur, Satyam Bhatti*, Olaoluwa R Popoola, Muhammad Ali Imran, Rami
Ghannam,
Qammer H Abbasi and Hasan T Abbas  James Watt School of Engineering,
University of Glasgow, United Kingdom
Email: {j.kaur.1<EMAIL_ADDRESS>
{Olaoluwa.Popoola, Muhammad.Imran, Rami.Ghannam, Qammer.Abbasi and
<EMAIL_ADDRESS>
###### Abstract

The pervasive nature of wireless telecommunication has made it the foundation
for mainstream technologies like automation, smart vehicles, virtual reality,
and unmanned aerial vehicles. As these technologies experience widespread
adoption in our daily lives, ensuring the reliable performance of cellular
networks in mobile scenarios has become a paramount challenge. Beamforming, an
integral component of modern mobile networks, enables spatial selectivity and
improves network quality. However, many beamforming techniques are iterative,
introducing unwanted latency to the system. In recent times, there has been a
growing interest in leveraging mobile users’ location information to expedite
beamforming processes. This paper explores the concept of contextual
beamforming, discussing its advantages, disadvantages and implications.
Notably, the study presents an impressive 53% improvement in signal-to-noise
ratio (SNR) by implementing the adaptive beamforming (MRT) algorithm compared
to scenarios without beamforming. It further elucidates how MRT contributes to
contextual beamforming. The importance of localization in implementing
contextual beamforming is also examined. Additionally, the paper delves into
the use of artificial intelligence schemes, including machine learning and
deep learning, in implementing contextual beamforming techniques that leverage
user location information. Based on the comprehensive review, the results
suggest that the combination of MRT and Zero forcing (ZF) techniques,
alongside deep neural networks (DNN) employing Bayesian Optimization (BO),
represents the most promising approach for contextual beamforming.
Furthermore, the study discusses the future potential of programmable
switches, such as Tofino, in enabling location-aware beamforming.

###### Index Terms:

Beamforming Classification, Adaptive and Contextual Beamforming, Machine and
Deep Learning, Wireless Communication Networks, Localisation

##  I Introduction

Every subsequent generation of cellular communication has brought advancements
in data speeds and capabilities, with each generation offering significant
improvements over its predecessor [1]. The first-generation (1G) introduced
the concept of cell phones, while the second-generation (2G) enabled text
messaging services. The advent of the third-generation (3G) brought about
internet streaming capabilities, and the fourth-generation (4G) revolutionised
the mobile landscape with broadband internet coverage. However, as user
demands continue to escalate rapidly, 4G networks have reached their capacity
limits, necessitating the need for more data to cater to the growing number of
smartphones and smart devices.

The arrival of fifth-generation (5G) cellular technology promises to address
these challenges by providing networks capable of carrying significantly
higher traffic volumes than currently available networks[2]. 5G networks are
expected to evolve at a rate ten times faster than the long-term development
of 4G (LTE), these have the potential to facilitate the development of
technologies such as augmented reality (AR), autonomous vehicles, the internet
of things (IoT) [3]. At the core of 5F technology are five key advancements:
full-duplex, massive multi-input multi-output (MIMO), millimetre waves
(mmWaves), smart cell, and beamforming (BF). While smartphones and electronic
devices operate within radio frequency (RF) frequencies that are typically
less than 6 GHz [4, 5, 6]. This range is becoming increasingly congested due
to the proliferation of communication technologies and multiple mobile
carriers. The limited RF spectrum available in the industrial, scientific and
medical (ISM) band poses challenges for accommodating the growing demand for
data transmission, resulting in slower services and more frequent lost
connections[7, 8].

To address this issue, researchers have explored higher frequency bands
ranging from 30 to 300 GHz [7, 8]. Although millimetre waves(mm-wave) have
been utilized in satellite communication for some time, their use in mobile
communications is a recent development. While offering a wider frequency
spectrum, mm-waves face a major challenge due to their limited ability to
penetrate obstacles such as infrastructure. This characteristic leads to
signal loss or absorption when mm-waves encounter environmental obstacles [9].
Subsequently, smart cell networks provide a solution to overcome this problem.
Unlike traditional cellular connections relying on large high-power cell
towers that transmit signals over long distances, smart cell networks leverage
thousands of small low-power access points (APs) [10, 11]. These APs are
strategically placed in close proximity and grouped spatially to relay signals
around obstructions. By eliminating the reliance on line of sight (LOS), smart
cell networks ensure uninterrupted cellular service, even when users move
behind obstacles. When a user equipment (UE) travels behind an obstruction, it
seamlessly switches to a new AP, maintaining a consistent connection [12, 13].

Another significant advancement in 5G technology is the use of Massive MIMO,
which involves deploying a higher number of antennas compared to traditional
MIMO systems. Massive MIMO leverages beamforming techniques to direct wireless
signals towards their intended receivers and enables spatial multiplexing of
multiple data streams over the same frequency band. Despite its drawbacks,
Massive MIMO can significantly enhance communication performance and multiply
the capacity of a mobile ad-hoc network by a factor of 22 or more[1, 14]. In a
time-division multiplexing system, user equipment (UE) needs to alternate
between transmitting and receiving, which can introduce delays and reduce
communication efficiency. In traditional cellular base stations (BS), antennas
can only broadcast or receive signals at a given time. Multiplexing can
improve performance, but transmit and receive signals are typically propagated
at different frequencies. Conventional cellular antennas broadcast signals in
all directions simultaneously, leading to potential interference [15]. Figure
1 provides an illustration of the beamforming process (broadcasting signals in
a specific direction) in rural, semi-urban, urban, and highway areas.

Figure 1: Illustration of BF according to the scenario (rural, semi-urban,
urban, highway).

Beamforming, on the other hand, offers several advantages in cellular
communication. It enables more reliable and faster data transmission by
establishing a more direct connection between transmitters and receivers.
Beamforming has become an essential technology in various applications,
including the 5G standard for cellular networks and radar-detection systems.
However, implementing beamforming requires significant processing resources,
which can pose challenges related to cost, hardware, and energy consumption.
In the past, radar systems relied on mechanically moving and steering antennas
to direct signals. However, advancements in signal processing techniques have
made it possible to manipulate radio waves and focus them towards specific
locations using electromagnetic beams. This eliminates the need for physical
movements and reduces dependence on the physical structure of antennas.

The development of antenna systems for 5G networks must meet the requirements
of compact size and low power consumption. To enhance spectrum efficiency and
throughput, antenna arrays with larger dimensions, such as 64 x 64 MIMO and
beyond, are being utilized. However, the accuracy of these antenna arrays
significantly affects the performance of beamforming. As wavelengths decrease,
component sizes, including RF transceivers with features like ADCs, also
decrease. Exploring new materials, such as 40 nM CMOS, is helping to reduce
the size and power consumption of essential components in 5G networks.
Traditional RF power amplifiers made with materials like GaAs and other III-V
semiconductors are not power-efficient and do not integrate well with other
capabilities. This is where advancements in 40 nM CMOS technology can play a
role in further reducing the size and power consumption of these critical
components. Moreover, as the number of beams created by individual gNBs
increases, more advanced signal processing techniques, including neural
network methods, are required. This pushes power budgets and space
restrictions even further. Despite these challenges, beamforming holds a
promising future in various application areas.

Contextual beamforming is a promising technique for enhancing the performance
of 5G communication systems. It enables the use of millimeter wave frequencies
and massive MIMO technologies to achieve high data rates and low latency.
Contextual beamforming adapts beamforming parameters in real-time based on
environmental conditions and user requirements. This is achieved through
feedback from the network and user devices, as well as the utilization of
machine learning algorithms to optimize the beamforming process. Contextual
Beamforming has applications in mobile edge computing (MEC), where low-latency
computing and networking services are provided to mobile users. By dynamically
adjusting the beamforming parameters based on the location, movement, and
traffic conditions of the users, the quality of the wireless links between the
user devices and the MEC servers can be improved. In VR/AR, contextual
beamforming can improve the quality of audio and video streams used in VR/AR
applications by selectively enhancing relevant signals and suppressing
irrelevant or distracting ones.

To optimize the beamforming process and make it more efficient, AI is used in
5G technology. AI algorithms analyze real-time data from the network to
determine the optimal beamforming pattern based on various factors, such as
the user’s location and behaviour, and the surrounding environment.
Additionally, AI can help with beamforming in scenarios involving multiple
users, where each user requires a different beamforming pattern. By
dynamically adjusting the beamforming patterns for each user, optimal signal
strength can be ensured. In complex scenarios involving multiple users and
dynamic environments, AI is a crucial tool for optimizing the beamforming
process in 5G networks.

###  I-A Contribution to the Literature

In our review paper, we analyzed the role of Beamforming in the field of
mobile communication. We conducted a review of the applications of beamforming
in the algorithms, antenna fabrication, and the discovery of new AI
(artificial intelligence) approaches. Our article is an effort to provide a
review of contextual beamforming. The following are the major contributions of
this article:

  1. 1.

We shortlisted research articles related to beamforming techniques that can
help in context-aware beamforming.

  2. 2.

We review the literature on beamforming types both standard and using ML and
AI techniques.

  3. 3.

Various ML techniques facilitated the estimation of user location and beam
management in the study.

  4. 4.

We investigated the techniques used for the optimization of beamforming with
the help of ML.

  5. 5.

We highlighted the challenges associated with using ML techniques for
contextual beamforming.

###  I-B State of the Art

The field of contextual beamforming in 5G technology is in a constant state of
evolution, with ongoing research and development efforts aimed at improving
its efficiency and effectiveness. In recent years, a surge in the use of
conventional, machine learning (ML), and artificial intelligence (AI)
techniques for beamforming has been observed. Several recent advances have
been made in this area. By considering location-unaware systems with
benchmarking techniques [16], a location-aware system can be developed and
make location estimation more accurate. Additionally, opportunistic
beamforming is used as feedback to smart antennas using channel delay
information[17].

In [18], the authors propose a recursive matrix shrinkage method to estimate
the interference-plus-noise covariance matrix along with the desired signal
steering vector mismatch. A two-stage design approach was utilized in [19],
with the first stage dealing with beamforming, and the second with adaptive
power allocation and modulation. Another recent study by [20] proposed a novel
and general approach to deriving the statistical distribution of the signal-
to-noise ratio (SNR) by exploiting the array structure, beamforming type, and
slow fading channel coefficients. This approach was used to design power and
modulation adaptation strategies. [21] presented the scheme that uses
coordinated beam search from a small beam dataset within the error offset, and
then the selected beams are used to guide the search for beam prediction.

Additionally, [22] proposed an end-to-end deep learning technique to design a
structured compressed sensing (CS) matrix that is well-suited to the
underlying channel distribution. This technique leverages sparsity and the
spatial structure that appears in vehicular channels. In contrast, [23] noted
that current millimetre-wave (mmWave) beam training and channel estimation
techniques do not typically make use of prior beam training or channel
estimation observations. Moreover, [24] presented that determining the optimal
beamforming vectors in large antenna array mmWave systems necessitates
significant training overhead, which can have a significant impact on the
efficiency of these mobile systems. However, a significant drawback in these
studies was that limited ML approaches were discussed, and the optimization
scope and implementation of proposed beamforming algorithms were not
considered in real environments.

As various ML techniques have been adopted for BF, this paper aims to provide
a detailed review of different ML-based BF techniques. These ML techniques
include the procedure to preprocess the input data and various ML algorithms
in any environment. Our systematic review goes beyond existing literature,
showcasing how various ML techniques can be used to screen large numbers of
beamforming approaches for potential location estimation applications and to
optimize the approaches using high computational power. Accordingly, the
following sections will describe the in-depth analysis of currently popular
beamforming techniques and how AI can improve their overall performance by
mitigating their limitations.

###  I-C Organisation of the Review

The rest of the paper is organized depending on finding the gap from the
state-of-the-art in the recent literature review and is described as follows.
The adopted methodology is presented in section 2 of the systematic review,
followed by the results and analysis of the systematic review are discussed in
section 3. In addition, section 4 elaborates on the challenges associated with
using the recent AI, ML, and DL techniques for contextual beamforming.
Moreover, section 5 incorporates the potential applications of contextual
beamforming. Subsequently, section 6 discusses our key contributions towards
the development of Localisation and Beamforming. Lastly, section 7 of the
systematic review concludes the article.

Figure 2: Base station to vehicle scenario.

##  II Review Methodology

This section of the systematic review presents our review methodology
depending on the defined research objectives and questions that were used for
shortlisting the relevant research articles on ML algorithms for contextual
beamforming techniques.

###  II-A Research Objectives

The four key objectives of our systematic review article are:

  1. RO1:

To review the range of Adaptive beamforming and ML-based beamforming using
priori user data.

  2. RO2:

To identify the ML techniques used specifically for Contextual beamforming.

  3. RO3:

From a practical perspective, identify the specific ML and optimization
techniques used for real-time implementation.

  4. RO4:

To identify ML algorithms specifically used for the beamforming for low
latency, high throughput and SINR.

###  II-B Research Questions

Our systematic review aims to answer the following four research questions:

  1. RQ1:

What are the various location-assisted BF techniques?

  2. RQ2:

What are the datasets required for classifying the Contextual Beamforming?

  3. RQ3:

How can the Contextual Beamforming models be optimized for real-time
processing?

  4. RQ4:

What are the different types of ML and AI techniques used with respect to
Contextual Beamforming?

###  II-C Review Protocol

For structuring our systematic review, we instigated a review protocol, and
the following are the perquisites of the adopted analogy. In this section, we
discuss the search strategy, inclusion criteria, exclusion criteria, and
screening mechanisms for selecting relevant research papers.

####  II-C1 Search Strategy

The most recent research papers from renowned publishing houses like IET,
Science Direct, Nature, AIP, Wiley, IEEE Explorer, IoP science, ACS
publications, and MDPI were taken into account during our review. We also
included unreviewed papers from arXiv in our search. As a result, we evaluated
and critically analysed the grey literature (research and publications
produced by organisations not usually linked with academic or commercial
publishing organisations) using the AACODS (Authority, Accuracy, Coverage,
Objectivity, Date, Significance) criteria.

We commence by queering every repository that contains various study items. To
compile our study articles, we defined keywords like ”Machine Learning,” ”Deep
Learning,” ”Beamforming,” ”location,” ”context information,” ”5G,” and
”Vehicular Communication.” Based on the article’s title and abstract as well
as a full-text read of the papers, articles were scanned. To link these
keywords, we also created search strings using the Boolean operators AND and
OR.

####  II-C2 Inclusion Criteria

The following are the parameters used in the inclusion criteria.

  1. 1.

We included only English-language articles involving the data-driven
approaches of beamforming using conventional and ML techniques and were
pertinent to the study issues such as poor data quantity and data quality.

  2. 2.

We included the pertinent articles facilitating the discovery of only low-
latency beamforming algorithms using ML methods before determining their
eligibility.

  3. 3.

We included comparative studies involving the optimization and robustness of
beamforming techniques designed from ML services.

  4. 4.

We targeted only articles that discussed ML for beamforming, location
information, and publications on ML integration on contextual beamforming.

####  II-C3 Exclusion Criteria

The following is a list of the exclusion criteria for shortlisting the
research papers based on our research objectives and targeted research
questions.

  1. 1.

English-language research articles released in other languages.

  2. 2.

Research papers without a complete text version.

  3. 3.

Editorials, review articles of surveys, abstracts, and short papers concerning
secondary studies are not accepted.

  4. 4.

Articles that didn’t discuss how to combine ML techniques with beamforming.

####  II-C4 Screening Phase

Articles were further screened in two phases. In the first phase, we examined
the title and the abstract of each research article to check whether they
satisfied our inclusion criteria. In the second phase, we further shortlisted
our articles based on their full text. It is worth mentioning that the same
piece of writing frequently appeared in various publications. For example,
conference papers frequently appear in journals. We take into account the
original writing each item was reviewed throughout the screening stage two. At
least two of the contributors of this paper who were entrusted with
classifying the items as either pertinent or not pertinent might require more
research, as finalized until any such item is either published or the authors
have a discussion tagged as relevant or not. Survey and review papers were
excluded from our review. Finally, each article was carefully classified and
evaluated thematically.

##  III Results and Analysis of the Review

This section of the review paper summarizes the research articles that are
shortlisted using the defined research objectives as well as aims to answer
the predefined research questions.

###  III-A What are the recent BF techniques [RQ:1]

With an extensive utilization of GPS coordinates worldwide, various location-
assisted BF techniques are becoming exponentially important. Contextual
beamforming and adaptive beamforming are two techniques used in signal
processing to improve the quality of transmitted or received signals.
Contextual beamforming refers to using prior knowledge about the environment
to design a beamforming algorithm that optimizes the signal quality in that
specific environment. This prior knowledge can include information about the
location and number of signal sources, the acoustic properties of the
environment, and other factors that can affect the quality of the received
signal. On the other hand, adaptive beamforming refers to using real-time
feedback from the received signal to continuously adjust the beamforming
algorithm to improve the quality of the signal. This is particularly useful in
dynamic environments where the signal sources or environmental conditions may
change over time. Contextual beamforming is based on prior knowledge of the
environment, while adaptive beamforming uses real-time feedback to
continuously adjust the beamforming algorithm. Both techniques have their
strengths and weaknesses.

Figure 3: Base station to vehicle scenario

####  III-A1 Adaptive Beamforming

An adaptive beamformer is a tool for performing adaptive spatial signal
processing using an array of transmitters or receivers. The signals are
integrated in such a way that the signal intensity to and from a specific
direction is increased. Signals from and to other directions are combined
constructively or destructively, resulting in the degradation of the signal
from and to the undesired direction. This method is utilised in both RF and
acoustic arrays to achieve directional sensitivity without physically changing
the receivers or transmitters [25, 26, 27].

Adaptive BF was first developed in the 1960s for military sonar and radar
applications. There are various modern applications for BF, with commercial
wireless networks such as long-term evolution (LTE) being one of the most
popular. Adaptive BF’s first applications in the military were primarily
focused on radar and electronic countermeasures to counteract the effects of
signal jamming. In phased array radars, BF can be seen. These radar
applications use either static or dynamic/scanning BF; however, they are not
truly adaptive. Adaptive BF is used in commercial wireless standards such as
3GPP LTE and IEEE 802.16 WiMAX to enable important services within each
standard [28]. The concepts of wave transmission and phase relations are used
in an adaptive BF system. A greater or lower amplitude wave is formed, for
example, by delaying and balancing the received signal, using the concepts of
superimposing waves.

Figure 4: Basic block diagram of Adaptive BF

The adaptive BF system is adaptive in real-time to maximize or minimize
desirable parameters, including the signal-to-interference noise ratio (SINR).
There are numerous approaches to BF design, the first of which was achieved by
Applebaum in 1965 by increasing the signal-to-noise ratio (SNR) [29]. This
method adjusts the system parameters to maximize the power of the received
signal while reducing noise (jamming or interference). Widrow’s least mean
squares (LMS) error method and Capon’s maximum likelihood method (MLM)
introduced in 1969 are two further approaches. The Applebaum and Widrow
algorithms are quite similar in that they both converge on the best option.
However, these strategies have difficulties in terms of implementation. Reed
demonstrated a technique called sample matrix inversion (SMI) in 1974 [30].
Unlike Applebaum and Widrow’s approach, SMI determines the adaptive antenna
weights directly [25, 26, 27].

The Weiner solution can be used to create statistically optimal weight vectors
for adaptive BF in data-independent BF design methods. On the other hand, the
asymptotic $2^{nd}$ order statistics of SINR were assumed. Statistics
fluctuate over time in cellular networks where the target is mobile and
interferes with the cell area. An iterative update of weights is required to
follow a mobile user in a time-varying signal propagation environment [31].
This enables the spatial filtering beam to adjust to the time-varying DOA of
the target mobile user and to provide the desired signal to the user. To
address the challenge of statistics (which can vary over time), adaptive
algorithms that adapt to changing environments are frequently used to
determine weight vectors. The functional block diagram of an adaptive array of
n elements includes an antenna array of n elements and a digital signal
processor with a feedback and/or control loop algorithm. The signal processing
unit receives the data stream gathered by an array and computes the weight
vector using a specific control method.

On the contrary, the adaptive antenna array is divided into two categories: a)
steady-state and b) transient state. These two categories are determined
according to the array weights of stationary environment and time-varying
environment. If the reference signal for the adaptive method is known from
prior information, the system can update the weights adaptively through
feedback [32]. To change the weights of the time-varying environment at every
instance, several adaptive algorithms (mentioned in the further section) can
be utilized. Figure 4 shows the block diagram for adaptive BF which consist of
a digital signal processor (DSP), RF chain, splitter, and N-phase shifter
followed by antenna assembly along with an adaptive system providing feedback
to shifters.

Figure 5: A standard procedure based on the Kalman filter.

####  III-A2 Contextual Beamforming

The ability to forecast the next location of the receiver which is based on
tracking previous movements can be useful for creating intelligent
applications like automobiles, robotics, augmented/virtual reality etc. The
advancement of location prediction apps and services is enabled by the growth
of methodologies for predicting and projecting the receiver’s position in the
future [33]. A wireless system, in general, controls a location-predicting
framework by capturing and communicating critical data before application. The
sender must be able to determine the receiver’s location at any given time to
interact effectively with them. Machine learning (ML) methods have already
been used to predict the receiver’s location. Context is created by recording,
processing, and transcribing the receiver’s status data at a certain time.
Several machine learning algorithms, such as Deep Neural Networks, Convolution
Neural Networks, Generative Adversarial Networks, and others, have been
recognised as aiding in the technological advancement of location forecasting.
Furthermore, depending on the application, machine learning algorithms can be
modified and customised to match their objectives [34].

The majority of the existing mmWave beam tracking research focuses on
communication-only protocols. The unusual beam tracking technique requires the
transmitter to send information to the receiver, which then determines the
angular position and sends it back to the transmitter. It is worth noting that
in high-mobility communication circumstances, such as the one depicted in
Figure 3, it is not enough to merely track the beam. To meet the crucial
latency requirement, the transmitter should be capable of predicting the beam
[35]. The state prediction and tracking designs in Figure 5 is based on the
classic Kalman filtering process.

Figure 6: Basic block diagram of Location-Assisted BF

####  III-A3 Location-assisted Beamforming

The a priori information on the location of the user can enable the system to
work more efficiently. The sorting of the prior information can reduce energy
footprints. As an example, the branch predictor [36] in computer architectures
can improve the flow in the instruction pipeline to achieve highly effective
performance. In the case of location-aided or location-aware BF, a similar
concept has been seen. Figure 7 shows the block diagram for predictive or
location-assisted BF which consist of a digital signal processor (DSP), RF
chain, splitter, and N-phase shifter followed by antenna assembly along with a
feedback loop providing current target user location to shifters. Line of
sight (LOS) communication in mmWave transmission systems provides multi-
gigabit data transmission with BF toward the user direction to mitigate the
substantial propagation loss. However, abrupt performance degradation caused
by human obstruction remains a major issue, thus using possible reflected
pathways when blocking occurs should be considered [37].

In this line, location is a critical factor for Contextual beamforming in 5G
networks because 5G relies on higher frequencies and smaller cells than
previous generations of wireless networks. These higher frequencies have
shorter wavelengths, which means that the signal is more easily obstructed by
obstacles such as buildings, trees, and other objects. As a result, the
location of the user and the position of the cell tower are crucial for
ensuring reliable and efficient communication. Contextual beamforming in 5G
networks involves directing the transmission and reception of signals towards
the user’s location, which can significantly improve signal quality and reduce
interference from other sources. This can be done using beamforming
techniques, which use phased array antennas to focus the signal in a specific
direction towards the user. By adjusting the direction and shape of the beam,
beamforming can improve signal strength and quality, reduce interference, and
increase the capacity of the network. In addition to beamforming, 5G networks
also rely on other location-based technologies, such as geolocation and
network slicing. Geolocation can be used to determine the user’s location and
provide location-specific services, while network slicing allows for the
creation of virtualized networks with different performance characteristics
for different locations and applications.

The usage of location-aware beamforming (BF) and interference mitigation
techniques in ultra-dense 5G networks composed of densely scattered access
nodes (AN) has been investigated in the literature. The development of user
environment area networks (UEAN) with short distances in a packed environment
results in higher levels of signal interference, but network densification
enhances the chance of line-of-sight (LoS) and, as a result, leads to more
accurate UE placement. This enables the use of spatial dimensions by BF and
interference reduction. The accuracy of radio network positioning systems
currently available is inferior to that of fibre optic communication systems
in radar stations and atomic clock-based satellite navigation systems. Future
5G networks are expected to provide positioning accuracy on the order of one
meter. Lu et al [16] proposed approaches such as weighted centroid geometric
(WCG) and a joint positioning and tracking framework based on the extended
Kalman filter (EKF) to achieve accurate and reliable 3D positioning for
industrial IoT systems where anchor locations are not precisely known. They
also suggested position-aided beamforming (PA-BF) approach that outperforms
conventional BF in terms of initial access latency and spectral efficiency,
especially for UE moving at a speed greater than 0.6 m/s.

Sellami et al [38] proposed a neighbour-aided localization algorithm for
outdoor UEs operating in challenging channel conditions. The algorithm selects
two neighbours based on reference signal power measurement, and the BS
performs beamforming over an angular interval determined by the calculated
distance and angle of arrival (AoA) of the first neighbour to discover two
candidates for the UE post. [39] provided location assistance (LA) direction
of departure (DOD)-based beamforming technique that is appropriate for
wireless communication in high-speed rail (HSR). The algorithm’s goal is to
modify the phase at the transmitter to increase the output signal-to-noise
ratio (SNR) at the receiver. Both the performance of ideal DOD beamforming and
approximated DOD with location error-related variation are assessed.

Figure 7: Basic block diagram of Location-Assisted BF

The study described in [40] suggests LAMA (Location-Assisted Medium Access), a
Medium Access Control (MAC) protocol based on locally shared position data for
position awareness beaconing. Their contention-free method manages to
effectively minimize interference, especially hidden-terminal type, through
coordinated spatial reuse and scales effectively with high neighbour numbers.
In [41], the authors implemented location-aware beamforming and interference
mitigation techniques in 5G ultra-dense radio networks to improve the use of
space. They also estimated the positioning accuracy limitations of the user
equipment using the direction of arrival measurement processing in three-
dimensional space with metrics of the Cramer-Rao lower bound ellipsoid.

Similarly, [42] proposed a location-aware beamforming design for the RIS-aided
millimetre-wave (mmWave) communication system without the channel estimation
process, which took into account the limitations of the conventional channel
state information (CSI) acquisition techniques for the RIS-aided communication
system. They also created a worst-case robust beamforming optimization problem
to counteract the impact of location inaccuracy on the beamforming design. In
[43], a spatial estimation approach based on the theory of observers in the
control systems literature was proposed to handle the essential issue of
beamforming in UAV-based communications. They effectively anticipated the
positions of the target UAVs in the presence of uncertainty by using a delay-
tolerant observer-based predictor. The method functioned consistently in the
presence of channel blockage and interference.

The likelihood of positioning-aided beamforming systems experiencing an outage
was investigated in [44]. The authors took into consideration positioning
error, link distance, and beamwidth to generate closed-form outage probability
constraints. They demonstrated that the beamwidth should be maximized with the
transmit power and connection distance to reduce the likelihood of an outage.
In [45], a deep learning-based location-aware predictive beamforming technique
was proposed to follow the beam for UAV communications in a dynamic
environment. They developed a long short-term memory (LSTM)-based recurrent
neural network (LRNet) to predict the UAV’s expected location, which could be
used to calculate a forecast angle between the UAV and the base station for
efficient and quick beam alignment.

In a multi-cell, multiple input, multiple outputs (MIMO) communication system
aided by optical positioning, [39] suggested a location-based energy-efficient
optimization approach for the beamforming matrix. They increased the system’s
achievable ergodic rate by estimating the channel coefficient matrix based on
the location data. In [46], position-aided beamforming (PABF) architecture was
proposed for improved downlink communications in a cloud-oriented mmWave
mobile network. The authors demonstrated that the proposed PABF outperformed
the traditional codebook-based beamforming in terms of effective transmit
ratio and initial access latency, demonstrating its potential to accommodate
high-velocity mobile users.

Finally, [34] proposed an effective beam alignment solution for mmWave band
communications by utilizing the mobile user’s location data and potential
reflectors. The suggested method enabled the base station and mobile user to
jointly search a small number of beams within the error bounds of the noisy
location information. Additionally, [47] proposed a method for beamforming
that tracked the spatial correlation of the strong pathways that were
currently accessible between the transmitter and the receiver. They
demonstrated the robustness of their approach to position information
uncertainty and how it could reliably maintain a connection with a user who
was travelling along a trajectory.

In summary, all the above-mentioned research shows the effectiveness of
knowing the user location either static or on the move to leverage the
existing cellular communications. The location information can assist in
predicting the precoding weights accurately and eventually steering the beam
in the direction of the user with low latency and high throughput.

Figure 8: Datasets for Contextual Beamforming Classification

###  III-B Datasets for Contextual Beamforming Classification [RQ:2]

Researchers often need datasets that contain location data, audio signals, and
other pertinent elements to identify contextual beamforming approaches. The
following are a few examples of datasets that have been applied in earlier
research:

####  III-B1 TUT Acoustic Scenes 2017 dataset:

This dataset includes location-specific metadata and audio recordings of
diverse acoustic scenes, including street traffic, parks, and retail malls. It
has been employed in numerous research to gauge how well contextual
beamforming methods function [48].[49] introduced the acoustic scene
classification task of DCASE 2018 Challenge and the TUT Urban Acoustic Scenes
2018 dataset provided for the task, and evaluates the performance of a
baseline system in the task. The TUT Urban Acoustic Scenes 2018 dataset
consisted of 10 different acoustic scenes recorded in 6 large European cities,
making it more acoustically variable than previous datasets used for this
task. The dataset included high-quality binaural recordings as well as data
recorded with mobile devices. The baseline system consisting of a
convolutional neural network achieved good performance in the subtasks using
the recommended cross-validation setup. Also, [48] introduced the TUT Acoustic
Scenes 2016 database, which was a collection of binaural recordings from 15
different acoustic environments. A subset of this database called the TUT
Sound Events 2016, was annotated to mark sound events. The paper presented the
recording and annotation procedure, the database content, and the performance
of a supervised acoustic scene classification system and event detection
baseline system.

####  III-B2 DEMAND dataset:

The DEMAND dataset includes audio recordings of metropolitan settings,
including as parking lots, train stations, and street traffic. Associated
metadata is also included, such as GPS positions and noise levels. Studies to
assess the efficacy of contextual beamforming methods have utilised this
dataset [50]. [50] introduced the Diverse Environments Multi-channel Acoustic
Noise Database (DEMAND), which was a set of 16-channel noise files recorded in
a variety of indoor and outdoor settings. The data was recorded using a planar
microphone array consisting of four staggered rows, with the smallest distance
between microphones being 5 cm and the largest being 21.8 cm.

####  III-B3 The CHiME-4 dataset:

This collection of audio recordings comprises conversations that were
overheard in places like offices, homes, and cafes. Additionally, it contains
metadata like noise levels and position data. Studies evaluating the
effectiveness of contextual beamforming methods for enhancing speech
recognition accuracy have made use of the CHiME-4 dataset [51]. This dataset
is used in some research such as in [52], a deep eigenvector beamformer was
proposed as a front-end for robust speech recognition in adverse environments.
Data augmentation was performed by modulating the amplitude and time scale of
the audio. The proposed system achieved a word error rate of 4.22% on the real
development and 8.98% on the real evaluation data for 6 channels and 6.45% and
13.69% for 2 channels, respectively. Also, in [53], the CHiME corpus was
designed to enable noise-robust speech processing research. The corpus
included 40 hours of background recordings from a head and torso simulator in
a domestic setting and a comprehensive set of binaural impulse responses. The
data had been mixed to produce a controlled and natural range of SNRs for
speech separation, enhancement, and recognition algorithms

####  III-B4 Dataset for TAU Spatial Sound Events 2019:

This collection includes location-related metadata and audio recordings of a
variety of spatial sound occurrences, including footfall, glass breaking, and
doors closing. Studies have used it to gauge how well contextual beamforming
systems perform at locating and detecting sound occurrences [54]. In [55], The
STARS22 dataset was a high-resolution dataset of spatial recordings of real
scenes with sound event annotations. The dataset was captured with a high-
resolution spherical microphone array and delivered in two 4-channel formats,
ﬁrst-order Ambisonics and tetrahedral microphone array. Also, [48] introduced
the TUT Acoustic Scenes 2016 database, which was a collection of binaural
recordings from 15 different acoustic environments. A subset of this database
called the TUT Sound Events 2016, was annotated to mark sound events. The
paper presents the recording and annotation procedure, the database content,
and the performance of a supervised acoustic scene classification system and
event detection baseline system.

####  III-B5 Vehicular Networks Dataset (VeND):

The University of California, Los Angeles (UCLA) created this dataset, which
includes observations from a vehicular network testbed. The dataset contains
data about the cars and their movements in addition to details about the
wireless channel, such as the signal-to-noise ratio (SNR) and the channel
impulse response (CIR) [56].

[57] presented a realistic synthetic dataset, covering 24 hours of car traffic
in a $400-{km}^{2}$ region around the city of koln, in Germany. The dataset
captures both the macroscopic and microscopic dynamics of road traffic over a
large urban region. Incomplete representations of vehicular mobility may
result in over-optimistic network connectivity and protocol performance.

####  III-B6 5G-VICTORI:

is a project financed by the European Union that aims to create 5G
technologies for a range of applications, including vehicular communication.
With regard to vehicular communication, the project has created a number of
datasets, including assessments of the radio frequency (RF) channel and
network performance in practical settings [58].

[59] discussed how the new 5G network technology would impact the
digitalization of various industries, including modern railway transportation.
The Future Railway Mobile Communication System (FRMCS) service requirements
and system principles were well-mapped to 5G concepts, but deployment
paradigms needed to be established to prove their effectiveness. The
5G-VICTORI project aimed to deliver a complete 5G solution for railway
environments and FRMCS services, and this paper discussed the Key Performance
Indicators and technical requirements for an experimental deployment in an
operational railway environment in Greece.

####  III-B7 5G-EmPOWER:

This project, which is also supported by the European Union, aims to develop
5G technology for a range of applications, including vehicular communication.
With regard to vehicular communication, the project has created a number of
datasets, including assessments of the RF channel and network performance in
practical settings [60].

3GPP is embracing the concept of Control-User Plane Separation (a cornerstone
concept in SDN) in the 5G core and the Radio Access Network (RAN). An open-
source SDN platform for heterogeneous 5G RANs has been introduced, which
builds on an open protocol that abstracts the technology-dependent aspects of
the radio access elements. The effectiveness of the platform has been assessed
through three reference use cases: active network slicing, mobility
management, and load-balancing [60].

####  III-B8 NS-3:

The Network Simulator 3 (NS-3) is an open-source network simulator that is
useful for simulating and modelling vehicular communication in 5G networks. In
addition to mobility models for simulating the movement of vehicles, NS-3 has
various built-in modules for modelling the wireless channel [61].

[62] presented a framework for the ns-3 network simulator for capturing data
from inside an experiment, subjecting it to mathematical transformations, and
ultimately marshalling it into various output formats. A framework for
capturing data from inside an experiment, subjecting it to mathematical
transformations, and ultimately marshalling it into various output formats is
presented. The application of this functionality is illustrated and analyzed
via a study of common use cases. The design presented provides lessons
transferrable to other platforms.

####  III-B9 Connected automobiles and Cities:

The National Renewable Energy Laboratory (NREL) created this dataset, which
contains information from a field investigation of connected automobiles in a
smart city setting. The dataset contains details about, among other things,
network performance, traffic flow, and vehicle trajectories[63].

In [64], big data from the cellular network of the Vodafone Italy Telco
operator can be used to compute mobility patterns for smart cities. Five
innovative mobility patterns have been experimentally validated in a real
industrial setting and for the Milan metropolitan city. These mobility
patterns can be used by policymakers to improve mobility in a city, or by
Navigation Systems and Journey Planners to provide final users with accurate
travel plans.

####  III-B10 DeepSense6G:

DeepSense 6G is a collection of data that includes different types of sensing
and communication information, such as wireless communication, GPS, images,
LiDAR, and radar. This data was gathered in real-life wireless environments
and represents the world’s first large-scale dataset of this kind. The dataset
contains over one million samples of this multi-modal sensing-communication
data and was collected in over 30 different scenarios to target various
applications. The collection of data was done at several indoor and outdoor
locations with high diversity and during different times of the day and
weather conditions. Additionally, there are tens of thousands of data samples
that have been labelled both manually and automatically.

Also in [65], the DeepSense 6G dataset is a large-scale dataset based on real-
world measurements of co-existing multi-modal sensing and communication data.
The DeepSense dataset structure, adopted testbeds, data collection and
processing methodology, deployment scenarios, and example applications are
detailed in the paper. The paper aims to facilitate the adoption and
reproducibility of multi-modal sensing and communication datasets. The
researchers ([66]) had a 400 GB dataset containing hundreds of thousands of
WiFi transmissions collected ”in the wild” with different Signal-to-Noise
Ratio (SNR) conditions and over different days. They also had a dataset of
transmissions collected using their own software-defined radio testbed, and a
synthetic dataset of LTE transmissions under controlled SNR conditions.

####  III-B11 SUMO:

SUMO (Simulation of Urban Mobility) is an open-source traffic simulation
software that allows modelling and simulating traffic flow in urban areas. It
can simulate individual vehicles, pedestrians, public transportation, and
various road networks. SUMO has a variety of applications, including traffic
planning, intelligent transportation systems, and autonomous driving. A
synthetic dataset generator was developed to support research activities in
mobile wireless networks. The generator uses traces from the Simulation of
Urban MObility (SUMO) simulator and matches them with empirical radio signal
quality and diverse traffic models. A dataset was created in an urban scenario
in the city of Berlin with more than 6h of duration, containing more than
40000 UEs served by 21 cells [67].

The development and evaluation of contextual beamforming techniques for
vehicle communication in the 5G network rely heavily on datasets. Researchers
have utilized various datasets from the literature to evaluate and investigate
the effectiveness of these techniques. This systematic review critically
analyzes the datasets used by researchers worldwide to incorporate contextual
beamforming techniques in vehicular communication. [68] presented the 5G3E
dataset, which contains thousands of time series related to the observation of
multiple resources involved in 5G network operation. The dataset was created
to support 5G network automation. The 5G3E dataset contained thousands of time
series related to the observation of multiple resources involved in 5G network
operation. The variety of collected features ranged from radio front-end
metrics to physical server operating system and network function metrics. The
testbed was deployed to support the creation of traffic starting from real
traffic traces of a commercial network operator.

Another dataset is the 5G trace dataset from a significant Irish mobile
operator introduced by [69]. This paper presented a 5G trace dataset collected
from a major Irish mobile operator. The dataset was generated from two
mobility patterns (static and car) and across two application patterns (video
streaming and file download). The dataset was composed of client-side cellular
key performance indicators (KPIs) comprised of channel-related metrics,
context-related metrics, cell-related metrics and throughput information.
Additionally, The authors provided a 5G large-scale multi-cell ns-3 simulation
framework to supplement our real-time 5G production network dataset. This
framework allowed other researchers to investigate the interaction between
users connected to the same cell through the generation of their own synthetic
datasets.

[70] curated SPEC5G, the first publicly accessible 5G dataset for natural
language processing (NLP) research. The dataset contains 134M words in
3,547,587 phrases taken from 13 online websites and 13094 cellular network
specs. The authors utilized this dataset for security-related text
categorization and summarization by utilizing large-scale pre-trained language
models. For protocol testing, pertinent security-related attributes were also
extracted using text classification techniques. Additionally, [71] presented a
novel mobility dataset generation method for 5G networks based on users’ GPS
trajectory data. It aggregated the user’s GPS trajectories and models his
location history by a mobility graph representing the cell base stations he
passed through. The generated dataset contained the mobility graph records of
128 users. The user mobility dataset for 5g networks based on GPS geolocation
is valuable for predicting user mobility patterns.

In another study, [72] discussed a methodology for collecting a labelled
dataset for a 5G network. It described how to build a 5G testbed and use it to
collect data. This data can then be used to construct a 5G-based labelled
dataset. A 5G testbed was built to observe 5G network features by replaying
the collected data. A specialized network collector system was implemented to
collect 5G edge network traffic data. A re-collecting methodology using the
proposed 5G testbed and network collector can be used to construct a 5G-based
labelled dataset for supervised learning methods.

TABLE I: Table listing datasets related to contextual beamforming

Dataset Name | Source | Characteristics
---|---|---
| IEEE 802.11n
---
Channel Measurement
| IEEE 802.11n
---
working group
| Channel state information (CSI)
---
from multiple antennas
KTH Localization Dataset |  | KTH
---
Royal Institute of Technology
| Received signal strength (RSS) and
---
Angle of arrival (AoA) from multiple antennas
Ground truth location data
CSI-Hotel Dataset |  | University of California,
---
Santa Barbara
| CSI data from multiple antennas
---
in an indoor environment
DeepMIMO Dataset | New York University |  | Synthetic data generated by a ray-tracing tool
---
for a variety of scenarios, including urban and
indoor environments
iNEMO Dataset | STMicroelectronics |  | Acceleration, magnetic field, and
---
angular velocity data,
along with ground truth location data

Also, [73] used the results of a publicly available measurement campaign of 5G
users and analyzes various figures of merit. The analysis showed that the
downlink and uplink rates for static and mobile users can be captured either
by a lognormal or a Generalized Pareto distribution. Downlink and uplink rates
for static and mobile users can be captured either by a lognormal or a
Generalized Pareto distribution. Time spent in the same cell by a mobile
(driving) user can be captured to the best extent by a Generalized Pareto
distribution. Prediction of the number of active users in the cell is
possible.

Furthermore, [74] discussed 5G Tracker - a crowdsourced platform that includes
an Android app to record passive and active measurements tailored to 5G
networks and research. It has been used for over 8 months and has collected
over 4 million data points. The platform is useful for building the first-of-
a-kind, interactive 5G coverage mapping application. 5G Tracker is a
crowdsourced platform to enable research using commercial 5G services. 5G
performance is affected by several user-side contextual factors such as user
mobility level, orientation, weather, location dynamics, and environmental
features. 5G Tracker has been used to collect over 4 million data points,
consuming over 50 TB of cellular data across multiple 5G carriers in the U.S.

Moreover, bringing computational and storage technologies closer to end users
with strategically deployed and opportunistic processing and storage
resources, mobile edge computing in the 5G network was developed by [75] as a
very attractive computation architecture. This paper used data mining and
statistical methods to analyze Baidu website data. The analysis results gave
suggestions to improve the design and development of 5G services. Data mining
and statistical analysis of Baidu cloud services in 5G network revealed that
clustering, outlier detection, prediction, and statistical methods can be used
to evaluate smart city services. The analysis results provided insights into
the design and development of 5G services (API website). The findings
suggested that mobile edge computing in 5G networks can be used to improve the
performance of smart city services.

Finally, [76] discussed a project to collect ”three-level” edge layer data
from equipment manufacturers, equipment users, and spare parts manufacturers.
The goal was to establish a unified data standard and help companies build
general services around equipment data. 5G+ Industrial Internet is used to
collect data from three levels of edge layers: spare parts manufacturers,
equipment manufacturers and equipment users. Data is assimilated and unified
into a single standard. A large-scale and shallow informatization project of
incremental equipment is implemented in the Yangtze River Delta in a short
period of time. Also, the table lists some 5G data sets consisting of Channel
state information (CSI), Phase, Received Power, etc. that can be used for user
localisation.

###  III-C Optimization techniques for Contextual Beamforming [RQ:3]

Contextual beamforming models can be optimised using various strategies to
ensure real-time processing. One method to accelerate computations is through
hardware acceleration techniques like GPU processing. Additionally, pre-
trained models or reducing the number of parameters in the model design can be
used to optimise the model. To decrease the model size and boost computational
effectiveness, techniques like pruning, quantization, and knowledge
distillation can be applied. Improving the feature extraction and input data
pre-processing phases can also help with real-time processing. Creating
specialised algorithms and optimisation strategies adapted for certain
hardware and deployment conditions can also enhance the performance of
contextual beamforming models.

Below are some of the possible ways to optimise contextual beamforming models
for real-time processing:

Figure 9: Optimization Techniques for Contextual Beamforming

####  III-C1 Model simplification:

Simplifying the model architecture, such as reducing the number of layers or
the number of neurons in each layer, can improve computational efficiency and
reduce processing time. For instance, the research suggests a strategy that
uses machine learning and hardware performance counter data to optimise power
and performance for GPU-based systems. The model can accurately detect power-
performance bottlenecks and provide optimization techniques for a variety of
sophisticated compute and memory access patterns. The model, which has been
validated on NVIDIA Fermi C2075 and M2090 GPUs as well as the Keeneland
supercomputer at Georgia Tech, is more reliable and accurate than existing GPU
power models [77].

####  III-C2 Hardware acceleration:

Dedicated hardware, such as graphics processing units (GPUs), field-
programmable gate arrays (FPGAs), or application specific integrated circuits
(ASICs), can speed up the processing of contextual beamforming models by
performing parallel computations. In order to determine the direction of
incoming signals, beamforming is a signal processing technique that combines
signals from a number of receivers. Although it overcomes noise interference,
adaptive beamforming (ABF) is computationally expensive. On current graphics
processing units (GPUs), ABF can be implemented in parallel. ABF can be
parallelized on an NVIDIA GPU using the author’s method, which has a lesser
throughput than the serial implementation but is still able to be improved
[78].

####  III-C3 Optimization techniques:

Various optimization techniques, such as weight pruning, quantization, and
knowledge distillation, can be applied to contextual beamforming models to
reduce their computational complexity and memory footprint without significant
loss in accuracy. This research [79] explores the use of the deep neural
network (DNN) model as the teacher to train recurrent neural networks (RNNs),
specifically long short-term memory (LSTM), for automated voice recognition
(ASR). The method successfully trains RNNs without the use of additional
learning methods, even with a small amount of training data.

####  III-C4 Preprocessing and postprocessing:

Preprocessing the input data to reduce its dimensionality or complexity, and
postprocessing the output data to refine the results or reduce noise, can help
improve the performance and efficiency of contextual beamforming models. With
the use of deep learning, a low-complexity precoding design approach for
multiuser MIMO systems is suggested in [80]. The suggested method uses methods
such as input dimensionality reduction, network pruning, and recovery module
compression to produce a performance that is comparable to the conventional
WMMSE algorithm with relatively little computational cost.

####  III-C5 Real-time learning:

Using online or incremental learning algorithms, instead of offline or batch
learning, can enable contextual beamforming models to adapt to changing
conditions in real-time and reduce the need for frequent retraining. In [81],
two adaptive learning approaches such as ADAM and RAL are proposed for the
real-time detection of network assaults in Internet network traffic. These
methods achieve excellent detection accuracy even in the presence of idea
drifts by dynamically learning from and adapting to non-stationary data
streams while lowering the demand for labelled data.

####  III-C6 Model parallelism:

Breaking the model into smaller sub-models and processing them in parallel can
improve the overall processing speed of contextual beamforming models. This
can be done using techniques such as data parallelism or model parallelism.
With a focus on model and data parallelization, [82] addresses distributed
machine learning architecture and topology. It analyses machine learning
algorithms and offers parallelization suggestions. The specific needs and
demands of communications networks, such as resource allocation and trade-offs
between privacy and security, are not addressed.

####  III-C7 Early termination:

Stopping the model’s processing early when a certain threshold is reached can
reduce unnecessary computation, especially in cases where the output has
already converged. [83] promotes early pausing before convergence to prevent
overfitting and suggests the use of cross-validation to detect overfitting
during neural network training. The study uses multi-layer perceptrons with
RPROP training to assess the effectiveness and efficiency of 14 distinct
automatic stopping criteria from three classes for a variety of activities.
The findings indicate that slower stopping criteria slightly improve
generalisation, although training time often increases by a factor of four.

The choice of optimization techniques will depend on the specific requirements
of the application and the constraints of the hardware platform. A combination
of these techniques can be used to achieve the best balance between
performance and efficiency for the real-time processing of contextual
beamforming models.

###  III-D AI, ML and DL approaches for Contextual Beamforming [RQ:4]

AI, is a term that encompasses a vast array of techniques and technologies
that grant machines the ability to perform tasks that would usually demand
human intelligence, such as learning, problem-solving, and decision-making.
Within AI, there are two primary categories: narrow or weak AI and general or
strong AI. Narrow or weak AI machines are designed to accomplish specific
tasks, while general or strong AI strives to create machines capable of
performing any cognitive task a human can do. In contrast, machine learning
(ML) is a subfield of AI that specializes in the development of algorithms and
statistical models that enable machines to improve their performance on a task
over time by learning from data. ML algorithms can be classified into three
primary categories: supervised learning, unsupervised learning, and
reinforcement learning. In supervised learning, the algorithm is trained on
labelled data, where the correct output is already known, to forecast new
outputs for unseen data. In unsupervised learning, the algorithm is trained on
unlabeled data to identify patterns or structures in the data. In
reinforcement learning, the algorithm learns through trial and error,
receiving feedback in the form of rewards or penalties based on its actions.

Regarding beamforming, AI can refer to any technique that allows machines to
enhance the quality or efficiency of beamforming by learning from data, making
predictions or decisions based on that data, and adapting to changing
conditions. Machine learning is a specific subset of AI that utilizes
algorithms and statistical models to enable machines to learn from data
without explicit programming. The amalgamation of beamforming and artificial
intelligence (AI) represents a compelling advancement in signal processing and
communication systems. Beamforming, a technique used in radio communications
and signal processing, involves the direction of a signal toward a particular
location or direction. By integrating signals from multiple antennas,
beamforming amplifies signals in the desired direction while suppressing
interference from other directions. On the other hand, AI employs
computational algorithms and computer programs that can learn from existing
data to make decisions or predictions. AI finds application in various
domains, including natural language processing (NLP), image recognition, and
robotics.

By combining beamforming and AI, communication systems can witness remarkable
improvements in their performance. AI algorithms can scrutinize signals
received by multiple antennas and determine the optimal beamforming
configuration for a given situation. This can result in enhanced signal
quality and reduced interference. Additionally, AI can dynamically adjust
beamforming parameters in response to current environmental and signal
characteristics using reinforcement learning or other AI techniques, which is
particularly useful in complex and dynamic environments where traditional
beamforming techniques may struggle to adapt. AI is also beneficial for
optimizing beamforming algorithms themselves by adjusting the parameters
employed to combine signals from different antennas. This can enhance the
accuracy and efficiency of the beamforming process, leading to more reliable
communication. Furthermore, beamforming and AI can significantly improve the
performance of communication systems in various applications, from cellular
networks to satellite communication systems.

Recent research has delved into AI-assisted contextual beamforming, which can
be optimized using AI algorithms to filter out unwanted noise from the signal
or to automatically identify the location of a sound source. This can be
accomplished by training models on datasets of sound signals and corresponding
locations and using the models to predict the location of new sound sources or
to identify and remove noise from new signals. By pointing the microphone
array towards the predicted location, sound can be captured more effectively.
In multi-user multiple-input-single-output (MISO) systems, beamforming is a
useful way to improve the quality of incoming signals. Traditionally, finding
the best beamforming solution has relied on iterative techniques, which have
significant processing delays and are unsuitable for real-time applications
[84]. With recent advancements in deep learning (DL) algorithms, identifying
the best beamforming (BF) solution in real-time while taking into account both
performance and computational delay has become possible. This is accomplished
by offline training of neural networks before online optimization, allowing
the trained neural network to identify the optimal BF solution. This approach
reduces computational complexity during online optimization, requiring only
simple linear and nonlinear operations [84].

Figure 10 illustrates the neural network architecture for BF, which comprises
input, neural layers, and output to extract features for further processing.
In complicated indoor or outdoor contexts with multiple pathways, propagation
loss, noise, and Doppler effects create additional issues. Chong Liu’s
approach involves employing a machine learning regression method based on
efficient BF transmission patterns to predict the position of users on the
move, following the collection of large volumes of Line-of-Sight (LOS) and
Non-Line-of-Sight (NLoS) data [85]. In the domain of location estimation,
Bhattacharjee et al. presented two distinct approaches for training neural
networks, one using channel parameters as features and the other using a
channel response vector, and evaluated the results using preliminary computer
simulations [86, 87]. The same group also conducted experimental work on the
localization of drones and other application areas using different approaches
[88]. Wang et al. proposed a weighted loss function to enhance the performance
of localization with sparse sensor layouts, achieving an accuracy boost of
over 50% [89]. We also presented results for future location estimation of
mobile users using a deep neural network in [90].

Contextual Beamforming in 5G vehicle communication has been implemented using
various ML and AI techniques. The performance and precision of beamforming
systems, which are essential for efficient communication in moving situations,
are to be improved by these techniques. ML has the potential to significantly
advance 5G technology, as evidenced by the growing complexity of constructing
cellular networks. Deep learning has demonstrated effectiveness in ML tasks
like speech recognition and computer vision, with performance growing as more
data is accessible. The proliferation of deep learning applications in
wireless communications is constrained by the scarcity of huge datasets. To
create channel realisations that accurately depict 5G scenarios with mobile
transceivers and objects, this study describes an approach that combines a car
traffic simulator with a raytracing simulator. The following section of the
review offers a unique dataset along with various ML as well as AI techniques
used for examining millimetre wave beam selection methods for car-to-
infrastructure communication. The application of datasets produced with the
suggested methodology is demonstrated by experiments including deep learning
in classification, regression, and reinforcement learning problems [91].

TABLE II: Table to compare the performance of different contextual beamforming
techniques based on results from various studies

Technique Name | Description | Advantages | Limitations | Type of Data Required
---|---|---|---|---
Geometric-Based |  | Determines the location of the user using
---
the arrival times of signals from multiple antennas
-Low computational cost |  | -Limited accuracy in indoor environments
---
-Vulnerable to multipath fading
| -Antenna array data
---
-User location data
| Channel State Information
---
(CSI)-Based
| Uses CSI data from multiple antennas
---
to estimate the user’s location
| -High accuracy
---
-Robustness to multipath fading
| -Requires high-quality CSI data
---
-Complex algorithms
| -CSI data from multiple antennas
---
-User location data
Hybrid-Based |  | Combines geometric and CSI-based
---
techniques to improve accuracy and robustness
| -High accuracy
---
-Robustness to multipath fading and noise
| -Requires complex algorithms
---
-May have high computational cost
| -Antenna array data
---
-CSI data from multiple antennas
-User location data
Machine Learning-Based |  | Uses machine learning algorithms to
---
learn the relationship between antenna
array data and user location
| -High accuracy
---
-Can adapt to changing environments
| -Requires large amounts of training data
---
-May have high computational cost during training
| -Antenna array data
---
-User location data for training
Deep Learning-Based |  | Uses deep learning algorithms to learn the
---
relationship between antenna array data and user location
| -High accuracy
---
-Can adapt to changing environments
-Can handle large amounts of data
| -Requires even larger amounts of training data
---
than machine learning-based techniques
-May have high computational cost during training
| -Antenna array data
---
-User location data for training

####  III-D1 Deep Learning Techniques:

The extraction of valuable characteristics from input signals and the
provision of more precise predictions have been accomplished using deep
learning techniques like CNNs and RNNs.

For instance, Wang et al. used deep learning to simplify beamforming weight
estimation in 5G systems. They developed a channel model and trained
convolutional neural networks on generated data. The networks predicted
beamforming weights based on channel data, reducing complexity. Results show
the potential of deep learning for digital and hybrid beamforming, and
performance comparison with conventional techniques was presented [92]. Also,
[93] proposed a method that aims to improve the performance of Random Forest,
Multilayer Perceptron, and k-Nearest Neighbors classification models by
increasing the amount of data through synthetic data inclusion. Their
experimental results showed that the inclusion of synthetic data improved the
macro F1 scores of the models. The Random Forest, Multilayer Perceptron, and
k-Nearest Neighbors achieved macro F1 scores of 0.9341, 0.9241, and 0.9456,
respectively, which are higher than those obtained with the original data
only.

[94] proposed a deep learning-based fast-beamforming design method for sum
rate maximization under a total power constraint. The method was trained
offline using a two-step training strategy. Simulation results demonstrated
that the proposed method is fast while obtaining a comparable performance to
the state-of-the-art method. They derived a heuristic solution structure of
the downlink beamforming through the virtual equivalent uplink channel based
on the optimum MMSE receiver. BPNet is designed to perform the joint
optimization of power allocation and VUB design and is trained offline using a
two-step training strategy. A DL-enabled beamforming neural network (BFNN) is
proposed which can optimize the beamformer to attain better spectral
efficiency. Simulation findings reveal that the proposed BFNN achieves
significant performance gain and high robustness to imperfect CSI. The
proposed BFNN greatly decreases the computational complexity compared to
conventional BF algorithms. Spectral Efficiency, Performance Gain, Robustness
To Imperfect Csi, and Computational Complexity (Measured In Floating Point
Operations) are the main outcomes of BFNN [95].

[84] proposed a beamforming neural network (BNN) for the power minimization
problem in multi-antenna communication systems. The BNN was based on
convolutional neural networks and the exploitation of expert knowledge. It
achieved satisfactory performance with low computational delay. A deep fully
convolutional neural network (CNN) was used for beamforming, providing
considerable performance gains. The CNN was trained in a supervised manner
considering both uplink and downlink transmissions with a loss function based
on UE receiver performance. The neural network predicted the channel evolution
between uplink and downlink slots and learned to handle inefficiencies and
errors in the whole chain, including the actual beamforming phase [96]. A deep
learning model that learns how to use these signatures to predict the
beamforming vectors at the BSs. [24] discussed a novel integrated machine
learning and coordinated beamforming solution to support highly-mobile mmWave
applications. The solution used a deep learning model to learn how to use
signatures to predict the beamforming vectors at the base stations. This
rendered a comprehensive solution that supports highly mobile mmWave
applications with reliable coverage, low latency, and negligible training
overhead.

[97] proposed a deep learning–based energy beamforming scheme for a multi-
antennae wireless powered communication network (WPCN). We used offline
training for the deep neural network (DNN) to provide a faster solution to the
real-time resource allocation optimization problem. Simulation results showed
that the proposed DNN scheme provided a fair approximation of the traditional
sequential parametric convex approximation (SPCA) method with low
computational and time complexity.

####  III-D2 Supervised ML Techniques:

Different sorts of acoustic environments have been classified and predicted
using supervised learning methods like SVMs and decision trees. A modified SVM
technique is proposed for 3D MIMO beamforming in 5G networks. The Advanced
Encryption Standard algorithm is employed for more security, and interference
is reduced in two stages. The suggested ML-3DIM method outperforms existing
methods in terms of throughput, SINR, and SNR by up to 20%, 30%, and 35%,
respectively, according to simulation results [98]. [99] investigated the
machine learning-based beamforming design in two-user MISO interference
channels. It proposed a machine learning structure that takes transmit power
and channel vectors as input and then recommends two users’ choices between
MRT and ZF as output. The numerical results showed that our proposed machine
learning-based beamforming design well finds the best beamforming combination
and achieved a sum rate of more than 99.9% of the best beamforming
combination.

[100] introduced an SVM-based approach for linear array processing and
beamforming. It showed how the new minimization approach can be applied to the
problem of linear beamforming. BER performance of LS and SVM for different
noise levels from 0 to 15 dB. A machine learning (ML) beamforming approach
based on the k-nearest neighbours ( ${k}$ -NN) approximation has been
considered, which was trained to generate the appropriate beamforming
configurations according to the spatial distribution of throughput demand.
Performance was evaluated statistically, via a developed system-level
simulator that executes Monte Carlo simulations in parallel. The ML-assisted
beamforming framework achieved up to 5 Mbits/J and 36 bps/Hz in terms of
energy efficiency (EE) and spectral efficiency (SE), respectively, with
reduced hardware and algorithmic complexity [101]. BeamMaP was a beamforming-
based machine learning model for positioning in massive MIMO systems.
Simulation results showed that BeamMaP achieved Reduced Root-Mean-Squared
Estimation Error (RMSE) performance with an increasing volume of training
data. BeamMaP was more efficient and steady in the positioning system compared
with kNN and SVM [102].

[103] discussed a machine learning method for beamforming at the receiver side
antennas for deploying Line-of-Sight (LOS) communication in Satellite
Communication (Satcom). It described how the antenna array weights are pre-
calculated for a number of beam directions and kept as a database. The signal
weights that were calculated for each array element by using their progressive
measured phase difference were due to the arriving signal, which was given as
input to a linear regression machine learning model and the direction of
arrival (DOA) of the signal is predicted. A method for determining an
appropriate precoder from the knowledge of the user’s location only was
proposed. The proposed method involved a neural network with a specific
structure based on random Fourier features allowing to learn functions
containing high spatial frequencies. The proposed method was able to handle
both line-of-sight (LOS) and non-line-of-sight (NLOS) channels [104].

####  III-D3 Unsupervised ML Techniques:

Unsupervised learning methods like clustering and PCA have been used to spot
trends and put related data points in one category. For instance, [105]
proposes a beamforming algorithm for fifth-generation and later communication
systems. The approach combines the benefits of conventional optimization-based
beamforming techniques with deep learning-based techniques. To create initial
beamforming, a novel neural network architecture is proposed, and performance
is increased by building a deep unfolding module. The entire algorithm is
unsupervised and trained, and simulation results demonstrate enhanced
performance and reduced computing complexity when compared to current
approaches.

[106] proposed a novel unsupervised learning approach to design the hybrid
beamforming for any subarray structure while supporting quantized phase
shifters and noisy CSI. No beamforming codebook was required, and the neural
network is trained to take into account the phase-shifter quantization.
Simulation results showed that the proposed deep learning solutions can
achieve higher sum rates than existing methods.

Figure 10: A Basic Architecture of a deep neural network that consists of
input (extracted features), neural layers(as per required framework), output
(desired results) and a feed for post-processing.

####  III-D4 Reinforcement ML Techniques:

Contextual Beamforming system performance has been enhanced using
reinforcement learning techniques like Q-learning and policy gradient methods.
For instance, to make network design and maintenance more straightforward, a
brand-new intelligent algorithm for massive MIMO beamforming performance
optimisation is proposed in this research. To produce accurate user mobility
patterns, pertinent antenna designs, and an estimate of the effectiveness of
the generated antenna diagrams, the system uses three neural networks that
apply deep adversarial reinforcement learning workflow. This method has the
advantage of learning independently and without requiring big training
datasets [107].

[108] investigated the use of deep reinforcement learning to predict
coordinated beamforming strategy in an ultra-dense network. It was found that
the optimal solution is a balanced combination of selfish and altruistic
beamforming. The beamforming vectors were obtained efficiently through the
learned balancing coefficients. A reinforcement learning (RL) based algorithm
for cognitive beamforming was proposed for multi-target detection in massive
multiple input multiple outputs (MMIMO) cognitive radars (MMIMO CR). The
proposed RL-based algorithm outperformed the conventional omnidirectional
approach with equal power allocation in terms of target detection performance.
The performance improvement was even more remarkable under environmentally
harsh conditions such as low SNR, heavy-tailed disturbance and rapidly
changing scenarios [109].

[110] proposed a blind beam alignment method based on RF fingerprints of user
equipment obtained from base stations. They used deep reinforcement learning
on a multiple-base station cellular environment with multiple mobile users.
Achieved a data rate of up to four times the data rate of the traditional
method without any overheads. [111] proposed a novel multiagent reinforcement
learning(MARL) formulation for codebook-based beamforming control. It took
advantage of the inherently distributed structure in a wirelessly powered
network and laid the groundwork for fully locally computed beam control
algorithms. A cognitive beamforming algorithm based on Reinforcement Learning
(RL) framework is proposed for colocated MIMO radars. The proposed RL-based
beamforming algorithm is able to iteratively sense the unknown environment and
synthesize a set of transmitted waveforms tailored to the acquired knowledge.
The performance of the proposed RL-based beamforming algorithm is assessed in
terms of Probability of Detection $(P_{D})$ [112].

[113] proposed a reinforcement learning (RL) approach called combinatorial
multi-armed bandit (CMAB) framework to maximize the overall network throughput
for multi-vehicular communications. They proposed an adaptive combinatorial
Thompson sampling algorithm, namely adaptive CTS, and a sequential Thompson
sampling (TS) algorithm for the appropriate selection of simultaneous beams in
a high-mobility vehicular environment. Simulation results showed that both of
our proposed strategies approach the optimal achievable rate achieved by the
genie-aided solution.

TABLE III: Table summarizing the different types of machine learning and
artificial intelligence techniques used in contextual beamforming

Technique Name | Description | Advantages | Limitations
---|---|---|---
Support Vector Machines (SVM) |  | Supervised learning algorithm that
---
learns a decision boundary between classes
| -Can handle high-dimensional data
---
-Effective in binary classification tasks
-May overfit with noisy or imbalanced data
Random Forest (RF) |  | Ensemble learning method that
---
combines multiple decision trees
to improve performance
| -Can handle high-dimensional data,
---
-Can handle missing or noisy data
-Can provide feature importance measures
-May overfit with noisy or imbalanced data
Convolutional Neural Networks (CNN) |  | Neural network architecture that
---
uses convolutional layers to extract
features from input data
| -Highly effective for image and signal processing tasks
---
-Can learn complex spatial patterns
| -May require large amounts of training data
---
-May be computationally expensive
Recurrent Neural Networks (RNN) |  | Neural network architecture that
---
can process sequential data by
maintaining a memory of past inputs
| -Effective for time-series data and natural language processing tasks
---
-Can handle variable-length inputs
| -May be prone to vanishing or exploding gradients
---
-May require large amounts of training data
Reinforcement Learning (RL) |  | Learning paradigm in which an
---
agent learns to make decisions
through trial and error in an environment
| -Can adapt to changing environments
---
-Can handle complex decision-making tasks
| -May require significant computational resources
---
-May require careful tuning of hyperparameters

####  III-D5 Hybrid Techniques:

Contextual Beamforming system performance has also been improved using hybrid
methods that incorporate several ML and AI techniques, such as deep
reinforcement learning. To address the hybrid beamforming issue in huge MIMO
systems, deep reinforcement learning is suggested. The suggested techniques
reduce computing complexity while achieving spectral efficiency performance
that is close to ideal [114]. Hybrid beamforming, combining digital baseband
precoders and analogue RF phase shifters, is an effective technique for
millimetre wave (mmWave) communications and massive multiple-in-multiple-out
(MIMO) systems. Machine learning techniques can be used to improve the
achievable spectral efficiency of hybrid beamforming systems. The proposed
two-step algorithm can attain almost the same efficiency as that can be
achieved by fully digital architectures [115].

[116] described the design of ML-based hybrid beamforming for multiple users
in systems that use millimetre waves (mmWaves) and massive MIMO architectures.
The simulation results showed that the ML-based hybrid beamforming
architecture can achieve the same spectral efficiency (bits/sec/Hz) as the
fully digital beamforming designs with negligible error for both single-user
and multi-user Massive-MIMO scenarios. [117] proposed a novel RSSI-based
unsupervised deep learning method to design the hybrid beamforming in massive
MIMO systems. They proposed a method to design the synchronization signal (SS)
in initial access (IA) and a method to design the codebook for the analog
precoder. They showed that the proposed method not only greatly increases the
spectral efficiency especially in frequency-division duplex (FDD)
communication by using partial CSI feedback, but also has a near-optimal sum-
rate and outperforms other state-of-the-art full-CSI solutions.

Deep neural networks (DNNs) can be used to approximate the singular value
decomposition (SVD) and design hybrid beamformers. DNN-based hybrid
beamforming improved rates by up to 50-70% compared to conventional hybrid
beamforming algorithms and achieved a 10-30% gain in rates compared with the
state-of-the-art ML-aided hybrid beamforming algorithms. The proposed approach
had low time complexity and memory requirements [118]. A federated learning
(FL) based framework for hybrid beamforming was proposed, where the model
training was performed at the base station (BS) by collecting only the
gradients from the users. A convolutional neural network was designed, in
which the input was the channel data, yielding the analog beamformers at the
output. FL was demonstrated to be more tolerant to the imperfections and
corruptions in the channel data as well as having less transmission overhead
than centralized machine learning (CML) [119].

##  IV Challenges associated with using AI, ML and DL techniques for
contextual beamforming

Contextual beamforming is a technique used in signal processing and
communication systems to improve the quality of sound or data transmission by
focusing the transmitted or received signals in a specific direction or area
of interest. Machine learning (ML) techniques have been increasingly used to
optimize the performance of contextual beamforming systems. However, there are
several challenges associated with using ML techniques for contextual
beamforming:

  1. 1.

Lack of training data: ML techniques require a large amount of data to be
trained effectively. However, in contextual beamforming, it may be difficult
to collect enough data that accurately represents the various environments and
scenarios in which the system will be used. This can result in underfitting or
overfitting of the model, leading to poor performance.

  2. 2.

Complexity of the models: ML models used for contextual beamforming can be
quite complex, with many parameters that need to be tuned. This can make the
training process difficult and time-consuming, and can also increase the risk
of overfitting.

  3. 3.

Robustness to environmental changes: Contextual beamforming systems need to be
robust to changes in the environment, such as changes in noise levels or the
location of sound sources. ML models may not be able to adapt to these changes
quickly enough, resulting in reduced performance.

  4. 4.

Limited interpretability: ML models can be difficult to interpret, which can
make it hard to understand why the system is behaving in a certain way or to
diagnose problems when they occur.

  5. 5.

Limited generalizability: ML models trained on one set of data may not
generalize well to other datasets or environments. This can limit the
applicability of the system in real-world scenarios.

TABLE IV: Table comparing the computational complexity and processing time of
different contextual beamforming models

Model |  Computational Complexity |  Processing Time |  Advantages |  Limitations
---|---|---|---|---
Linear Beamforming |  Low |  Fast |  -Simple to implement -low computational complexity |  Cannot perform well in non-line-of-sight environments
Maximum Ratio Transmission (MRT) |  Low |  Fast |  -Simple to implement -low computational complexity |  Cannot perform well in interference-limited environments
Minimum Variance Distortionless Response (MVDR) |  High |  Slow |  -Provide better performance in non-line-of-sight environments |  Computationally intensive
Neural Network-Based Beamforming |  High |  Slow |  -Learn complex non-linear relationships between inputs and outputs |  Require significant computational resources for training
Reinforcement Learning-Based Beamforming |  High |  Slow |  -Adapt to changing environments and optimize performance through trial and error |  Require significant computational resources and careful tuning of hyperparameters

To address these challenges, researchers are exploring new techniques such as
transfer learning, which involves pretraining models on large datasets and
then fine-tuning them on smaller, task-specific datasets. They are also
working on developing more interpretable ML models and incorporating
robustness and adaptability into the models. These challenges have been
discussed in several research papers, including Another study proposed by
[120] discussed a novel approach to enhance the Feature Engineering and
Selection (eFES) Optimization process in ML. eFES was built using a unique
scheme to regulate error bounds and parallelize the addition and removal of a
feature during training. Results showed the promising state of eFES as
compared to the traditional feature selection process. A weak convolutional
network can be used to provide rough label maps over the neighbourhood of a
pixel. Incorporating this weak learner in a bigger network can improve the
accuracy of state-of-the-art architectures. The approach in [121] was generic
and can be applied to similar networks where contextual cues are available at
training time.

A Multicriteria technique has been developed that allows for the control of
feature effects on the model’s output. Knowledge functions have been
integrated to accommodate for more complex effects and local lack of
information. A Deep Learning training process that was both interpretable and
compliant with modern legislation has been developed by [122]. [123] proposed
a technique to improve the interpretability in transfer learning tasks by
defining interpretable features. They examined the interpretability of
transfer learning by applying a pre-trained model with defined features to
Korean character classification. Feature Network (FN) consisted of Feature
Extraction Layer and a single mapping layer that connected the features
extracted from the source domain to the target domain. Also, [124] proposed an
actor-critic model that allowed better generalization across goals and scenes.
AI2-THOR framework enabled agents to take actions and interact with objects,
allowing for efficient collection of training samples. Model converged faster
than state-of-the-art deep reinforcement learning methods, generalized to real
robot scenarios with minimal fine-tuning, and is end-to-end trainable.

##  V Potential Applications of Contextual Beamforming

Some potential applications of contextual beamforming for 5G technology
include:

  1. 1.

Improved coverage: Contextual beamforming can help extend the coverage of 5G
networks by focusing the transmission beam towards the receiver. This can help
overcome obstacles such as buildings and trees that may obstruct the signal.

  2. 2.

Higher data rates: By directing the signal towards the receiver, contextual
beamforming can help increase the data rates of 5G networks. This can enable
faster downloads and uploads, as well as smoother streaming of high-definition
content.

  3. 3.

Reduced interference: Contextual beamforming can help reduce interference from
other devices or networks by steering the transmission beam away from sources
of interference. This can improve the reliability and quality of 5G
connections.

  4. 4.

Energy efficiency: By directing the transmission beam towards the receiver,
contextual beamforming can reduce the amount of energy required to transmit
the signal. This can help improve the energy efficiency of 5G networks, which
is an important consideration for mobile devices that rely on battery power.

##  VI Our Contribution Towards Localisation and Beamforming

Optimal BF strikes a balance between giving maximum power to a single user
while decreasing or eliminating signal interference at other users. When the
maximum ratio transmission (MRT) BF technique is used in an MU-MIMO system,
the transmitter transmits a beam to every user according to its weight vector.
The resultant power received by each user for the signal intended for that
user is calculated as the product of the channel gain and weight vector.
Because the MIMO system transmits to multiple users at the same frequency, a
critical performance metric for the system is the signal-to-interference-plus-
noise ratio (SINR) for each user. This concept has been proved in [125] that
shows how SINR can be significantly improved by 28.83 dBm and/ or 53% by using
MRT in comparison with no BF.

We have also published some of the work in context to localization in [126,
125, 90, 127]. These works show how location datasets can be extracted through
ray tracing tools and how data can be utilized for location prediction using
deep neural networks. In our recent paper([128]), the usage of the core
network is discussed to enable beamforming after calculating the direction of
arrival, or in simple terms, the user’s location at the MAC layer. The basic
concept of this paper is shown in Figure 6.

##  VII Conclusion

In this study, we provide an overview of advanced adaptive BF in which
artificial intelligence techniques such as deep learning (DL) can be used.
More importantly, we have shown that with access to contextual information
such as prior user location, a wireless network’s performance can be improved
through deep learning techniques. With the development of exciting new
technologies such as edge computing and federated learning, we believe the
next generation of mobile networks will unlock new opportunities.
Communication systems will continue to evolve as closed-loop systems where
data extracted by observing a mobile user will be exploited to improve
connectivity and network performance, such as the signal-to-noise ratio (SNR).
We have touched upon some of the studies already underway that can harness a
user’s location, and develop a DL-enabled contextual beamforming strategy that
can improve the SNR by 53% on average.

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