Received: 28 January 2021
Revised: 5 July 2021
Accepted: 10 August 2021
DOI: 10.1002/ett.4352
S U R V E Y PA P E R
Machine learning for cooperative spectrum sensing and
sharing: A survey
Dimpal Janu1
Kuldeep Singh1
1
Department of Electronics and
Communication Engineering, Malaviya
National Institute of Technology, Jaipur,
India
Sandeep Kumar2
Abstract
With the rapid development of next-generation wireless communication technologies and the increasing demand of spectrum resources, it becomes nec-
2
Central Research Laboratory, Bharat
Electronics Ltd, Ghaziabad, India
Correspondence
Sandeep Kumar, Central Research
Laboratory, Bharat Electronics Ltd,
Ghaziabad, Uttar Pradesh, India.
Email: sann.kaushik@gmail.com
essary to introduce learning and reasoning capabilities in cognitive radio networks (CRN). In particular, our focus is on two fundamental applications in
CRNs, namely spectrum sensing (SS) and spectrum sharing. The application
of machine learning (ML) techniques has added new aspects to SS and spectrum sharing. This paper offers a survey on various ML-based algorithms in
the cooperative spectrum sensing (CSS) and dynamic spectrum sharing (DSS)
domain, with its emphasis on types of features extracted from primary user
signal, types of ML algorithm, and performance metrics utilized for evaluation of ML algorithms. Starting with the basic principles and challenges
of SS, this paper also justifies the applicability of supervised, unsupervised,
and reinforcement ML algorithms in the CSS domain. The application of
ML algorithms, to solve the DSS problem has also been reviewed. Finally,
the survey paper is concluded with some suggested open research challenges
and future directions for ML application in next-generation communication
technologies.
1
I N T RO DU CT ION
Global data traffic has been increasing since the past years and is expected to grow faster; mobile subscribers will
increase from 5.1 billion in 2018 to 5.7 billion in 2023.1 According to a Cisco report, mobile devices will grow
from 8.8 billion in 2018 to 13.1 billion in 2023. 5G connection speed will be 13 times higher than the average
mobile connection and is expected to be 575 Mbps by 2023. Along with the significant growth in data traffic, new
applications of wireless communications such as IoT, wearable devices, etc., are continuously generating a huge
amount of data. To address the spectrum scarcity problem and utilize available energy resources, the future wireless communication system must support intelligent processing. This has led to research in the field of cognitive
radio (CR).
The CR is an intelligent device that senses its electromagnetic environment and can dynamically change/configure
its radio operating parameters.2 The concept of CR came into existence to overcome the scarcity problem of limited radio
spectrum by exploiting the underutilization of the available spectrum.3 The CR devices opportunistically utilize the spectrum allocated to primary user (PU) when PU is detected idle by it. Due to shadowing4,5 and multi-path fading,6 the
quality of the PU signal received at the secondary user (SU) is degraded, leading to wrong sensing decisions about the
presence/absence of PU. In certain situations, due to the geographical constraint, the SU cannot receive the signal of
Trans Emerging Tel Tech. 2022;33:e4352.
https://doi.org/10.1002/ett.4352
wileyonlinelibrary.com/journal/ett
© 2021 John Wiley & Sons, Ltd.
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the PU, which is known as the hidden node problem. To overcome these issues, the collaborative spectrum sensing (SS)
is performed, which improves the system’s sensing performance.7,8 CR technologies have capabilities to provide intelligent spectrum management to fulfill the ever-increasing demand for spectrum resources. Figure 1 presents the complete
spectrum management framework and it shows the four main functions of cognitive radio network (CRN).
a) Spectrum sensing: First step is to discover the spectrum holes on the arrival of SUs. SS enables to detect spectrum
holes (in terms of duration of availability, frequency and location) and PU activity by continuously sensing the
spectrum.
b) Spectrum sharing: As the multiple SUs are trying to access the available spectrum, there is possibility of collision
among the SUs. Spectrum sharing deals with coordination among the SUs to access the spectrum.
c) Spectrum decision: It includes spectrum analysis by choosing the appropriate spectrum bands as per the users’ quality
of service requirement and decision making. After the spectrum analysis, a decision has been made to access the
vacant spectrum bands.
d) Spectrum mobility: Main function of spectrum mobility is spectrum handoff and connection management. SUs need
to switch from one spectrum hole to another on the arrival of PU. Connection management ensures continuous data
transmission from SU in new spectrum hole.
As mentioned above, the various applications of CR are briefly discussed. In this paper, our main focus is on the two
fundamental applications such as SS and spectrum sharing.
In the cooperative spectrum sensing (CSS) framework, SUs of a CRN exchange their sensing results with a
common receiver known as a fusion center (FC), making the final decision for PU’s presence or absence. There
are various conventional SS techniques such as energy detection, matched filter detection, cyclostationary detection, etc. These techniques require the optimum threshold to detect the presence of PU and each design has a
different algorithm for the calculation of the threshold value. If the threshold is too high, then it can lead to the
probability of missed detections. If the threshold is too low, it can lead to the probability of false alarms. After
completing the process of detecting the spectrum holes, the concept of spectrum sharing was introduced to solve
the spectrum scarcity problem. Spectrum sharing is a means to optimize spectrum usages by enabling the sharing of the same frequency band among the multiple users having different priorities without causing interference
to each other. The main advantages of spectrum sharing are increment in capacity and improvements in spectrum
utilization.
Therefore, to alleviate the problems encountered in conventional SS techniques and dynamic spectrum sharing (DSS), machine learning (ML)-based frameworks are employed for CSS and DSS. The ML algorithms can solve
FIGURE 1
Spectrum management framework for CR network
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complex problems by analyzing and interpreting structures and patterns in data as they are capable of learning, reasoning, and decision making. Applying ML algorithms in CRN increases perception capability and reconfigurability
as well as automation level and network flexibility.9 The three types of ML algorithms: unsupervised, supervised,
and reinforcement learning (RL) have been utilized for SS in recent years. Various traditional SS techniques are
being used, but ML has been introduced in SS to enhance the probability of detection and accuracy of the
system.
1.1
Existing survey papers
To the best of our knowledge, an exclusively survey paper on ML-based CSS and DSS is not presented to date. This survey
is very specific to the application of ML techniques for CSS and DSS. All the existing survey papers which have a brief
discussion on ML-based CSS methods have been discussed here. Alshawaqfeh et al10 discussed ML applications for CSS,
learning engine, power allocation, and modulation classification in CRN. Bkassiny et al11 characterized the learning problems in CRs and highlighted the importance of ML in developing practical CRs. In Reference 12, authors have reviewed
how intelligent wireless networks can be created with the help of CR and how ML algorithms can be utilized to improve
the spectrum efficiency and energy efficiency of the wireless networks. To enhance the intelligence in future wireless
communication networks, authors have discussed different ML approaches, environment perception, interference sensing, modeling, multi-dimensional SS, and spectrum measurements. Arjoune and Kaabouch13 provide an overview of
advancement in SS technique and discuss the methods that describe the incorporation of compressive sensing and ML
into CRN. Table 1 lists the existing survey papers in which ML-based algorithms for CSS and DSS have need discussed in
a subsection.
In our work, we have presented a detailed discussion on the ML-enabled CSS and DSS techniques from the perspective
of various features used, different ML algorithms and performance evaluation metrics and recent development in DSS
domain as well. We have reviewed 45 papers related to CSS and 10 papers related to DSS utilizing machine learning
algorithms in the recent years 2010-2021.
1.2
Key contributions of the work
In this paper, we present a comprehensive survey of the key research work on two applications of CR namely CSS and
DSS using ML algorithms. Our main contributions can be summarized as follows:
• Various ML-based techniques have been recently applied in the field of CSS to provide efficient solutions to the challenges associated with the traditional CSS methods. Therefore, before introducing the ML-based CSS methods, the
conventional approaches are briefly discussed first.
T A B L E 1 Existing survey papers
Existing
survey paper
Number of papers employing ML
Major area discussed
CSS
DSS
Duration
10
Utilization of ML algorithms for power allocation,
modulation classification, and for CSS
6
0
2010-2013
11
A study of ML algorithms and characterization of
their applications in CRN
1
0
2009
12
A detailed study on CR technology such as
spectrum sensing, spectrum access and
resource allocation and application of ML
algorithms in CRN and future wireless
communication systems
7
0
2010-2014
13
Advancement of ML techniques, Incorporation of
ML and compressive sensing
10
3
2013-2017
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• The ML algorithms have been extensively used for solving the issues in CSS in this decade. However, to the best of our
knowledge, a survey paper reviewing the advances of ML in the field of CSS is not presented to date. We are presenting
the first survey paper exclusively for ML-based CSS techniques.
• The classification of CSS techniques based on the different categories of ML algorithms is presented. An extensive
review of the feature computation methodologies for implementing ML techniques is also presented in this survey
paper.
• We have also catered to the performance evaluation perspective of ML-based CSS techniques. Various metrics that have
been utilized for performance evaluation of these algorithms are reviewed and discussed.
• Along with CSS, we have projected light on DSS domain with emphasizing on ML-based spectrum sharing techniques,
which are the key features to improve the spectral efficiency and energy efficiency of a CRN.
The rest of the paper is organized as follows. In Section 2, traditional approaches for CSS have been discussed. Section 3
elaborates on the application of ML in the field of CSS. In this section, different types of ML, such as supervised, unsupervised, and reinforcement ML used for SS in the literature is discussed along with a brief introduction of these techniques
and in the subsection of this Section 3, the reviewed papers are characterized based on the feature computation techniques
and the evaluation metrics utilized for performance comparison. In Section 4, DSS with fundamental approaches along
with the application of ML into DSS have been discussed. In the last, conclusions and future scope are discussed for building intelligent wireless communication systems. In order to improve the readability of this paper, we define abbreviations
in Table 2.
T A B L E 2 Nomenclature
Acronyms
Definitions
A3C
Asynchronous advantage actor critic
AWGN
Additive White Gaussian Noise
ACF
Auto correlation function
ANN
Artificial neural networks
BSs
Base stations
BCDLN
Bidirectional cognitive deep learning nodes
BP-HMM
Beta process-hidden Markov model
CA
Classification accuracy
CSS
Cooperative spectrum sensing
CR
Cognitive radio
CRN
Cognitive radio network
CH
Cluster heads
CNN
Convolutional neural network
CN
Cognitive node
CRTP
Convex relaxation with tree pruning
CSI
Channel state interference
CVAE
Conventional variational autoencoder
DL
Deep learning
DNN
Deep neural network
DSS
Dynamic spectrum sharing
DSLSTM
dynamic spectrum sharing long short term memory
DPPO
distributed proximal policy optimization
DT
Decision tree
(Continues)
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T A B L E 2 (Continued)
Acronyms
Definitions
DMM
Difference between maximum and minimum eigenvalue
DRL
Deep reinforcement learning
DMEAE
Difference between maximum eigenvalue and average energy
DAR
Decomposition and recombination
ECOC
Error correcting output codes
EM
Expected maximum
EMD
Empirical mode decomposition
FCM
Fuzzy C-means
FD
Full duplex
FC
Fusion center
FFA
Firefly algorithm
GAC
Genetic algorithm clustering
GMM
Gaussian mixture model
GPU
Graphics processing unit
ICA
Independent component analysis
IQ
In-phase quadrature-phase
IoT
Internet of things
I-DAR
Interval decomposition and recombination
KNN
k-Nearest neighbors
LTE
Long-term evolution
MAC
MAC Medium access control
MDP
Markov decision process
MME
Ratio of maximum and minimum eigenvalue
MSE
Difference between the maximum eigenvalue and mean eigenvalue
ML
Machine learning
NP
Non-parallel
NSGA
Non-dominated sorting genetic algorithm
NSGA-RL
Non-dominated sorting genetic algorithm reinforcement learning
OFDM
Orthogonal frequency division multiplexing
O-DAR
Order decomposition and recombination
PCA
Principle component analysis
RMET
Ratio of maximum eigenvalue to matrix trace
RL
Reinforcement learning
ROC
Receiver operating characteristic
SS
Spectrum sensing
SVM
Support vector machine
SNR
Signal-to-noise ratio
SVD
Singular value decomposition
SAS
Spectrum access sharing
SARSA
State action reward state
SU
Secondary User
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2
C S S M ET H O D S
In this section, various SS techniques, such as conventional SS and advanced SS techniques with their mathematical
models, have been discussed. Figure 2 highlights the classification of several SS techniques that handle many challenging
problems related to SS in CRN.
2.1
Conventional SS techniques
Before starting the data transmission, a SU must ensure the availability of the spectrum. In CR networks, a SU identifies the PU activity without creating any disturbance to the network infrastructure of the PU. A hypothesis testing
problem can be analyzed where we consider H0 hypothesis for the absence of PU’s signal and H1 hypothesis for the
presence of PU’s signal. This problem can be considered as similar to a binary classification problem in the context
of ML.
The model for the analysis of the SS problem14 can be expressed as:
y[i] = w[i]; H0 when PU is absent
(1)
y[i] = h ∗ s[i] + w[i]; H1 when at least one PU′s signal is present
(2)
i = 1, 2, 3, 4, … … ..N
where y[i] represents the received signal at the SU node, s[i] is the transmitted signal by the PU node, and w[i] denotes
additive White Gaussian noise (AWGN) with zero mean and variance 𝜎 2 , h is the channel gain, i is the sensing time, and N
Energy detection
Cyclostationary feature
detection
Conventional spectrum
sensing techniques
Matched filter detection
Waveform based detection
Cooperative
Spectrum
Sensing
Wideband compressive
Adaptive compressive
Advanced spectrum
sensing techniques
Covariance based
Machine Learning
FIGURE 2
Cooperative spectrum sensing techniques
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FIGURE 3
Traditional CSS model
represents the number of sampling points. The sensing techniques’ performance can be evaluated in terms of probability
of detection and probability of false alarm. We can define the probabilities as:
pf = p(H0 ∕H1 )
(3)
pd = p(H0 ∕H0 )
(4)
Figure 3 depicts various components and their work in a traditional CSS model. Different conventional SS techniques
are discussed in the following subsections.
2.1.1
Energy detection-based sensing technique
The energy detection at SU is the most straightforward technique which does not require prior information of PU’s signal characteristics.15,16 The main advantage of this technique is low implementation and computational complexities in
comparison with other sensing techniques.17,18 This method estimates the energy of samples received and compares it
with a predefined threshold value to check the spectrum’s availability. If the received signal’s energy is lower/higher than
the threshold value, PU is considered absent/present.19 However, this technique’s major challenge is to determine the
threshold value for the identification of PU activity without causing any interference to it. Energy estimated at SU node
can be defined as
1∑
|y[n]|2
N n=1
N
XED =
The mathematical expressions are as below.
XED < 𝜆 ∶ H0 is considered.
XED > 𝜆 ∶ H1 is considered.
(5)
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There are certain issues with the energy detection technique such as noise uncertainty, fading effect of the channel
between the PU and SU, etc. Moreover, this method does not give good performance for the communication system
that operates on low signal-to-noise ratio (SNR). The performance of the energy detection-based method is analyzed in
literature by considering different fading models.
2.1.2
Cyclostationary feature detection-based sensing technique
The statistical parameters of signal, such as modulation rate and carrier frequency, are periodic in nature and are considered as cyclostationary features. If the mean and the auto correlation function (ACF) of the received signal are periodic,
then the signal is called a cyclostationary signal. The periodicity of mean and ACF of a signal is utilized by cyclostationary
features-based detectors to accomplish the sensing task. Mathematically, periodicity of mean and ACF can be defined as:
R𝛿y (𝜏) = R𝛿y (n + N0 , 𝜏)
(6)
my (n) = E[y(n)] = my (n + 𝜏)
(7)
∑
N−1
my (n) =
[y[n]e−j2𝜋𝛿n ]
(8)
n=1
R𝛿y (𝜏) = E[y[n]∗ y[n + 𝜏]ej2𝜋𝛿n ]
(9)
where, E[.] represents expectation operator, N0 represents time period of received signal, 𝜏 represents time period, 𝛿
represents cyclic frequency. R𝛿y (𝜏) represents autocorrelation function, and my (n) represents mean of the received signal
y[n].
Compared to the energy detection technique, the cyclostationary feature detection technique is less sensitive to
noise uncertainty and, therefore, has less probability of false alarm. Further, this technique can detect low SNR signal values. The cyclostationary features-based approach provides better detection performance than the energy detector.
This is because noise is stationary and has no correlation and be easily discriminated from the signal by calculating the spectral correlation function.20,21 The noise signals are uncorrelated and non-periodic in nature, so it is
easy for cyclostationary detection to distinguish between the PU signal and noise as the PU signal exhibits periodic
properties. This technique’s major drawback is that it needs more power consumption, processing complexity, and
sensing time.
2.1.3
Matched filter-based sensing technique
The Matched filter-based sensing technique requires complete information about the characteristics of the signal transmitted by the PU.22,23 The PU signal’s principal properties are various modulation techniques, operating frequency,
order, bandwidth, frame structure, etc. In this technique, there is a requirement of SU to demodulate the received signal. As the PU signal information is available to SU, this technique is most preferred to recognize the PU activity.
The received signal at SU can be correlated with the parameters of PU’s signal. Mathematically test statistics can be
defined as:
1∑
=
y[n]xp∗ (n)
N n=1
N
TMF
(10)
where, xp∗ (n) represents test samples collected from PU transmitter, N represents the total number of samples. A
comparison is made between the test statistics and the predefined threshold value to determine PU’s presence.
TMF < 𝜆 ∶ H0 is considered.
TMF ≥ 𝜆 ∶ H1 is considered.
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This technique uses a dynamically calculated threshold value rather than the static threshold used in the
above-discussed methods. The main disadvantage of this technique is that the SU must have accurate knowledge about
the spectrum, but this is difficult to acquire information about the spectrum.
2.1.4
Waveform-based sensing technique
This technique is known as coherent sensing. Synchronization can be made at the receiver end by using well-known
patterns. These known patterns are called waveform and consist of various key elements such as spreading sequence,
hopping sequence, pilot signal patterns, etc. To perform waveform-based sensing, the same copy of the signal is used to
correlate the signal at the receiver.24
2.2
Advanced SS techniques
The conventional sensing techniques deal with a single frequency spectrum band at a particular time. However, the recent
advancements in sensing techniques identify multiple frequency spectrum bands by cooperation among multiple SUs and
improve sensing precision. Moreover, conventional sensing techniques require more sensing time, but advanced sensing
techniques can reduce the sensing time with cooperative decision-making. Despite having advantages of these techniques,
there are few challenges, such as overhead problems caused due to the sharing of sensing results among SUs and the
presence of malicious SUs in the CR network, which affects the cooperation and reduces the network’s reliability. Various
recent advancements in the sensing techniques are discussed in the following subsections, which overcome conventional
sensing techniques’ limitations.
2.2.1
Wideband compressive sensing
As the advanced technologies in wireless communication systems require high data rates and increased bandwidth,
SU is necessary to sense a wide frequency spectrum band. Because of hardware limitations on the sampling rate, the
above-discussed techniques sense only a single frequency channel at a time. This technique is employed when signal
bandwidth is more significant than the coherence bandwidth. In compressive sensing, sparse signals are recovered from
a few measurements.25 In wideband SS, the band is divided into multiple frequency channels, and each channel is sensed
concurrently. But this approach requires very high sensing time, hardware cost, and computational complexity. To reduce
the sensing time and to expedite the sensing process, compressive sensing was introduced. Compressive sensing, which
includes sampling and compressing in one step, is an acquisition process in which signal is compressed with the necessary information and reconstructed by sampling at the Nyquist rate or less than the Nyquist rate.26 To perform this
process, there are three primary stages; the first one is the sparse representation of signals. In the second stage, signals
are compressed and sampled. In the final step, reconstruction algorithms are performed to recover the wideband signal.
2.2.2
Adaptive compressive sensing technique
To sense the wideband spectrum by using an appropriate number of measurements, the adaptive compressive sensing
technique27 utilize compressed sensing features. The main idea behind adaptive compressive sensing is that the signal
can be reconstructed using a sub-Nyquist sampling rate or if a band is sparse in nature. In this technique, wideband
signals are recovered in blocks from various mini-slots, and the spectrum is reconstructed from compressed samples.
To perform SS within a frequency band, a specific time slot from the complete periodic time is utilized for sensing, and
remaining time slots are utilized for data transmission. Consider that a SU aims to detect the spectrum holes within a
particular frequency band (from 0-W Hz). The complete time period T is considered, where from 0 to 𝜏 sec used for
SS and the remaining time period from 𝜏 to T second reserved for data transmission. The continuous signal received
at frontend of SU mainly consists of PU signal and noise signal as all SU keep quiet to follow medium access control
(MAC) protocols during sensing time. If the sampling rate is fN over the observation time 𝜏, the discrete time samples are
obtained as:
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(
y(n) = yc
n
fN
)
, n = 0, 1, 2, 3, 4.., N − 1
(11)
where N = 𝜏fN is considered a natural number.
2.2.3
Covariance-based sensing technique
Covariance-based sensing technique employs the covariance matrix of received signal and singular value decomposition
(SVD) to detect the presence of PUs.28,29 Ideally, correlations of noise signals would be zero if the AWGN channel is
assumed, and the correlations of received signals would be non-zero if calculated at different time instances. The PU
signal can be easily differentiated from noise signals as they have zero correlation. A sample covariance matrix of the
received signal can be evaluated, and the covariance matrix’s eigenvalues are determined by using SVD method.30,31 The
sample covariance matrix can be calculated using the following expression:
Ry (N) =
L−2+Ns
1 ∑
̂
y(n)̂
yT (n)
N n=L−1
(12)
From the covariance, matrix eigenvalues can be calculated. A test statistic is calculated by taking the ratio between the
maximum eigenvalue Emax to the minimum eigenvalue Emin of the covariance matrix. To decide between test hypotheses,
the test statistics is compared with a threshold value 𝛾c . The test statistics can be calculated as:
Tcov [y] =
Emax
Emin
(13)
If Tcov [y] < 𝛾c ∶ H0 is considered.
else Tcov [y] < γc ∶ H1 is considered.
where 𝛾c is a predetermined threshold value.
2.2.4
ML-based sensing techniques
To make the spectrum resources available to all the users, there should be more intelligence involved in the CRN. To
cater this requirement, the ML techniques have been used to solve various problems in CRN.32,33 ML can provide a
Machine Learning
Supervised Learning
SVM, KNN, Naïve
bayes,
decision trees , linear
regression,
ANN, Bayesian Learning
FIGURE 4
Summary of ML algorithms for CSS
Unsupervised Learning
K-Means, PCA,
GMM, Fuzzy C-means,
K-medoids ,
Genetic Algorithm
Reinforcement Learning
Markov Decision Process,
Multi-agent learning
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promising solution to this problem and can introduce intelligence to CR technology. ML algorithms can solve complex
problems by analyzing and interpreting structures and patterns in data as they are capable of learning, reasoning, and
decision making. Application of ML algorithms in CR networks increases perception capability and reconfigurability
and automation level, and network flexibility. This section is aimed to provide an extensive survey of the state-of-the-art
performance of the ML algorithm in the field of CSS. Figure 4 portrays the family tree of ML algorithms utilized
in CSS.
3
MACHINE LEARNING FO R CSS
ML algorithms provide systems the ability to learn from experiences while performing a particular task T, aiming to improve the task’s performance measured by performance metric P by using the experience E.34 ML-based
algorithms are broadly classified into three categories supervised learning, unsupervised learning, and RL. In supervised learning techniques, a task is performed by learning from training data which are provided with corresponding labels or outputs. The goal is to derive an objective function based on the labeled training data to predict
the correct output for future training samples. In contrast to supervised learning techniques, unsupervised learning
exploits unlabeled training data aimed to extract hidden features in the input data. The RL technique is used when
some information about the environment is known, and a decision-making agent learns and adapts to the environment. The agent’s main objective is to take appropriate actions and maximize the reward by interacting with the
environment.
ML algorithms are used for pattern classification, where a feature vector extracted from the pattern is fed into the
classifier, which determines a particular class of the feature vector.14 SS process can be formulated as a binary classification
problem that can be solved by a supervised or unsupervised ML algorithm, where the classifier has to determine between
the availability and unavailability of the two possible states of the radio frequency channel. To detect the availability of
the spectrum, the classifier makes use of energy vectors or probability vectors, also referred to as a feature vector in the
context of ML. Figure 5 highlights an ML-based CSS framework. In this section, various studies and research papers have
been reviewed related to ML’s application for SS in the CRN.
FIGURE 5
ML-based CSS model
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3.1
System architecture and paradigms
Considering P number of PUs, where PUs are indexed as p = 1, 2, 3, … .P, and M number of SUs indexed as m =
1, 2, 3 … .M. The SUs are assumed to be located in a d-dimensional feature space. SUs can be equipped with a single
antenna or multiple antennae. Each SU senses the channel and then reports to the FC, which makes the final decision
about the availability of spectrum holes in the case of CSS. The FC estimates feature vector based on the sample values
received from all SUs. For feature vector realization, different methodologies are described further in detail. If at least
one PU is active, the channel is unavailable to access. The channel is considered available when all the PUs is inactive in
transmission. The channel availability A can be defined as
A=
{
}
− 1, when at least one PU active (channel unavailable)
1, when all PUs are inactive (channel available)
(14)
Let Ym (i) is the ith signal sample observed by mth SU. Ym (i) received at the respective SU is summation of all the
signals from all the PUs14 can be defined as:
⎧𝜂 (i)
H0 ∶ When PU signal is absent ⎫
⎪
⎪ Pm
Ym (i) = ⎨ ∑
⎬
⎪ hp,m Sp (i) + 𝜂m (i) H1 ∶ when PU signal is present⎪
⎭
⎩p=1
(15)
where hp,m represents the channel gain from the pth PU to mth SU, Sp (i) is the signal transmitted by the pth PU, 𝜂m (i) is
the noise signal, and 𝜔 represents the bandwidth of the signal. Various methods are used for feature realization such as
principle component analysis (PCA) analysis to remove the noise, energy detection, covariance matrix realization, etc. To
achieve high detection probability, it becomes necessary to select unique and relevant features. The FC constructs feature
vector, then ML and deep learning (DL) models are employed for CSS. The feature vector can be realized as by using
various preprocessing techniques such as PCA, energy detection-based methods.
3.2
3.2.1
ML techniques
Supervised ML techniques
In the case of supervised ML algorithms, the attributes of input and output datasets are labeled, and the objective of
these algorithms is to build a model to map the input to the output. The supervised ML tasks can be further divided
into a classification or regression tasks if the output is discrete or continuous. SS problem can be formulated as a
classification problem as the output is a binary value representing channel availability. Assuming there are L number
of training data points z = {z(1), z(2), z(3) … ..z(L)} with corresponding output labels in terms of channel availability or unavailability a = {a(1), a(2), a(3) … ..a(L)} are fed into the classifier for training as this method requires prior
information related to channel availability for each training feature vector. Now when an unknown data point z(i)
is presented to the trained model, the ML algorithm has to classify it to one of the class a(i), that is, channel available (a(i) = 1) or unavailable class (a(i) = −1). The supervised ML algorithms such as support vector machine (SVM),
k-nearest neighbors (KNN), Bayesian networks, and artificial neural network (ANN) used in SS are introduced before discussing their CSS applications. Table 3 summarizes the use of a supervised ML algorithm for CSS along with the features
used.
The SVM algorithm is mainly used for classification tasks as the spectrum availability variable can only a discrete binary value. In the SVM algorithm, training vectors can be separated from a linear hyperplane. This algorithm
aims to find a maximal margin hyperplane with the help of support vectors (training vectors nearest to the decision
surface) to separate the training feature vectors into two classes. When training vectors are not linearly separable,
different kernel methods are used for non-linear transformation of input feature vectors. Hence, mapping the training vectors to a higher dimensional feature space by a non-linear function makes the training feature vectors linearly
separable. SVM algorithm with the linear kernel is easy to train for the input feature vectors which are linearly
separable.
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T A B L E 3 Supervised ML methods for CSS
References
Supervised ML technique
Features
14
KNN, SVM-Linear, SVM-Polynomial
Energy statistics
35
SVM, KNN, decision tree, Naïve Bayes classifier
Energy statistics
36
SVM
Probability vector
37
SVM with FFA, naive Bayesian classifier, decision
trees, SVM, linear regression
Spectrum status
38
Two-Phase SVM
Energy statistics
39
Kernel-SVM
Eigenvalues
40
SVM
Derived from eigenvalues
41
Non-Parallel-Fuzzy SVM
Energy statistics
42
Multi-class SVM
Energy statistics
43
SVM
Energy statistics
44
SVM
Derived form received samples
45
Naïve Bayes Classifier
Energy statistics
46
Bayesian Learning
Energy statistics
47
Bayesian Learning
Energy statistics
48
SVM, Logistic regression, K-NN, Gaussian Naive
Bayes, and Decision Trees
Power and SNR levels
49
SVM
Energy statistics
50
ANN
Energy statistics and Zhang statistic
from the likelihood ratio statistic
The KNN is also a classification algorithm that is based on the majority voting of nearest neighbors. In KNN, K refers
to the number of nearest neighbors involved in the majority voting process. This algorithm is based on the feature vectors
similarity, classifies a new test vector based on the class of its majority voting of neighbors. To determine a test vector class,
the KNN classifier finds K number of nearest training vectors among the whole training set based on a specific distance
measure. To find K number of neighbors, the test vector’s distance from all training vectors is calculated and then sorted
in ascending order. The classifier then chooses the first K training vectors as nearest neighbors and calculates the number
of neighbors corresponds to channel available class and channel, unavailable class. Whichever count is maximum, that
particular class is assigned to the test vector.
Artificial neural networks (ANNs) are very effective tools to solve complex real-world problems as they can adapt
to signals’ non-linear characteristics. ANNs are constituted by a set of layers, including one input layer, one or more
hidden layers, and one output layer. Both the hidden and output layers are composed of neurons. Hidden layers
receive the input vector and output layer and calculate the system’s output through a non-linear activation function. The ANNs learn specific parameters of the complete network from the given training data set. The modern
DL architectures such as deep neural network (DNN) and convolutional neural network (CNN) are derived from
the ANNs.
The decision tree (DT) classifier forms a binary tree based on the node error splitting rule to classify input
data points. The DT classifier has two types of nodes; one is a decision node that makes decisions and has multiple branches, whereas the second node is a leaf node, which is the output of those decisions and represents class
labels.
Naïve Bayes classifier is an independent feature model where the classes are independent of each other, and the prior
probabilities for the given classes are computed from the given training examples. To estimate the probability of a test data
point belonging to a particular class, “posterior probability” is calculated using the Naïve Bayes assumption and Bayes
rule.
Bayesian learning, which has roots from Bayes theorem, formulate the knowledge about the situation probabilistically.
The model specifies the prior probabilities of unknown parameters and based on the available examples, the posterior
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probability distribution for the parameters is computed. These posterior distributions are further utilized for making
decisions or conclusions about a new data point.
Thilina et al14 proposed a CSS model in CRN using supervised learning techniques such as SVM, weighted KNN
algorithm. The energy vector estimated at SUs is treated as a feature vector and fed to the classifiers to detect whether
the channel is available or not. The SVM classifier with linear kernel for linear inputs provides better detection probability and requires lesser training and classification delay. Mikaeil et al35 proposed an ML-based algorithm for a data
FC to train the model per frame in real-time and detect the channel availability. The performance of four supervised
ML algorithms KNN, SVM, Naive Bayes, and DT were compared and concluded that KNN and DT classifiers outperform the other two classifiers in terms of accuracy. In Reference 36, a low-dimensional probability vector is treated
as a feature vector along with SVM classifier to accomplish the task of CSS. Making use of low dimensional probability vector instead of N-dimensional energy vector, this method requires less training time, less classification time, and
provides equal classification accuracy (CA). Azmat et al37 have presented a spectrum occupancy model in CRN using
various ML algorithms such as DT, SVM, SVM with firefly algorithm, and linear regression to achieve higher CA where
spectrum status is considered as an input feature vector. Ghazizadeh et al38 utilized two-phase SVM to enhance SVM
performance in terms of detection probability and misclassification risk. The energy levels of PUs are treated as a feature vector to train the classifier. In Reference 39, sample covariance matrix eigenvalue estimation-based features and
the SVM classifier are blended for SS in the multi-antenna CRs framework. Coluccia et al40 experimented in terms of
features by mapping the received signal to high-dimensional feature space and further classified using SVM. The feature vector consists of decision statistics such as energy detection, maximum-minimum eigenvalues ratio, and their
higher-order combinations. In Reference 41, a fuzzy SVM algorithm with non-parallel hyper-plane is utilized to address
the performance issues of conventional parallel hyper-plane SVM algorithm degraded due to the noise uncertainty, which
causes overlapping of different classes. To reduce the noise uncertainty effect, this approach introduces fuzziness in
decision-making for SS with the Kernel shadow C-means algorithm’s help. The performance of different supervised classification algorithms, that is, SVM, Logistic regression, KNN, Gaussian Naives Bayes, and DT, were compared based on
power and SNR features in Reference 48. Li et al49 tackled the issue of cooperation overhead through user grouping. The
idea is to group the CR users based on energy data samples and the SVM model before their participation in the CSS
process, which brings safety and reduces the redundancy in the network. In Reference 50, a hybrid SS scheme was presented, which uses a combination of classical energy detection and Likelihood Ratio Test statistics-based features to train
the ANN.
The above-discussed methods have attempted only binary classification problems, which are limited to temporal SS
(detecting unused spectrum bands at a specific time). However, it is also essential to detect spectrum holes both at a particular time and geographic location in SS. In Reference 42, authors have addressed the joint spatio-temporal SS problem
under multiple PUs scenarios. The binary classification problem was reformulated as multiple class signal detection problems, where each class further sub-categorized into one or more categories representing system states. Error correcting
output codes (ECOC)-based multi-SVM algorithm is used for Spatio-temporal SS. The work was further extended in Reference 43, for multi-antenna SUs. Multi-state signal detection problem under multiple PUs scenario is solved by using
multiple antennas with beamforming capabilities and ECOC-based multi-class SVM algorithms. The Beamforming-based
preprocessing technique increases the received SNR in order to increase the detection capability of the SVM algorithm. A
CSS model has been proposed based on multiple class hypotheses to maximize the throughput of secondary links.44 The
problem can be solved using the SVM algorithm with linear, polynomial, and radial basis kernel. The multi-class hypothesis can be classified based on the quantized regions of sensed energy from the received signal and residual energy of the
secondary node’s battery.
In contrast to the above-mentioned multi-class problem, the hypothesis that PU is absent is further sub-divided
based on sensed energy and residual energy of SU node. The input feature vector is composed of three statistical features extracted from the received PU signal and the percentage of residual energy as the fourth feature. The authors
claim that this is the first work to select such type of features. SVM with polynomial kernel provides higher throughput and higher accuracy as compared to the conventional two-class hypothesis-based sensing problem using SVM. Tian
et al45 have addressed the SS problem for the orthogonal frequency division multiplexing (OFDM) system, which is
formulated as a multi-classification problem, adapting to new SNR variations. The average received signal power and
cyclic-prefix-induced correlation are considered two-dimensional signal features and further a naïve Bayes classifier is
trained to detect the spectrum occupancy. A novel class-reduction assisted prediction method is also introduced to reduce
the SS time. The variational Bayesian learning-based CSS model was implemented in Reference 46 for a multi-antenna
SU scenario realizing the energy vector as the features. This novel technique has the advantage of finding the number of
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active PUs. The proposed sensing technique’s performance is quantified in terms of probability of detection vs probability
of false alarm. Another Bayesian ML-based SS method to obtain the global spectrum states was proposed for the challenging scenario of a large-scale heterogeneous network.47 In the earlier SS techniques, the SUs were assumed to be static;
however, this work exploited the mobility of SUs since the mobility of SU could increase the spatial-temporal diversity
of the received signal and increase the sensing reliability and throughput. This framework is based on non-parametric
Bayesian learning, referred to as beta process-hidden Markov model, which finds spatial-temporal correlation within the
time series data acquired by SU to discover the total number of spectrum states. Bayesian inference is carried out to perform group sensing, and then based on the inference results, the proposed novel refinement mechanism identifies the
availability of spectrum globally.
Although the supervised techniques have shown promising results in detecting spectrum availability, various challenges are associated with these algorithms. One main concern is that they require labeled datasets, which is usually
quite difficult in practice. Consequently, some researchers employ unsupervised algorithms to mitigate this problem.
Besides, most of the above-discussed methods have been tested on simulated data. The performance of these algorithms
on real-world data needs to be extensively analyzed.
3.2.2
Unsupervised ML techniques
The unsupervised learning techniques do not require any prior information or labels corresponding to each training
feature vector as supervised learning techniques. These algorithms extract patterns and similarity in the input data
and build profound associations with the help of internalized heuristics. The main function of unsupervised learning is clustering, dimensionality reduction, and determining the distribution of data. A clustering algorithm classifies
the input data so that objects with similar attributes are grouped, forming a cluster. The dimensionality reduction
involves projecting a high-dimensional data to a low-dimensional space. The third category determines the distribution of data or estimates unknown parameters in the distribution. The following subsections briefly explain different
unsupervised learning techniques and their usage in SS. Table 4 summarizes the unsupervised ML algorithms applied
in CSS.
K-means clustering: The process involves grouping data into different groups, where data within a particular group or
cluster have certain similar features. K-means clustering is one of the most popular centroid-based clustering algorithms
used to classify unlabeled data. A dataset of L samples having the form {xi }li=1 , where each sample is N-dimensional,
is used for K-means clustering. Here K represents the number of clusters, and the word “means” represents an average of all data points in a particular cluster, that is, also referred to as centroid of the cluster. The clusters are formed
such that the cost function, the sum of distances of sample data points in a particular cluster to the centroid, is
minimized. K-means clustering algorithm has been extensively used in SS with a wide variety of features derived
from received signal at SU such as Energy Vector,14,51 Eigen Values derived features52,53,55-57 and Probability vector.36
K-medoids clustering technique54,58 with the variant of K-means is also applied in the SS domain. To improve the sensing performance of the multiple antennae equipped SUs model, genetic algorithm clustering60 is used for decision
statistics.
Dimensionality reduction: Dimensionality reduction techniques are used for data compression, feature extraction
without loss of useful information. The most popular dimensionality reduction technique is PCA and independent
component analysis (ICA). PCA’s goal is to project data from high-dimensional space to lower dimensional space and
extract meaningful information of data that preserves high variance in the data. PCA has been widely used for data
preprocessing for extracting features. In contrast to PCA, ICA aims to preserve maximum statistical independence in
the component. In Reference 52, the K-means clustering algorithm is exploited for CSS, where input feature vectors
are extracted by PCA analysis. The maximum eigenvalue and trace of the eigenvalues obtained from the sample covariance matrix are selected as features. Compared to the energy feature vector, eigenvalue-based features are more robust
to noise.
Gaussian mixture model (GMM): GMM is a widely used clustering algorithm that uses multiple Gaussian distributions
as parameter models according to the number of clusters. The expected maximum (EM) algorithm obtains the most optimal Gaussian distribution parameters by using the training samples. In SS, the PU signal’s presence and absence can be
considered two different Gaussian distributions. In Reference 14, authors have utilized the GMM algorithm for temporal SS. Sobabe et al53 use the GMM algorithm, where the leading eigenvector and maximum and minimum eigenvalues
are considered an input feature vector. Making use of more robust features leads to an increase in the computational
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T A B L E 4 Unsupervised ML methods for CSS
References
Unsupervised ML technique
Features
14
K-means clustering, Gaussian mixture model
Energy detection
36
K-means clustering
Probability vector
41
Kernel shadow C-means
Energy detection
51
K-means clustering
Energy vector
52
K-means clustering
The largest eigenvalue and sum of all the eigenvalues
are feature vector
53
K-means clustering and Gaussian mixture model
Maximum eigenvector, eigenvalues
54
K-means and K-medoids clustering
Derived from eigenvalues
55
K-means clustering
Derived from eigenvalues
56
K-means clustering
Empirical mode decomposition and wavelet
transform, derived from eigenvalues
57
Fuzzy c-means clustering
Derived from eigenvalues
58
K-medoids clustering
Empirical mode decomposition, geodesic distance
59
Fuzzy C-means clustering
Geodesic distance
60
Genetic algorithm clustering
Preprocessed by PCA, Riemann distance
61
Gaussian mixture model
Signal preprocessed by Wavelet transform, derived
value from eigenvalues
62
Fuzzy C-Means clustering
Energy detection
63
Optimal fuzzy c-means clustering
Energy detection (cluster-based approach)
64
Kernel fuzzy c-means clustering
Energy detection (cluster-based approach)
complexity of the system model. Still, the performance of the CSS model outperforms as compared to using conventional
energy-based features.
SS performance is affected due to fading, shadowing, noise, and other interfering factors, reducing the impact of these
factors, multiple antennae equipped SUs-based model is proposed under the k − 𝜇 fading channel.61 Signals received
by SUs are preprocessed by wavelet transform to remove noise from signals, and two-dimensional input features are
formulated with the combination of difference between maximum and minimum eigenvalue (DMM) eigenvalue and ratio
of maximum eigenvalue to matrix trace (RMET) eigenvalue. GMM technique is used for the classification of these signal
features.
Fuzzy C-means (FCM) clustering algorithm: FCM is a clustering algorithm mostly used for pattern recognition. Each
data points assigned a certain degree of membership with respect to all clusters’ center, whose summation should
be one. Kernel FCM enables mapping of inseparable energy data sensed at low SNR to a higher dimensional space
where data can be more easily separated into clusters. In Reference 57, authors have used the FCM algorithm to
classify signal features obtained from the covariance matrices’ eigenvalues. To improve the accuracy of extracted features, signals are preprocessed by the Null Space Pursuit algorithm and in-phase quadrature-phase (IQ) decomposition.
Zhang et al59 have proposed a CSS model based on empirical mode decomposition (EMD) and information geometry theory, where the FCM clustering algorithm is used for the classification of features based on geodesic distances.
As the performance of FCM degrades at low SNR, some variations in FCM63,64 are also applied to address these
problems.
Cluster-based CSS: To make the CR energy efficient, clusters-based CSS approach is utilized. A large number of SUs
in cooperation sense the channel and report the information to the FC, so a considerable amount of energy is wasted. All
SUs involved in CSS share their sensing information (FC); this situation leads to excessive computational overhead for
FC. A cluster-based CSS approach is proposed to overcome this issue, which groups all the SUs in CRN into clusters. The
cluster is formed using ML algorithms, and cluster heads (CHs) are chosen from each cluster. Some selected SUs from
each cluster forward sensing information to CH, which forwards the sensed result to FC. Bhatti et al65 have proposed
a cluster-based CSS model to increase the energy efficiency of the network, where clusters of SUs are formed using a
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FCM clustering algorithm. Further, FCM was utilized for cluster formation of SUs, and the CHs are chosen based on
the channel error detection technique by Bhatti et al.62 In Reference 66, a novel ML affinity propagation algorithm was
proposed for cluster formation of SUs. The affinity propagation algorithm is based on message passing between users, and
thus best delegated SUs are selected for cluster formation. Since the training feature vectors play a crucial role in building
the classification models, it is essential to calculate accurate feature vectors; otherwise, inaccurate training models may
lead to a wrong decision.
3.2.3
Reinforcement learning
RL is useful in control systems to optimize decisions and in the case when some knowledge about an environment is
known and an agent, which is the optimal decision-maker, learns from the environment and adapts to it by interacting
with the surrounding environment. A finite state machine is generally used to model the environment with inputs such
as action received from the agent, and outputs such as rewards and observations sent from environment to agent. The
agent’s primary goal is to achieve optimum decisions by exploiting the known decision and exploring new decisions. In
the RL-based model, an agent interacts with the environment and receives a response as the next state’s reward and the
next action selection. Based on the reward received from the environment, the agent chooses the best action to maximize
the reward. The reward is useful for analyzing the performance of the complete learning process. Figure 6 depicts the
RL-based framework for CSS.
Markov decision process is the most popular RL framework to model the SS problem. An Markov decision process (MDP) problem can be represented by four terms such as ⟨S, A, T, R⟩.Where S represents a set of finite states,
and A represents a set of all actions taken by the agent, T represents state transition probability, and R represents the
reward functions. Action is the decision made by an agent in each state. In this MDP model, the agent (FC) interacts
with SUs present in the environment and requests for local decisions. After receiving the local decisions from selected
SUs, the agent uses majority rule to find the final decision on the presence of PU. The agent receives a reward from
neighboring SU for the next state, and based on the received reward agent, selects the next action. As the sequence
of decisions to be made by an agent on selecting the cooperating SUs, this is the main reason for using MDP. The
agent learns from the sequence of decisions received from selected SUs by exploring the unknown states and receiving the rewards from known states. The two major tasks of MDP and agent are a selection of action and calculation
of reward.
FIGURE 6
Reinforcement learning framework for CSS
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Lo et al67 proposed a CSS model based on MDP to minimize the cooperation overhead problem and energy inefficiency.
This approach finds an optimal set of cooperative SUs that minimize the reporting delay and improve the detection performance. In Reference 68, multi-agent RL-based SS framework has been proposed to discover more number of the available
frequency spectrum as well as to achieve the desired diversity gain. This approach is based on the state action reward
state (SARSA) algorithm and linear function approximation as it reduces the dimensionality of SS state-action space. In a
multi-agent learning-based sensing policy, each SUs employs a reinforcement algorithm. This approach employs user collaboration to find more frequency bands simultaneously, and an action selection algorithm is utilized to select the number
of SUs in collaboration. The work was extended in Reference 69, the multiuser multiband SS problem is formulated as a
partially observable stochastic game and is solved using a multi-agent RL algorithm. This approach aims to maximize the
number of available spectrum bands by addressing the trades off between the sensing reliability and discovering more
frequency channels under the constraint of missed detection probability. It is proved that the proposed model of SS in a
multi-agent scenario is computationally efficient and can be deployed in the networks with a large number of SUs and a
set of a different frequency band.
In contrast to single-agent RL, multi-agent RL addresses more challenges, such as non-stationary and coordination. In
a CR ad-hoc network, to make the network energy efficient and to achieve higher throughput, Oksanen et al70 proposed a
sensing model based on the RL. The sensing policy employs 𝜀-greedy method for selecting frequency bands to be sensed
and for the selection of sensing assignments as well as this sensing policy guides the SU to concentrate the search of
vacant spectrum to the frequencies that provide high data rates. Zhang et al71 proposed a distributed CSS model based on
RL to solve data fusion data between users having different reputations. The main contribution of this work is to reduce
the interference of malicious users by selecting honest users and improves the performance of the whole CRN to make
it more intelligent and stable. By reasoning which ML algorithm has been used and which have not been used for SS,
the provided categorization allows the researchers to identify potential research gaps and, thus, possible future research
directions to explore.
3.3
Characterization of surveyed papers
In this section, we have characterized each reviewed paper based on feature computation, types of SUs (SU devices
equipped with a single antenna or multiple antennas) and the performance metrics used to evaluate the ML algorithm.
3.3.1
Feature computation
In ML-based classification problems, feature computation and feature selection have significant importance. Choosing
appropriate features improves the classifier’s performance in terms of CA, computational cost, and classification time.
A feature selection scheme can be utilized to fulfill the requirement of selecting a better feature combination from the
features pool. In this subsection, we have discussed various feature computation methodologies applied to the sensing
signal received at SU.
Energy detection-based features: The energy of the sensing signal received at SUs is calculated to construct a feature
vector. Energy vector has been considered as a feature vector in various research papers.14,38,42,46,51 Energy features are
realized using beamforming techniques, which increases the SNR of the received signal to enhance the detection capability of classifier-based learning algorithms.43 The SS model’s performance using the energy feature vector is degraded
due to the overlapping of different classes caused by noise uncertainty. The energy vector can be realized as:
Zn =
𝜔t
∑
|Yn (i)|2
(16)
i=1
Z = (Z1 , Z2 … … … ..ZN )T , where energy vector Z can be treated as a feature vector.
The energy vector-based feature computation is easy to implement though they are not robust to noise.
Features derived from Eigenvalues: Various researchers use features derived from eigenvalues obtained from the
sample covariance matrix of received sensing signals as they are more robust to noise uncertainty. In Reference 39,
eigenvalues of the covariance matrix of the received signals at SUs are considered as a feature vector. Sobabe et al53 has
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considered leading eigenvector, maximum and minimum eigenvalues as a feature vector for implementing the unsupervised ML algorithm. To obtain the eigenvalues of the covariance matrix of the received signal can be determined
from Equation (5). Let us consider eigenvalues of the covariance matrix 𝜆max > 𝜆2 > 𝜆3 > 𝜆4 > 𝜆min , where 𝜆max be the
maximum eigenvalue and 𝜆min minimum eigenvalue. The derived features from eigenvalues TDMM , TMME , TRMME , and
TMSE can be calculated from the equations as below. One of the feature TDMM is calculated as difference between maximum and minimum eigenvalue, and another one TMME is calculated as ratio of maximum eigenvalue to minimum
eigenvalue.
TDMM = 𝜆max − 𝜆min
(17)
TMME = 𝜆max ∕𝜆min
(18)
TRMET = 𝜆max ∕trace of covariance matrix
(19)
TMSE = 𝜆max − (Sum of all numer of eigenvalue∕total eigenvalues)
(20)
TRMET is calculated as the ratio of maximum eigenvalue and trace of the covariance matrix and TMSE is calculated
as the difference between maximum eigenvalue and mean of eigenvalues. Further, the feature vector can be derived
from the computed eigenvalue-based features pool T = {TMME , TDMM , TRMET , TMSE } such as a two-dimensional vector or
three-dimensional vector can be defined as {TMME , TDMM }, {TRMET , TMSE , TMME } respectively.
Various preprocessing methods are implemented to reduce the noise and redundant signals from the sensed signal,
such as PCA analysis and EMD. In Reference 52, PCA is applied as a preprocessing step, and further, the maximum eigenvalue and trace of the eigenvalues were obtained from the sample covariance matrix to compute the
features. The two-dimensional input feature vector is obtained from I and Q decomposition of the received signal.
Zhang et al54 used features derived from eigenvalues such as RMET, MME, and MSE, as defined above. In Reference 55 also, the features derived from eigenvalues such as MME, MSE, and RMET were used to train the K-mean
clustering algorithm for SS. Wang et al56 have applied the EMD method and wavelet transform method on the sensing signal to remove the noise component and other interfering signals and further calculated feature vectors as
DMM and DMEAE. In Reference 57, the received signals are preprocessed by Null Space Pursuit algorithm and IQ
decomposition to extract the two- dimensional features, and eigenvalues of covariance matrices are considered as a
feature vector. Zhang et al61 applied a preprocessing technique based on wavelet transform to remove noise from
sensed signals and computes a two-dimensional input features composed of DMM, RMET. The features derived from
eigenvalues are more robust to noise. However, acquiring more robust features leads to an increase in the computational complexity of the system model, but the CSS model’s performance is better compared to using energy-based
features.
Information geometry theory-based features: In the information geometry theory, geodesic distance is the most popularly used method to measure the distance between two probability distributions. This distance can be measured between
two probability distributions on a statistical manifold. The distance between the two points on manifold curvature
depends on the curve’s choice between these points. A curve that minimizes the distance between two points can be
defined as a geodesic, and the related distance is called geodesic distance. Consider any two points 𝜃1 and 𝜃2 on the curve
and the curve between two points can be defined as 𝜃(t) (t1 ≤ t ≤ t2 ). The geodesic distance between two points can be
defined in Reference 72 as
D(𝜃1 , 𝜃2 ) ≜
t2
∫t1
√
(
d𝜃
dt
)T
G(𝜃)
d𝜃
dt
dt
(21)
where G(𝜃) is the Fisher information matrix in geometry theory for statistical matrix, and it can be expressed as
[
G(𝜃) = E
𝜕 ln p(x|𝜃) 𝜕 ln p(x|𝜃)
⋅
𝜕𝜃i
𝜕𝜃j
]
(22)
where, p(x|𝜃) is the probability density function considered in information geometry theory, x denotes the n dimensional
sample related to a random variable, and 𝜃 denotes the parameter vector. In the context of CR technology, the SS signal
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matrix is a multivariate Gaussian distribution family with different covariance matrices and the same mean.73 Assuming
the two covariance matrices R1 and R2 , geodesic distances between them is calculated in Reference 74 as
√
1
−1∕2
−1∕2
tr log2 (R1 R2 R1 )
2
√
√ n
√1∑
log2 𝜂u
D(R1 , R2 ) = √
2 u=1
D(R1 , R2 ) =
−1∕2
(23)
(24)
−1∕2
where, 𝜂u denotes the uth eigenvalue of the matrix R1 R2 R1 .
In the context of CSS, decomposition and recombination (DAR) is applied to the signal matrix perceived at SU to
increase the matrix dimensions. The DAR can be divided into O-DAR and I-DAR. In the process of applying the O-DAR
and I-DAR on the signal vector perceived by the SU, corresponding high-dimensional matrices YO−DAR and YI−DAR and
covariance matrices RO and RI can be calculated.
( )
1
T
RO =
YO−DAR YO−DAR
(25)
s
( )
1
T
(26)
YI−DAR YI−DAR
RI =
s
Here s is the length of the split signal vector after splitting. Now, when the SUs observe K environmental noise matrices,
the corresponding covariance matrices ROk (k = 1, 2 … … K) and RIk (k = 1, 2 … … K) are calculated after processing the
noise matrix using O-DAR and I-DAR. Further, the Riemann mean objective functions are defined as:
K
( )∑
1
D(ROk , R−O )
K k=1
K
( )∑
1
D(RIk , R−I )
Ψ(R−I ) =
K k=1
Ψ(R−O ) =
(27)
(28)
R−O and R−I are the Riemann Mean of RO and RI (minimizes the Ψ(.)) and D(−, −) is the geodesic distance between two
points on the manifold.
For the feature extraction, R−O and R−I are considered as reference points and the geodesic distances from RO to R−O
and RI to R−I are calculated individually.
d1 = D(RO , R−O )
(29)
d2 = D(RI , R−I )
(30)
These geodesic distances represent the distances between the sensing signals and the reference points on the manifold
in the context of information geometry. Further, a feature vector G = [d1 , d2 ] can be constructed for the training of the ML
algorithm. Geodesic distance-based features have been utilized in a few research works.58,59 These methods applied preprocessing on the sensed signal using the EMD method, and the signal detection problem is transformed into geometric
problems by applying information geometry theory. In Reference 60, the authors proposed an information fusion-based
method to avoid complicated matrix decomposition algorithms, as discussed above. Instead of geodesic distance, the Riemann distance is used to extract the signal feature vector. In the information geometry context, the geodesic is considered
as the shortest curve between two points on the manifold; however, the Riemann distance is the length of the geodesic,
which can be used to analyze the similarity between distributions. The Riemann distance is calculated as:
−1∕2
D2 (R1 , R2 ) = ||log2 (Rc
−1∕2
Rd Rc
)||
2
= Tr[log (R−1
c Rd )]
=
n
∑
log2 𝜂u
u=1
(31)
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where, ||.|| denotes the Frobenius norm, Rc and Rd represent the coordinate points on the statistical manifold, Tr[.] denotes
−1∕2
−1∕2
trace of the matrix and 𝜂u denotes the uth eigenvalue of the matrix Rc Rd Rc . For the SS scenario, R1 and R2 can be
considered as the covariance matrices of the divided signal matrix collected at SUs corresponding to authorized channel
and Rw1 and Rw2 can be considered as the corresponding covariance matrices in the absence of PU signal. Further, the
Riemann distances can be calculated using the reference points
dr1 = D2 (Rw1 , R1 )
(32)
dr2 = D2 (Rw2 , R2 )
(33)
Rw1 and Rw2 are the corresponding Riemann mean and considered as a reference point. The two-dimensional signal
feature vector T = [dr1 , dr2 ] can be used to train the models.
Other features: Few researchers have utilized other attributes of the sensing signals received at SU as feature vectors.
Azmat et al37 have used spectrum status vector of all the considered time slots and frequency bins in the CR network. In
Reference 36, a low dimensional probability vector derived from the high dimensional energy vector has been considered
a feature vector for training the SVM classifier and K-means clustering algorithm. Basumatary et al48 have treated the
Power and SNR levels measured at the testbed’s independent CR device as a feature vector.
3.3.2
Performance evaluation metrics
The performance of the ML algorithm for SS can be evaluated using different performance metrics. The most popular
metrics for assessing the performance of the ML models are discussed below.
a) Classification accuracy is defined as the ratio of correct predictions to the total predictions made by the model. CA can
be evaluated at different SNR levels, and the graph between CA and SNR can be analyzed for performance evaluation.
CA is defined as:
CA = Nc ∕Nt × 100%
(34)
where Nc denotes correctly predicted states and Nt denotes the total number of states.
b) Receiver operating characteristic curve is a plot between the probability of detection against the probability of false
alarm. The receiver operating characteristic curve portrays the primary signal detection performance of the algorithm.
c) Average throughput is defined as the amount of data that can be transmitted and received at the destination
successfully within a timeframe. It is measured in megabits per second or bits per second.
d) Energy efficiency is defined as the ratio of average throughput of the network to the total energy dissipated by the
whole network.
𝜀=
𝛽
E
(35)
where 𝛽 represents the average throughput of the system and E represents total energy dissipated from the network.
e) Probability of detection and probability of false alarm: Probability of detection is defined as the probability of a SU that
declares the presence of a PU when the spectrum is actually occupied by the PUs. The probability of false alarm is
defined as the probability of a SU that declares the presence of PU when the spectrum is detected free. The formula
for the probability of detection and probability of false alarm is given in Equations (3) and (4).
Table 5 summarizes the usage of different evaluation metrics in the reviewed research papers.
3.3.3
Type of secondary user
The surveyed research papers are characterized into two categories based on the types of SUs considered in the CRN. The
first category belongs to SU with a single antenna and the second category involves SU equipped with multiple antennas.
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T A B L E 5 Performance evaluation metrics
References
Performance
evaluation metrics
14,36
Average training time, average classification delay, ROC, detection probability
35,40,41,47,51,54,59-61
ROC curve
37
Classification accuracy
38
ROC curve, detection probability, average misclassification error rate
39
Probability of detection
42
ROC curve, classification accuracy
43
ROC curve, classification accuracy, probability of detection, probability of false alarm
44
Accuracy, average throughput
45
ROC curve, probability of detection, average prediction time vs, SS error rate
46
Probability of detection and probability of false alarm
48
Training time, classification time
52,53
ROC curve, Probability of detection
62
Average throughput, energy efficiency
63,64
ROC curve, probability of detection, average energy consumption, average energy consumption,
probability of detection
65
Energy efficiency, throughput, energy consumption
66
Average throughput, efficiency
67
Cumulative reward, ROC curve, probability of detection, and probability of false alarm
68
Number of frequency bands sensed
70
Probability of detection, probability of missed detection, expected throughput
71
ROC curve, detection time
Authors have concluded that SU equipped with multiple antennas improves the SS performance by overcoming the
problems such as path losses and shadows, which is predominantly found in SU equipped with a single antenna.
Further, multiple antennas improve spatial diversity and spatial multiplexing gain and hence enhance the SS performance. In References 39,43,46,60,61, the CSS problem is solved using multiple antennae equipped SU; however,
in the rest of the ML-based methods reported in the reviewed papers, single antenna-based SUs have been used
for SS.
4
DY NA MIC SPECTRUM SHARING
In the case of static spectrum sharing technique, spectrum has to be divided in advance and a fixed spectrum band is
shared among the users statically, which leads to inefficient utilization of spectrum. Hence, for the efficient utilization of
spectrum bands in 5G technology, spectrum sharing has to be addressed to overcome the spectrum shortage issues. The
5G networking technology has been considered as a new communication network that is capable of providing higher
data rates and higher-capacity of data transmission.
It is flexible to support various devices and services, which makes it possible to combine with IoT. 5G network has the
capability to enable wide range of machine type applications and hence leads to development of IoT. In future, IoT technology enabled with 5G can provide various kinds of IoT services with low power consumption, low cost, big data, wide
coverage and large connection, which supports mobile designs to support massive IoT with variety of services requirements and enhanced broadband communications. Hence, the major challenge in these new emerging technologies is the
requirement of spectrum and bandwidth resources. Now, it becomes necessary to dynamically share the available spectrum to fulfill the resource demand. DSS allows network equipment to share the spectrum amongst the users connected
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to long-term evolution and 5G communication technologies. Cognitive IoT combined with CR, which is considered as
an effective spectrum access technology for DSS, provides robust operation in a highly dynamic spectrum environment.
A multichannel IoT has been proposed in Reference 75 to share the 5G communications dynamically, where IoT nodes
are designed in a way to perform 5G communication and IoT communication simultaneously.
4.1
Classification of spectrum sharing techniques
Spectrum sharing techniques are classified into three main approaches: network architecture, spectrum access and spectrum allocation behavior. A schematic diagram for the classification of various spectrum sharing techniques is shown in
Figure 7. Network architecture categorized into centralized (infrastructure-oriented) and distributed (infrastructure-less).
This technique includes distributed sensing devices, base station (BS) and software defined network controller in the
system. By using this method incorrect decisions influenced by inconsistent quality of service can be reduced. Spectrum
access sharing (SAS) is an emerging method nowadays gaining attention classified into three techniques: dynamic exclusive, open access, and hierarchical model. Dynamically advanced SAS method consists of a common model, shared use,
and exclusive use. The open access model can be used any service as it provides unrestricted access of spectrum. Hierarchical model includes interweave, underlay, and overlay mechanism for spectrum sharing. In underlay and overlay
spectrum sharing schemes, SU can access the spectrum along with PU transmission by maintaining the constraint that
it will not degrade the performance and protect the PU from interference by keeping SU power below than predetermined threshold. Whereas, in interweave scenario, the SU can dynamically access the spectrum when PU is in idle state.
Spectrum allocation behavior methods are categorized into cooperative and non-cooperative.
4.1.1
Cooperative spectrum sharing
Advanced spectrum sharing techniques can increase the energy efficiency and spectral efficiency from the cost effective
perspective. Yang et al76 has performed a survey on cognitive and cooperative spectrum sharing techniques to increase the
spectral efficiency and energy efficiency. They have discussed cooperative spectrum sharing methods from two perspectives: economic perspective, which mainly focus on spectrum trading and spectrum leasing and cross layer perspective,
which emphasizes on spectrum mobility, routing, relaying, and harvesting.
To improve the spectrum utilization, a novel economic tool size-negotiable auction mechanism-based spectrum
sharing framework has developed in Reference 77, which aimed at providing the solution between auction and
FIGURE 7
Classification of spectrum sharing techniques
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negotiation for multiple buyers sharing a common spectrum band. MAC protocol plays important role in spectrum
sharing. A distance-dependent multichannel MAC protocol78 is utilized to increase CRN throughput, as it uses a probabilistic channel assignment mechanism that exploits the dependence between the transmission distance and signal’s
attenuation model, and allows multiple SUs to make more concurrent transmission on a channel under moderate and
high traffic loads. Although, there are numerous spectrum sharing techniques utilized have been studied in order to
enhance the spectral efficiency and energy efficiency of future wireless network. The efficient utilization of spectrum
achieved by DSS can be further increased by utilizing the full duplex (FD) communication technology. Due to its advantages including doubling the capacity, increased secondary throughput and increased spectrum utilization efficiency,
this technology is considered to be candidate for the 5G wireless network and beyond the wireless communication
networks. In Reference 79, various FD-enabled DSS techniques and recent advancement are studied and a communication framework is proposed by considering a power control-based self-interference mitigation technique in order to
make possible the concurrent sensing and transmission in DSS systems, which improves the system throughput of the
network.
4.2
DSS using machine learning
The above discussed works are less energy efficient for allocation of spectrum resources as the learning capability of CR
devices has not been explored for determining the optimal strategies. Hence, to obtain the potential solution for energy
efficient resource allocation, machine learning integrated with CR can be seen as a potential solution. Machine learning
is an important research topic for next generation wireless communication and has been widely used in SS and spectrum
sharing. To satisfy the quality of service requirement from multiple users, the resource allocation problem is addressed
in Reference 80. An efficient heuristic algorithm is developed by considering multiple resource dimensions (frequency,
power consumption, and antenna direction) for the optimization as well as multiple objectives also simultaneously optimized. Three algorithms such as convex relaxation with gradual removal, convex relaxation with tree pruning (CRTP),
and GA, where GA has shown promising results approaching CRTP with the less computational effort by comparing with
two other techniques.
RL is considered as most suitable approach for obtaining optimal solution as it does not form any extensive model
and it learns with interaction with environment. In multi-agent RL, multiple agent access channel state information
from environment and find optimal policy with cooperative or non-cooperative manner. In Reference 81, a model free
multi-agent RL approach based on Q-learning and SARSA algorithms with cooperative framework have been utilized
to determine the optimal strategy for resource allocation in energy efficient manner in complex dynamic environment,
which reduces the channel state interference and maximizes the channel capacity of CR network while maintaining the
users’ quality of service. An another solution to DSS is the application of heuristically accelerated RL approach which
speed up the RL algorithm in multi-agent domain by guiding the learning process of cognitive cellular system by using
additional heuristic information.82
To solve the continuous and high dimensional problem, such as power control for spectrum sharing, a DNN concept
along with RL is introduced as the traditional RL approaches are not capable to solve these problems. A deep RL-based
power control method is developed for spectrum sharing in scenario where PU and SU work in non-cooperative manner
and SU does not know PU’s transmit power, which a SU can intelligently adjust its transmit power such that is can
share the spectrum with PU with desired quality of service requirement.83 The power control problem is addressed in
Reference 84 by constructing an information interaction model among PU, SU and wireless sensors and investigating
with the application of deep RL methods such A3C and distributed proximal policy optimization, etc. Another work for
spectrum management and resource allocation is presented by exploring the deep learning (DL) algorithms. Generative
model conventional variational autoencoder (CVAE) is utilized by solving linear sum assignment problem for enabling
spectrum sharing underlay device to device communication.85 Performance is evaluated for CVAE by exploring three
different architectures such as CNN architecture auto-encoder, feed-forward neural network auto-encoder and hybrid
auto-encoder.
Jacob et al86 present a new spectrum sharing technique using 5G enabled bidirectional cognitive deep learning nodes
(BCDLN) along with DSS long short term memory (DSLSTM), where the BCDLN self-learning nodes forward the information to other cognitive node at a constant spectrum sharing targets and cooperate to each other with DSLSTM. The
dynamic spectrum allocation is performed with CP-OFDM. In Reference 87, spectrum resource allocation problem is
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considered as decentralized contention-based spectrum access for BS and solved by incorporating its features to a distributed RL framework. This problem is formulated as a decentralized partially observable MDP with reward structure
that provides long term proportional fairness in terms of throughput and a two-stage MDP is introduced, which uses SS
decision and reception quality information to make a medium access decision. A SS and spectrum allocation framework
is proposed for CR-enabled IoT cellular network consisting of SU devices as IoT objects and multiple PU base-stations,88
where SU devices discover the spectrum holes in multiband PU spectrum by using SVM and forward spectrum allocation request to intelligent FC. This request is processed through a multi-dimensional feature set, where the SVM classifier
assigns vacant spectrum PU band to the SU-IoT device which need for spectrum access in the CR-IoT framework. Spectrum allocation issue is addressed by considering the two objectives such as network capacity and spectrum efficiency
has been modeled as a multi-objective optimization problem in CR network.89 To handle these conflicting objectives
non-dominated sorting genetic algorithm-II which combines features of evolutionary algorithms and machine learning
based on non-dominated sorting genetic algorithm RL has incorporated.
5
CO N C LUSION AN D FU T U RE S CO PE
It is well-known that billions of wireless communicating devices have connected to the Internet, so it has become a big
challenge for users to access the radio spectrum. CR technology has become a promising solution to address the issues
related to spectrum scarcity. The CR technology and ML provide the ability to automatically learn and adapt to the
environment and have the potential to dynamically access the spectrum and to utilize spectrum resources. Considering
the popularity of ML in the CSS domain, we have presented an exclusive survey of relevant literature. We characterize
the surveyed papers based on the information about the sensing signal they use (ie, the features), what ML techniques
they apply and what evaluation metrics they use to check the performance of the algorithm. In this survey, we summarized the most up-to-date ML-based SS methods, along with their limitations and advantages. We hope that the
outcomes of this survey can bring new directions and methodologies to enhance the research in the CSS domain. DL
is becoming the most popular research direction as it can automatically capture complex and hierarchically organized
features and outperform the ML-based methods with carefully hand-crafted features. This manual feature extraction
process is time inefficient and requires retraining of the network with new sets of data collection whenever the inputs
feature changes. DL has shown great success in various fields such as computer vision, natural language processing,
signal detection, and resource allocation in wireless communication. In practical scenarios, GPU-enabled computing
is becoming more and more popular, a larger volume of complex data is available, hence more effective training algorithms are developed. DL has become a promising research direction for building an intelligent wireless communication
system as it plays a major role in predictive analysis. Although researchers have explored neural network architectures
in SS, spectrum sharing, and resource allocation, still there is a need to improve spectral efficiency and energy efficiency, hence it becomes necessary to develop the DL algorithms for building the intelligent wireless communication
systems.
DATA AVAILABILITY STATEMENT
Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.
ORCID
Kuldeep Singh https://orcid.org/0000-0002-2350-1700
Sandeep Kumar https://orcid.org/0000-0002-5750-6112
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How to cite this article: Janu D, Singh K, Kumar S. Machine learning for cooperative spectrum sensing and
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