1

arXiv:1801.04223v1 [] 12 Jan 2018

Machine Intelligence Techniques for
Next-Generation Context-Aware Wireless
Networks
Tadilo Endeshaw Bogale1,2 , Xianbin Wang1 and Long Bao Le2
Western University, London, Canada1
Institute National de la Recherche Scientifique (INRS),
Université du Québec, Montréal, Canada2
Email: {tadilo.bogale, long.le}@emt.inrs.ca, xianbin.wang@uwo.ca
Abstract
The next generation wireless networks (i.e. 5G and beyond), which would be extremely dynamic and
complex due to the ultra-dense deployment of heterogeneous networks (HetNets), poses many critical
challenges for network planning, operation, management and troubleshooting. At the same time, generation
and consumption of wireless data are becoming increasingly distributed with ongoing paradigm shift from
people-centric to machine-oriented communications, making the operation of future wireless networks even
more complex. In mitigating the complexity of future network operation, new approaches of intelligently
utilizing distributed computational resources with improved context-awareness becomes extremely important.
In this regard, the emerging fog (edge) computing architecture aiming to distribute computing, storage, control,
communication, and networking functions closer to end users, have a great potential for enabling efficient
operation of future wireless networks. These promising architectures make the adoption of artificial intelligence
(AI) principles which incorporate learning, reasoning and decision-making mechanism, as natural choices
for designing a tightly integrated network. Towards this end, this article provides a comprehensive survey
on the utilization of AI integrating machine learning, data analytics and natural language processing (NLP)
techniques for enhancing the efficiency of wireless network operation. In particular, we provide comprehensive
discussion on the utilization of these techniques for efficient data acquisition, knowledge discovery, network
planning, operation and management of the next generation wireless networks. A brief case study utilizing the
AI techniques for this network has also been provided.
Keywords– 5G and beyond, Artificial (machine) intelligence, Context-aware-wireless, ML, NLP, Ontology

I. I NTRODUCTION
The advent of the fifth generation (5G) wireless
network and its convergence with vertical applications
constitute the foundation of future connected society
which is expected to support 125 billion devices by
2030 (IHS Markit). As these applications and devices
are featured by ubiquitous connectivity requirements,
future 5G and beyond networks are becoming more
complex. Besides the complexity increase of base
stations (BSs) and user equipments (UEs), significant
challenges arise from the initial network planning to
the deployment and situation-dependent operation and
management stages.
The network architecture of 5G and beyond will
be inevitably heterogeneous and multi-tier with ultra-

dense deployment of small cells to achieve the anticipated 1000 fold capacity increase cost-effectively.
For instance, the mixed use of planned and centrally
controlled macro-BSs and randomly deployed wireless
fidelity (WiFi) access points or femto-BSs in the ultradense heterogeneous network (HetNet) raises several
unexpected operation scenarios, which are not possible
to envision at the network design stage. This requires
future wireless networks to have self organizing, configuring and healing capability based on the operational condition through tight coordination among
different nodes, tiers and communication layers. These
challenges make the existing network design strategies
utilizing a fairly simple statistics experience unacceptable performance (for example, in terms of spectrum

Network interoperability

Distributed & affordable
computing resources

AI opportunities
for wireless
networks

Predictive network
characteristics

Massive data availability

Fig. 1. Favorable conditions for the adoption of machine intelligence techniques in the next generation wireless networks.

and energy efficiency, coverage, delay and cost) [1],
[2].
The rapidly growing number of machine-typecommunication (MTC) devices contribute a considerable portion on the complexity of this ultra-dense network. Many of the future MTC applications supported
by 5G and beyond will require the underlying wireless
networks to achieve high availability, reliability and
security, very short transit time and low latency [3].
Furthermore, in such use cases, uninterrupted and
safe operation is often the top priority (for instance,
connected vehicles). Taking a MTC application offline
for any reason can cause significant business loss or
non-tolerable customer experience, and many of the
MTC devices are resource-constrained and will not be
able to rely solely on their own limited resources to
fulfill their processing demands [4].
As a result, these latency critical applications cannot
be moved to the network controller or cloud due to delay, bandwidth, or other constraints. Moreover, the data
sets generated from these devices will be extremely
diverse and may have large-scale missing (inaccurate)
values [5]. In addition, a number of new data hungry
MTC immersive use-cases will arise including wearables, virtual realities, intelligent product and supportsystems where most of them will use built-in backend data infrastructure and analytics engine to provide
context-aware services. All these necessitates the next
generation network (i.e. 5G and beyond) to adopt an
intelligent and context-aware approach for network
planning, design, analysis, and optimization.
We are in the beginning phase of an intelligent era
that has been driven by the rapid evolution of semiconductor industries, computing technologies, and diverse
use cases. This is witnessed by the tight integration of
networked information systems, sensing and communication devices, data sources, decision making, and

cyber physical infrastructures. The proliferation of tiny
wireless sensors and MTC devices, and smart phones
also show clear evidences for the exceptional processing capability and cost-effectiveness of semiconductor devices. These promising developments facilitate
distributed computing resources not only in the cloud
but also in the fog and edge nodes. Both fog and
edge computing attempt pushing the intelligence and
processing capabilities down closer to where the data
originates.
The edge computing aims to integrate intelligence
and processing power capabilities closest to the original data source. The edge node, for example, intelligent programmable automation controllers (PACs),
determines which raw input data should be stored
locally or sent to the fog (cloud) for further analysis.
On the other hand, in the fog computing, all the raw
input data will first be converted to the appropriate
Internet protocol (such as HTTP) before sending to
the fog nodes. Thus, higher-level data contents are
processed, stored and sent to the cloud for further analysis in the fog devices (for example, intelligent routers,
access points, Internet of things (IoT) gateways). Thus,
the edge and fog enabled network allows distributed
computing, storage, control, communication and networking functions by reducing the data transmitted and
work load of the cloud, latency and system response
time especially for applications demanding localized
and location dependent information [6]. Moreover, the
node, user, sensor, or MTC device is potentially capable of generating row and processed data at different
granularity levels which ultimately help the network
to have a massive amount of data likely exhibiting
some pattern. This will help different nodes to leverage data mining and analytics techniques to predict
relevant network metrics such as user mobility, traffic
behavior, network load fluctuation, channel variations,
and interference levels.
All these opportunities enable efficient and flexible
resource allocation and management, protocol stack
configuration, and signaling procedure and physical
layer optimization, and facilitate existing devices to
harness the powers of sensors, edge, fog and cloud
based computing platforms, and data analytics engines
[7]–[9]. These also create favorable conditions to engineer a tightly integrated wireless network by adopting
the AI principles (see Fig. 1) incorporating learning,
reasoning and decision making mechanisms which are
crucial to realize context-awareness capability. A typical next generation network utilizing the AI principles

at different nodes is shown in Fig. 2. Towards this end,
the current paper provides a comprehensive survey
on the utilization of AI integrating machine learning,
data analytics and natural language processing (NLP)
techniques for enhancing the efficiency of wireless
systems. We particularly focus on the utilization of
these techniques for efficient wireless data acquisition
and knowledge discovery, planning, and operation and
management of the next generation wireless networks.
A brief case study showing the utilization of AI
techniques for this network has also been provided.
The paper is organized as follows. In Section II,
we discuss data acquisition and knowledge discovery
approaches used in AI enabled wireless. Then, a comprehensive discussion on how these knowledge can be
used in network planning, operation and management
of the next generation wireless is given in Sections
III and IV. A case study discussing the applications
of AI techniques for channel impulse response (CIR)
prediction and context-aware data transmission is then
provided in Section V. Finally, conclusions are drawn
in Section VI.
II. DATA ACQUISITION AND K NOWLEDGE
D ISCOVERY
Efficient data acquisition and knowledge discovery
is one of the requirements of future wireless networks
as it helps to realize situation aware and optimized
decisions as shown in Fig. 3. The gathered data may
need to be processed efficiently to extract relevant
knowledge. Furthermore, as the available data may
contain a large amount of erroneous (missing) values,
a robust knowledge discovery may need to be devised
[5].
A. Data Acquisition
The AI based tools relying on machine learning for
the input data mining and knowledge model extraction
at different levels could be applied [10]. This includes
the cell level, cell cluster level and user level. In
general, one can collect data from three main sources;
network, user, and external. The network data characterizes the network behavior including outage and
usage statistics of services or nodes, and load of a
cell. The user data could comprise user subscription
information and user device type. And, the external
data contains a user specific information obtained
from different sources such as sensors and channel
measurements [11].

One way of collecting wireless data is by employing
content caching where the idea is to store popular
contents at the network edge (at BSs, devices, or
other intermediate locations). In this regard, one can
enable the proactive cache type if the traffic learning algorithm predicts that the same content will be
requested in the near future [12], [13]. Moreover,
since different users may request the same content
with different qualities, each edge node may need to
cache the same content in different granularity (for
example, caching video data with different resolution).
This further requires the edge device to apply coded
(adaptive) caching technique based on the quality of
service (QoS) requirement of the requester [14]. Coded
caching also enables devices to create multicasting
opportunities for certain contents via coded multicast
transmissions [15].
In some cases, a given edge (fog) may collect date
from more than one sources with different connectivity criteria [16]. In a fog enabled wireless network,
this is facilitated by employing IoT devices which
leverage a multitude of radio-access technologies such
as wireless local area network (WLAN) and cellular
networks. In this regard, context-aware data collection
from multiple sources probably in a compressed format by employing appropriate dimensionality reduction techniques under imperfect statistical knowledge
of the data while simultaneously optimizing multiple
objective functions such as delay and transmission
power can be enabled [13].
B. Knowledge Discovery
Efficient knowledge discovery is critical for optimized operation and management of the network. The
network may need to use a novel learning technique
such as deep learning to extract the hidden contextual
information of the network which are crucial for
knowledge base (KB) creation. In general, context is
related to any information used to characterize the
situation of an entity, including surrounding location,
identity, preferences, and activities. Context may affect
the operation and management procedures of complex
systems at various levels, from the physical device
level to the communication level, up to the application level [17]. For instance, uncovering the relation
between the device and network information (user
location, velocity, battery level, and other medium
access control (MAC) and higher layer aspects) would
permit adaptive communication and processing capa-

IoT
edges

Cloud

Microwave
mmWave

Fog

Fog

BS

BS

Cellular edges

ITS
edges

Fig. 2. Typical next generation network adopting AI principles with learning, reasoning and decision making.

Network, user,
external data

Data acquisition
Knowledge discovery
Reasoning and decision
Optimized
network
Fig. 3. Optimized network design with AI techniques.

bilities based on the changes in the environment and
application [2], [17].
Analyzing wireless data contextually (semantically)
facilitate wireless operators to optimize their network
traffic. To realize semantic aware traffic optimization,
however, the network may need to understand the
content of the signal. One way of understanding the
information content of a data is by creating semantic
aware ontology using predefined vocabulary of terms
and concepts [18]. The ontology specification can
provide an expressive language with many logical
constructs to define classes, properties and their relationships. In this regard, the authors of [18] propose

a semantic open data model for sensor data called
MyOntoSens and write using ontology web language
2 description logic language. The proposed KB has
been implemented using protégé and pre-validated
with pellet reasoner. In a similar context, an ontology
for wireless sensor networks (WSNs) dedicated to the
description of sensor features and observation has been
presented in [19], [20].
Understanding the context also helps to produce
a context-aware compressed (summary) information
which will utilize less radio resource for transmission.
For instance, if a BS would like to transmit text
information to a user, the BS can transmit only its
contextual coded data. The user will, then, extract
the desired content just from the context by utilizing
appropriate decoder and big-data analytics technique
such as NLP. As context differs from the user’s world
knowledge about the content, the coding and decoding
technique may vary among users [17]. In general,
two types of content summarizing approaches are
commonly adopted; abstractive and extractive. The
extractive approach uses only the relevant content
from the original information whereas, the abstractive
approach may use new words (expressions or contents) as part of the summary information [21], [22].
Although most of the existing methods can extract
useful information for the summary, they are very
far from generating human understandable summary.
One of the main reasons is the loose associations

and unordered information distribution which make it
hard to extract syntactically correct and semantically
coherence information from the summary. In fact,
modeling the coherence of information summary is
one of the active areas of research [21].
III. N ETWORK P LANNING
One of the most critical aspects determining the performance of a wireless network is the initial planning.
This includes infrastructure (node deployment), frequency, number of parameters and their configuration
setting procedures, and energy consumption to run the
network in both idle (no data communication takes
place between the network and a user) and active (data
communication takes place between the network and
a user) conditions. A well planned network may need
to guarantee satisfactory user QoS (in terms of data
rate, reliability and latency) and the network operator
requirements (in terms of cost). The machine learning
technique can be used for planning different parts of
the network by utilizing the network and user data.
A. Node Deployment, Energy Consumption and RF
Planning
The future generation wireless networks will be
extremely dense and heterogeneous, equipped likely
with moving and flying BSs, and featured by continually varying network conditions [23]. This fact makes
the existing network planning techniques, which are
mainly static and designed from expensive field tests,
not suitable for the future wireless networks [2], [24].
The utilization of AI techniques for network planning
has recently received an interest in the research community. For instance, a machine learning technique
is suggested for content popularity and request distribution predictions of a cache where its design in
the network considers several factors including cache
placement and update strategy which are determined
by utilizing the users’ content request distribution and
frequency, and mobility pattern [25].
In [26], an AI based system which leverages graph
theory based problem formulations for the fiber to
home network is proposed to automate the planning
process. To solve the problems, mixed integer linear
programming (MILP), ant colony optimization (ACO)
and genetic algorithm (GA) have been applied. The
authors of [10] employ the principles of AI for radio
access network (RAN) planning which includes new
cell, radio frequency (RF) and spectrum of the 5G

wireless. The analysis is performed by processing input data from multiple sources, through learning based
classification, prediction and clustering models, and
extracting relevant knowledge to drive the decisions
made by 5G network.
Wireless networks contribute an increasing share in
the energy consumption of the information communications technology (ICT) infrastructure. Over 80%
of a wireless network power consumption is used by
the RANs, especially at the BS, since the present BS
deployment is designed on the basis of peak traffic
loads and generally stays active irrespective of the
huge variations in traffic load [27]. This makes the
current energy planning inefficient for the future smart
cities aiming to realize green communication. To enable energy efficient wireless, different AI techniques
have been suggested. For instance, the authors of
[28] propose a method to realize the efficient use
of electricity by autonomously controlling network
equipments such as servers, air-conditioners in an
integrated manner. Furthermore, the authors of [29]
suggest predictive models, including neural network
and Markov decision, for the energy consumption of
IoT in smart cities. Along this line, a BS switching
solution for traffic aware greener cellular networks
using AI techniques has also been discussed in [27].
B. Configuration Parameter and Service Planning
The number of configurable parameters in cellular networks fairly increases when moving from one
generation to the next. For instance, in a typical 3G
and 4G nodes these parameters are around 1000 and
1500, respectively. It is anticipated that this trend will
continue and the recently suggested 5G network node
will likely have 2000 or more parameters. In addition,
unlike the current and previous generation networks
which provides static services, the next generation network may need to support the continuously evolving
new services and use cases, and establish sufficient
network resource and provisioning mechanisms while
ensuring agility and robustness. These necessitate
the next generation network to understand parameter variations, learn uncertainties, configure network
parameters, forecast immediate and future challenges,
and provide timely solutions by interacting with the
environment [27]. In this direction, the utilization of
big data analytics has been discussed for protocol stack
configuration, signaling procedure and physical layer
procedure optimizations in [9].

Future smart cities require well planned wired and
wireless networks with ubiquitous broadband connectivity, and flexible, real time and distributed data processing capability. Although most modern cities have
multiple cellular networks that provide adequate coverage and data processing capability, these networks
often have limited capacity and peak bandwidths and
fail to meet the real time constraint of different emerging tactile applications. These make the realization
of advanced delay critical municipal services envisioned in a smart city (e.g., real-time surveillance,
public safety, on-time advisories, and smart buildings)
challenging [1]. One way of addressing this challenge
would be by deploying an AI integrated fog based
wireless architecture which allows data processing of
the network using a number of distributed nodes.
This will help for analyzing network status, detecting
anticipated faults and planning new node deployment
using AI techniques [1].
IV. N ETWORK O PERATION AND M ANAGEMENT
Energy and spectrum efficiency, latency, reliability,
and security are the key parameters that are taken
into account during the network operation stage. And
properly optimizing these parameters usually yield
satisfactory performance for both the service providers
and end users. In addition, these optimization parameters usually require simple and real time learning and
decision making algorithms.
A. Resource Allocation and Management
Different AI techniques have been proposed for
resource allocation, management and optimization of
wireless networks such as cellular, wearable, WSN,
body area network (BAN) [24]. In [30], the potential of AI in solving the channel allocation problem in wireless communication is considered. It is
demonstrated that the AI based approach has shown
better performance than those of randomized-based
heuristic and genetic algorithms (GAs). In [31], radio access technology (RAT) selection utilizing the
Hopfield neural networks as a decision making tool
while leveraging the ability of AI reasoning and the
use of multi-parameter decision by exploiting the
options of IEEE 802.21 protocol is proposed. A machine learning based techniques including supervised,
unsupervised, and reinforcement learning techniques,
have been exploited to manage the packet routing in
many different network scenarios [32]. Specifically, in

[33], [34], a deep learning approach for shortest traffic
route identification to reduce network congestion is
presented. A deep learning technique aiming to shift
the computing needs from rule-based route computation to machine learning based route estimation
for high throughput packet processing is proposed in
[32]. Along this line, a fog computing based radioaccess network which exploits the advantage of local
radio signal processing, cooperative radio resource
management, and distributed storage capability of fog
has been suggested to decrease the load on the front
haul and avoid large scale radio signal processing in
the centralized baseband controllers [1].
The utilization of unlicensed spectrum as a complement to licensed one receives an interest to offload network traffic through the carrier aggregation
framework, while critical control signaling, mobility,
voice and control data will always be transmitted on
the licensed bands. In this aspect, the authors of [35]
propose a hopfield neural network scheme for multiradio packet scheduling. The problem of resource
allocation with uplink-downlink decoupling in a long
term evolution-unlicensed (LTE-U) system has been
investigated in [36] for which the authors propose a
decentralized scheme based on neural network. The
authors in [37] propose a distributed approach based
on Q-learning for the problem of channel selection
in an LTE-U system. Furthermore, in a multi-RAT
scenario, machine learning techniques can allow the
smart use of different RATs wherein a BS can learn
when to transmit on each type of frequency band based
on the network conditions. For instance, one can apply
a machine learning to predict the availability of a line
of sight (LoS) link, by considering the users’ mobility
pattern and antenna tilt, thus allowing the transmission
over the millimeter wave band.
B. Security and Privacy Protection
The inherent shared nature of radio propagation
environment makes wireless transmissions vulnerable
to malicious attacks, including eavesdropping and
jamming. For this reason, security and privacy protection are fundamental concerns of today’s wireless
communication system. Wireless networks generally
adopt separate security level at different layers of the
communication protocol stack. Furthermore, different
applications usually require different encryption methods [42]. The utilization of AI techniques for wireless
security has received a significant interest.

TABLE I
M AIN I SSUES IN AI- ENABLED W IRELESS N ETWORK
Ref.
[13]
[14], [15]
[18]–[20]
[5]
[10], [26]
[38]
[27], [28]
[1], [9]
[30], [31]
[1], [32]
[35]
[39]–[41]
[5], [38]

7.4

Average SE (bps/Hz)

Data acquisition and knowledge discovery
• Context-aware data acquisition from single/multiple sources
• Coded (adaptive) caching
• Semantic-aware Ontology (KB) creation from network data
• Robust knowledge discovery from erroneous (missing) data
Network planning
• Node deployment and radio frequency allocation
• Caching and computing placement and content update
• Energy consumption modeling and prediction (idle/active)
• Parameter and service configuration procedure
Network operation and management
• Resource allocation: RAT and channel selection,
packet routing, distributed storage and processing,
multi-RAT packet scheduling
• Security: Spoofing attack and intrusion detection
• Latency: Context-aware edge computing and scheduling

7.2

7

6.8

Predicted CIR: Analytical (RLS)
Predicted CIR: ML (Linear regression)
Perfect CIR

6.6
1

2

3

4

5

6

7

8

CIR prediction index (in OFDM block)

In [39], a spoofing attack detection scheme using
random key distribution based artificial immune system (AIS) has been proposed. In a similar path, an
approach based on GA and AIS, called GAAIS, for
dynamic intrusion detection in mobile ad-hoc network
(MANETs) is suggested in [41]. In [40], advanced
detection of intrusions on sensor networks (ADIOS)
based intrusion detection and prevention system is
developed. The ADIOS is designed to mitigate denialof-service attacks in wireless sensor networks by capturing and analyzing network events using AI and
an expert system developed using the C language
integrated production system tool. In a similar work,
the authors of [43] propose AI based scheme to secure
the communication protocol of connected vehicles.
C. Latency Optimization for Tactile Applications
The next generation wireless networks are featured by several mission critical (tactile) applications
such as lane switching in automated vehicles. For
vehicular networks, different levels of automation has
been defined by the USA department of transportation
(DOT) ranging from a simple driver assistance (level
1) to the full automation mode (level 5). For this
application, one can apply different message representations including warning alarm, picture and audio
information to request intervening. In fact, recently it
is demonstrated via experiment that the use of natural
language generation techniques from imprecise data
improves the human decision making accuracy. Such
a linguistic description of data could be designed by
modeling vague expressions such as small and large,
which are norms in daily life conversation, using fuzzy
logic theory [44]. All these facilitates the utilization of
predictive machine learning as in [45], [46].

Fig. 4. Comparison of analytical and machine learning (ML)
approaches in terms of achieved average SE for different future
OFDM block index CIR.

From the computing side, edge devices can be
used for effective low-latency computations, using the
emerging paradigm of mobile edge computing. However, optimizing mobile edge computing faces many
challenges such as computing placement, computational resource allocation, computing task assignment,
end-to-end latency, and energy consumption. In this
aspect, a machine learning technique can be used to
address these challenges by utilizing historical data.
Predicting computational requirements enable the network devices to schedule the computational resources
in advance to minimize the global latency. In this
aspect, the authors of [38] propose a cross-system
learning framework in order to optimize the long-term
performance of multi-mode BSs, by steering delaytolerant traffic towards WiFi. Furthermore, in a fog
enabled wireless system, latency can be addressed by
exploiting different levels of awareness at each edge
network. In fact, a number of learning techniques can
be applied to achieve these awareness including incremental, divide and conquer, parallel and hierarchical
[5]. A brief summary of different issues in the AIenabled wireless network is presented in Table I.

V. D ESIGN C ASE S TUDIES
This section discusses typical design case studies
in which the AI techniques can be applied for the
context-aware wireless network.

A. Machine Learning for CIR Prediction
This study demonstrates the utilization of machine learning tools for optimizing wireless system
resources. In this aspect, we select the wireless CIR
prediction as the design objective. To solve this design
objective, the first possibility could be to apply different analytical CIR prediction techniques (for example,
the recursive least square (RLS) prediction proposed in
[45]). The second possibility could be to predict future
CIRs by leveraging the past experience. The former
possibility is very expensive particularly when real
time prediction is needed. Furthermore, in most cases,
the analytical prediction approach may fail whenever
there is a modeling error or uncertainty. The latter
possibility, however, is simple as it employs the past
experience and applies standard vector multiplication
and addition operations [47]. This simulation compares the performances of RLS and machine learning
prediction approaches. For the machine learning, we
employ the well known multivariate linear regression.
For the comparison, we consider an orthogonal
frequency domain multiplexing (OFDM) transmission
scheme where a BS equipped with N antennas is
serving a single antenna IoT device. The CIR is
modeled by considering a typical scenario of the IEEE
802.11 standard with channel correlation both spatially
and temporarily. The spatial channel covariance matrix
is modeled by considering the uniform linear array
(ULA) structure, and the temporal channel correlation
is designed by the well known Jake’s model [48].
The number of multipath taps L = 4, fast Fourier
transform (FFT) size M = 64, N = 8, OFDM
symbol period Ts = 166µs, RLS window size Sb = 8,
forward prediction window size Sf = 8, carrier frequency 5.6GHz, and mobility speed of the IoT device
30km/hr. The signal to noise ratio (SNR) for each subcarrier is set to 10dB. With these settings, Fig. 4 shows
the average spectrum efficiency (SE) obtained by the
RLS and machine learning approaches for sub-carrier
s = 4. In both cases, the achieved SE decreases as
the future OFDM-block index increases. This is expected since the number of unknown CIR coefficients
increase as the future block index increases leading
to a degraded CIR prediction quality. However, for
a fixed future prediction index, the machine learning
approach yields better performance than the RLS one1 .
1

Note that similar average performance is observed for other
sub-carriers.

Sample Traffic Data
1
13%

9%

3%

19%

2
3

56%

4
5

Fig. 5. Data traffic of a sample abstract information: In this figure,
(Sets) 1 to 5 denote, Background, Objective, Method and Result,
Conclusion, and Related and Future Works, respectively.

B. Context-Aware Data Transmission using NLP Techniques
Context (semantic) aware information transmission
is crucial in the future generation network. To validate
this, we employ abstract texts from scientific articles
[49]. According to this paper, each scientific abstract
text consists of different types of information including
research background, methodology, main results etc.
Fig. 5 shows the expert annotated data size for different types for 2000 biomedical article abstracts. As can
be seen from this figure, different information types
use different portions of the overall data set. And for
a given user, one can transmit the desired information
according to the context. For instance, for a user who
is interested in the basics about the article, transmitting the background information could be sufficient
which accounts for only 9% of the total traffic. This
shows that semantically enabled data transmission
will reduce the network traffic while simultaneously
maintaining the desired QoS experience of users.
Such a transmission, however, is realized when
sentences of similar types are clustered correctly for
each abstract. In scientific papers, the location and
part-of-speech voice of a sentence are crucial features
to identify its class set [49]. We have employed these
features with the commonly used data clustering algorithms (i.e., K-means and Agglomerative) and present
the accuracy achieved by these algorithms for each
type as shown in Table II. As can be seen from this
table, different clustering algorithms yield different
accuracy. One can also notice from this table that
significant research work may need to be undertaken

TABLE II
ACCURACY OF DIFFERENT CLUSTERING METHODS
Clustering Method
K-Means
Agglomerative

Set 1
0.34
0.21

Set 2
0.17
0.18

Set 3
0.35
0.38

Set 4
0.31
0.30

Set 5
0.16
0.15

to reach the ideal performance.
VI. C ONCLUSIONS
The next generation wireless networks, which would
be more dynamic, complex with dense deployment
of BSs of different types and access technologies,
poses many design challenges for network planning,
management and troubleshooting procedures. Nevertheless, wireless data can be generated from different sources including networked information systems,
and sensing and communication devices. Furthermore,
the emerging fog computing architecture aiming for
distributed computing, storage, control, communication, and networking functions closer to end users
contribute for the efficient realization of wireless systems. This article provides a comprehensive survey
on the utilization of AI integrating machine learning,
data analytics and NLP techniques for enhancing the
efficiency of wireless networks. We have given a
comprehensive discussion on the utilization of these
techniques for efficient wireless data acquisition and
knowledge discovery, planning, operation and management of the next generation wireless networks. A brief
case study showing the utilization of AI techniques has
also been provided.
R EFERENCES
[1] A. Munir, P. Kansakar, and S. U. Khan, “IFCIoT: integrated
Fog Cloud IoT: A novel architectural paradigm for the future
internet of things,” IEEE Consumer Electronics Magazine,
vol. 6, no. 3, pp. 74–82, July 2017.
[2] T. E. Bogale and L. Le, “Massive MIMO and mmWave for
5G wireless HetNet: Potential benefits and challenges,” IEEE
Vehic. Techno. Mag., vol. 11, no. 1, pp. 64 – 75, Mar. 2016.
[3] M. Weiner, M. Jorgovanovic, A. Sahai, and B. Nikoli,
“Design of a low-latency, high-reliability wireless communication system for control applications,” in Proc. IEEE Int.
Conf. Commun. (ICC), 2014, pp. 3829 – 3835.
[4] M. Chiang and T. Zhang, “Fog and IoT: An overview of
research opportunities,” IEEE Internet of Things, vol. 3, no.
6, pp. 854 – 864, Dec. 2016.
[5] X. Wang and Y. He, “Learning from uncertainty for big data:
Future analytical challenges and strategies,” IEEE Systems,
Man, and Cybernetics Magazine, vol. 2, no. 2, pp. 26–31,
April 2016.

[6] Ruilong Deng, Rongxing Lu, Chengzhe Lai, Tom H Luan,
and Hao Liang, “Optimal workload allocation in fog-cloud
computing toward balanced delay and power consumption,”
IEEE Internet of Things Journal, vol. 3, no. 6, pp. 1171–
1181, 2016.
[7] M. Chiang, S. Ha, C. L. I, F. Risso, and T. Zhang, “Clarifying
Fog computing and networking: 10 questions and answers,”
IEEE Communications Magazine, vol. 55, no. 4, pp. 18–20,
April 2017.
[8] Y. Sun, H. Song, A. J. Jara, and R. Bie, “Internet of things
and big data analytics for smart and connected communities,”
IEEE Access, vol. 4, pp. 766 – 773, 2016.
[9] S. Han, C. L. I, G. Li, S. Wang, and Q. Sun, “Big data
enabled mobile network design for 5G and beyond,” IEEE
Communications Magazine, vol. PP, no. 99, pp. 2–9, 2017.
[10] J. Prez-Romero, O. Sallent, R. Ferrs, and R. Agust,
“Knowledge-based 5G radio access network planning and
optimization,” in 2016 International Symposium on Wireless
Communication Systems (ISWCS), Sept 2016, pp. 359–365.
[11] J. Prez-Romero, O. Sallent, R. Ferrs, and R. Agust, “Artificial intelligence-based 5G network capacity planning and
operation,” in 2015 International Symposium on Wireless
Communication Systems (ISWCS), Aug 2015, pp. 246–250.
[12] E. Bastug, M. Bennis, and M. Debbah, “Living on the edge:
The role of proactive caching in 5G wireless networks,” IEEE
Communications Magazine, vol. 52, no. 8, pp. 82–89, Aug
2014.
[13] E. Bastug, M. Bennis, M. Medard, and M. Debbah, “Toward
interconnected virtual reality: Opportunities, challenges, and
enablers,” IEEE Communications Magazine, vol. 55, no. 6,
pp. 110–117, 2017.
[14] G. Paschos, E. Bastug, I. Land, G. Caire, and M. Debbah,
“Wireless caching: Technical misconceptions and business
barriers,” IEEE Communications Magazine, vol. 54, no. 8,
pp. 16–22, August 2016.
[15] Y. Fadlallah, A. M. Tulino, D. Barone, G. Vettigli, J. Llorca,
and J. M. Gorce, “Coding for caching in 5G networks,” IEEE
Commun. Magazine, vol. 55, no. 2, pp. 106 – 113, 2017.
[16] D. Ohmann, A. Awada, I. Viering, M. Simsek, and G. P.
Fettweis, “Achieving high availability in wireless networks
by inter-frequency multi-connectivity,” in 2016 IEEE International Conference on Communications (ICC), May 2016,
pp. 1–7.
[17] F. Chiti, R. Fantacci, M. Loreti, and R. Pugliese, “Contextaware wireless mobile autonomic computing and communications: Research trends and emerging applications,” IEEE
Wireless Commun., vol. 23, no. 2, pp. 86 – 92, Apr. 2016.
[18] L. Nachabe, M. Girod-Genet, and B. El Hassan, “Unified
data model for wireless sensor network,” IEEE Sensors
Journal, vol. 15, no. 7, pp. 3657–3667, July 2015.
[19] R. Bendadouche, C. Roussey, G. De Sousa, J. Chanet, and
K-M. Hou, “Extension of the semantic sensor network ontology for wireless sensor networks: The stimulus-WSNnodecommunication pattern,” in Proc. 5th Int. Conf. Semantic
Sensor Networks - Vol. 904, 2012, pp. 49–64.
[20] M. Compton, A. A. Henson, L. Lefort, H. Neuhaus, and A. P.
Sheth, “A survey of the semantic specification of sensors,”
in Proc. CEUR Workshop, 2009, pp. 17–32.
[21] E. Cambria and B. White, “Jumping NLP curves: A review
of natural language processing research,” IEEE Comp.
Intelligence Mag., pp. 48 – 57, May 2014.
[22] T. Hirao, M. Nishino, Y. Yoshida, J. Suzuki, N. Yasuda,
and M. Nagata, “Summarizing a document by trimming the

[23]

[24]

[25]

[26]

[27]

[28]

[29]

[30]

[31]

[32]

[33]

[34]

[35]

discourse tree,” IEEE/ACM Trans. Audio, Speech, and Lang.
Process, vol. 23, no. 11, pp. 2081 – 2092, Nov. 2015.
M. Alzenad, A. El-Keyi, F. Lagum, and H. Yanikomeroglu,
“3-D placement of an unmanned aerial vehicle base station
(UAV-BS) for energy-efficient maximal coverage,” IEEE
Wireless Communications Letters, vol. 6, no. 4, pp. 434–437,
Aug 2017.
Z. Fadlullah, F. Tang, B. Mao, N. Kato, O. Akashi, T. Inoue,
and K. Mizutani, “State-of-the-art deep learning: Evolving
machine intelligence toward tomorrow’s intelligent network
traffic control systems,” IEEE Communications Surveys
Tutorials, vol. PP, no. 99, pp. 1–1, 2017.
M. Chen, M. Mozaffari, W. Saad, C. Yin, M. Debbah, and
C. S. Hong, “Caching in the sky: Proactive deployment
of cache-enabled unmanned aerial vehicles for optimized
quality-of-experience,” IEEE JSAC, vol. 35, no. 5, pp. 1046
– 1061, 2017.
K. F. Poon, A. Chu, and A. Ouali, “An AI-based system
for telecommunication network planning,” in 2012 IEEE
International Conference on Industrial Engineering and Engineering Management, Dec 2012, pp. 874–878.
R. Li, Z. Zhao, X. Zhou, G. Ding, Y. Chen, Z. Wang, and
H. Zhang, “Intelligent 5G: When cellular networks meet
artificial intelligence,” IEEE Wireless Communications, vol.
PP, no. 99, pp. 2–10, 2017.
K. Awahara, S. Izumi, T. Abe, and T. Suganuma, “Autonomous control method using AI planning for energyefficient network systems,” in 2013 Eighth International
Conference on Broadband and Wireless Computing, Communication and Applications, Oct 2013, pp. 628–633.
W. Ejaz, M. Naeem, A. Shahid, A. Anpalagan, and M. Jo,
“Efficient energy management for the internet of things in
smart cities,” IEEE Communications Magazine, vol. 55, no.
1, pp. 84–91, January 2017.
S. I. Suliman, G. Kendall, and I. Musirin, “Artificial
immune algorithm in solving the channel assignment task,”
in 2014 IEEE International Conference on Control System,
Computing and Engineering (ICCSCE 2014), Nov 2014, pp.
153–158.
V. Rakovic and L. Gavrilovska, “Novel RAT selection mechanism based on Hopfield neural networks,” in International
Congress on Ultra Modern Telecommunications and Control
Systems, Oct 2010, pp. 210–217.
B. Mao, Z. M. Fadlullah, F. Tang, N. Kato, O. Akashi,
T. Inoue, and K. Mizutani, “Routing or computing? the
paradigm shift towards intelligent computer network packet
transmission based on deep learning,” IEEE Transactions on
Computers, vol. PP, no. 99, pp. 1–1, 2017.
N. Kato, Z. M. Fadlullah, B. Mao, F. Tang, O. Akashi,
T. Inoue, and K. Mizutani, “The deep learning vision for
heterogeneous network traffic control: Proposal, challenges,
and future perspective,” IEEE wireless commun. Mag, vol.
24, no. 3, pp. 146–153, 2017.
F. Tang, B. Mao, Z. M. Fadlullah, N. Kato, O. Akashi,
T. Inoue, and K. Mizutani, “On removing routing protocol
from future wireless networks: A real-time deep learning
approach for intelligent traffic control,” IEEE Wireless
Commun. Mag, 2017.
Y. Cui, Y. Xu, R. Xu, and X. Sha, “A multi-radio packet
scheduling algorithm for real-time traffic in a heterogeneous

[36]

[37]

[38]

[39]

[40]

[41]

[42]

[43]

[44]

[45]

[46]

[47]
[48]
[49]

wireless network environment,” Information Technology
Journal, vol. 10, pp. 182 – 188, Oct. 2010.
M. Chen, W. Saad, C. Yin, and M. Debbah, “Echo state
networks for proactive caching in cloud-based radio access
networks with mobile users,” IEEE Trans. Wireless Commun., vol. 16, no. 6, pp. 3520 – 3535, Jun. 2017.
J. Perez-Romero, O. Sallent, R. Ferrus, and R. Agusti, “A robustness analysis of learning-based coexistence mechanisms
for LTE-U operation in non-stationary conditions,” in Proc.
of IEEE Vehicular Technology Conference (VTC Fall), Sep.
2015.
M. Bennis, M. Simsek, A. Czylwik, W. Saad, W. Valentin,
and M. Debbah, “When cellular meets WiFi in wireless small
cell networks,” IEEE Commun. Magazine, vol. 51, no. 6, pp.
44 – 50, 2013.
E. S. Kumar, S. M. Kusuma, and B. P. V. Kumar, “A
random key distribution based artificial immune system for
security in clustered wireless sensor networks,” in Electrical,
Electronics and Computer Science (SCEECS), 2014 IEEE
Students’ Conference on, March 2014, pp. 1–7.
V. F. Taylor and D. T. Fokum, “Securing wireless sensor networks from denial-of-service attacks using artificial
intelligence and the clips expert system tool,” in 2013
Proceedings of IEEE Southeastcon, April 2013, pp. 1–6.
F. Barani, “A hybrid approach for dynamic intrusion detection in ad hoc networks using genetic algorithm and artificial
immune system,” in 2014 Iranian Conference on Intelligent
Systems (ICIS), Feb 2014, pp. 1–6.
Y. Zou, J. Zhu, X. Wang, and L. Hanzo, “A survey on
wireless security: Technical challenges, recent advances, and
future trends,” Proceedings of IEEE, vol. 104, no. 9, pp.
1727 – 1765, Sept. 2016.
P. Sharma, H. Liu, H. Wang, and S. Zhang, “Securing
wireless communications of connected vehicles with artificial
intelligence,” in 2017 IEEE International Symposium on
Technologies for Homeland Security (HST), April 2017, pp.
1–7.
D. Gkatzia, O. Lemon, and V. Rieser, “Data-to-text generation improves decision-making under uncertainty,” IEEE
Computational Intelligence Magazine, vol. 12, no. 3, pp. 10–
17, Aug 2017.
T. E. Bogale, X. Wang, and L. B. Le, “Adaptive channel
prediction, beamforming and scheduling design for 5G V2I
network,” in Proc. IEEE Veh. Technol. Conf (VTC-Fall).,
Sep. 2017.
K. Zhang, Y. Mao, S. Leng, Y. He, and Y. ZHANG, “Mobileedge computing for vehicular networks: A promising network paradigm with predictive off-loading,” IEEE Vehicular
Technology Magazine, vol. 12, no. 2, pp. 36–44, June 2017.
T. E. Bogale, X. Wang, and L. B. Le, “Adaptive channel
prediction, beamforming and feedback design for 5G V2I
network,” in IEEE (In preparation for submission), 2017.
D. Schafhuber and G. Matz, “MMSE and adaptive prediction
of time-varying channels for OFDM systems,” IEEE Tran.
Wireless Commun., vol. 4, no. 2, pp. 593–602, March 2005.
Y. Guo, A. Korhonen, I. Silins, and U. Stenius, “A weaklysupervised approach to argumentative zoning of scientific
documents,” in Proc. of Conf. on Empir. Methods in Nat.
Lang. Process., 2011, pp. 273 – 283.

