mimic the resource usage behaviour, learning is necessary to train the
virtualization manager placed on the SDN controller. It is able to adjust the
resource (i.e. bandwidth) based on the user demand and QoS requirements so
that network fairness among users can be achieved. In the line of discussion,
DL has been used in [194] to manage SDN resources in vehicular networks. To do
that, a flow management scheme is designed and integrated in the SDN
controller so that resource utilization is optimized. Due to the different
priority levels and data requests of different vehicular users, a dynamic
resource allocation algorithm is vitally important to avoid over resource
usage as well as resource scarcity at any users. CNN is deployed on the
network controller to manage the flow control for multiple users, aiming to
optimize resource usage. Based on the network information such as bandwidth,
available resource, user requests, the network controller can learn the new
resource usage patterns for better allocation. The authors in [195] focus on
the use of DRL for resource management in network slicing. Here, a deep
Q-network controller tries to seek a bandwidth sharing solution to achieve a
fair and optimal bandwidth resource with respect to available resource and
dynamic user demands without knowing the specific network model. Another work
in [196] also considers DRL as a solution for resource management in network
slices. In the presence of uncertain network slice demands, designing an
autonomous controller with the ability to interact and explore the environment
to make decisions for resource allocation is desired. Deep Q network can
fulfil this requirement by using an agent that estimate the state-action
distribution where slice demands are regarded as states while allocated
resources are seen as actions. The implementation results show that the
combination of a reward-clipping scheme and duelling deep network improves the
bandwidth allocation under different slicing scenarios.

###  V-D Lessons learned

The main lessons acquired from the survey on the Access AI domain are
highlighted as the following.

  * •

AI well support the designs of three key network layers: PHY layer, MAC layer,
and Network layer. For PHY layer, AI has been applied to facilitate CSI
acquisition tasks in wireless communications such as CSI estimation [124],
learning for CSI sensing and recovery [125], and CSI feedback compression
[128]. AI provides promising solutions by its learning and prediction
capabilities to simplify the precoding design under uncertain environments and
unknown network traffic patterns [141]. Further, AI also facilitates channel
coding/decoding for wireless systems [146].

  * •

Many AI-based solutions have been proposed for MAC layer, mainly in three
domains, namely channel allocation, power control and interference mitigation.
In the multi-channel communications with less CSI and high channel variation,
AI especially DL is able to estimate dynamically the channel allocation by
establishing the adaptive channel assignment policy with low computational
complexity. AI has been also investigated to solve power control issues in
complex and dynamic wireless communications with recent successes [156],
[157], [161]. Specially, some emerging research results demonstrate that AI is
particularly useful for interference management in small cell networks or
mmWave networks [168].

  * •

AI also gain enormous interests in network layer designs from four fundamental
domains, spectrum sharing, data offloading, multi-connectivity, and network
resource management. For instance, many AI-based approaches have been
introduced to solve the critical spectrum sharing issues in wireless
communications, ranging from wireless spectrum sharing [177] to IoT spectrum
exchange [178]. Meanwhile, the learning capability of AI is also helpful to
estimate data traffic and channel distribution to realize flexible data
offloading [180], multi-connectivity [190], and resource management [193] in
wireless networks.

In summary, we list Access AI use cases in the taxonomy TABLE V to summarize
the contributions of each reference work.

TABLE V: Taxonomy of Access AI use cases.  Category | Ref. |  Use case |  AI techniques applied |  Main contributions
---|---|---|---|---
PHY layer AI | [123] |  CSI acquisition |  Support vector regression-SVR |  A ML-based CSI acquisition architecture for massive multiple input multipleoutput (MIMO) with frequency division duplex (FDD).
[124] |  CSI estimation |  CNN |  A ML-based time division duplex (TDD) scheme in which CSI can be estimated via a ML-based predictor.
[126] |  CSI compression |  DL (CsiNet+) |  A multiple-rate compressive sensing neural network framework for CSI compression in MIMO.
[127] |  CSI compression |  Convolutional DL |  A convolutional DL architecture, called DeepCMC, for efficient compression of the channel gain matrix.
[137] |  Hybrid channel precoding |  Neural networks |  Designing the hybrid precoding metrics by using the prior observations of the channel with neural networks.
[138] |  Channel precoding |  DNNs |  A decentralized robust precoding framework in multi-user MIMO settings.
[139] |  mmWaves hybrid precoding |  CNN |  A DL scheme using a convolutional neural network (CNN) for a new mmWaves hybrid precoding architecture.
MAC layer AI | [148] |  Distributed channel access |  Online ML |  A distributed channel access scheme for Wireless Networked Control Systems.
[149] |  Channel assignment |  Online ML |  A DL-based scheme for channel assignment for multi-channel communication in IoT networks.
[151] |  Channel allocation |  RL |  A RL based ML approach is designed for optimizing channel allocation in wireless sensor networks.
[156] |  Power control |  Q-learning |  A power control scheme using ML for D2D networks.
[157] |  Transmit power optimization |  ML |  A learning-based scheme for optimizing transmit power over the wireless channel under a certain power budget.
[165] |  Interference management |  Stochastic gradient leaning |  A scheme for interference management in multi-user LTE cellular networks with the aid of data-driven ML.
[166] |  Intercell interference control |  Distributed RL |  A scheme for intercell interference in OFDMA networks.
Network layer AI | [177] |  Spectrum sharing |  Q-learning |  An inter-operator spectrum sharing (IOSS) model that takes advantage of the benefits of spectral proximity between users and BSs.
[178] |  Spectrum sharing |  Supervised learning |  A Database-based spectrum sharing model for PU-SU networks.
[179] |  Spectrum sharing |  RL |  A scheme for spectrum sharing in vehicular networks with V2V and V2I links.
[180] |  Data offloading |  Compressed neural networks |  An AI-enhanced offloading scheme for industrial IoT.
[183] |  Vehicular traffic offloading |  Deep Q-learning |  A vehicular traffic offloading scheme for vehicles.
[186] |  Computation offloading |  DRL |  A computation offloading scheme for wireless MEC networks.
[190] |  Multi-connectivity |  LSTM-based DL |  A DL scheme for mobility management caused by frequent handovers in ultra-dense small cells
[191] |  Multi-connectivity |  Neural networks |  A model for supporting connectivity in 5G vehicular networks
[193] |  Network resource management |  LSTM and CNN |  A DL-based scheme for 5G wireless networks.

##  VI User Device AI

The next Wireless AI function of the data life cycle in Fig. 2 is User Device
AI where AI have been embedded into end devices to create a new on-device AI
paradigm. In this section, we study the User Device AI function for wireless
networks by analyzing the integration of AI in mobile user devices as shown in
Fig. 8.

###  VI-A Embedded AI functions

Figure 8: User Device AI function.

In this sub-section, we present how AI is integrated into mobile devices to
develop on-develop AI functions.

Problem formulation: With the rapid growth of smart mobile devices and
improved embedded hardware, there is interest in implementing AI on the device
[197], for both ML and DL functions. In the future networks with higher
requirements in terms of low-latency data processing and reliable
intelligence, pushing AI functions to mobile devices with privacy enhancement
would be a notable choice to wireless network design.

Drawbacks of conventional methods:  Traditionally, AI functions are placed on
remote cloud servers, which results in long latency for AI processing due to
long-distance transmissions. Further, the reliance on a central server faces a
serious scalable problem when processing data from distributed mobile devices.
The use of external servers to run AI functions is also vulnerable to security
and data privacy issues due to the third parties.

Unique advantages of using wireless AI techniques:  AI can be run directly on
mobile devices to provide smart and online learning functions for mobile
networks at the edge. The key benefits of embedded AI functions consist of
low-latency learning and improve the intelligence of mobile devices, which can
open up new intesting on-device applications, such as mobile smart object
detection, mobile human recognition.

Recent works: Some important works are highlighed with the integration of AI
on mobile devices, including embedded ML functions and embedded DL functions.

  * •

Embedded ML functions: The work in [198] introduces nnstreamer, an AI software
system running on mobile devices or edge devices. This software is able to
execute directly real-time ML functions, such as neural networks, with complex
mobile data streams by simplifying ML implementations on mobile devices
without relying on cloud servers. The feasibility of nnstreamer has been
investigated via real-world applications, such as event detection through
neural networks with sensor stream samples as the input data for training and
classification. In [199], a GPU-based binary neural networks (BNN) engine for
Android devices is designed, that optimizes both software and hardware to be
appropriate for running on resource-constrained devices, i.e. Android phones.
More specific, the authors exploit the computation capability of BNN on mobile
devices, decoupled with parallel optimizations with the OpenCL library toolkit
for enabling real-time and reliable neural network deployments on Android
devices. Another research effort in buiding AI on devices is in [200] that
applies ML to Android malware detection on Android smart devices. They build
an Application Program Interface (API) call function and integrate a ML
classification algorithm to detect abnormal mobile operations or malicious
attack on the device. As a trial, some typical ML classifiers are considered,
including SVM, decision tree, and bagging to characterize the designed API
scheme, showing that ML can assist to recognize malware, showing that the ML-
based bagging classifier achieves the best performance in terms of higher
malicious application detection rate. Moreover, a ML-based API module is
designed in [201] to recognize malware on Android phones. There are 412
samples including 205 benign app and 207 malware app used in the test for
classification by integrating with SVM and tree random forest classifiers. The
trial results show that ML classifiers can achieve high malware detection
rates, with over 90% for cross validation examination. Similarly, a ML-based
malware detection scheme called Talos is also presented in [202] on Android
operating systems. Talos only relies on on-device ML to identify and detect
malicious apps on Android devices without the need of external computing
services for running the ML algorithm.

  * •

Embedded DL functions: Software and hardware support for performing DL on
mobile devices is now evolving extremely fast. In [203], the authors run DNNs
on over 10,000 mobile devices and more than 50 different mobile system-on-
chips. The most complex task that can be run with the help of TensorFlow
Mobile or TensorFlow Lite is image classification with MobileNet or Inception
CNNs at the moment. The work in [204] proposes Minerva as a highly automated
co-design approach across the algorithm, architecture, and circuit levels to
optimize DNN hardware accelerators. Minerva enables highly accurate, ultra-low
power DNN accelerators (in the range of tens of milliwatts), making it
feasible to deploy DNNs in power-constrained IoT and mobile devices. Another
example of mobile AI function is in [205] that develops a MobileNet app
running lightweight CNN on mobile devices for mobile localization
applications. Images captured by phone cameras can be recognized by CNN to
specify the centroid of the object (i.e. the human hand), which is useful to
various applications in human motion detections. CNN is also applied to build
classification functions for preserve user privacy on embedded devices [206].
With the popularity of Android apps on the market, malicious apps can attack
mobile operating systems to exploit personal information, which breaches
mobile data over the Internet. By using a CNN-based training model, the mobile
processor can recognize attack activities by integrating the trained model
into the malicious apps. Therefore, all behaviour and data streams on mobile
websites can be identified and detected.

CNN inference is becoming necessary for improving user experience with recent
applications, such as information extraction from mobile data streams, virtual
reality. The authors in [207] present a hardware design that considers to
integrate CNN inference into embedded AMR devices. They implement a Kernel-
levels scheme which represents the four-layer CNN across heterogeneous cores.
Different cores would process different layers based on the consecutive images
in a stream. The improvement of overall throughput of the CNN architecture
stems from the computation resources of multi-cores and on-chip data memory.
In addition to CNN, DNN has been explored for mobile learning [208]. In this
work, an on-chip DL model is designed for edge AI devices. Due to the overhead
of computation on mobile CPUs, an improved DNN inference engine is considered
with half precision support and optimized neural network layers to be suitable
for resource-limited devices. This model potentially enhances the GPU
throughput by 20 times and saves 80% memory usage. As a different AI platform,
a collaborative AI scheme is proposed in [209] where cloud servers are used to
support AI training, while AI model is executed on the mobile devices. All
features of an AI model (i.e. DNN) are offloaded to cloud for processing. A
loss function is created that calculates the compressibility of intermediate
features in the model, and it is then integrated with the overall loss
function. This solution would accelerate the learning process for multi-tasks,
and mitigate the workload offloaded to cloud through compression.

In particular, CNN can be integrated in edge devices to create an on-device
CNN architecture [210]. CNN layers can learn the features collected from the
data samples on wearable sensor devices. The edge board such as SparkFun
Apollo3 chip is used to run the DL algorithm. The novelty of this work is to
use batch normalization for recognition tasks with CNN on mobile devices. To
optimize the performance of on-chip DL-based system, a memory footprint
optimization technique is also involved and compared with conventional
statistical ML technique, showing a lower execution delay for running mobile
DL models. Recently, an on-board AI model is designed for mobile devices for
sensing and prediction tasks [211]. To do this, a CNN architecture is
considered, with two convolutional blocks, two linear blocks with a sigmoid
block to perform learning and classification on embedded devices. A use-case
using CNN for detecting the seed germination dynamics in agriculture is taken
with seed images as training data inputs, showing the high accuracy in seed
recognition with power savings for running DL algorithm.

Figure 9: Federated learning for mobile wireless networks.

###  VI-B Federated learning at user side

In this sub-section, we present the recent development of federated learning
(FL) in wireless networks. Conceptually, FL requires a cross-layer
implementation involving user devices, network devices, and the access
procedure. However, since almost all of the AI-related computations are
carried out by user devices in FL, we put FL under the category of this
section ”User device AI”.

Problem formulation:  In the large-scale mobile networks, it is in-efficient
to send the AI learning model to remote cloud or centralized edge servers for
training due to high communication costs and AI data privacy issues. With
widely distributed and heterogeneous user data located outside the cloud
nowadays, centralized AI would be no longer a good option to run AI in
wireless networks. FL can preserve privacy while training a AI model based on
data locally hosted by multiple clients. Rather than sending the raw data to a
centralized data-center for training, FL allows for training a shared model on
the server by aggregating locally computed updates black [212].

Drawbacks of conventional methods:  The traditional AI models rely on a cloud
or edge server to run AI functions, which put high pressure on such a
centralized data center in terms of high data storage and processing demands.
Further, offloading to remote servers to run AI function may result in data
loss due to the user reluctancy of providing sensitive data, which in return
degrades data privacy.

Unique advantages of using wireless AI techniques:  FL allows to train AI
models in a distributed manner where local data can be trained at mobile
devices for low-latency learning and data privacy protection. It also reduce
the flow of data traffic to avoid network congestion.

Recent works:  In [213], the authors integrate the DRL techniques and FL
framework with mobile edge systems, for optimizing, caching in mobile edge
computing. The work in [214] presents a collaborative FL network for smart IoT
users. The FL includes two steps, including model training on the smartphones
using data collected from IoT devices and model sharing over the blockchain
network [215]. To encourage more users to join the FL, an incentive mechanism
is designed to award coins to users, which in return enhances the robustness
of the mobile FL network. To support low-latency vehicle-to-vehicle (V2V)
communications, the authors in [216] employ FL that enables to interconnect
vehicular users in a distributed manner. In this context, each user transmits
its local model information such as vehicle queue lengths to the roadside unit
(RSU) where a centralized learning module is available for perform learning
for maximum likelihood estimation.

FL is also useful to DRL training in the offloading process of the MEC-based
IoT networks [217]. In the offloading scheme, each IoT device takes actions to
make offloading decisions based on system states, i.e. device transmit power,
CSI conditions. In such a context, FL allows each IoT user to use his data to
train the DRL agent locally without exposing data to the edge server, which
preserves the privacy of personal information. Each trained model on
individual IoT user is then aggregated to build a common DRL model for. By
using FL, the IoT data is trained locally that eliminates the channel
congestion due to a large amount of data transmitted over the network. In
addition to training support, FL is able to preserve the privacy of DL
schemes, i.e. in the neural network training process [218]. Here, the training
model consists of centralized learning on the edge server and FL at the users.
Specially, in FL, both users and servers collaborate to train a common neural
network. Each user keeps local training parameters and synchronize with the
cloud server to achieve an optimal training performance. By implementing the
FL, user privacy can be ensured due to the local training. Moreover, a FL
scheme is introduced in [219] to support training the collaborative model over
a network of agents of users, service providers and agents. These entities can
incorporate to predict the information sharing using a privacy-preserved
centralized model. Here, the centralized model is stored in the cloud server,
but learning parameter settings or updates are implemented by each local
agent. The cloud server then averages the updates to produce the shared model.
All training data is kept on the local user agent, and no updates are stored
on the cloud server for privacy protection.

Another solution introduced in [220] uses FL for building a self-learning
distributed architecture that aims to monitor security of IoT devices (i.e.
detecting compromised devices). In this case, FL is used to aggregate
detection profiles of the IoT network where the mobile gateways can train data
locally without sharing with remote servers. This would not only mitigate the
latency overhead of the training process, but also ensure IoT data privacy by
using available resource for model training. Besides, to support low-latency
communication in mobile wireless networks, a federated edge computing scheme
is proposed in [221] as depicted in Fig. 9. User data can be trained locally
using local datasets and model updates can be transmitted over the multi-
access channel to the edge server for aggregation of local models. The process
is repeated until the global model is converged. Further, an on-device FL
model for wireless network is suggested by [222]. The main focus is on a
design for communication latency mitigation using a Newton method and a scheme
for reducing error during the FL model update based on difference-of-convex
functions programming. Different from these works, the authors in [223] pay
attention to privacy preservation for mobile FL. The FL model includes key
generation centre, cloud server and participants (users). Each user has
private dataset and performs training locally, then extracts the model updates
(gradient parameters). These updates would be exchanged with the cloud server
to achieve a global training. Here, to preserve privacy for FL, a distributed
Gaussian scheme is integrated that allows to encrypt local data to achieve a
secure aggregation protocol.

###  VI-C Lessons learned

With the rapid growth of smart mobile devices and improved hardware
technology, AI functions have been embedded into mobile devices to create a
new paradigm: on-device AI. This would open up new opportunities in
simplifying AI implementation in wireless networks by eliminating the need of
external servers, i.e. clouds. In fact, the feasibility of on-device AI model
has been demonstrated via recent successes with designs of mobile ML functions
[198] and mobile DL functions [203]. For example, DNN inference now can be run
on mobile devices such as Android phones for local client applications such as
image classification [205], object recognition [210]. Developing deep AI
models on mobile devices is predicted to revolutionize the way future
intelligent networks operates and provides user services, especially in the
booming era of the Internet of Things. Moreover, AI can be implemented by the
cooperation of distributed mobile devices, which gives birth to FL paradigms.
Rather than sending the raw data to a centralized data-center for training, FL
allows for training a shared model on the server by aggregating locally
computed updates [212]. FL can support training the collaborative model over a
network of agents of users, service providers and agents with low training
latency, traffic congestion mitigation and privacy preservation [217]. We list
User Device AI use cases in the taxonomy TABLE VI to summarize the
contributions of each reference work.

TABLE VI: Taxonomy of User Device AI use cases.  Category | Ref. |  Use case |  AI techniques applied |  Main contributions
---|---|---|---|---
User Device AI | [198] |  Embedded ML functions |  Neural networks |  An AI software system running on mobile devices or edge devices.
[199] |  Mobile ML engine |  Bsinary neural networks |  A GPU-based binary neural networks (BNN) engine for Android devices.
[200] |  Malware detection |  SVM, decision tree |  An Android malware detection on Android smart devices.
[201] |  Malware detection |  SVM, tree random forest |  A ML-based API module to recognize malware on Android phones.
[203] |  Image classification |  DNNs |  A scheme for image classification using deep neural networks (DNNs) on mobile devices.
[205] |  Mobile localization |  CNN |  A MobileNet app, which run lightweight CNN on mobile devices for mobile localization applications.
[206] |  Mobile privacy protection |  CNN |  A scheme for building classification functions for preserve user privacy on embedded devices.
[208] |  DNN inference engine |  DNN |  An on-chip DL model for edge AI devices.
[210] |  Mobile activity recognition |  CNN |  An on-device CNN architecture for support human activity recognition.
[211] |  Mobile sensing and prediction |  CNN |  An on-board AI model for mobile devices for sensing and prediction tasks
[214] |  Collaborative federated learning |  FL |  A collaborative federated learning network for smart IoT users.
[216] |  Vehicular communications |  FL |  A FL model for supporting low-latency vehicle-to-vehicle (V2V) communications.
[219] |  Collaborative learning |  FL |  A FL scheme for training the collaborative model over a network of agents of users, service providers and agents.
[220] |  Security monitoring |  FL |  A self-learning distributed architecture to monitor security of IoT devices.
[221] |  Low-latency communication |  FL |  A federated edge computing for supporting low-latency communication in mobile wireless networks.
[222] |  Communication latency mitigation |  FL |  An on-device FL model for wireless networks.

##  VII  Data-provenance AI

The final Wireless AI function of the data life cycle in Fig. 2 is Data-
provenance AI where AI can provide various useful services, such as AI data
compression, data privacy, and data security. We will focus on discussing the
roles of AI for such important services that are shown in Fig. 10.

Figure 10: Data-provenance AI function.

###  VII-A AI Data compression

We here analyze how AI can help data compression tasks in wireless networks.

Problem formulation: In the era of big data, i.e. big sensor data, sensor data
needs to be compressed to mitigate network traffic and save resources of
wireless networks. There are two important data compression tasks in wireless
networks, including data aggregation and data fusion. Here, data aggregation
is a process that aims to minimize the data packets from different sources
(i.e. sensor nodes) to alleviate the transmit workload. Due to the resource
constraints of wireless sensor devices, data aggregation is of paramount
importance for achieving efficient data collection while preserving energy
resources on wireless networks. Meanwhile, in the data fusion, data from
ubiquitous sensors can be fused to analyze and create a global view on the
target subject. For example, in a smart home setting where sensors in each
room collect data that represents the location information.

Drawbacks of conventional methods:  Many data compression algorithms have been
proposed in [23], [25], but they remain some unsolved issues. For example, a
wireless sensor network often consists of heterogeneous sensor nodes with
multi-media data associated with different statistical properties, which makes
traditional data compression approaches using mathematical formulations
inefficient. Further, in the future networks, the number of sensor nodes would
increase rapidly with higher data complexity and high data volume, how to
compress data with high scalability and robustness without loss of valuable
data is a challenge.

Unique advantages of using wireless AI techniques:  The major benefits of AI
for data compression tasks in wireless networks are intelligence and
scalability. Thanks to the online learning and prediction, AI can extract the
useful information to compress the ubiquitous sensor data and identify the
most important features that are necessary for later data processing.
Moreover, AI can provide scalable data learning, data processing and data
computation for a large dataset. The use of DNN would overcome the limitations
of current ML approaches to map and learn the big data collected from sensors.

Recent works: Some important works are highlighed with the exploitation of AI
for data compression tasks, including sensor data aggregation and sensor data
fusion.

  * •

Sensor data aggregation: The work in [224] presents a sensory data aggregation
scheme for wireless sensor clusters. The main focus is on classifying the
collected data using a supervised intrusion detection system. Here a hybrid ML
system including a misuse detection and an anomaly detection system are
designed to monitor the data aggregation process and detect data attacks. To
investigate the feasibility of the proposed approach, a knowledge discovery in
data mining dataset [225] is employed as the dataset for training and testing,
showing that ML can help achieve above 99% detection rate and 99.8% accuracy.
A new tree-based data aggregation framework is introduced in [226] for sensor
networks. Sensory data can be collected by an aggregator and then a part of
data is transmitted to the sink associated with a function that represents the
characteristics of all data, while the remaining data is processed in the sink
for transmit energy savings. The classification of the aggregated data is
implemented by a Bayesian belief network, which can achieve 91% accuracy via
numerical simulations.

The efficiency of data aggregation in wireless networks can be further
improved with DL approaches [227]. In this study, the focus is on privacy
preservation for sensor data on the link between gateways and servers. Here, a
DL autoencoder is used to ensure security for the sensor communication channel
by reconstructing the obfuscated data from the original data, which can hidden
data from unauthorized parties. In some practical scenarios, aggregators may
not be willing to collect data due to the lack of motivation (i.e. less
aggregation revenues). To solve this issue, the work in [228] implements a
payment-privacy preservation scheme that enables aggregators to collect
securely data while paying (i.e. rewards) to them. This process can formulated
as a MDP problem and since the transition probabilities of payment is unknown
to the aggregators, a model-free DRL approach using deep Q-learning is
proposed to learn the model via trial and error with an agent. The reward here
correlates with the optimal payment so that the agent explores the data
aggregation network to find an optimal policy for maximizing revenues.

  * •

Sensor data fusion: The study in [229] presents a multi-sensor data fusion
that serves human movement classification based on ML. A network of
heterogeneous sensors of a tri-axial accelerometer, a micro-Doppler radar and
a camera is used to collect data that is then fused and classified by a ML
algorithm with SVM and KNN. In [230], a sensor fusion architecture is proposed
for object recognition. Parameters or information measured from sensors can be
fused with those collected from other sensors to create a global view on the
target subject, which helps evaluate it more comprehensively. To facilitate
the fusion process, a processing model based on unsupervised ML is integrated
that helps classify the features from the fused datasets. Experiments use
camera images as the input data for training and classification, showing that
ML can recognize the object well in different parameter settings. Another
solution for sensor fusion in wireless sensor networks is presented in [231].
In particular, a system setting with various rooms is considered, where
sensors in each room collect data that represents the location information.
Then, data collected from different sensors is fused and a ML-based K-Spectral
Centroid algorithm is adopted to cluster the data from different rooms
together.

In practice, heterogeneous sensory data fusion may be challenging due to the
insufficient information of collected data sources and the difficulty to
represent the multimodal data, which can make data estimation infeasible. To
solve this issue, a new multimodal data fusion for sensor networks is proposed
in [232]. More specific, a deep multimodal encoder based on neural networks is
designed to perform learning from data collected from sensors to capture the
correlated features, which addresses the missing data issue. Importantly, an
unsupervised learning algorithm is derived to compress data by learning joint
features among the set of features in the raw dataset. Meanwhile, a Bayesian
network is employed in [233] for sensor data fusion. The complex non-Gaussian
sample dynamics in sensor signal patterns are fused and classified based on an
offline Bayesian non-parametric Dirichlet Process model, which achieves a
higher accuracy performance compared with traditional Bayes network and SVM
classifiers.

###  VII-B AI Data Privacy

In this sub-section, we present how AI can help improve data privacy in
wireless networks.

Problem formulation: At the network edge, data privacy protection is the most
important service that should be provided. The dynamicity of the data
collection, data transmisison and data distribution in future networks would
put at risks of user’s information leakage. For example, the data collection
can expose sensor’s information to third parties which makes privacy (i.e.
privacy of user address, user name) vulnerable. Therefore, protecting data
privacy is of paramount importance for network design and operations. By
ensuring data privacy, a wireless network can appeal to a larger number of
users which increases the utility of such a system.

Drawbacks of conventional methods:  Many existing approaches have been
proposed to solve data privacy issues, mainly using encryption protocols [23],
[26]. Although such techniques can keep the content of user’s information
private from the others and third parties, it is hard to apply to scalable
wireless networks with large data. Further, they often apply a specific
encryption protocol to a group of multi-media data collected from sensors and
devices, which is infeasible to future large-scale networks.

Unique advantages of using wireless AI techniques:  AI potentially provides
privacy functions for sensed data via classification and detection to
recognize potential data threats. AI is also useful to user location privacy.
In fact, AI can also support protection of user location privacy by building
decision-making policies for users to adjust their location information and
selecting privacy location preference.

Recent works: From the perspective of data generation process, we consider two
AI data privacy domains, including data sensing privacy and user location
privacy.

  * •

Data sensing privacy: The work in [234] concerns about privacy preservation
for mobile data sensing. Differential privacy is used to protect the
individual information during the data collection process. To authenticate the
user identity based on their own data, a ML-based classifier approach using
two-layer neural networks is designed. It classifies the privacy sensitivity
levels (0-9) and evaluates the privacy vulnerability with leakage possibility.
To overcome the limitations of existing encryption solutions in terms of
computation complexity and the lack of ability to solving large-scale domains,
a multi-functional data sensing scheme with awareness of different privacy is
proposed in [235]. The key objective is to provide flexible sensing functions
and aggregation properties to serve different demands from different users.
Besides, it is important to preserve the collected sensor data against
adversarial attacks for better user information protection. In such contexts,
the aggregation queries from sensors are collected as the dataset for training
that is implemented by a ML algorithm, while the multifunctional aggregation
can be performed by a Laplace differential privacy technique. Then, learning
helps to estimate the aggregation results without revealing user privacy. As
an example, a distributed privacy-preserved ML scheme for crowd sensing is
presented in [236] that combines sensing, learning and privacy protection via
a classifier design on mobile devices. The behind ML algorithm is a stochastic
gradient descent model that is implemented on each local device. This solution
not only distributes learning workload over the network of devices, but also
keeps data private due to no need to transfer data on the network. Meanwhile,
the work in [237] solves the data collection issue with privacy awareness
using K-mean clustering. Indeed, to ensure data privacy, a technique of adding
Laplace noise to the data compression process. Also, aggregated data is
distributed over the mobile device network where each device holds a
differentially-private sketch of the network datasets.

In the multimedia IoT networks, the sensor nodes can sense data from the
environment and transmit collected data to cloud servers for further
processing. A critical issue here is the data sensing and transmission can
reveal private data, i.e. location information. It is thus important to
protect data privacy during the data sensing process. The work in [238]
proposes a cluster approach that divides sensor devices into different groups.
Each cluster controller ensures the privacy of its sensor group, associated
with a neural network that classifies the collected data and extracts useful
features for data learning. Such data processing can be helped by using MEC
services that not only provides low-latency computation but also enhances
privacy for physical sensor devices [16]. A DL scheme for privacy protection
and computation services for physical sensors can be seen in Fig. 11.

  * •

User location privacy: An example is in [239] that introduces a privacy
preservation scheme to help users to manage their location information. The
key objective is to enable users to obtain the privacy preferences based on
their choice factors such as QoS, trust levels. Based on that, a k-learning
model is built that can make decisions for users in preserving location
privacy. The authors in [240] are interested in privacy in mobile social
networks. They first perform a real experiment to collect user location
information in a campus network and investigate the possibility of location
privacy leakage. Then, a privacy management scheme is designed to provide
privacy control for mobile users. This model is empowered by a ML-based
decision tree algorithm that learns the user privacy preference and classifies
location contexts in the social ecosystem.

At present, the location of mobile devices is mostly identified by GPS
technology, but this platform remains inherent limitations in terms of energy
overheads and reliance on the third party (i.e. service providers), which
poses user information at privacy risks. A new method presented in [241] can
achieve a user privacy location guarantee with energy savings. By using the
features of current location that represents the privacy location preference,
a ML approach is applied to learn the privacy model, aiming to classify
privacy preferences depending on whether the user want or not want to share
its location information based on their activities. It is noting that
capturing features only relies on sensors available on smart phones without
using GPS that is power saving and eliminates the need of third party. To
preserve user location privacy in crowd sensing, a learning scheme is
presented in [242] to model the correlations between sensitive and non-
sensitive scenarios according to the relations with private locations. The
main aim is to find a strategy so that the system utility is maximized while
user location is protected against malicious attacks. The work in [243]
jointly takes context factors, i.e. occasion and time, and individual factors
into considerations to establish a privacy location preservation protocol in
social networks. A ML-based regression model is involved to evaluate the
privacy levels based on such features, showing that the occasion factor
dominates in determining the privacy degree during the location sharing.

Figure 11: DL for MEC privacy preservation.

###  VII-C AI Data Security

In addition to privacy protection, AI also has the great potential for
enhancing security for wireless networks. We here present how AI can help to
protect data security.

Problem formulation:  Data security is the top research concern in wireless
network design. A wireless network system is unable to work well and robust
under the presence of security threats and attacks. The openness and
dynamicity of wireless networks makes them prone to external data threats,
while unauthorized users can access the resource of the network.

Drawbacks of conventional methods:  Wireless networks have gained increasing
influences on modern life that makes cyber security an significant research
area. Despite installed defensive security layers, the wireless networks
inherit cyber attacks from the IT environments. Many security models have been
proposed, mainly using middle boxes such as Firewall, Antivirus and Intrusion
Detection Systems (IDS) [244]. These solutions aims to control the traffic
flow to a network based on predefined security rules, or scan the system for
detecting malicious activities. However, many traditional security mechanisms
still suffer from a high false alarm rate, ignoring actual serious threats
while generating alerts for non-threatening situations. Another issue of the
existing security systems is the impossibility of detecting unknown attacks.
In the context of increasingly complex mobile networks, the human-device
environments change over time, sophisticated attacks emerge constantly. How to
develop a flexible, smart and reliable security solution to cope with various
attacks and threats for future wireless networks is of paramount importance
for network operators.

Unique advantages of using wireless AI techniques:  AI has been emerged as a
very promising tool to construct intelligent security models. Thanks to the
ability of learning from massive datasets, AI can recognize and detect attack
variants and security threats, including unknown ones in an automatic and
dynamic fashion without relying on domain knowledge [245]. Also, by
integrating with on-device learning algorithms, it is possible for AI to
recognize the unique features of a certain user (i.e. through AI classifiers),
aiming for the user classification against threats.

Recent works: We here introduce some important security services that AI can
offer, including user authentication and attack detection.

  * •

AI for user authentication: Some recent works are interested in exploiting AI
for user authentication in wireless networks. An example in [246] introduces a
user authentication model using millimetre sensors. Each radar sensor is
equipped with four receive antennas and two transmit antennas. Signals that re
reflected from a part of human body are analyzed and extracted to produce a
feature dataset that is then fed to a ML-based random forest classifier.
Through training and learning, the classifier can recognize the biometric
properties of any individuals. In [247], a user authentication scheme is
proposed based on CSI measurements working for both stationary and mobile
users. For the former case, the authors build a user profile with
considerations of spoofing attacks, while for the later case, the correlations
of CSI measures are taken to implement user authentication checking.
Specially, an authenticator is created that runs a ML algorithm to learn from
the CSI profiles and determine whether the CSI value measured in the coming
packet matches with the one created in the profile storage. If they match, the
authentication is successful, otherwise that user is denied for further
processing.

The Wi-Fi signals can be exploited to build datasets of learning for user
authentication. The authors in [248] try the first time for a new user
authentication system using human gait biometrics from Wi-Fi signals. Here,
CSI value samples are collected from Wi-Fi devices. The core component of the
scheme is a 23-layer DNN network that is modelled to extract the important
salient gait features collected from gait biometrics in the CSI measurement
dataset. DNN would act as a classifier to extract unique features of a user
and identify that user among the mobile user set. A user authentication scheme
for Wi-Fi networks is also suggested in [249] which can be installed on mobile
devices to detect malicious access and prevent spoofing attacks. By
integrating with on-device learning algorithms, it is possible to recognize
the unique features of a certain user (i.e. through ML classifiers) where
visible light can be an indicator input for the user classification. As a
different viewpoint of user authentication, the work in [250] analyzes CSI
data for user authentication in wireless networks. First, a CNN classifier is
built to learn the extracted features from CSI dataset such that pooling
layers are utilized to simplify the computation process for faster learning.
To analyze CSI features, a RNN network is then designed, including some
recurrent layers and a fully connected layer where user recognition is
implemented. The combination of CNN and RNN models leads to a new scheme,
called CRNN, aiming to use the capability of information representation of CNN
and information modelling of RNN. Thus, the accuracy for user authentication
is improved that is also confirmed through computer simulations. It is noted
that in wireless networks, the authentication between two users on the
physical network layer based on traditional approaches is challenging due to
the time variance of channels and mobility of users.

  * •

AI for attack detection: The work in [251] introduces a scalable intrusion
detection system (IDS) for classifying and recognizing unknown and
unpredictable cyberattacks. The IDS dataset collected from a large-scale
network-level and host-level events is processed and learned by a DNN
architecture that can classify the network behaviours and intrusions. Compared
to commercial IDSs that mainly rely on physical measurements or defined
thresholds on feature sets to realize the intrusions, learning can assist
flexible IDS without any further system configurations, and work well under
varying network conditions such as network traffic, user behaviour, data
patterns. Another solution in [252] formulates an intrusion detection system
using RNN. The main focus is on evaluating the detection rate and false
positive rate. The intrusion detection can be expressed equivalently to a
multi-class classification problem for recognizing whether the traffic
behaviour in the network is normal or not. The key motivation here is to
enhance the accuracy of the classifiers so that intrusive network behaviours
can be detected and classified efficiently. Motivated by this, a RNN-based
classifier is leveraged to extract abnormal traffic events from the network
features using the well-known NSL-KDD dataset [253]. Specially, the attacks
can be divided into four main types, including DoS (Denial of Service
attacks), R2L (Root to Local attacks), U2R (User to Root attack), and Probe
(Probing attacks). The results of deep training and learning show a much
better classification accuracy of RNN in comparison with ML techniques.

In addition to that, wireless networks also have to solve spoofing attacks
where an attack claims to be a legitimate node to gain malicious access or
perform denial of service attacks. Conventional security solutions, such as
digital signatures, cannot fully address the spoofing issues, especially in
the dynamic environment with unknown channel parameters. Recently, AI gains
interest in spoofing detection tasks. As an example, a solution proposed in
[254] uses ML to detect and identify spoofing attacks in massive MIMO 5G
networks. By collecting the virtual path gain of massive MIMO channel, a
virtual channel space is created to detect the anomaly among the network
users. In particular, a learning-based algorithm is built to estimate the
anomalies and recognize spoofing attacks without knowing the knowledge of
channel/signal information. Further, the work in [255] also leverages the
potential of ML to detect spoofing attacks in wireless sensor networks. By
analyzing the correlation of signal strength on the mobile radio channel, the
learning algorithm receives signal strength samples as the input data for
abnormal behaviour analysis. To enhance the detection accuracy, the collected
samples can be categorized into small windows that may include the attacking
nodes and legitimate nodes. Then, a collaborative learning scheme with k-NN
and k-means is applied to identify the spoofing attacks in the dense networks.
Meanwhile, the authors in [256] use RL to implement a spoofing detection model
from the PHY layer perspective for wireless networks. The key concept is to
exploit the CSI of radio packets to detect spoofing vulnerabilities. The
spoofing authentication process is formulated as a zero-sum game including a
receiver and spoofers. Here, the receiver specifies the test threshold in PHY
spoofing detection, while spoofers select attack frequency to optimize its
utility with respect to Bayesian risks. Specially, the threshold selection is
achieved by trial and error process using Q-learning without knowing CSI such
as channel variations.

###  VII-D Lessons learned

The main lessons acquired from the survey on the Data-provenance AI domain are
highlighted as the following.

  * •

Recent studies also raise the discussion on the AI adoption for data-driven
applications during the data generation process. They mainly concentrate on
four specific domains, namely AI data compression, data clustering, data
privacy, data security. As an example, AI has been applied for supporting
sensor data aggregation and fusion, by classifying useful information from the
collected data samples [224]. Also, the classification of AI helps monitor the
data aggregation process, aiming to analyze the characteristics of all data
and detect data attacks [226].

  * •

At the network edge, usage privacy and data security protection are the most
important services that should be provided. From the perspective of data
generation process, the applications of AI in data privacy are reflected in
two key domains, including data sensing privacy and user location privacy.
Currently, AI-based data classification algorithms can be implemented on each
local device. This solution not only distributes learning workload over the
network of devices, but also keeps data private due to no need to transfer
data on the network [236]. Interestingly, recent studies in [239], [240] show
that ML can help learn the user privacy preference and classify location
contexts in the social ecosystem.

  * •

In addition to privacy protection, AI also has the great potential for
enhancing security for wireless networks. In the literature, AI mainly provide
some important security services, including user authentication and access
control. In practical wireless networks, the authentication between two users
on the physical network layer based on traditional approaches is challenging
due to the time variance of channels and mobility of users. AI may be an ideal
option to recognize legitimate users from attackers by training the channel
data and perform classification. Meanwhile, DRL has proved extremely useful in
building access control mechanisms [257], [258] that can learn from the user
behaviours and estimate the access events for appropriate security actions.

In summary, we list Data-provenance AI use cases in the taxonomy TABLE VII to
summarize the contributions of each reference work.

TABLE VII: Taxonomy of Data-provenance AI use cases.  Category | Ref. |  Use case |  AI techniques applied |  Main contributions
---|---|---|---|---
AI Data compression | [224] |  Sensory data aggregation |  Supervised ML |  A sensory data aggregation scheme for wireless sensor clusters.
[226] |  Sensory data aggregation |  Bayesian belief network |  A new tree-based data aggregation framework for sensor networks.
[227] |  Data aggregation |  DL autoencoder |  A scheme for data aggregation tasks with a focus on privacy preservation.
[229] |  Multi-sensor data fusion |  SVM and KNN |  A multi-sensor data fusion that serves human movement classification based on ML.
AI Data Privacy | [234] |  Data privacy preservation |  Neural networks |  A ML scheme for privacy preservation in mobile data sensing.
[235] |  Different privacy |  Neural networks |  A multifunctional data sensing scheme with awareness of different privacy.
[237] |  Privacy awareness |  K-mean clustering |  A model for the data collection issue with privacy awareness using K-mean clustering.
[238] |  Data privacy |  Neural networks |  A scheme for protecting data privacy during the data sensing process.
[239] |  User location privacy |  k-learning |  A privacy preservation scheme that helps users to manage their location information.
[243] |  Privacy location preservation |  Regression learning |  A privacy location preservation protocol in social networks.
AI Data Security | [246] |  User authentication |  Random forest |  A user authentication model using millimetre sensors.
[248] |  User authentication |  Multi-layer DNN |  A new user authentication system using human gait biometrics from Wi-Fi signals.
[250] |  User authentication |  CNN |  A scheme for analyzing CSI data for user authentication in wireless networks.
[251] |  Intrusion detection |  DNN |  A scalable intrusion detection system (IDS) for classifying and recognizing unknown and unpredictable cyberattacks.
[252] |  Intrusion detection |  RNN |  An intrusion detection system for evaluating the detection rate and false positive rate.
[254] |  Spoofing detection |  Supervised learning |  A ML approach to detect and identify spoofing attacks in massive MIMO 5G networks.
[255] |  Spoofing detection |  k-NN and k-means |  A learning model for detecting spoofing attacks in wireless sensor networks.
[256] |  Spoofing detection |  RL |  A spoofing detection model from the PHY layer perspective for wireless networks.

##  VIII  Research Challenges and Future Directions

Different approaches surveyed in this paper clearly show that AI plays a key
role in the evolution of wireless communication networks with various
applications and services. However, the throughout review on the use of AI in
wireless networks also reveals several critical research challenges and open
issues that should be considered carefully during the system design. Further,
some future directions are also highlighted.

###  VIII-A Research challenges

We analyze some important challenges in wireless AI from three main aspects:
security threats, AI limitations, and complexity of AI wireless
communications.

####  VIII-A1 Security threats

Security is a major concern in wireless AI systems. Data collected from
sensors and IoT devices are trained to build the model that takes actions for
the involved wireless applications, such as decision making for channel
allocation, user selection, or spectrum sharing. Adversaries can inject false
data or adversarial sample inputs which makes AI learning invalid. Attackers
can also attempt to manipulate the collected data, tamper with the model and
modify the outputsc [259]. For instance, in reinforcement learning, the
learning environment may be tampered with an attacker so that the agent senses
incorrectly the information input data. Another concern can stem from the
manipulation on hardware implementation by modifying hardware settings or re-
configuring system learning parameters. The work in [260] investigated the
multi-factor adversarial attack issues in DNN with input perturbation and DNN
model-reshaping risks which lead to wrong data classification and then damages
the reliability of the DNN system. Evaluating security threats on AI models
and providing creative solutions to prevent attack risks and protect AI
operations is vitally important to AI-based wireless applications.

####  VIII-A2  Limitations of AI

While AI shows impressive results in wireless communication applications, its
models still remain performance limitations. For example, the imperfection
bottleneck in the training process of DNN can be exploited by adversarial
attacks to tamper with input data that leads to misclassification [261].
Further, an investigation in [262] reveals the false confidence of DNN in
estimating images. By generating fooling samples (i.e. images) in a way
imperceptible to humans, a DNN can mislabel the image and thus recognize
wrongly the object. Another limitation is the high cost of training the AI
model. Indeed, it is not uncommon to take some days for the AI training on
mobile devices or even edge devices with limited computation resources.

A more challenge is the scarcity of real datasets available for wireless
communications necessary to for the AI training, i.e. in supervised learning
[263]. In many wireless scenarios where only a small dataset is not sufficient
for the training which degrades the performance of AI model in terms of low
classification or prediction accuracy, and high model overfitting, and thus
the confidence of AI model may not be guaranteed. This drawback would be an
obstacle for deployment of AI in real-world wireless applications since the
feasibility of AI should be demonstrated in testbeds or experiments in the
real environments.

####  VIII-A3  Complexity of AI wireless communication networks

The complexity may stem from wireless data and network architecture. In fact,
in AI wireless communication networks, the data is produced from both
application traffic and information of devices and objects. Considering the
complex wireless network with various data resources, it is challenging to
extract data needed for the AI training of a specific task. In some cognitive
network scenarios, how to label the massive learning data for training AI
models is also highly challenging. From the network architecture perspective,
the traditional protocol of wireless communication is based on a multi-layer
network architecture. However, currently it is difficult to determine which
and how many layer should be configured to run AI functions and deploy AI
technologies in devices. Therefore, it is essential to consider intelligent
protocol architectures with the simplified and adaptive capability according
to AI functions and requirements [264].

###  VIII-B Future directions

We discuss some of the future research directions on wireless AI motivated
from the recent success in the field.

####  VIII-B1 Specialized AI architecture for wireless networks

The AI architecture has significant impacts on the overall performance of the
wireless AI systems, while the current AI mode design is still simple. Many AI
architectures proposed in [21], [22], [24], [26] are mostly relied on
conventional algorithms with learning iterations, data compression, decoding
techniques. The performance of wireless AI algorithms can be reduced thanks to
recent AI advances with sub-block partition solutions for neural networks
[146] or data augmentation for DL [265]. However, these AI approaches suffer
from dimensionality problems when applying to wireless applications due to the
high complexity of the wireless systems, and data training would be
inefficient when the network dimension is increased with the increase of
mobile devices and traffic volumes. Therefore, developing adaptive AI
architectures specialized in wireless communication scenarios is the key for
empowering future intelligent applications. As suggested by [266], by adapting
with the dynamicity of wireless communications, AI can model effectively the
wireless systems and discover the knowledge of data generated from network
traffic and distributed devices. Besides, motivated by [24], [26], AI
functions should be implemented and deployed in the wireless network in a
distributed manner. This architecture not only influences the wireless data
transmission infrastructure, but also modernize the way that wireless
communications are organized and managed [267].

Furthermore, with the ambition of deploying AI on mobile devices/edge devices
to extend the network intelligence to local clients, developing specialized
hardware/chipsets for AI is vitally important. Recent advances in hardware
designs for high-bandwidth CPUs and specialized AI accelerators open up new
opportunities to the development of AI devices at the edge of the network
[268], [269]. Motivated by this, recently Facebook bring ML inference to the
mobile devices and edge platforms [270] thanks to specialized AI functions
with high data training speed and low energy costs. The work in [271] also
introduces a DL inference runtime model running on embedded mobile devices.
This scheme is able to support data training and MIMO modelling using a
hardware controller empowered by mobile CPUs with low latency and power
consumption with bandwidth savings.

####  VIII-B2 From simulation to implementation

Reviewing the literature, most of the AI algorithms conceived for wireless
communication networks are in their simulation stages. Thus, the researchers
and practitioners needs to further improve AI functions before they are
realized in real-world wireless networks. We here propose some solutions that
can be considered in practical wireless AI design. First, real-world datasets
collected from physical communication systems must be made available for ready
AI function implementation. The sets of AI data generated from simulation
environments such as deepMIMO dataset [272] can contain the important network
features to perform AI functions, but they are unable to reflect exactly the
characteristics of a real wireless system. Second, as discussed in the
previous section, designing specialized hardware/chipsets for AI is extremely
important to deploy AI applications on edge/mobile devices. These new hardware
architectures should provide functions specializing AI to support on-device
training, modelling and classification at the edge of the network. Third, the
multidimensional assessment for wireless AI applications in various scenarios
is also vitally necessary to realize the feasibility of the AI adoption in
practice. By comparing with simulation performances, the designers can adjust
the model parameters and architecture settings so that AI can well fit the
real channel statistical characteristics.

####  VIII-B3  AI for 5G and beyond

The heterogeneous nature of 5G and beyond wireless networks features multi-
access ultra-dense networks with high data transmission capability and service
provisions. AI has been regarded as the key enabler to drive the 5G networks
by providing a wide range of services, such as intelligent resource
allocation, network traffic prediction, smart system management. For example,
the integration of AI enables network operators to realize intelligent
services, such as autonomous data collection across heterogeneous access
networks, dynamic network slicing to provide ubiquitous local applications for
mobile users, and service provisioning management [272]. Wireless AI also acts
as AI-as-a-service that can realize the potentials of Massive MIMO, a key 5G
technology. For example, AI can be used to predict the user distribution in
MIMO 5G cells, provide MIMO channel estimation, and optimize massive MIMO
beamforming [273]. With the rapid development of edge 5G services, AI would
potentially transform the way edge devices perform 5G functionalities. In
fact, the network management and computation optimization of mobile edge
computing (MEC) would be simplified by using intelligent AI software through
learning from the historical data. Lightweight AI engines at the 5G devices
can perform real-time mobile computing and decision making without the
reliance of remote cloud servers.

Beyond the fifth-generation (B5G) networks, or so-called 6G, will appear to
provide superior performance to 5G and meet the increasingly service
requirements of future wireless networks in the 2030s. The 6G networks are
envisioned to provide new human-centric values with transformative services,
such as quantum communications, wearable computing, autonomous driving,
virtuality, space-air-ground networks [274], [275], and the Internet of Nano-
Things [276]. AI technologies are expected to play an indispensable role in
facilitating end-to-end design and optimization of the data processing in 6G
wireless networks [277]. The future cellular networks will be much more
complex and dynamic, and the number of parameters to be controlled will
increase rapidly. In such scenarios, AI can provide smart solutions by its
self-learning and online optimization capabilities to support advanced data
sensing, data communication, and signal processing [278]. These benefits that
AI bring about would accelerate the deployment of future 6G networks and
enable newly innovative wireless services.

The application of AI in wireless is still at its infancy and will quickly
mature in the coming years for providing smarter wireless network services.
The network topology model and wireless communication architecture will be
more complex with the dynamicity of data traffic and mobile devices. AI is
expected to play a key role in realizing the intelligence of the future
wireless services and allow for a shift from human-based network management to
automatic and autonomous network operations. Therefore, the integration of AI
in wireless networks has reshaped and transformed the current service
provisions with high performances. This detailed survey is expected to pave a
way for new innovative researches and solutions for empowering the next
generation of wireless AI.

##  IX Conclusions

In this paper, we have presented a state-of-the-art survey on the emerging
Wireless AI in communication networks. We have first proposed a novel Wireless
AI architecture from the data-driven perspective with a focus on five main
themes in wireless networks, namely Sensing AI, Network Device AI, Access AI,
User Device AI and Data-provenance AI. Then, we have focused on analyzing the
use of AI approaches in each data-driven AI theme by surveying the recent
research efforts in the literature. For each domain, we have performed a
holistic discussion on the Wireless AI applications in different network
scenarios, and the key lessons learned from the survey of each domain have
been derived. Based on the holistic survey, we have pointed out the important
research challenges to be considered carefully for further investigation on
the AI applications in wireless networks. Finally, we have outlined potential
future research directions towards to AI 5G wireless networks and beyond. We
hope that this article stimulates more interest in this promising area, and
encourages more research efforts toward the full realization of Wireless AI.

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