# Data-driven Integrated Sensing and Communication: Recent Advances,
Challenges, and Future Prospects

Hammam Salem, MD Muzakkir Quamar, Adeb Mansoor, Mohammed Elrashidy, Nasir
Saeed Mudassir Masood

###### Abstract

Integrated Sensing and Communication (ISAC), combined with data-driven
approaches, has emerged as a highly significant field, garnering considerable
attention from academia and industry. Its potential to enable wide-scale
applications in the future sixth-generation (6G) networks has led to extensive
recent research efforts. Machine learning (ML) techniques, including
$K$-nearest neighbors (KNN), support vector machines (SVM), deep learning (DL)
architectures, and reinforcement learning (RL) algorithms, have been deployed
to address various design aspects of ISAC and its diverse applications.
Therefore, this paper aims to explore integrating various ML techniques into
ISAC systems, covering various applications. These applications span
intelligent vehicular networks, encompassing unmanned aerial vehicles (UAVs)
and autonomous cars, as well as radar applications, localization and tracking,
millimeter wave (mmWave) and Terahertz (THz) communication, and beamforming.
The contributions of this paper lie in its comprehensive survey of ML-based
works in the ISAC domain and its identification of challenges and future
research directions. By synthesizing the existing knowledge and proposing new
research avenues, this survey serves as a valuable resource for researchers,
practitioners, and stakeholders involved in advancing the capabilities of ISAC
systems in the context of 6G networks.

###### Index Terms:

Integrated sensing and communication, joint communication and sensing, joint
radar and communication, 6G, machine learning, deep learning, reinforcement
learning, data-driven approaches.

##  I Introduction

Integrated Sensing and Communication (ISAC) systems hold immense promise in
revolutionizing the performance of next-generation wireless networks. By
enabling the seamless sharing of critical resources like time, frequency,
waveform design, and hardware, ISAC empowers a wide range of applications in
6G that merge sensing and communication systems [1]. Its impact on future
wireless systems cannot be overstated, as it perfectly caters to the demands
of various domains, such as precise localization, advanced tracking, gesture
recognition, activity monitoring, and augmented human reality [1]. With its
ability to deliver both high data rates for communication and unparalleled
accuracy for sensing, ISAC emerges as an indispensable force driving the
future of wireless technology [2].

Moreover, the increasing trend of using machine learning (ML) techniques in
next-generation communication systems aim to improve signal processing,
spectrum management, predictive maintenance, and security features. In
particular, using deep learning (DL) in ISAC systems aims to reduce hardware
dependency, minimize complexity, and overcome the difficulties imposed on the
system’s respective tasks by the dynamic environment. Therefore, data-driven
approaches are expected to enhance automation and efficiency in the overall
operation of ISAC systems [3, 4].

Nevertheless, the implementation of ISAC systems presents several challenges.
One such challenge is developing novel waveform designs that can offer the
right balance between the requirements of communication and sensing
functionalities [5]. Beamforming also poses a significant challenge for ISAC
systems, given the conflicting preferences of communication and sensing
systems. While communication systems favor narrowband configurations to
optimize transmission between transmitters and receiver terminals, sensing
systems thrive on wideband setups to capture extensive environmental
information [4]. Fortunately, researchers have proposed various approaches to
tackle these challenges. For instance, in the realm of localization
enhancement, a distributed inference framework leveraging device-to-device
(D2D) communication was put forth [6]. Another strategy employed full-duplex
operation to address the design intricacies of ISAC systems [7]. To achieve
simultaneous communication and sensing, the authors in [8] utilized massive
multi-input multi-output (MIMO) arrays, enabling communication with moving
units while conducting environmental sensing. Additionally, orthogonal
frequency-division multiplexing (OFDM) emerged as a viable solution for
merging communication and sensing systems, as discussed in [9].

Although these classical methods have made notable strides in improving ISAC
system performance, they often encounter a common drawback: the inherent
complexity arising from reconciling the distinct requirements of communication
and sensing tasks. This complexity can hinder the seamless integration and
optimization of these two functionalities. However, the rapid advancement of
ML techniques can provide solutions to mitigating such challenges in ISAC
systems. These ML-based solutions have the potential to revolutionize the
field, rendering futuristic ISAC technologies more secure, reliable, and
efficient. By unlocking the capabilities of ML, we can unlock new dimensions
of efficiency, reliability, and security in ISAC systems. Therefore, this
paper highlights the significant contributions of ML algorithms in tackling
challenges within the realm of ISAC. By conducting a comprehensive review of
the literature, this study aims to present a state-of-the-art analysis of the
diverse applications of ML-ISAC. These applications encompass various domains,
such as vehicles, THz (terahertz) systems, radar technology, beamforming,
tracking and localization, spectrum sensing, edge computing, communication
environment, intelligent reflective surfaces (IRS), and more. Through this
exploration, a comprehensive understanding of the utilization of ML algorithms
in ISAC can be obtained, shedding light on the advancements and potential
future directions in this field.

###  I-A Relevant Surveys

Given that ISAC is an emerging field, several surveys in the literature have
approached the topic from different perspectives. As an example, Shao et al.
[10] presented a comprehensive survey on sensing techniques based on channel
state information (CSI) in wireless local access network environments. The
authors investigated three categories of techniques: model-based, data-based,
and hybrid model-data techniques. They delved into data-based techniques in
particular, discussing pattern recognition and deep learning algorithms in
detail. Liu et al. [11] focused on performance metrics related to
communication, sensing, and ISAC, utilizing estimation and information
theories. They examined these metrics at individual and joint levels,
discussing the limitations of device-based and device-free sensing algorithms.
Zhang et al. [12] surveyed new signal-processing techniques for integrating
communication and radar sensing by exploring technologies employed for
communication-centric, radar-centric, and joint optimization and design for
joint radar and communication (JRC) systems. In [13], JRC was surveyed from a
waveform and network architecture perspective, discussing waveform generation,
processing, and network design.

Furthermore, specific applications of ISAC have also garnered attention in the
literature. For instance, Zhang et al. [14] provided insights into the
methodologies, challenges, and future directions of applying ISAC in mobile
network systems, focusing on signal processing aspects. He et al. [15]
surveyed works that employed IRS in joint localization and communication
(JLC), a particular case of ISAC. They explored use cases in communication,
localization, and radar-like sensing, emphasizing channel estimation (CE) in
IRS-assisted JLC networks. The surveyed works fell into three categories: beam
alignment, compressive sensing, and data-driven approaches. Despite these
valuable surveys, a comprehensive review specifically focused on the existing
solutions of ML algorithms used in ISAC literature needs to be improved. We
compare our work against other works in terms of many aspects as shown in
table I.

TABLE I:  Comparing this work against other works in terms of the following items: 1) DL/AI roles, 2) ISAC, 3) AVs application, 4) THz application 5) Radar application 6) Beamforming application 7) localization application, 8) spectrum sensing, 9)edge computing, 10) IRS application, 11) channel estimation. Ref |  1 |  2 |  3 |  4 |  5 |  6 |  7 |  8 |  9 |  10 |  11
---|---|---|---|---|---|---|---|---|---|---|---
[10] |  ✓ |  ✓ |  - |  - |  ✓ |  ✓ |  - |  - |  - |  - |  -
[11] |  - |  ✓ |  ✓ |  - |  ✓ |  ✓ |  ✓ |  ✓ |  - |  ✓ |  ✓
[12] |  - |  ✓ |  ✓ |  - |  ✓ |  ✓ |  - |  - |  - |  - |  ✓
[13] |  - |  ✓ |  - |  - |  ✓ |  - |  ✓ |  - |  - |  - |  -
[14] |  ✓ |  ✓ |  ✓ |  - |  ✓ |  ✓ |  ✓ |  - |  ✓ |  - |  ✓
[15] |  ✓ |  ✓ |  |  |  |  ✓ |  ✓ |  |  |  ✓ |
[16] |  ✓ |  - |  ✓ |  ✓ |  - |  ✓ |  - |  - |  ✓ |  ✓ |  ✓
this work |  ✓ |  ✓ |  ✓ |  ✓ |  ✓ |  ✓ |  ✓ |  ✓ |  ✓ |  ✓ |  ✓

###  I-B Contributions of this Survey

Despite the above-discussed surveys, a notable gap exists for an in-depth and
comprehensive review dedicated explicitly to exploring the existing solutions
of ML algorithms utilized in ISAC. This gap represents a critical opportunity
to delve into the realm of ML applications within the context of ISAC,
uncovering novel methodologies, advancements, and key insights. By bridging
this gap, this article explores how ML algorithms have been harnessed in the
literature to tackle the challenges associated with ISAC. The primary
objective of this study is to present a state-of-the-art review that showcases
the diverse range of use cases where ML-ISAC has been successfully employed.
These use cases encompass various applications, including vehicular networks,
THz systems, radar technology, beamforming, tracking and localization,
spectrum sensing, edge computing, communication environment, IRS, and many
others. By encompassing these various domains, this work offers a
comprehensive overview of the extensive utilization and advancements of ML
algorithms within the realm of ISAC, shedding light on the transformative
potential of this technology. Such an endeavor will contribute to academic
knowledge and serve as a valuable resource for researchers, practitioners, and
industry professionals seeking to navigate the complex intersection of ML and
ISAC systems.

###  I-C Organization

The rest of the paper is organized as follows (c.f., Fig. 1). Section II
provides a technical background about the mostly used ML algorithms in the
literature. Then, use cases for ISAC are discussed in section III. Section IV
presents the key challenges in ML-ISAC. Finally, future directions and
conclusions are presented in sections V and VI, respectively. Table II
includes the important abbreviations used in this work.

Figure 1: Overview diagram of the paper’s structure TABLE II: List of key
abbreviations.

Acronym |  Definition |  Acronym |  Definition
---|---|---|---
6G |  Sixth generation |  IRS |  Intelligent reflective surface
AI |  Artificial intelligence |  ISAC |  Integrated sensing and communication
APS |  Azimuth power spectrum |  JRC |  Joint radar and communication
ASPP |  Atrous spatial pyramid pooling |  KNN |  K-nearest neighbors
AV |  Autonomous vehicles |  LSTM |  Long short-term memory
CE |  Channel estimation |  ML |  Machine learning
CNN |  Convolutional neural network |  R2C |  Radar to communication
CSI |  Channel state information |  RL |  Reinforcement learning
DDQN |  Double deep Q-network |  RNN |  Recurrent neural network
DL |  Deep learning |  RSU |  Roadside unit
DNN |  Deep neural network |  SS |  Spectrum sensing
DQN |  Deep Q-network |  SVM |  Support vector machine
DRL |  Deep reinforcement learning |  UAV |  Unmanned aerial vehicle
EC |  Edge computing |  UE |  User equipment
FCNN |  Fully connected neural network |  V2X |  Vehicle to everything
GNN |  Graph neural network |  V2I |  Vehicle to infrastructure

##  II Summarizing deep learning techniques

Deep learning (DL) is a subset of machine learning that uses artificial neural
networks with multiple layers. These networks, typically known as deep neural
networks (DNNs) [17, 18], can learn intricate features from data and generate
accurate predictions. DL has revolutionized artificial intelligence (AI) and
has transformative applications in areas such as computer vision, speech
recognition, and autonomous vehicles [19], communications [20], and many more.

DL techniques have significantly influenced communication systems, leading to
breakthroughs in various crucial applications. In the realm of communication,
DL has been instrumental in channel coding [21], modulation recognition [22],
beamforming [23], resource allocation [24], signal processing [25], and
channel estimation [26]. These applications have leveraged DL’s ability to
optimize performance, enhance system efficiency, and effectively address the
complexities of communication tasks. Notably, DL techniques have gained
considerable traction in the context of integrated sensing and communication
systems, especially within the framework of 6G networks and terahertz (THz)
technologies, where they offer promising avenues for innovation and
improvement. In the following, we briefly introduce major DL algorithms used
in communication and sensing systems.

Fully Connected Neural Networks (FCNNs): FCNNs, inspired by the structure and
function of the human brain, are neural networks (NNs) that comprise
interconnected nodes that represent data features via learnable weights and
biases. FCNNs work by processing data through a series of layers, as shown in
Fig. 2, where each layer contains a set of nodes. Data is received first by
the input layer, and conveyed to the hidden layers for further feature
extraction. The inputs at each layer are combined linearly and passed through
an activation, non-linear, function. The process is repeated depending on the
number of hidden layers. The final predictions are developed at the output
layer. FCNNs can be used for many applications, including classification and
regression [27, 28] .

Figure 2:  A schematic diagram of FCNN.

Convolutional Neural Networks (CNNs): A CNN is an NN that is used for a
variety of tasks, particularly when spatial structure of the data is
considered. CNNs consist of layers that perform 2D or 1D convolution, where
the convolving masks are learnable parameters. Usually, a convolutional layer
is followed by pooling layers, batch normalization layers, and activation
layers. The 2D output of the last convolutional block is usually flattened and
passed through fully-connected layers to get the final output [29, 30]. A
example CNN is shown in Fig. 3.

Figure 3: A schematic diagram of CNN.

Recurrent Neural Networks (RNNs): RNNs are NNs that consideres the sequential
structure of the data (e.g., time sequences). A general scheme for RNNs is
shown in Fig. 4. RNNs consist of sequential units, each of which can be
represented mathematically by a state. The state depends on the input data and
the information received from the previous state. While feedforward networks
(e.g., FCNNs and CNNs) assign different parameters for each layer, RNNs share
the same parameters between the states. There are various types of RNN units
such as gated recurrent units (GRUs) [31] and long short-term memory (LSTM)
units [32, 33].

Figure 4:  A schematic diagram of RNN.

Reinforcement Learning (RL): RL is a sub-field of ML that involves training an
agent to make decisions based on observations and feedbacks. The goal is to
maximize a reward scalar function, which requires the agent to learn a policy
that maps environment states to actions. The RL framework comprises three main
components: the agent, the environment, and the reward. The agent observes the
environment’s state, chooses an action based on its policy, receives a reward,
and updates its policy based on the reward and the new state. This cycle
continues until the agent achieves the optimal policy or meets the stopping
criterion [34, 35]. Fig. 5 illustrates the basic RL framework.

Figure 5:  A schematic diagram of RL.

The reader is assumed hereafter to understand the main function of each
mentioned ML technique. The next section discusses the vital role of the ML
algorithms in many use cases of ISAC.

##  III Use cases of Data-driven ISAC systems

This section discusses different applications and system settings where ISAC
and ML techniques were employed in the literature. It is evident that many
works had an overlap of different use cases. Therefore, we emphasize the works
done for every use case in their respective sections and refer to the other
works that adopted a certain use case as a part of their complete framework
for comprehensiveness.

###  III-A Data-driven approached for ISAC-assisted autonomous vehicular
networks

The robustness of autonomous vehicular networks using ISAC technology will be
improved with 6G technology. An example vehicular network is depicted in Fig.
6, where an ML-powered base station and two autonomous vehicles (AVs) extract
sensing information from reflected signals. The base station uses ML to
extract environment-related data and insights about nearby locations, other
AVs, power allocation, and beamforming. The two AVs also communicate using
reflected signals, extracting each other’s locations and detecting obstacles
using ML algorithms.

Figure 6: An illustration of a vehicular network that utilizes ISAC where: 1)
A BS communicates with vehicles and uses the reflected signal for sensing
purposes. 2) Two vehicles communicate with each other and utilize the
reflected signal for traffic management.

Waveform optimization is a significant research area in ISAC-assisted AV
networks. The preamble data in ISAC packet-based transmission systems can
serve as a sensing source. However, a trade-off exists between increasing it
to improve sensing accuracy and reducing it to maximize data rate. This trade-
off complicates ISAC signal design for AV networks. To tackle this challenge,
Chu et al. [5] proposed an RL-based approach that uses channel quality and
maximum packet information as a state input, rather than relying on complete
surroundings knowledge. The authors used two algorithms, a Q-table-based
method and a deep RL (DRL) approach using an FCNN, to discover optimal signal
design in dynamic channel environments. Zhang et al. [36] directed their
attention to ISAC vehicular networks where an independent system communicates
with multiple users while detecting multiple vehicular targets simultaneously.
The authors proposed ISAC signal schemes based on OFDM and Non-Orthogonal
Multiple Access (NOMA). For the communication task, they trained a deep neural
network (DNN) for demodulation, and for tracking, they employed the
YOLOv5-SORT algorithm. The studies in [5, 36] contribute to advancing ISAC-
assisted AV networks by addressing signal design challenges and exploring the
potential of RL and DL techniques in optimizing communication and sensing
capabilities.

Several other studies have explored DL approaches for predictive beamforming
and function selection in ISAC-assisted AVs. For instance, in [2], Mu et al.
designed and trained an FCNN to predict the relative angles of the $k$-th
vehicle concerning a Road Side Unit (RSU) at a given time $n$. The input to
the DL model consisted of received echoes from the target vehicles, and the
estimated angular parameters was utilized to design an appropriate beamformer.
Then, the authors in [37] focused on ISAC-assisted AVs and proposed a function
selection process to determine whether communicate or sense should take
precedence at a specific time slot. Markov Decision Process (MDP) was employed
for this purpose with the aid of two optimization methods: Q-learning and
Double Deep Q-Network (DDQN). Xu et al. [38] introduced a distributed
distributional Deep Deterministic Policy Gradient (D4PG) approach for each RSU
in the system. This approach aimed to specify sensing information, frequency,
uploading priority, power of transmission, and bandwidth for the system’s
vehicles. The proposed model incorporated local networks and target networks,
comprising a critic network and a policy network. Experiences were stored in
an initialized buffer for replay, and the actions taken by the agent network
aimed to maximize the quality of view through sensing while minimizing costs.

Unmanned aerial vehicles (UAVs) are AVs that are widely applied to many
strategies related to sensing the environment. Wang et al. [39] devised an RL-
based resource allocation strategy for a group of UAVs equipped with an ISAC
system. A combination of mutual information and communication rate was
formulated as the reward function. The authors proposed different algorithms,
including Q-learning and deep Q-network (DQN), to learn a resource allocation
policy that maximizes the reward.

Table III presents a summary of the mentioned ML approaches in ISAC for AVs.
Other ISAC-ML based works that considered AV systems include [40] for ISAC-
assited AVs, [41, 42, 43] for vehicular networks, [44, 45, 46] for vehicle to
everything (V2X), [47, 48] for vehicle to infrastructure (V2I), and [49, 50]
for UAVs.

TABLE III: Summary of key works in ML-ISAC for autonomous vehicular networks.

Ref. |  Technology |  AI technique |  Criteria |  Input |  Output
---|---|---|---|---|---
[5] |  Waveform design |  RL |  1.Duelling FCNN 2.Q-learning |  Observation: number of packets perqueue and channel quality |  1.Action: Number of frames per coherent processing interval 2.A mapping between the observations and the actions (Q-table)
[36] |  1.Demodulation 2.Tra-cking |  DL |  1.FCNN 2.YOLOv5 [51] |  1.Received signal 2.Range-velocity spectrum image |  1.Recovered symbols 2.Target bounding boxes in the input image
[2] |  Beamforming |  DL |  FCNN |  Received echo |  Relative angle of a target vehicle
[37] |  System design |  RL |  1.FCNN-based DDQN 2.Q-learning |  Observation: states of the channel, the road, the weather, self speed, and the state of nearby vehicles |  1.Action: radar mode or communication mode 2.Q-table
[38] |  Resource allocation |  RL |  A couple of critic-action FCNNs(4 FCNNs) |  State: Time slot index, RSU index, the set of distances between vehicles and the RSU, information that can be sensed by the vehicle, cached information in the RSU, and the set of views required by the RSU |  Action: sensing information and frequencies, uploading priorities, and transmission power

###  III-B Data-driven ISAC-assisted THz Communication

In the realm of 6G networks, THz Integrated Sensing, Communication, and
Intelligence (ISCI) technology holds immense promise due to its utilization of
a vibrant spectrum [52]. Sharing resources between THz ISCI counterparts can
lead to increased spectrum efficiency, elimination of additional hardware
costs, and improved energy efficacy [53]. Achieving these advantages relies
heavily on the extent of shared resources. THz ISCI systems are particularly
inclined towards single-carrier ISAC waveforms, owing to THz channels’ minimal
delay spread and power amplifier efficiency. However, deploying wireless
technologies operating at such high frequencies and short wavelengths poses
challenges, such as limited range and increased attenuation levels. To address
these challenges, researchers have explored the use of ultra-massive Multiple-
Input Multiple-Output (MIMO) systems [54]. In this context, the authors in
[55] developed a model-based hybrid beamforming approach for ultra-massive
MIMO systems operating in the low-THz band where ISAC was deployed. They
proposed a cascaded combination of CNNs to predict the angles at which targets
exist and to design hybrid beamformers with lower complexity than traditional
model-based algorithms.

Some other works focused on designing the receiver of THz-ISAC systems using
DL techniques. For instance, a multi-task neural network (NN) was formulated
in [53] to develop an AI detector mechanism for sensing DFT-spread-OFDM in an
ISAC system. A DL technique is implemented in [56] to design a receiver for
THz ISAC signals. Two FCNNs were designed. One FCNN was trained to predict the
sensing parameters, range, and velocity, while the other FCNN was a two-level
network that was trained to recover the data symbols. Both FCNNs were trained
in a supervised fashion with the received data blocks in the frequency domain
as inputs. A summary of the data-driven methods in THz-ISAC is presented in
Table IV.

TABLE IV: Summary of key works in DL-ISAC for THz.

Ref. |  Application |  ML technique |  Criterion |  Input |  Output
---|---|---|---|---|---
[55] |  Beamforming |  DL |  CNN |  Received radar signal covariance matrix |  Hybrid beamformer vectors
[53] |  Receiver design |  DL |  multi-task FCNN |  Received signal |  Recovered data symbols and sensing parameters
[56] |  Receiver design |  DL |  2 FCNNs |  Received signal |  Recovered data symbols and sensing parameters

###  III-C Data-driven ISAC-assisted Radar Systems

DL has many applications in JRC systems. Possible scenarios of both radar-
centric and communication-centric JRC systems are depicted in Fig. 7.

Figure 7: Shows an overview diagram for both sensing-centric and
communication-centric. Sensing-centric presents how communication setups can
help radar function, while communication-centric shows how sensing equipment
could enhance the communication performance

For instance, Chen et al. [45] proposed two CNN-based DL methods to reduce the
beam training overhead for link configuration in V2X systems. The authors
proposed a radar-aided method that performs a radar-to-communication (R2C)
translation. One model performs R2C by predicting the azimuth power spectrum
(APS) of the communication link given the radar APS. The other model estimates
R2C covariance columns given radar spatial covariance columns. The authors
reported that the APS-based and covariance-based DNNs increased the rate by
13.3% and 21.9%, respectively. V2X was also considered in [46], where a
resource allocation strategy based on DRL was sought. In the case of multi-
agent scenarios (i.e., the existence of multiple radar-assisted vehicles), a
narrow-band control channel was preserved for vehicles to communicate and
coordinate their use of sensory data. This process was arranged using a graph
neural network (GNN), where nodes represented the radar-assisted vehicles and
the edges represented the control channel. The proposed DRL solution aimed to
maximize the transmission of useful data while keeping timely radar operation
through division of time and directional communication. Link configuration
between road infrastructure and vehicles was discussed in [42]. The authors
proposed three DNNs to convert the features from the radar domain to the
communication domain. In [40], an RL-based framework was used to learn a
quantized transmit beamforming vector for a sparse transmit array in mobile
JRC systems. The authors in [57] addressed the problem of beam alignment in
radar-aided mmWave systems by a DL approach to reduce the training overhead.
The radar system measurements were used to extract the range, angle, and
velocity of the target via 2D and 3D Fourier transforms. The extracted
information is passed through a CNN with average pooling layers. The output of
the CNN indicates the beam index.

Table V summarizes the data-driven techniques used for ISAC-assisted Radar
systems. Other ML works that considered radar-assisted communication systems
include [36, 58, 59].

TABLE V: Summary of key works in ML-ISAC for radar.

Ref. |  Technology |  AI technique |  Criterion |  Input |  Output
---|---|---|---|---|---
[45] |  Link configuration |  DL |  2 CNNs |  1\. Radar APS 2\. Radar spatial covariance columns |  1\. Communication APS 2\. R2C covariance columns
[46] |  Resource allocation |  RL |  GNN (for multi-agent) |  State of the local environment, local sensory data, and neighborhood links |  Action: radar detection, data transmission, the direction of data transmission, and the null action
[42] |  Link configuration |  DL |  1.CNN 2.6-layer FCNN 3\. 5-layer FCNN |  1.Radar APS 2.Radar spatial eigenvector 3\. Radar covariance vector |  1.Communication APS 2.Communication link spatial eigenvector 3\. Communication covariance vector
[40] |  Beamforming |  RL |  Actor-critic networks with KNN mapping |  State of the transmit array quantized phase shifter |  An action that is mapped to a certain beamformer vector
[57] |  Beamforming |  DL |  CNN |  Range, angel, and velocity of the target |  Beam index

###  III-D Data-driven Approaches for Beamforming in ISAC

The design of predictive beamforming is a crucial aspect in the realization of
ISAC systems. Using DL techniques, ISAC systems can extract information from
the reflected signal or the output of other sensing tools, such as a camera,
to help with beamforming as shown in Fig. 6. Charan et al. [49] suggested two
data-driven methods (using visual data and positioning features) for quick and
precise beam prediction in UAV networks. A CNN and an FCNN were the respective
network architectures for the two approaches. Similarly, Xu et al. [44] used
cameras in V2X systems and proposed a vision-based DNN for optimal beamformers
inference. Their DNN consisted of 1D convolutional layers for input spatial
information, a transformer encoder, and fully connected layers.

The accuracy of CE or angular parameter estimation is essential for predictive
beamforming. However, traditional methods suffer degraded performance due to
errors in estimated historical CSI [60, 61, 62]. In [47], the authors proposed
CLRNet for ISAC-assisted vehicular networks to improve angle prediction for
predictive beamforming. By combining LSTM and CNN, the CLRNet leverages
spatial and temporal dependencies from historical angle estimates to
facilitate precise angle prediction. Liu et al. [41] focused on reducing the
complexity of conventional predictive beamforming algorithms, imposed by CE
and channel tracking, in ISAC-assisted vehicular systems. The proposed
architecture is composed of CNN and LSTM modules, allowing the model to
extract spatial and temporal features of the input historical CSI. An
unsupervised training scheme was used to minimize the negative sum rate, where
ISAC constraints were added to the loss function as penalty terms. Adhikary et
al. [63] designed a holographic MIMO transceiver, where 3D beams are designed
using a DL model. The DL architecture is composed of a variational autoencoder
that reconstructs the estimated distances by the far-field signal, and a GRU
for beamforming.

Moreover, an ISAC high-speed railway (HSR) mmWave wireless network was
considered in [59] by using a DRL scheme to control two mmWave beams, the
communication beam, and the radar beam. The action space controls the space
between the two beams and their beam widths. The proper action is predicted by
a deep Q-network (DQN) given the state of the dynamic environment and previous
actions. Liu et al. [64] proposed a DL network architecture for interference
management in ISAC systems via proper power allocation. Unsupervised learning
was utilized, and then transfer learning was used to predict a proper
beamforming scheme for interference management.

Another DRL-based beam management scheme is proposed in [65], where user
location uncertainty was considered in mmWave networks in a joint vision-aided
sensing and communication system. Features were extracted from satellite
images to enhance the localization accuracy. To manage beamforming for the
localized UEs, a clustering method was first implemented, where a single beam
is dedicated to a cluster. A DRL technique was implemented to design the
corresponding beamformers depending on the clusters and channel information.

Table VI summarizes the different ML techniques utilized for beamforming
design. Several other ML works considered the problem of beamforming in
various ISAC settings, such as [2, 66, 37, 38] in AV networks, [40, 57] in
radar, and [55] in radar. More details about these works are found in their
respective use case subsections.

TABLE VI: Summary of key works in DL-ISAC for beamforming.

Ref. |  Technology |  AI technique |  Criteria |  Input |  Output
---|---|---|---|---|---
[49] |  UAV network |  DL |  1\. CNN 2\. FCNN |  1\. BS camera footage snapshots 2.UAV’s height, GPS position, and distance from the BS |  Beam index
[44] |  V2X |  DL |  1D-CNN, self-attention module, feed-forward module and an MLP |  Mobile station (MS) location and vehicle distribution features from the MS camera snapshots |  Beam index pair
[47] |  V2I |  DL |  CLRNet (CNN, LSTM, FC) |  Historical angles |  Current angle
[41] |  V2I |  DL |  CNN, LSTM units, and an FC layer |  Historical channel information |  Predictive beamforming matrix
[63] |  Holographic MIMO |  DL |  1.Variational autoencoder 2.A gated recurrent unit |  1\. Noisy distance estimation 2.Reconstructed distance vector |  1.Reconstructed distance vector 2.3D beams and power allocation
[59] |  HSR |  RL |  CNN- based DQN |  State: Train positions, sub-6GHz SNR, mmWave SINR, historical sensing and communication beamwidths |  Action: Beam separation, radar beamwidth, communication beamwidth
[65] |  Generic multi-user mmWave networks |  ML |  1.FCNN 2.UK-Medoids clustering 3.LSTM network policy |  1\. Ratio measures and pixel features extracted from satellite images 2.Noisy UE locations 3.State: Channel condition between the UE and the BS |  1\. Noisy UE locations 2.UE clusters, where a single beam is dedicated to every cluster 3.Action: Allocating a Resource Block Group to a user in each beam

###  III-E Data-driven methods in ISAC for tracking and localization

ISAC technology has potential for improving localization and tracking in next-
generation communication systems [67]. Localization has been studied in
various ISAC systems (e.g., [68, 69, 70, 71, 72]), but ML techniques are
rarely used. A data-generation approach and a DL-supervised training scheme
for localization in UAV-ISAC systems were proposed in [50]. In [73], an
integrated indoor-outdoor (IO) sensing and positioning scheme based on a
random forest classifier was proposed. The authors used weighted $K$-nearest
neighbors (KNN) for online positioning upon receiving a request from user
equipment (UE). In [74], a federated learning framework was developed to
design a multi-user ISAC system, where the sensing functionality is dedicated
to indoor positioning via visible light. The multi-task network shared by
users was used to jointly estimate the positions and the channels. Gao et al.
[75] proposed a dataset generation method that obtains indoor positioning
features in MIMO channels. The authors used the dataset to first train well-
known DNNs, such as ResNet-18 [76] and GoogLeNet [77]. They then trained a
novel multi-path CNN architecture using the generated dataset, where better
performance was achieved in terms of mean square error (MSE) and standard
deviation. Indoor localization in ISAC systems was also considered in [78]. A
signal processing scheme at the receiver was formulated to obtain channel
information in the angular domain. Then, given the obtained information, the
authors trained an FCNN to predict the total number of users in the area of
operation and the number of users in every sector of the readily segmented
area.

Table VII summarizes the mentioned ML approaches in the field of tracking and
localization for ISAC. Other localization works discussed in other use cases
include [65, 43, 63].

TABLE VII: Summary of key works in DL-ISAC for tracking and localization.

Ref. |  Technology |  AI technique |  Criteria |  Input |  Output
---|---|---|---|---|---
[73] |  Cellular networks |  ML |  Random-forest-classifier and weighted KNN |  User measurement reports |  UE position
[74] |  Indoor positioning via visible light |  Federated learning |  Custom RNN, LSTM units and FCNN |  Shared sparsity-aware network |  Position and the channel
[75] |  Indoor positioning |  DL |  multi-path CNN |  Channel frequency response |  UE position
[78] |  Indoor positioning |  DL |  FCNN |  Channel information in the angular domain |  Total number of users and number of users per area segment

###  III-F Data-driven methods in ISAC for spectrum sensing

Another evident, yet rarely acknowledged, application of ISAC technology is
spectrum sensing (SS) [79]. While some works discuss SS in the context of ML
techniques (e.g., [80, 81, 82, 83, 84]), less work studied SS in the context
of ISAC use-cases and ML technologies. For instance, Liu et al. [85]
considered spectrum sensing in space-air-ground integrated networks (SAGIN)
systems, where spectrum management is usually challenging. The authors
proposed a data-driven approach for SS to eliminate the uncertainty associated
with noise and pinpoint the channels occupied by the primary transmission. A
CNN classifier was utilized to predict channel occupancy. A similar binary
classification objective was approached by [86], but with a different DL
architecture (c.f., Table VIII). Then, Ahmed et al. [43] proposed a joint
framework for spectrum sensing and localization in Internet of Vehicle (IoV)
networks. They proposed a CNN-based model with skip connection layers and an
Atrous Spatial Pyramid Pooling (ASPP) [87] module. The CNN is trained to
classify the spectrogram of the received signal whether it is a noise or a
primary signal. Based on the network’s decision, a support vector machine
(SVM) was used for localization.

Table VIII summarizes various data-driven approaches in ISAC for spectrum
sensing applications.

TABLE VIII: Summary of key works in DL-ISAC for spectrum sensing.

Ref. |  Technology |  AI technique |  Criteria |  Input |  Output
---|---|---|---|---|---
[85] |  SAGIN |  DL |  CNN |  Received signal covariance matrix |  Class: channel occupancy assumptions $H_{0}$:No primary signal $H_{1}$:Primary signal
[86] |  Cognitive radio |  DL |  A multi-layer perceptron combined with an input propagation trainable path |  Signal power |  Class: channel occupancy assumptions $H_{0}$:The null hypothesis (Noise) $H_{1}$:Primary signal
[43] |  IoV |  DL |  CNN, skip connections, and ASPP module |  Received signal spectrogram |  Class: channel occupancy assumptions \- Spectrum hole (no transmission) \- No spectrum hole (primary transmission)

###  III-G Data-driven approaches in ISAC-assisted edge computing

Edge computing (EC) plays important roles in modern and future ISAC-enabled
networks, such as reducing heavy computational requirements from the user
devices when DL/DRL algorithms are employed [88, 89]. Few works focused on the
use of DL and DRL for systems that employ ISAC and edge computing. Yuan et al.
[90] considered smart road systems that enable vehicle operations via
capabilities that can be viewed in two integrated domains, transportation
domain (e.g., road structure) and information domain (e.g., communication,
sensing, and computing). The authors proposed a DRL approach to optimally
allocate resources from the cross-domain (e.g., data transmission, path
planning, computation offloading). The multi-agent framework is based on CNNs
that analyze the traffic state to predict the optimum action. Souza et al.
[91] proposed a path planning framework for self-driving vehicles with the
capabilities of performing mobile EC (MEC) and ISAC. In the proposed
framework, MEC aims to predict congestion points using an LSTM-based
architecture. Given such predictions, road structure (represented by a graph),
and the starting and end points, a Q-learning algorithm was proposed to
formulate a Q-table of the most reliable path.

The complexity and the dynamic of road environments render autonomous driving
offline-trained DL systems ungeneralizable and prone to the curse of
dimensionality. Such issues were addressed in [92]. The authors proposed a
multi-access EC (MAEC) framework to train a group of DNNs, each of which is
suited to a specific road segment. The models are trained by MAEC nodes via a
blockchain system. Table IX summarizes various data-driven techniques and
models used for ISAC-assisted EC works.

TABLE IX: Summary of key works in DL-ISAC for edge computing.

Ref. |  Application |  AI Technique |  Criteria |  Input |  Output
---|---|---|---|---|---
[90] |  Smart roads |  RL |  1.CNN-based value iteration network (VIN) 2\. CNN-based two-branched DQN |  1\. Observation: destination and current traffic status 2.Observation: origin-destination, traffic status, host RSU server, and edge computing loads |  1.Action: Route selection 2.Action: Agent migration and route planning
[91] |  ISAC-MEC-AVs |  1.DL 2.RL |  1.LSTM-based RNN (representing MEC) 2.Q-learning |  Observation: congestion state (RNN prediction) at a certain time stamp, road graph, urban dynamics |  Q-table: observation-routing pairs

Figure 8: Schematic representation of an IRS-assisted ISAC system.

###  III-H Data-driven methods for channel estimation and IRS

Efficient CE is crucial for characterizing received symbols in wireless
systems with time-varying and/or frequency-selective channels. Huang et al.
[58] proposed a MIMO radar-based CE framework that divides into two stages:
AoA/AoDs estimation at the radar module and gain estimation at the
communication module. arrival/departure (AoA/AoDs) were obtained using a
model-based algorithm, while gains were obtained using a residual denoising
autoencoder (RDAE). The RDAE was trained to denoise input signals, where the
least squares (LS) method is applied to the output for gain estimation.

On the other hand, IRS is a physical surface with reflecting elements that are
set up in propagation environments to overcome the coverage holes that occur
in wireless communication [93]. Fig. 8 shows examples for IRS deployment in
different propagation settings. Dynamic V2I scenarios in IRS-assisted ISAC
systems were considered by Wang et al. [48]. To estimate AoAs at both the base
station and deployed IRS, the authors proposed CAP-Net, a DNN consisting of
convolutional layers and LSTM units. CAP-Net was trained using historical
covariance data of received echo to predict AoAs.

CE becomes critical in IRS-assisted systems as passive IRS cannot perform
signal processing. Therefore, Zhang et al. [94] presented a self-supervised
learning approach to IRS-CE. During training, a DNN learns to output signals,
similar to the original, when provided with the noisy version of the signal.
The trained denoiser is used to improve channel estimation. The approach is
shown to be suitable for various communication systems, including ISAC.

CE in multiple-input-single-output (MISO) IRS-assisted ISAC systems was
considered in [95]. The problem was divided into three stages: estimating
direct SAC channels in the first stage, individually reflecting communication
and sensing channels in the second and third stages, respectively. To
accommodate the inherent propagation differences between direct and reflected
channels in a full-duplex ISAC base station, two CNNs were designed and
trained to make predictions at all three stages. Table X summarizes the works
using DL-ISAC techniques in for IRS/CE.

TABLE X: Summary of key works in DL-ISAC for channel estimation and IRS.

Ref. |  Technology |  AI technique |  Criteria |  Input |  Output
---|---|---|---|---|---
[48] |  IRS-assisted V2I |  DL |  CAP-Net (conv layers and LSTM) |  Historical covariance of the received echo |  AoAs
[94] |  General applications |  DL |  CNN (Trained as a denoiser) |  Channel estimated by LS method |  Channel estimation
[95] |  IRS-assisted ISAC MISO |  DL |  1.Direct estimation CNN (DE-CNN) 2.Reflected estimation CNN (RE-CNN) |  1.Received direct signals 2.Total received signals and DE-CNN estimation |  Direct (DE-CNN) and reflected (RE-CNN) sensing and communication channels

###  III-I Data-driven methods for other ISAC applications

Some DL-ISAC-oriented works cannot be grouped into one of the previous
categories because they are rather focused on dispersed yet important
applications.

Gesture recognition: Qi et al. [96] proposed a federated transfer learning
framework for gesture recognition with Wi-Fi sensing as an indoor deployment
for ISAC.

Signal design: Xie et al. [97] trained an autoencoder, in an unsupervised
fashion, to extract the features of an input ISAC signal. Thus, the trained
encoder is expected to fuse the ISAC signal into a lower-dimensional signal
before broadcasting.

Hardware impairments: The problem of hardware impairments’ effect on
beamforming in ISAC systems when standard mathematical modeling is used (i.e.,
standard model-based methods) was addressed in [3]. The authors proposed an
MDL approach, where some parameters of the mathematical framework are learned.
The learning process adapts with hardware impairments, such as inaccurate
antenna spacing, resulting in better results than the standard model-based
approaches’, where mathematical models are derived by assuming perfect
hardware design. The authors also designed an FCNN to directly obtain the
target parameters and compared the three approaches (i.e., the standard model-
based, the MDL, and the data-driven approaches).

Atmospheric sensing: In [98], climate change sensing using THz communication
was discussed. The advantage of environmental gases absorption by the THz
signal was utilized. ML techniques were employed to analyze the power spectral
density and signal path loss to determine the quantity of various gases that
have an impact on the climate.

Agriculture: A result-oriented literature review presented in [99] focused on
the study of vital resource preservation in precision farming field to enhance
the production and harvesting of agriculture through 6G smart management
systems. Nano sensors are considered for such infrastructure so that early-
stage plant health can be monitored. Through a designated line of
communication, the gathered information is uploaded to a distant cloud server.
One use of THz communication is to establish active communication links
between nano-sensors. The pioneering work on in nano-network communication was
presented by [100].

##  IV key challenges

Data-driven approaches have shown promise compared to model-driven methods,
but their implementation in ISAC systems comes with key challenges. In the
following, we discuss some of these challenges.

###  IV-A Limited data availability

One of the main challenges in data-driven approaches is data availability.
This is particularly true in ISAC systems due to the sensitivity or
confidentiality of the data, making it difficult to be collected and used [8].
Additionally, the data collection process can be considerably expensive and
time-consuming. This constraint can limit data availability and potentially
lead to incomplete or biased outcomes.[41]. This makes it challenging to build
accurate models that can generalize effectively and prevent selective bias.

###  IV-B Data quality and reliability

Data quality and reliability can vary due to factors like sensor noise,
measurement errors, and data transmission loss. This can create inaccurate or
biased models that do not represent the underlying system behavior [16].
Therefore, selected data must be pre-processed to remove outliers and missing
values, ensuring it’s in a format understood by the DL algorithm.

###  IV-C Model complexity

The development of accurate models for ISAC systems can be challenging due to
their inherent complexity, which encompasses multiple sensors, communication
channels, and system components. While DL models have shown great promise in
accurately predicting the behavior of systems in different scenarios, they
come with computational complexity, particularly during the training phase
[7]. Consequently, building models that capture the system complexity requires
significant resources and time investment. Additionally, the complexity of the
models can lead to overfitting, where the model performs well on the training
data but poorly on new, unseen data. Therefore, developing models that balance
the complexity of the DL models with their predictive capabilities remains an
ongoing challenge.

###  IV-D Limited interpretability

Interpreting data-driven approaches can be challenging, particularly when
dealing with complex models, which can limit the understanding of the
underlying mechanisms driving the system’s behavior. This can make it
difficult to identify and address any issues that may arise [39, 73],
highlighting the importance of developing interpretable models for informed
decision-making and improved system performance.

###  IV-E Integration with traditional methods

Although data-driven approaches have shown promise, integrating them with
traditional methods such as physics-based models or heuristics may be
necessary to achieve optimal results. However, integrating these methods can
be challenging due to differences in data types, assumptions, and algorithms.
To develop accurate and reliable models, it is crucial to balance data-driven
and traditional methods appropriately, depending on the specific application
and available resources, while combining the strengths of both approaches
[101]. Nonetheless, some classical methods may not be suitable for integrating
with data-driven models [102].

###  IV-F Privacy and security concerns

As discussed in IV-A, ISAC systems have the potential to collect sensitive
information, including personal health data and location information.
Therefore, it is imperative to collect and use this data in a way that
safeguards the privacy and security of individuals [81].

##  V Future research directions

ML algorithms show promise for accelerating the adoption of ISAC technologies,
but there are still gaps and unanswered questions that require further
research. This section highlights some of these difficulties and gaps to aid
researchers in filling them.

###  V-A Algorithms based on less amount of data

Although recent advancements in ML algorithms have shown significant progress,
it is still necessary to utilize vast amounts of data to achieve meaningful
results. Fortunately, sensor fusion techniques [103] and IRS provide potential
solutions for this problem, particularly in non-line-of-sight scenarios, by
utilizing various data sources. For example, outdoor data can be used for
localization and tracking, enhancing personalized services. Moreover, real-
world data on traffic flows can be combined with sensory data gathered by AVs,
further improving performance. Maps, on the other hand, represent a reliable
data source that can significantly enhance ML algorithms in the context of AVs
and intelligent roads [6].

###  V-B Handling time-varying systems

Highly time-varying models require advanced ML approaches. Channel estimation
errors increase as channels change with time more quickly [11]. Improved ML
algorithms are needed to estimate channel states in such scenarios and improve
ISAC performance across different mobility modes.

###  V-C Privacy and security

When developing appropriate data sharing and access control mechanisms, it’s
crucial for researchers to carefully consider data privacy and security
policies. The development of ISAC introduces new security challenges, such as
susceptibility to manipulation, eavesdropping, and jamming attacks [104].
These security weaknesses may cause apprehension from using ISAC in many
applications. Therefore, it’s imperative to devote a significant amount of
effort toward proposing effective ML/AI methods that can handle these security
challenges and ensure the secure operation of ISAC.

###  V-D Training time issue

Training is a crucial yet a time-consuming issue when using large data sets.
Novel techniques with low latency and proper training are needed to achieve
required performance, especially for time-sensitive systems like communication
[105]. Distributed deep learning algorithms and algorithms with acceptable
performance using less data are two potential solutions [106].

###  V-E Integrating data-based and model-based algorithms

Creating a new architecture for each component of ISAC systems is not always
necessary for ISAC development. Certain elements of the communication and
sensing systems can still be utilized with some modifications. To expedite the
integration of ML algorithms in ISAC and facilitate their adoption, it is
crucial to develop novel approaches that enable the effective integration of
data-based algorithms with model-based algorithms. Such methods would need to
be tailored to the specific requirements of ISAC technology to offer improved
efficiency and accuracy in the systems.

###  V-F Complexity issue

ML algorithms have an advantage over model-based algorithms for their ability
to handle complex systems without explicit modeling. As ISAC system complexity
increases, more advanced AI algorithms are needed. DL and NN algorithms are
promising solutions for this challenge. However, further studies are necessary
to demonstrate the practical characteristics of ML algorithms in ISAC. This
will enable the development of advanced algorithms that can be effectively
deployed in various applications to meet the demands of complex systems [107].

###  V-G Exploring novel ISAC use cases

The use of ML in various ISAC applications has yet to be explored, such as
automatic modulation recognition (AMR) for received signals. AMR can enhance
ISAC’s sensing module, e.g., spectrum monitoring [108]. However, there is
little work using DL for AMR in ISAC systems [109]. This is an interesting but
relatively unexplored area for DL deployment in ISAC. Another example is the
ISAC uplink design. In literature, few works have focused on designing and/or
analyzing ISAC uplink systems [89, 110, 111, 112, 113], and no work has been
done to design the uplink using ML techniques.

The use cases in Section III could be further explored. EC works, for example,
only proposed generic ML techniques in certain AV systems. Also, novel
configurations can be proposed for AV systems, where a certain application
(e.g., beamforming, CE, EC) can be targeted. An example of such configurations
is shown in Fig. 7.

##  VI Conclusion

This work highlights the role of data-driven ISAC systems in 6G and beyond. It
shows that ML algorithms can improve ISAC performance and reduce costs and
complexity. It reviews various deep learning applications for ISAC in domains
such as autonomous vehicles, THz communication, radar systems, beamforming,
tracking and localization, spectrum sensing, channel estimation, and more. It
also discusses the challenges and future directions of applying ML algorithms
in ISAC. This work demonstrates the importance and potential of ISAC in next-
generation wireless communication systems and provides a solid basis for
further research and innovation.

## References

  * [1] D. K. P. Tan, J. He, Y. Li, A. Bayesteh, Y. Chen, P. Zhu, and W. Tong, “Integrated sensing and communication in 6g: Motivations, use cases, requirements, challenges and future directions,” in _2021 1st IEEE International Online Symposium on Joint Communications & Sensing (JC&S)_. IEEE, 2021, pp. 1–6.
  * [2] J. Mu, Y. Gong, F. Zhang, Y. Cui, F. Zheng, and X. Jing, “Integrated sensing and communication-enabled predictive beamforming with deep learning in vehicular networks,” _IEEE Commun. Lett._ , vol. 25, no. 10, pp. 3301–3304, 2021.
  * [3] J. M. Mateos-Ramos, C. Häger, M. F. Keskin, L. L. Magoarou, and H. Wymeersch, “Model-driven end-to-end learning for integrated sensing and communication,” _arXiv preprint arXiv:2212.10211_ , 2022.
  * [4] U. Demirhan and A. Alkhateeb, “Integrated sensing and communication for 6g: Ten key machine learning roles,” _IEEE Commun. Mag._ , 2023.
  * [5] N. H. Chu, D. N. Nguyen, D. T. Hoang, Q.-V. Pham, W.-J. Hwang, and E. Dutkiewicz, “Ai-empowered joint communication and radar systems with adaptive waveform for autonomous vehicles,” _arXiv preprint arXiv:2202.11508_ , 2022.
  * [6] J. Lee, A. A. Badrudeen, and S. Kim, “6g integrated sensing and communication: Recent results and future directions,” in _2022 13th International Conference on Information and Communication Technology Convergence (ICTC)_. IEEE, 2022, pp. 1219–1221.
  * [7] C. B. Barneto, S. D. Liyanaarachchi, M. Heino, T. Riihonen, and M. Valkama, “Full duplex radio/radar technology: The enabler for advanced joint communication and sensing,” _IEEE Wireless Commun._ , vol. 28, no. 1, pp. 82–88, 2021.
  * [8] S. Buzzi, C. D’Andrea, and M. Lops, “Using massive mimo arrays for joint communication and sensing,” in _2019 53rd Asilomar Conference on Signals, Systems, and Computers_. IEEE, 2019, pp. 5–9.
  * [9] K. Wu, J. A. Zhang, X. Huang, and Y. J. Guo, “Joint communications and sensing employing multi-or single-carrier ofdm communication signals: A tutorial on sensing methods, recent progress and a novel design,” _Sensors_ , vol. 22, no. 4, p. 1613, 2022.
  * [10] S. Shao, M. Fan, C. Yu, Y. Li, X. Xu, and H. Wang, “Machine learning-assisted sensing techniques for integrated communications and sensing in wlans: Current status and future directions.” _Progress In Electromagnetics Research_ , vol. 175, 2022.
  * [11] A. Liu, Z. Huang, M. Li, Y. Wan, W. Li, T. X. Han, C. Liu, R. Du, D. K. P. Tan, J. Lu _et al._ , “A survey on fundamental limits of integrated sensing and communication,” _IEEE Commun. Surveys Tuts._ , vol. 24, no. 2, pp. 994–1034, 2022.
  * [12] J. A. Zhang, F. Liu, C. Masouros, R. W. Heath, Z. Feng, L. Zheng, and A. Petropulu, “An overview of signal processing techniques for joint communication and radar sensing,” _IEEE J. Sel. Topics Signal Process._ , vol. 15, no. 6, pp. 1295–1315, 2021.
  * [13] R. Thomä, T. Dallmann, S. Jovanoska, P. Knott, and A. Schmeink, “Joint communication and radar sensing: An overview,” in _2021 15th European Conference on Antennas and Propagation (EuCAP)_. IEEE, 2021, pp. 1–5.
  * [14] J. A. Zhang, M. L. Rahman, K. Wu, X. Huang, Y. J. Guo, S. Chen, and J. Yuan, “Enabling joint communication and radar sensing in mobile networks—a survey,” _IEEE Commun. Surveys Tuts._ , vol. 24, no. 1, pp. 306–345, 2021\.
  * [15] J. He, F. Jiang, K. Keykhosravi, J. Kokkoniemi, H. Wymeersch, and M. Juntti, “Beyond 5g ris mmwave systems: Where communication and localization meet,” _IEEE Access_ , vol. 10, pp. 68 075–68 084, 2022.
  * [16] H. Dahrouj, R. Alghamdi, _et al._ , “An overview of machine learning-based techniques for solving optimization problems in communications and signal processing,” _IEEE Access_ , vol. 9, pp. 74 908–74 938, 2021\.
  * [17] V. Sze, Y.-H. Chen, T.-J. Yang, and J. S. Emer, “Efficient processing of deep neural networks: A tutorial and survey,” _Proceedings of the IEEE_ , vol. 105, no. 12, pp. 2295–2329, 2017.
  * [18] R. Miikkulainen, J. Liang, E. Meyerson, A. Rawal, D. Fink, O. Francon, B. Raju, H. Shahrzad, A. Navruzyan, N. Duffy _et al._ , “Evolving deep neural networks,” in _Artificial intelligence in the age of neural networks and brain computing_. Elsevier, 2019, pp. 293–312.
  * [19] S. Kuutti, R. Bowden, Y. Jin, P. Barber, and S. Fallah, “A survey of deep learning applications to autonomous vehicle control,” _IEEE Trans. Intell. Transp. Syst._ , vol. 22, no. 2, pp. 712–733, 2021.
  * [20] I. Ahmad, S. Shahabuddin, H. Malik, E. Harjula, T. Leppänen, L. Lovén, A. Anttonen, A. H. Sodhro, M. Mahtab Alam, M. Juntti, A. Ylä-Jääski, T. Sauter, A. Gurtov, M. Ylianttila, and J. Riekki, “Machine learning meets communication networks: Current trends and future challenges,” _IEEE Access_ , vol. 8, pp. 223 418–223 460, 2020.
  * [21] M. Yang, C. Bian, and H.-S. Kim, “Ofdm-guided deep joint source channel coding for wireless multipath fading channels,” _IEEE Trans. on Cogn. Commun. Netw._ , vol. 8, no. 2, pp. 584–599, 2022.
  * [22] W. Xiao, Z. Luo, and Q. Hu, “A review of research on signal modulation recognition based on deep learning,” _Electronics_ , vol. 11, no. 17, p. 2764, 2022.
  * [23] H. A. Kassir, Z. D. Zaharis, P. I. Lazaridis, N. V. Kantartzis, T. V. Yioultsis, and T. D. Xenos, “A review of the state of the art and future challenges of deep learning-based beamforming,” _IEEE Access_ , vol. 10, pp. 80 869–80 882, 2022.
  * [24] A. Lakhan, M. A. Mohammed, O. I. Obaid, C. Chakraborty, K. H. Abdulkareem, and S. Kadry, “Efficient deep-reinforcement learning aware resource allocation in sdn-enabled fog paradigm,” _Automated Software Engineering_ , vol. 29, pp. 1–25, 2022.
  * [25] T. Gafni, N. Shlezinger, K. Cohen, Y. C. Eldar, and H. V. Poor, “Federated learning: A signal processing perspective,” _IEEE Signal Process. Mag._ , vol. 39, no. 3, pp. 14–41, 2022.
  * [26] A. Abdallah, A. Celik, M. M. Mansour, and A. M. Eltawil, “Deep-learning based channel estimation for ris-aided mmwave systems with beam squint,” in _ICC 2022 - IEEE International Conference on Communications_ , 2022, pp. 1269–1275.
  * [27] B. Müller, J. Reinhardt, and M. T. Strickland, _Neural networks: an introduction_. Springer Science & Business Media, 1995.
  * [28] O. I. Abiodun, A. Jantan, A. E. Omolara, K. V. Dada, N. A. Mohamed, and H. Arshad, “State-of-the-art in artificial neural network applications: A survey,” _Heliyon_ , vol. 4, no. 11, p. e00938, 2018.
  * [29] Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, “A survey of convolutional neural networks: analysis, applications, and prospects,” _IEEE Trans. Neural Netw. Learn. Syst._ , 2021.
  * [30] K. O’Shea and R. Nash, “An introduction to convolutional neural networks,” _arXiv preprint arXiv:1511.08458_ , 2015.
  * [31] R. Dey and F. M. Salem, “Gate-variants of gated recurrent unit (gru) neural networks,” in _2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS)_. IEEE, 2017, pp. 1597–1600.
  * [32] A. Sherstinsky, “Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network,” _Physica D: Nonlinear Phenomena_ , vol. 404, p. 132306, 2020.
  * [33] Y. Yu, X. Si, C. Hu, and J. Zhang, “A review of recurrent neural networks: Lstm cells and network architectures,” _Neural computation_ , vol. 31, no. 7, pp. 1235–1270, 2019.
  * [34] R. S. Sutton and A. G. Barto, _Reinforcement learning: An introduction_. MIT press, 2018.
  * [35] V. François-Lavet, P. Henderson, R. Islam, M. G. Bellemare, J. Pineau _et al._ , “An introduction to deep reinforcement learning,” _Foundations and Trends® in Machine Learning_ , vol. 11, no. 3-4, pp. 219–354, 2018.
  * [36] Z. Zhang, Q. Chang, J. Xing, and L. Chen, “Deep-learning methods for integrated sensing and communication in vehicular networks,” _Vehicular Communications_ , vol. 40, p. 100574, 2023.
  * [37] N. Q. Hieu, D. T. Hoang, D. Niyato, P. Wang, D. I. Kim, and C. Yuen, “Transferable deep reinforcement learning framework for autonomous vehicles with joint radar-data communications,” _IEEE Trans. Commun._ , vol. 70, no. 8, pp. 5164–5180, 2022.
  * [38] X. Xu, K. Liu, P. Dai, H. Zhang, and K. Xiao, “Maximizing quality and minimizing cost for vcps in isac-based vehicular networks: A deep reinforcement learning approach,” _arXiv preprint arXiv:2211.00007_ , 2022\.
  * [39] M. Wang, P. Chen, Z. Cao, and Y. Chen, “Reinforcement learning-based uavs resource allocation for integrated sensing and communication (isac) system,” _Electronics_ , vol. 11, no. 3, p. 441, 2022.
  * [40] L. Xu, R. Zheng, and S. Sun, “A deep reinforcement learning approach for integrated automotive radar sensing and communication,” in _2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM)_. IEEE, 2022, pp. 316–320.
  * [41] C. Liu, W. Yuan, S. Li, X. Liu, D. W. K. Ng, and Y. Li, “Predictive beamforming for integrated sensing and communication in vehicular networks: A deep learning approach,” in _ICC 2022-IEEE International Conference on Communications_. IEEE, 2022, pp. 1948–1954.
  * [42] A. Graff, Y. Chen, N. González-Prelcic, and T. Shimizu, “Deep learning-based link configuration for radar-aided multiuser mmwave vehicle-to-infrastructure communication,” _IEEE Trans. Veh. Technol._ , 2023.
  * [43] R. Ahmed, Y. Chen, B. Hassan, L. Du, T. Hassan, and J. Dias, “Hybrid machine-learning-based spectrum sensing and allocation with adaptive congestion-aware modeling in cr-assisted iov networks,” _IEEE Internet Things J._ , vol. 9, no. 24, pp. 25 100–25 116, 2022.
  * [44] W. Xu, F. Gao, X. Tao, J. Zhang, and A. Alkhateeb, “Computer vision aided mmwave beam alignment in v2x communications,” _IEEE Trans. Wireless Commun._ , 2022.
  * [45] Y. Chen, A. Graff, N. González-Prelcic, and T. Shimizu, “Radar aided mmwave vehicle-to-infrastructure link configuration using deep learning,” in _2021 IEEE Global Communications Conference (GLOBECOM)_. IEEE, 2021, pp. 01–06.
  * [46] J. Lee, Y. Cheng, D. Niyato, Y. L. Guan, and D. González, “Intelligent resource allocation in joint radar-communication with graph neural networks,” _IEEE Trans. Veh. Technol._ , vol. 71, no. 10, pp. 11 120–11 135, 2022.
  * [47] C. Liu, X. Liu, S. Li, W. Yuan, and D. W. K. Ng, “Deep clstm for predictive beamforming in integrated sensing and communication-enabled vehicular networks,” _Journal of Communications and Information Networks_ , vol. 7, no. 3, pp. 269–277, 2022.
  * [48] R. Wang, F. Xia, J. Huang, X. Wang, and Z. Fei, “Cap-net: A deep learning-based angle prediction approach for isac-enabled ris-assisted v2i communications,” in _2022 IEEE 22nd International Conference on Communication Technology (ICCT)_. IEEE, 2022, pp. 1255–1259.
  * [49] G. Charan, A. Hredzak, C. Stoddard, B. Berrey, M. Seth, H. Nunez, and A. Alkhateeb, “Towards real-world 6g drone communication: Position and camera aided beam prediction,” in _GLOBECOM 2022-2022 IEEE Global Communications Conference_. IEEE, 2022, pp. 2951–2956.
  * [50] J. Rodríguez-Piñeiro, W. Liu, Y. Wang, X. Yin, J. Lee, and M.-D. Kim, “Deep learning-based joint communication and sensing for 6g cellular-connected uavs,” in _2022 16th European Conference on Antennas and Propagation (EuCAP)_. IEEE, 2022, pp. 1–2.
  * [51] S. Li, Y. Li, Y. Li, M. Li, and X. Xu, “Yolo-firi: Improved yolov5 for infrared image object detection,” _IEEE Access_ , vol. 9, pp. 141 861–141 875, 2021.
  * [52] Z. Qadir, K. N. Le, N. Saeed, and H. S. Munawar, “Towards 6G internet of things: Recent advances, use cases, and open challenges,” _ICT Express_ , 2022.
  * [53] Y. Wu, C. Han, and Z. Chen, “Thz isci: Terahertz integrated sensing, communication and intelligence,” in _2021 46th International Conference on Infrared, Millimeter and Terahertz Waves (IRMMW-THz)_. IEEE, 2021, pp. 1–2.
  * [54] H. Sarieddeen, M.-S. Alouini, and T. Y. Al-Naffouri, “Terahertz-band ultra-massive spatial modulation mimo,” _IEEE J. Sel. Areas Commun._ , vol. 37, no. 9, pp. 2040–2052, 2019.
  * [55] A. M. Elbir, K. V. Mishra, and S. Chatzinotas, “Terahertz-band joint ultra-massive mimo radar-communications: Model-based and model-free hybrid beamforming,” _IEEE J. Sel. Topics Signal Process._ , vol. 15, no. 6, pp. 1468–1483, 2021.
  * [56] Y. Wu, F. Lemic, C. Han, and Z. Chen, “Sensing integrated dft-spread ofdm waveform and deep learning-powered receiver design for terahertz integrated sensing and communication systems,” _IEEE Trans. Commun._ , 2022.
  * [57] U. Demirhan and A. Alkhateeb, “Radar aided 6g beam prediction: Deep learning algorithms and real-world demonstration,” in _2022 IEEE Wireless Communications and Networking Conference (WCNC)_. IEEE, 2022, pp. 2655–2660.
  * [58] S. Huang, M. Zhang, Y. Gao, and Z. Feng, “Mimo radar aided mmwave time-varying channel estimation in mu-mimo v2x communications,” _IEEE Trans. Wireless Commun._ , vol. 20, no. 11, pp. 7581–7594, 2021.
  * [59] L. Yan, X. Fang, S. Li, Y. Li, and Q. Xue, “Drl based beam management for joint sensing and communications in hsr mmwave wireless networks,” in _2022 IEEE 95th Vehicular Technology Conference:(VTC2022-Spring)_. IEEE, 2022, pp. 1–6.
  * [60] N. Abuzainab, M. Alrabeiah, A. Alkhateeb, and Y. E. Sagduyu, “Deep learning for thz drones with flying intelligent surfaces: Beam and handoff prediction,” in _2021 IEEE International Conference on Communications Workshops (ICC Workshops)_. IEEE, 2021, pp. 1–6.
  * [61] S. Bhattacharya and A. K. Gupta, “Deep learning for thz channel estimation and beamforming prediction via sub-6ghz channel,” in _2022 IEEE International Conference on Signal Processing and Communications (SPCOM)_. IEEE, 2022, pp. 1–5.
  * [62] S. K. Moorthy and Z. Guan, “Beam learning in mmwave/thz-band drone networks under in-flight mobility uncertainties,” _IEEE Trans. Mobile Comput._ , vol. 21, no. 6, pp. 1945–1957, 2020.
  * [63] A. ADHIKARY, M. S. Munir, A. D. Raha, Y. Qiao, Z. Han, and C. S. Hong, “Integrated sensing, localization, and communication in holographic mimo-enabled wireless network: A deep learning approach,” 2023.
  * [64] X. Liu, H. Zhang, K. Long, A. Nallanathan, and V. C. Leung, “Distributed unsupervised learning for interference management in integrated sensing and communication systems,” _IEEE Trans. Wireless Commun._ , 2023.
  * [65] Y. Yao, H. Zhou, and M. Erol-Kantarci, “Joint sensing and communications for deep reinforcement learning-based beam management in 6g,” in _GLOBECOM 2022-2022 IEEE Global Communications Conference_. IEEE, 2022, pp. 5019–5024.
  * [66] C. Liu, W. Yuan, S. Li, X. Liu, H. Li, D. W. K. Ng, and Y. Li, “Learning-based predictive beamforming for integrated sensing and communication in vehicular networks,” _IEEE J. Sel. Areas Commun._ , vol. 40, no. 8, pp. 2317–2334, 2022.
  * [67] J. Sanusi, O. Oshiga, S. Thomas, S. Idris, S. Adeshina, and A. M. Abba, “A review on 6g wireless communication systems: Localization and sensing,” in _2021 1st International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS)_. IEEE, 2021, pp. 1–5.
  * [68] J. Wu, W. Yuan, F. Liu, Y. Cui, X. Meng, and H. Huang, “Uav-based target tracking: Integrating sensing into communication signals,” in _2022 IEEE/CIC International Conference on Communications in China (ICCC Workshops)_. IEEE, 2022, pp. 309–313.
  * [69] X. Jing, F. Liu, C. Masouros, and Y. Zeng, “Isac from the sky: Uav trajectory design for joint communication and target localization,” _arXiv preprint arXiv:2207.02904_ , 2022.
  * [70] Z. Yu, X. Hu, C. Liu, M. Peng, and C. Zhong, “Location sensing and beamforming design for irs-enabled multi-user isac systems,” _IEEE Trans. Signal Process._ , vol. 70, pp. 5178–5193, 2022.
  * [71] P. Gao, L. Lian, and J. Yu, “Cooperative isac with direct localization and rate-splitting multiple access communication: A pareto optimization framework,” _IEEE J. Sel. Areas Commun._ , vol. 41, no. 5, pp. 1496–1515, 2023.
  * [72] F. Dong and F. Liu, “Localization as a service in perceptive networks: An isac resource allocation framework,” in _2022 IEEE International Conference on Communications Workshops (ICC Workshops)_. IEEE, 2022, pp. 848–853.
  * [73] L. Zhang, X. Chu, and M. Zhai, “Machine learning-based integrated wireless sensing and positioning for cellular network,” _IEEE Trans. Instrum. Meas._ , 2022.
  * [74] T. Wei, S. Liu, and X. Du, “Visible light integrated positioning and communication: A multi-task federated learning framework,” _IEEE Trans. Mobile Comput._ , 2022.
  * [75] K. Gao, H. Wang, H. Lv, and W. Liu, “Toward 5g nr high-precision indoor positioning via channel frequency response: A new paradigm and dataset generation method,” _IEEE J. Sel. Areas Commun._ , vol. 40, no. 7, pp. 2233–2247, 2022.
  * [76] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in _Proceedings of the IEEE conference on computer vision and pattern recognition_ , 2016, pp. 770–778.
  * [77] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in _Proceedings of the IEEE conference on computer vision and pattern recognition_ , 2015, pp. 1–9.
  * [78] S. Khunteta, A. K. R. Chavva, and A. Agrawal, “Ai-based indoor localization using mmwave mimo channels at 60 ghz,” 2022.
  * [79] F. Liu, Y. Cui, C. Masouros, J. Xu, T. X. Han, Y. C. Eldar, and S. Buzzi, “Integrated sensing and communications: Towards dual-functional wireless networks for 6g and beyond,” _IEEE J. Sel. Areas Commun._ , 2022.
  * [80] J. Xie, C. Liu, Y.-C. Liang, and J. Fang, “Activity pattern aware spectrum sensing: A cnn-based deep learning approach,” _IEEE Commun. Lett._ , vol. 23, no. 6, pp. 1025–1028, 2019.
  * [81] C. Liu, J. Wang, X. Liu, and Y.-C. Liang, “Deep cm-cnn for spectrum sensing in cognitive radio,” _IEEE J. Sel. Areas Commun._ , vol. 37, no. 10, pp. 2306–2321, 2019.
  * [82] J. Gao, X. Yi, C. Zhong, X. Chen, and Z. Zhang, “Deep learning for spectrum sensing,” _IEEE Wireless Commun. Lett._ , vol. 8, no. 6, pp. 1727–1730, 2019.
  * [83] S. Zheng, S. Chen, P. Qi, H. Zhou, and X. Yang, “Spectrum sensing based on deep learning classification for cognitive radios,” _China Communications_ , vol. 17, no. 2, pp. 138–148, 2020.
  * [84] S. Solanki, V. Dehalwar, and J. Choudhary, “Deep learning for spectrum sensing in cognitive radio,” _Symmetry_ , vol. 13, no. 1, p. 147, 2021.
  * [85] R. Liu, Y. Ma, X. Zhang, and Y. Gao, “Deep learning-based spectrum sensing in space-air-ground integrated networks,” _Journal of Communications and Information Networks_ , vol. 6, no. 1, pp. 82–90, 2021.
  * [86] B. Soni, D. K. Patel, S. B. Shah, M. Lopez-Benitez, and S. Govindasamy, “Pu-detnet: Deep unfolding aided smart sensing framework for cognitive radio,” _IEEE Access_ , vol. 10, pp. 98 737–98 751, 2022.
  * [87] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” _IEEE Trans. Pattern Anal. Mach. Intell._ , vol. 40, no. 4, pp. 834–848, 2017.
  * [88] C. Ding, J.-B. Wang, H. Zhang, M. Lin, and G. Y. Li, “Joint mimo precoding and computation resource allocation for dual-function radar and communication systems with mobile edge computing,” _IEEE J. Sel. Areas Commun._ , vol. 40, no. 7, pp. 2085–2102, 2022.
  * [89] Z. Wang, X. Mu, Y. Liu, X. Xu, and P. Zhang, “Noma-aided joint communication, sensing, and multi-tier computing systems,” _IEEE J. Sel. Areas Commun._ , 2022.
  * [90] Q. Yuan, J. Li, H. Zhou, G. Luo, T. Lin, F. Yang, and X. S. Shen, “Cross-domain resource orchestration for the edge-computing-enabled smart road,” _IEEE Network_ , vol. 34, no. 5, pp. 60–67, 2020.
  * [91] A. M. de Souza, H. F. Oliveira, Z. Zhao, T. Braun, A. A. Loureiro, and L. A. Villas, “Enhancing sensing and decision-making of automated driving systems with multi-access edge computing and machine learning,” _IEEE Intell. Transp. Syst. Mag._ , vol. 14, no. 1, pp. 44–56, 2020.
  * [92] M. Wu, F. R. Yu, and P. X. Liu, “Intelligence networking for autonomous driving in beyond 5g networks with multi-access edge computing,” _IEEE Trans. Veh. Technol._ , vol. 71, no. 6, pp. 5853–5866, 2022.
  * [93] “Intelligent surfaces for 6G Wireless Networks: A survey of optimization and performance analysis techniques,” _IEEE Access_ , vol. 8, pp. 202 795–202 818, 2020.
  * [94] Z. Zhang, T. Ji, H. Shi, C. Li, Y. Huang, and L. Yang, “A self-supervised learning-based channel estimation for irs-aided communication without ground truth,” _IEEE Trans. Wireless Commun._ , 2023.
  * [95] Y. Liu, I. Al-Nahhal, O. A. Dobre, and F. Wang, “Deep-learning channel estimation for irs-assisted integrated sensing and communication system,” _IEEE Trans. Veh. Technol._ , 2022.
  * [96] W. Qi, R. Zhang, J. Zhou, H. Zhang, Y. Xie, and X. Jing, “A resource-efficient cross-domain sensing method for device-free gesture recognition with federated transfer learning,” _IEEE Transactions on Green Communications and Networking_ , 2023.
  * [97] Y. Xie, J. Mu, F. Zhang, Q. Zhou, and X. Jing, “Intelligent fusion scheme of broadcasting signal and sensing signal,” in _2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)_. IEEE, 2022, pp. 1–5.
  * [98] L. T. Wedage, B. Butler, S. Balasubramaniam, Y. Koucheryavy, and J. M. Jornet, “Climate change sensing through terahertz communications: A disruptive application of 6g networks,” _arXiv preprint arXiv:2110.03074_ , 2021.
  * [99] M. Usman, S. Ansari, A. Taha, A. Zahid, Q. H. Abbasi, and M. A. Imran, “Terahertz-based joint communication and sensing for precision agriculture: a 6g use-case,” _Frontiers in Communications and Networks_ , vol. 3, p. 3, 2022.
  * [100] M. Kaushik, S. H. Gupta, and V. Balyan, “Optimization of cooperative in vivo sensor network operating at terahertz frequency,” _IEEE Sensors J._ , vol. 21, no. 17, pp. 19 469–19 480, 2021.
  * [101] C. Liu, X. Liu, D. W. K. Ng, and J. Yuan, “Deep residual learning for channel estimation in intelligent reflecting surface-assisted multi-user communications,” _IEEE Trans. Wireless Commun._ , vol. 21, no. 2, pp. 898–912, 2021.
  * [102] B. Zheng and R. Zhang, “Intelligent reflecting surface-enhanced ofdm: Channel estimation and reflection optimization,” _IEEE Wireless Commun. Lett._ , vol. 9, no. 4, pp. 518–522, 2019.
  * [103] T. Wild, V. Braun, and H. Viswanathan, “Joint design of communication and sensing for beyond 5g and 6g systems,” _IEEE Access_ , vol. 9, pp. 30 845–30 857, 2021.
  * [104] H. M. Furqan, M. S. J. Solaija, H. Türkmen, and H. Arslan, “Wireless communication, sensing, and rem: A security perspective,” _IEEE Open Journal of the Communications Society_ , vol. 2, pp. 287–321, 2021.
  * [105] R. A. Khalil, N. Saeed, M. Masood, Y. M. Fard, M.-S. Alouini, and T. Y. Al-Naffouri, “Deep learning in the industrial internet of things: Potentials, challenges, and emerging applications,” _IEEE Internet Things J._ , vol. 8, no. 14, pp. 11 016–11 040, 2021.
  * [106] J. J. Dai, Y. Wang, X. Qiu, D. Ding, Y. Zhang, Y. Wang, X. Jia, C. L. Zhang, Y. Wan, Z. Li _et al._ , “Bigdl: A distributed deep learning framework for big data,” in _Proceedings of the ACM Symposium on Cloud Computing_ , 2019, pp. 50–60.
  * [107] K. Kim, J. Kim, and J. Joung, “A survey on system configurations of integrated sensing and communication (isac) systems,” in _2022 13th International Conference on Information and Communication Technology Convergence (ICTC)_. IEEE, 2022, pp. 1176–1178.
  * [108] Q. Zhou, R. Zhang, Z. Jing, and X. Jing, “Semi-supervised-based automatic modulation classification with domain adaptation for wireless iot spectrum monitoring,” _Frontiers in Physics_ , vol. 11, p. 252, 2023.
  * [109] X. Zhang, H. Zhao, H. Zhu, B. Adebisi, G. Gui, H. Gacanin, and F. Adachi, “Nas-amr: Neural architecture search-based automatic modulation recognition for integrated sensing and communication systems,” _IEEE Trans. on Cogn. Commun. Netw._ , vol. 8, no. 3, pp. 1374–1386, 2022.
  * [110] Z. He, W. Xu, H. Shen, D. W. K. Ng, Y. C. Eldar, and X. You, “Full-duplex communication for isac: Joint beamforming and power optimization,” _arXiv preprint arXiv:2211.00229_ , 2022.
  * [111] Y. Liu, S. Zhang, X. Mu, Z. Ding, R. Schober, N. Al-Dhahir, E. Hossain, and X. Shen, “Evolution of noma toward next generation multiple access (ngma) for 6g,” _IEEE J. Sel. Areas Commun._ , vol. 40, no. 4, pp. 1037–1071, 2022.
  * [112] C. Ouyang, Y. Liu, and H. Yang, “On the performance of uplink isac systems,” _IEEE Commun. Lett._ , vol. 26, no. 8, pp. 1769–1773, 2022.
  * [113] ——, “Performance of downlink and uplink integrated sensing and communications (isac) systems,” _IEEE Wireless Commun. Lett._ , vol. 11, no. 9, pp. 1850–1854, 2022.