# Spectrum Sensing and Signal Identification with Deep Learning based on
Spectral Correlation Function

Kürşat Tekbıyık, Özkan Akbunar,
Ali Rıza Ekti, Ali Görçin,
Güneş Karabulut Kurt, Khalid A. Qaraqe K. Tekbıyık, Ö. Akbunar, A.R. Ekti and
A. Görçin are with the Informatics and Information Security Research Center
(BİLGEM), TÜBİTAK, Kocaeli, Turkey. e-mail: {kursat.tekbiyik, ozkan.akbunar,
aliriza.ekti<EMAIL_ADDRESS>Ekti is with the Department of
Electrical-Electronics Engineering, Balıkesir University, Balıkesir, Turkey.
e-mail<EMAIL_ADDRESS>Görçin is with the Department of Electronics
and Communications Engineering, Yıldız Technical University, İstanbul, Turkey.
e-mail<EMAIL_ADDRESS>Tekbıyık and G.K. Kurt are with the Department
of Electronics and Communications Engineering, İstanbul Technical University,
İstanbul, Turkey. e-mail: {tekbiyik<EMAIL_ADDRESS>A. Qaraqe is with the
Department of Electrical and Computer Engineering, Texas A&M University at
Qatar, Doha, Qatar. e-mail<EMAIL_ADDRESS>work has received
funding from the European Commission Horizon 2020 research and innovation
programme BEYOND5 under grant agreement No. 876124.This study has been
supported by NPRP12S-0225-190152 from the Qatar National Research Fund (a
member of The Qatar Foundation).

###### Abstract

Spectrum sensing is one of the means of utilizing the scarce source of
wireless spectrum efficiently. In this paper, a convolutional neural network
(CNN) model employing spectral correlation function (SCF) which is an
effective characterization of cyclostationarity property, is proposed for
wireless spectrum sensing and signal identification. The proposed method
classifies wireless signals without a priori information and it is implemented
in two different settings entitled CASE1 and CASE2. In CASE1, signals are
jointly sensed and classified. In CASE2, sensing and classification are
conducted in a sequential manner. In contrary to the classical spectrum
sensing techniques, the proposed CNN method does not require a statistical
decision process and does not need to know the distinct features of signals
beforehand. Implementation of the method on the measured over-the-air real-
world signals in cellular bands indicates important performance gains when
compared to the signal classifying deep learning networks available in the
literature and against classical sensing methods. Even though the
implementation herein is over cellular signals, the proposed approach can be
extended to the detection and classification of any signal that exhibits
cyclostationary features. Finally, the measurement-based dataset which is
utilized to validate the method is shared for the purposes of reproduction of
the results and further research and development.

###### Index Terms:

Deep learning, spectrum sensing, cyclostationarity, signal classification,
spectral correlation function, convolutional neural networks.

##  I Introduction

Today’s wireless communication systems have to bear an unprecedented increase
in data transmission volume. It is essential for wireless communication
networks to utilize the limited source of spectrum as efficiently and
effectively as possible to meet the demand [1]. Furthermore, the efforts
including the deployment of small cells, utilizing mmWave bands, effective
spectrum usage algorithms, massive multiple-input multiple-output (MIMO)
systems[2], and cognitive radio networks target the same goal. Cognitive
radios aim to attend this purpose by sharing the spectrum dynamically among
users; thus, spectrum sensing and signal identification became major
techniques for cognitive radio networks.Considering joint communications,
sensing, and localization demanded by 6G and beyond, efficient spectrum
allocation will be more crucial for heterogeneous networks. For instance, 5G
NR Release 16 introduces dynamic spectrum sharing which is novel method
enabling parallel operation of 5G and Long-Term Evolution (LTE) in the same
band [3]. Furthermore, it is envisioned that radar and communications system
will share the same frequency band [4].

When spectrum sensing and signal identification techniques are considered, it
is seen that sensing techniques of energy detection and matched filtering
require a priori information such as number of second order noise statistics,
cyclic frequencies and particular pulse shaping filter characteristics to
operate. Moreover, after processing of the received signals, a statistical
decision mechanism should be implemented to complete the sensing process [5].
Such cumbersome process can hamper the agile decision making requirements of
5G and beyond networks, thus, classical sensing paradigm can not satisfy the
requirements of fast changing operation environment of contemporary and future
wireless communications networks. In this context, deep learning (DL) has been
proposed as a solution to the parameter adaptation issues of classical
techniques. This stems from the known ability of DL techniques in extracting
the intrinsic features of given inputs through a convolutional process. The
use of DL based approaches also eliminates the need for a statistical decision
mechanism at the end of the identification process. Along this line, the
recent study shows that DL methods outperform classical approaches in signal
detection in the spectrum [6]. To achieve the requirements for 5G and beyond
wireless networks, an intelligent radio design for spectrum sensing and signal
identification is required and such solution can be realized with the help of
machine learning (ML) algorithms [7] utilizing features such cyclostationarity
of signals [8].

###  I-A Related Work

When the literature on the implementation of artificial intelligence
techniques for spectrum sensing and signal identification purposes are
considered, it is initially seen that convolutional neural networks are
trained with high-order statistics of single carrier signals for modulation
classification [9]. A CNN classifier is used for modulation and interference
identification for industrial scientific medical (ISM) bands by utilizing FFT,
amplitude-phase representation (AP) and in-phase/quadrature (I/Q) features for
training [10]. Another study [11] focused on the protocol classification in
ISM band by utilizing fully connected neural networks. As another example of
the application of DL to signal classification, long short term memory (LSTM)
is deployed for modulation classification and identification of digital video
broadcast (DVB), Tetra, LTE, Global System for Mobile communications (GSM),
wide-band FM (WFM) signals by using AP and FFT magnitude for training [12].
The performance of the proposed model is high, however, it employs synthetic
data generated from MATLAB. In the real channels, there are numerous
phenomenons, which further complicate the signal characteristics.

On the other hand, cyclostationarity signal analysis has been explored for
modulation classification, parameter estimation and spectrum sensing for more
than 20 years. In addition to being an established method for spectrum sensing
in cognitive radio domain, cyclostationary features detection (CFD) is also
utilized to distinguish generic modulations such as M-PSK, M-FSK, and M-QAM
[8, 13]. When the radio access technology (RAT) identification [14] is
considered, second order cyclostationarity is employed for classification of
LTE and GSM signals [15]. Later, a tree-based classification approach is
proposed to identify GSM, cdma2000, universal mobile telecommunications system
(UMTS) and LTE signals [16].

###  I-B The Contributions

####  I-B1 Methodological novelty

CFD depends on extracting the underlying features using likelihood-based
techniques utilizing statistical decision mechanisms and for CFD to operate
under the dynamically changing communication medium, an additional mechanism
to adaptively adjust decision parameters such as thresholds and the number
samples is required [17]. On the other hand, even though employment of DL
techniques for the purposes of spectrum sensing and signal identification
implies considerable advantages in terms of performance and complexity,
utilization of FFT, AP and I/Q as input features to the intelligent networks
do not lead stable and dependable results due to the rapidly and significantly
changing wireless communications medium between the nodes. Therefore, this
study proposes application of SCF as input feature to CNNs for blind wireless
signal identification. The problems of spectrum sensing and signal
identification are framed into two particular contexts which utilize a novel
CNN model designed and trained with spectral correlation function (SCF) of
wireless signals without bi-frequency mapping. Therefore, the proposed method
can be employed either to decide whether the signal is present or not in the
spectrum or to distinguish signals from each other. Sensing and identification
performance of the method is tested and validated utilizing real-life over-
the-air signal measurements of GSM, UMTS, and LTE signals.

The proposed method approaches to the problems of sensing and identification
from the aspects of two cases; in CASE1, the designed CNN model is fed
directly with the SCFs of measurements of GSM, UMTS, LTE along with SCF of
spectrum which is only comprised of noise. Sensing and classification are
executed jointly for CASE1. On the other hand in CASE2, a two-step approach is
adopted; first, as a spectrum sensing method to measure the spectrum occupancy
is conducted and this stage is followed by a signal classification procedure.

####  I-B2 Novelty in terms of numerical studies

In terms of performance analysis, first, a comparative analysis is conducted
and superiority of SCF over the features of I/Q, AP and FFT is shown for the
purpose of training of DL networks. Second, comparison with the existing DL
methods such as convolutional long short term memory fully connected deep
neural network (CLDNN) [18], LSTM [12], DenseNet [19], ResNet [9] are given in
terms of accuracy, memory consumption and computational complexity. Third, it
is shown that the proposed method outperforms support vector machines trained
with SCF, which is our previous study. Fourth, the performance of the proposed
method is compared with the classical spectrum sensing technique of CFD, which
requires the cyclic frequencies as a priori information. The identification
results indicate important performance improvements over the aforementioned
techniques.

####  I-B3 Novelty in terms of experimental activities

Focusing on the valuable information in the dataset is an important metric for
the proposed method; thus, it is denoted that utilizing only the meaningful
part of the input matrices improves the classification performance along with
alleviation in training time and complexity. On the other hand, the general
dataset, which has been developed from measurements taken through a
comprehensive measurement campaign conducted in different locations and
frequency bands, is shared publicly in [20]. Therefore, the measurement-based
dataset is open to researchers as a comprehensive resource in the development
and validation of their work.

####  I-B4 Applicability for future research problems

Even though in this work the scope of implementation is focused on cellular
signals, the introduced identification system can be directly used for
detection and classification of any signal that exhibit cyclostationary
features. All the analyses are based on the real-world measurements taken
during an extensive measurement campaign conducted at different locations with
varying environmental conditions such as channel fading statistics and
signal-to-noise ratio (SNR) levels. Finally, the measurement data that this
work is experimented on is also shared for reproducibility of this work and to
support future research and development activities in this domain.

###  I-C Organization of the Paper

The rest of the paper is structured as follows. Background information on the
system model, cyclostationary analysis and CNNs is presented in Section II.
The problem statement is given in Section III. The proposed CNN model is
described in Section IV. The details of the measurement setup and dataset
utilized in this study are given in Section V. Section VI presents measurement
results and details the classification performance of the proposed method. The
concluding remarks are provided in Section VII.

##  II Background

Assuming that received signal is down converted to baseband before further
processing, first the complex baseband equivalent of the received signal,
$r(t)$ should be defined. When the presence of fading environment with thermal
noise, received signal can be given as

$\displaystyle r(t)=\rho(t)*x(t)+\omega(t),$ (1)

where $\omega(t)$ denotes the complex additive white Gaussian noise (AWGN)
with $\mathcal{C}\mathcal{N}(0,\sigma^{2}_{N})$ in the form of
$\omega(t)=\omega_{I}(t)+j\omega_{Q}(t)$ as both $\omega_{I}(t)$ and
$\omega_{Q}(t)$ being $\mathcal{N}(0,{\sigma^{2}_{N}}/2)$ and $j=\sqrt{-1}$;
the complex baseband equivalent of the transmitted signals is denoted as
$x(t)$; and $\rho(t)$ stands for the impulse response for the time-invariant
wireless channel because of extremely short observation time for a signal.

Depending on the idle or busy state of the mobile propagation channel in the
radio frequency (RF) spectrum, the signal detection process of deep learning
methods can be modelled as a binary hypothesis test

$r(t)=\left\\{\begin{matrix}\rho(t)x(t)+\omega(t),~{}~{}\hfill H_{1}\\\
\omega(t),~{}~{}\hfill H_{0}.\end{matrix}\right.$ (2)

$H_{0}$ and $H_{1}$ hypotheses stand for the presence of noise only and the
unknown signal, respectively. Therefore, the problem statement can be stated
as identification of the presence of the unknown signal, $x(t)$, and
classification of the $x(t)$.

###  II-A Cyclostationarity

Cyclostationary signal processing leads to extracting hidden periodicities in
a received signal, $r(t)$. Since these periodicities (e.g., symbol periods,
spreading codes, and guard intervals) exhibit unique characteristics for
different signals, they provide the necessary information for identification.
Thus, the unknown signals $x(t)$ can be identified by using cyclostationary
features to obtain the statistical characteristics of $r(t)$ in the presence
of $\omega(t)$ and multipath fading without a priori information. A nonlinear
transformation, second-order cyclostationarity of a signal can be expressed as

$\displaystyle
s_{\tau}(t)=\mathbb{E}\left\\{r(t+\tau/2)r^{*}(t-\tau/2)\right\\},$ (3)

where $\mathrm{s}_{\tau}(t)$ is the autocorrelation of $r(t)$. Assuming that
the autocorrelation function is periodic with $T_{0}$ for second-order
cyclostationary signals, a Fourier series expansion of $s_{\tau}(t)$ is given
as

$\displaystyle\mathbb{R}_{r}^{\alpha}\left(\tau\right)=\frac{1}{T_{0}}\int_{-T_{0}/2}^{T_{0}/2}s_{\tau}(t)e^{-j2\pi\alpha
t}dt,$ (4)

where $\mathbb{R}_{r}^{\alpha}\left(\tau\right)$ is the cyclic autocorrelation
function (CAF) and $\alpha$ values denote the cyclic frequencies.

The Fourier transform of the CAF for a fixed $\alpha$ is given with the cyclic
Wiener relation [8]

$\displaystyle\mathbb{S}_{r}(f)=\int_{-T/2}^{T/2}\mathbb{R}_{r}^{\alpha}\left(\tau\right)e^{-j2\pi
f\tau}d\tau,$ (5)

where $\mathbb{S}_{r}(f)$ is called as SCF which is equal to the power
spectral density (PSD) when $\alpha$ is zero.

The computational complexity of calculating SCF is relatively high. However,
this complexity can be decreased by using the  accumulation method (FAM) based
on time smoothing via FFT [21]. FAM estimates the SCF as

$\displaystyle\mathbb{S}_{r_{T}}$
$\displaystyle=\sum_{k}R_{T}(kL,f)R_{T}^{*}(kL,f)g_{c}(n-k)e^{-i2\pi kq/P},$
(6)

where $R_{T}(n,f)$ denotes the complex demodulates which is the
$N^{\prime}$-point FFT of $r(n)$ passed through a Hamming window and can be
computed by

$\displaystyle R_{T}(n,f)$
$\displaystyle=\sum_{k=-N^{\prime}/2}^{N^{\prime}/2}a(k)r(n-k)e^{-i2\pi
f(n-k)T_{s}},$ (7)

where $a(n)$ and $g_{c}(n)$ are both data tapering windows. The symbols
$N^{\prime}$, $T_{s}$, and $L$ denote the channelization length, sampling
period, and sample size of hopping blocks, respectively. The ratio between the
number of total samples and $L$ is employed as the length of second FFT, whose
length is denoted as $P$. The FAM has six implementation steps. These steps
are respectively channelization, windowing, $N^{\prime}$-point FFT, complex
multiplication, $P$-point FFT and bi-frequency mapping. In the study, the unit
rectangle and Hamming windows are employed as $g_{c}(n)$ and $a(n)$,
respectively. Fig. 1 illustrates SCFs results in bi-frequency plane, which are
estimated by FAM algorithm for GSM, UMTS, and LTE along with the noise.
Consequently, the input matrix, $\mathbb{X}_{k}^{SCF}$, to be fed into
classifier model is given as

$\displaystyle\mathbb{X}_{k}^{SCF}=|\mathbb{S}_{r_{T}}(nL,f)|,$ (8)

(a) AWGN

(b) GSM

(c) UMTS

(d) LTE

Figure 1: FAM based SCF estimates of cellular signals in bi-frequency plane.
It is easily observed that the signals show different cyclic characteristics.
The noise does not show cyclic characteristics as SCF of noise gives only peak
at the center of bi-frequency plane where the cyclic frequency is zero.

###  II-B Amplitude-Phase

The amplitude and phase values of time-domain I/Q data can be used to
establish a real-valued classification feature matrix, $\mathbb{X}_{k}^{AP}$.
This feature matrix is composed of the amplitude and phase vectors of the
received signal samples. So, $\mathbb{X}_{k}^{AP}$ is defined as

$\displaystyle\mathbb{X}_{k}^{AP}=\begin{bmatrix}\mathbb{x}_{A}^{T}\\\
\mathbb{x}_{\phi}^{T}\end{bmatrix},$ (9)

where $\mathbb{x}_{A}=({r_{q}}^{2}+{r_{i}}^{2})^{\frac{1}{2}}$ and
$\mathbb{x}_{\phi}=\arctan(\frac{r_{q}}{r_{i}})$ denote the amplitude and
phase vectors, respectively.

###  II-C Fast Fourier Transform

The characteristics of signals in frequency domain can be employed as
discriminating classification features. The FFT of the received signal is used
to obtain a real-valued classification feature matrix $\mathbb{X}_{k}^{FFT}$
as

$\displaystyle\mathbb{f}$
$\displaystyle=\mathcal{F}(r),~{}\mathbb{X}_{k}^{FFT}=\begin{bmatrix}\mathbb{f}_{re}^{T}\\\
\mathbb{f}_{im}^{T}\end{bmatrix},$ (10)

where $\mathcal{F}(\cdot)$ stands for the FFT of the received signals;
$\mathbb{f}_{re}$ and $\mathbb{f}_{im}$ are real and imaginary parts of
$\mathbb{f}$, respectively.

###  II-D The Convolutional Neural Networks

CNN is a class of deep neural networks which is mainly employed in image
classification and recognition. CNN process inputs like a visual system in
human. In other words, it extracts features in an input rather than fitting
data [22]. In this study, we utilize the input matrices which resemble image
consisting of features in a specific positions as seen in Fig. 1. Still, it
has been recently extended to several application areas. CNNs have two stages:
feature extraction and classification. In feature extraction, a convolutional
layer is followed by a pooling layer. In the convolution layer, the feature
matrix is convolved with different filters to obtain convolved feature map as
follows

$\displaystyle\mathrm{h}[i,j]=\sum_{p=1}^{m}\sum_{l=1}^{n}w_{p,l}\mathbb{X_{k}}[{i+p-1,j+l-1}],$
(11)

where $w_{p,l}$ is the element at $p$-th row and $l$-th column of the $m\times
n$ filter matrix, and $\mathbb{X_{k}}\left[\cdot,\cdot\right]$ denotes the
elements of feature matrix convolved by $w_{p,l}$. The convolution layer is
followed by the pooling layer to reduce computational complexity and training
time, and control over-fitting due to the fact that pooling layer makes the
activation less sensitive to feature locations [23]. The $u\times v$ maximum
pooling operation is described as

$\displaystyle\mathrm{g}[i,j]=$
$\displaystyle\max\left\\{h[i+a-1,j+b-1]\right\\},$ (12)

where $1\leq a\leq u\text{ and }1\leq b\leq v$. The output of the pooling
layer is a 3-D tensor. This output is then reshaped into a 1-D vector. This
vector is fed to the dense (fully-connected) layers for the final
classification decision.

##  III Problem Statement

The dynamic communications environment of next generation wireless networks
require fast, robust and adaptive sensing and identification of the multi-
dimensional communications medium to utilize the resources quickly and
efficiently [5]. In this context, spectrum sensing and signal identification
becomes important means of achieving effective resource utilization. To that
end, we approach the problems of sensing and identification via DL from two
aspects:

CASE1: In this case, first a novel CNN classifier is trained with all possible
classes, in this case GSM, UMTS, LTE and empty spectrum which can be referred
to as AWGN only. For each signal the cyclic spectrum is constructed based on
the procedures described in Section II-A. The cyclic spectrum is then fed to
the CNN classifier, which is trained with four possible inputs beforehand.
Finally, the classification is made.

CASE2: In this case a two-stage approach is adopted; at the first stage a CNN
detector (the same CNN model defined is employed for both detection and
classification for the sake of simplicity) is utilized to decide whether a
signal exists in the given band or not by training the CNN by two classes,
first comprised of GSM, UMTS, and LTE signals and second part with AWGN only.
Thus, in the first stage a decision is made about whether a signal exists in
the spectrum or not as in the case of classical spectrum sensing. If the
decision is made that there is an information bearing signal in the given
band, second stage is activated utilizing a CNN classifier, which is trained
in our case with three classes (i.e., GSM, UMTS, and LTE) and finally a
decision is made for the class of the signal occupying the spectrum.

Please note that the classification refers to identification of the signals,
and at the detection part of the approach $H_{1}$ and $H_{0}$ refers to the
existence and non-existence of a signal over the spectrum based on binary
hypothesis testing. Both CASE1 and CASE2 are illustrated in Fig. 2.

Firstly, we can define the accuracy for CASE1, $P_{\texttt{CASE1}}$ as:

$\displaystyle
P_{\texttt{CASE1}}=P(\hat{\mathbf{\chi}}_{k}=\mathbf{\chi}_{k}),\;k=0,1,2,3,$
(13)

where $\mathbf{\chi}_{k}$ denotes the label array of the transmitted signals
and $k$ represents the label of the classes AWGN, GSM, UMTS, and LTE,
respectively. $\hat{\mathbf{\chi}}_{k}$ is array for the predicted classes of
the received signals. In a short, $P_{\texttt{CASE1}}$ stands for the accuracy
of four-classes classification problem. For CASE2, it is required to define
two independent accuracy functions: the sensing accuracy,
$P_{\texttt{CASE2}}^{\texttt{S}}$ and the classification accuracy,
$P_{\texttt{CASE2}}^{\texttt{C}}$, which are defined as

$\displaystyle P_{\texttt{CASE2}}^{\texttt{S}}$
$\displaystyle=P(\hat{\mathbf{\chi}}_{\texttt{S}}=1|H_{1})+P(\hat{\mathbf{\chi}}_{\texttt{S}}=0|H_{0}),$
(14) $\displaystyle P_{\texttt{CASE2}}^{\texttt{C}}$
$\displaystyle=P(\hat{\mathbf{\chi}}_{k}=\mathbf{\chi}_{k}|H_{1}),\;k=1,2,3.$
(15)

$\hat{\mathbf{\chi}}_{\texttt{S}}$ is the prediction of
$\mathbf{\chi}_{\texttt{S}}$ regarding to the presence of a signal in the
spectrum. $\mathbf{\chi}_{k}$ stands for the predictions for the
classification part of CASE2. $\mathbf{\chi}_{\texttt{S}}$ is defined for the
transmitted signal as:

$\displaystyle\mathbf{\chi}_{\texttt{S}}=\left\\{\begin{matrix}0,~{}~{}\hfill
k=0,\\\ 1,~{}~{}\hfill k=1,2,3.\end{matrix}\right.$ (16)

The overall accuracy for CASE2 can be introduced in terms of
$P_{\texttt{CASE2}}^{\texttt{S}}$ and $P_{\texttt{CASE2}}^{\texttt{C}}$ by

$\displaystyle
P_{\texttt{CASE2}}=P_{\texttt{CASE2}}^{\texttt{S}}P_{\texttt{CASE2}}^{\texttt{C}}.$
(17)

##  IV The Proposed CNN Model

Figure 2: Two different approaches for the sensing and classification of signals. In CASE1, signal sensing and signal classification are jointly conducted. However, CASE2 firstly sense signal in the spectrum, then classify. TABLE I: The proposed CNN layout. Layer | Output Dimensions
---|---
Input | $8193\times 16$
Conv1 | $8193\times 16\times 64$
Leaky ReLU1 | $8193\times 16\times 64$
Max_Pool1 | $4097\times 8\times 64$
Conv2 | $4097\times 8\times 128$
Leaky ReLU2 | $4097\times 8\times 128$
Max_Pool2 | $2049\times 4\times 128$
Conv3 | $2049\times 4\times 64$
Leaky ReLU3 | $2049\times 4\times 64$
Max_Pool3 | $1025\times 2\times 64$
Flatten | $131200$
Dense1 | $256$
Dense2 | $4$
Trainable Par. | $33,736,772$

As indicated in Section III, the proposed method relies on a novel CNN model.
Design and implementation of CNN for classification of wireless mobile
communication signals is conducted via an open source machine learning
library, Keras [24]. The proposed CNN model consists of three convolution and
three pooling layers sequentially. The convolution layers have respectively
$64$, $128$, and $64$ filters. The network is terminated by two fully
connected layers. First hidden layer includes 256 neurons. Second hidden layer
consists of $4$ and $3$ neurons for CASE1 and CASE2, respectively. The leaky
rectified linear unit (ReLU) activation function with an alpha value $0.1$ is
used in each convolution layer to extract discriminating features. Leaky ReLU
is selected instead of ReLU. Unlike ReLU, leaky ReLU maps larger negative
values to smaller ones by a mapping line with a small slope. In each
convolution layer, $3\times 3$ filters are used. $2\times 2$ max pooling is
used to reduce the dimension and training time. A fully connected layer is
formed by $256$ neurons and Leaky ReLU activation function. Following the
fully connected layers, the probabilities for each class are computed by the
softmax activation function. In addition, the adaptive moment estimation
(ADAM) optimizer is utilized when determining the model parameters. In the
training phase, early stopping is employed to prevent the model from over-
fitting. The patience is chosen as $10$ epochs for early stopping function and
validation loss is monitored during the training. If the validation loss
converges a level and remain at this level during $10$ epochs, the training is
terminated and the weights at the end of training are used in the test. The
implementation layout for the proposed CNN model is given in Table I. The
input matrices, $\mathbb{X}_{k}^{AP}$, $\mathbb{X}_{k}^{FFT}$, and
$\mathbb{X}_{k}^{SCF}$ are used at the beginning of the proposed model by
convolving with filters. The overall block diagram for the proposed CNN model
is depicted in Fig. 3.

When the motivation behind designing such a CNN model is considered, it should
be noted firstly that the information about changes in the local regions of
the mapped output is extracted by using $3\times 3\times 64$ filters in the
first convolution layer. In this problem, because the SCF creates local
differences in frequency and cyclic frequency regions, the smaller filter size
is preferred to catch peaks in the feature matrices. Thus, local differences
are taken into account along the layers. After the first layer determines the
cyclic characteristics of all local terms as a general process, the second
layer examines the properties such as location and size related to these
characteristics. Here, it is aimed to deal with cyclic features in detail by
increasing the number of filters to $128$. In the last layer, all properties
are converted to an average of all information gathered and eventually sent to
the decisive layer which is dense layer. For this reason, the number of
filters in the last layer should be chosen so that sufficient information is
obtained without overfitting. Therefore, the number of filters is selected as
$64$ in the last layer.

Figure 3: The proposed CNN model consists of three convolutional layers and
two dense layers with Adam optimizer with learning rate of $10^{-5}$.

It is customary to quantify the performance of a classifier model in terms of
the precision ($\Pi$), recall ($\Psi$), and $F_{1}$-score performance metrics.
The precision metric quantifies how much positive results are actually
positive, the recall provides information on how much true positives are
identified correctly as positive, and $F_{1}$-score gives an overall measure
for the accuracy of a classifier model since it is the harmonic average of
precision and recall. These metrics are given as

$\displaystyle\Pi=\frac{\xi}{\xi+\upsilon},\;~{}~{}\Psi=\frac{\xi}{\xi+\mu},\;~{}~{}F_{1}\textrm{-score}=2\times\frac{\Pi\times\Psi}{\Pi+\Psi},$
(18)

where $\xi$, $\upsilon$, and $\mu$ denote the numbers of true positive, false
positive, and false negative, respectively.

##  V Measurement Methodology and Dataset Generation

Figure 4: This snapshot of spectrum denotes a sample from the dataset
comprised of cellular signals recorded during a comprehensive measurement
campaign. $900$ MHz band is represented here but the measurements are not
limited to that band; thus, cover all cellular bands. Figure 5: An overview of
the measurement area. The transmitters are located in the urban area, but the
receivers are in a sub-urban area.

The dataset to test and evaluate the proposed method is developed from the
measurements taken through an extensive measurement campaign conducted at
different locations and frequency bands. In order to make the model robust
against environmental changes, measurements have been conducted in different
locations as illustrated in Fig. 5. The locations of transmitters and
measurement points can be seen in Fig. 5. It can be seen that the signals
propagate through the urban area, and then reach the receivers in sub-urban
area. The measurement focuses on $800$, $900$, $1800$, and $2100$ MHz
frequency bands that are allocated for cellular communications. Rohde Schwarz
FSW26 spectrum analyzer and a set of Yagi-Uda antennas are employed at the
receiver. The measurements are unified as follows: for each signal observed in
the spectrum, $16384$ I/Q samples are taken. Measurements are conducted at
$15$ different SNR levels. Each level consists of the same number of signals
which is $4000$. Therefore, $60000$ signals in total are recorded and included
in the dataset. Sample power spectra of these signal types, obtained with the
Welch’s method, are shown in Fig. 4. When the proposed method is considered,
the dataset is split into test and train data with the proportion of $0.4$ and
$0.6$, respectively.

To better understand the effects of wireless communications channels over the
received signals, first, amplitude distributions of four different recordings
of all three signals are given in Fig. 6. The figure indicates differing power
and amplitude levels. The distribution of the received power changes
considerably since the measurements are taken at different locations, times
and frequency bands. Second, Fig. 7 illustrates the phase distribution of four
different recordings of all three signals. It is seen that the phase of
received signals are distributed almost uniformly in between $-\pi$ and $\pi$
radians. This result implies Rayleigh-like fading behavior stemming from the
amplitude and phase distributions of received signals. This is an expected
result when the measurement area and the locations of transmitters and
receivers are considered. Eventually the received power and phase of the
signals are obviously affected by the shadowing, multipath fading and path
loss as depicted in Fig. 6 and Fig. 7.

The dataset is shared in [20] in the format of SCF. The dataset covers $60000$
SCF matrices with the dimensions of $8193\times 16$ corresponding to received
I/Q samples of $16384$ for each signal.

Figure 6: Sample PDFs of the amplitude of received signals in the dataset. The
example PDFs show the different channel and received power characteristics.
Figure 7: Sample PDFs of the phase of received signals in the dataset. The
example PDFs show uniform distribution characteristics.

##  VI Classification Performance Analysis

Figure 8: Accuracy values with respect to SNR level of the received signals
for both cases. TABLE II: Classification performance metrics for the proposed
CNN model with SCF, AP, and FFT features for CASE2.

SNR | Feature | Signal | Precision ($\Pi$) | Recall ($\Psi$) | F1-Score
---|---|---|---|---|---
1dB | I/Q | UMTS | 0.33 | 1.00 | 0.49
LTE | 0.00 | 0.00 | 0.00
GSM | 0.00 | 0.00 | 0.00
Average | 0.11 | 0.33 | 0.16
AP | UMTS | 0.35 | 0.39 | 0.37
LTE | 0.31 | 0.29 | 0.30
GSM | 0.30 | 0.29 | 0.30
Average | 0.32 | 0.32 | 0.32
FFT | UMTS | 0.00 | 0.00 | 0.00
LTE | 0.21 | 0.50 | 0.30
GSM | 0.00 | 0.00 | 0.00
Average | 0.07 | 0.17 | 0.10
SCF | UMTS | 0.59 | 0.40 | 0.48
LTE | 0.68 | 0.46 | 0.54
GSM | 0.62 | 0.98 | 0.76
Average | 0.63 | 0.61 | 0.59
5dB | I/Q | UMTS | 0.33 | 1.00 | 0.49
LTE | 0.00 | 0.00 | 0.00
GSM | 0.00 | 0.00 | 0.00
Average | 0.11 | 0.33 | 0.16
AP | UMTS | 0.43 | 0.42 | 0.42
LTE | 0.39 | 0.43 | 0.41
GSM | 0.61 | 0.56 | 0.58
Average | 0.47 | 0.47 | 0.47
FFT | UMTS | 0.53 | 0.49 | 0.51
LTE | 0.25 | 0.51 | 0.34
GSM | 0.00 | 0.00 | 0.00
Average | 0.26 | 0.33 | 0.28
SCF | UMTS | 0.97 | 0.92 | 0.94
LTE | 0.92 | 0.95 | 0.94
GSM | 0.98 | 1.00 | 0.99
Average | 0.96 | 0.96 | 0.96
10dB | I/Q | UMTS | 0.33 | 1.00 | 0.49
LTE | 0.00 | 0.00 | 0.00
GSM | 0.00 | 0.00 | 0.00
Average | 0.11 | 0.33 | 0.16
AP | UMTS | 0.50 | 0.52 | 0.51
LTE | 0.50 | 0.53 | 0.52
GSM | 0.88 | 0.79 | 0.83
Average | 0.63 | 0.61 | 0.62
FFT | UMTS | 0.49 | 0.37 | 0.42
LTE | 0.27 | 0.62 | 0.38
GSM | 0.00 | 0.00 | 0.00
Average | 0.26 | 0.33 | 0.27
SCF | UMTS | 1.00 | 0.96 | 0.98
LTE | 0.96 | 0.99 | 0.98
GSM | 1.00 | 1.00 | 1.00
Average | 0.99 | 0.98 | 0.98
15dB | I/Q | UMTS | 0.33 | 1.00 | 0.49
LTE | 0.00 | 0.00 | 0.00
GSM | 0.00 | 0.00 | 0.00
Average | 0.11 | 0.33 | 0.16
AP | UMTS | 0.55 | 0.54 | 0.55
LTE | 0.55 | 0.57 | 0.56
GSM | 0.94 | 0.93 | 0.94
Average | 0.68 | 0.68 | 0.68
FFT | UMTS | 0.73 | 0.49 | 0.58
LTE | 0.35 | 0.82 | 0.49
GSM | 0.00 | 0.00 | 0.00
Average | 0.36 | 0.44 | 0.36
SCF | UMTS | 1.00 | 0.97 | 0.99
LTE | 0.97 | 0.99 | 0.98
GSM | 0.99 | 1.00 | 1.00
Average | 0.99 | 0.99 | 0.99

We evaluate the performance of the proposed classification model over the
comprehensive dataset described in Section V. Therefore, the dataset is
composed of GSM, wideband code division multiple access (WCDMA) for UMTS and
LTE signals which are recorded over-the-air at different locations with unique
conditions in terms of the number of channel taps, and fading, again as noted
in Section V. Training and test sets contain $9000$ and $6000$ signals for
each waveform. The I/Q signal length is 16384. CNN is trained and tested on
the graphics processing unit (GPU) server equipped with four NVIDIA Tesla V100
GPUs.

First, we focus on the results for CASE1. As stated before, CASE1 refers to
four-classes classification problem. As shown in Fig. 8, the test accuracy of
the model exceeds $90\%$ at $11$dB SNR. It takes a maximum accuracy value of
$92\%$ at $15$dB. The confusion matrices related to CASE1 are depicted in Fig.
9. Due to the low SNR values, the model mostly can not accurately clasify the
signals and identifies the signal as Noise. This case can be observed in Fig.
9. Therefore, dividing the problem into two parts becomes a viable
alternative: first sense, then classify. In this case, we analyse both CNN
detector and CNN classifier (see Fig. 2). For the sensing part of the
architecture, noise signals are labeled as $0$ and the rest of the set is
labeled as $1$. The detection results are plotted again in Fig. 8 as
$P^{\texttt{S}}_{\texttt{CASE2}}$. The detection accuracy follows $96\%$ at
almost all SNR values.

Following the steps above, assuming that the signal is present in the spectrum
at the output of CNN detector of CASE2 in Fig. 2, the performance of the CNN
classifier can be investigated. This stage is labeled as
$P^{\texttt{C}}_{\texttt{CASE2}}$ in Fig. 8 and it is observed that the
classification accuracy exceeds $90\%$ at $3$dB SNR. It gives the best
performance, $98.5\%$, at $9$dB and it is remained stable until $15$dB. The
Fig. 10 depicts the confusion matrices related to CNN classifier of CASE2 and
implies that even at low SNR regime, the classifier can identify GSM signals
with high accuracy; however, overall precision of the classifier is low i.e.,
in contrary to GSM signals, the classifier has difficulty in recognition of
UMTS and LTE signals in low SNR regime. But the accuracy and precision of the
classifier enhance as SNR increases in Fig. 10 and Fig. 10. This phenomena is
observed due to the dominance of characteristics in feature matrices which
follow Gaussian distribution. As known, GSM is associated with Gaussian
minimum shift keying (GMSK); therefore, GSM signals inherently show
characteristics defined by Gaussian distribution in the case of high SNR.
Decreasing in SNR leverages Gaussian characteristics in the received signal
because of AWGN. That is to say, UMTS and LTE signals with lower SNR values
become denoting Gaussian characteristics; thus, the model is prone to learn
Gaussian characteristics to decrease its loss function. When the trained model
is tested, it is expected that the model can identify the signals which have
dominant Gaussian characteristics. As a result, the model can identify GSM
signals, which inherently denote Gaussian attributes, in lower SNR regime
where UMTS and LTE signals lose their unique features. This statement shows
parallelism with the results given in CASE1 in Fig. 9. The model accurately
identifies AWGN at lower SNR regime as given in Fig. 9.

Figure 9: Confusion matrices for CASE1 at SNR levels of (a) 1dB, (b) 5dB, and
(c) 10dB. It should be noticed that the model does not randomly choose only
one signal at low SNR level.

Figure 10: Confusion matrices for the classification part of CASE2 at SNR
levels of (a) 1dB, (b) 5dB, and (c) 10dB. It should be noticed that the model
does not randomly choose only one signal at low SNR level.

The results for CASE2 are given in parts to this point. Now, we can examine
the overall performance of CASE2. Obviously, there is a loss of performance
due to some misdetection in the sensing phase. Both the detection rate in the
sensing stage and the accuracy in the classification stage are high at $3$dB
and thereafter, so overall performance does not suffer a significant loss. As
shown in Fig. 8, the overall performance of CASE2 is far superior to that of
CASE1. Especially at low SNR levels, the signals remaining after first
detecting and separating noise from the signal set by the CNN detector can be
classified with much better performance. In this way, the performance is
higher in CASE2. However, it should be noted that CASE2 is more costly than
CASE1 in terms of training time and the number of models. Obviously, CASE2 can
be predicted to perform better than CASE1 in the presence of a jammers
exhibiting Gaussian characteristics or other interfering signals.

Sensing performance can be considered that it is slightly lower than
conventional spectrum sensing methods like energy detector and matched filter.
However, it should be noted that this study employs real–world data rather
than simulation or synthetic data. For example, energy detectors can sense a
signal in a spectrum with optimal performance; however, it needs to know noise
variance. But even with a slight error on estimating the noise variance, the
sensing performance seriously decreases. Moreover, as the power of spread
spectrum signals (e.g. WCDMA in UMTS) is spreaded in a wide band, its power is
very close to the noise floor. By taking into this account, in a fading
environment, it can be said that ED cannot perform a satisfactory detection
rate for spread spectrum signals as stated in [25]. It is worth noting that
our measurements follow Rayleigh distribution as seen in Fig. 6 and Fig. 7. On
the other hand, matched filters are waveform-specific solution and they
require the perfect knowledge for signals.

###  VI-A Investigation for the Impact of Different Features

In this section, we compare the performance of other features of I/Q, AP, and
FFT which are frequently employed for sensing purposes with SCF. The features
are used as detailed in Section II. The results of this test are presented in
Table II. Unlike the modulation classification studies [9, 26], I/Q cannot
provide a meaningful input for the model due to the severe fading effect on
the phase of signal, which is seen in Fig. 7. The histograms of phase imply
that the signal phase is corrupted and the information on the phase is lost.
That is why I/Q shows poor performance. The average performances also indicate
that SCF outperforms I/Q, AP, and FFT for all SNR levels. Assuming that these
two are used along with I/Q as the main features for training, these results
show significant gains for real-world signals especially above $5$dB SNR
level. It is observed that AP performs better than FFT. The average training
time per epoch is approximately $60$s for SCF feature where both FFT and AP
take $7.5$s per epoch; however, both FFT and AP cannot show an acceptable
classification performance, $P_{\texttt{CASE2}}^{C}$. Although the cost of
computing both features is far behind the SCF, they are far from delivering
the desired performance. To visualize the vectors in input space, we employ
the t-distributed stochastic neighbor embedding (t-SNE) algorithm. Although
originally I/Q samples are not linearly separable, SCF clusters the vectors in
the space and allows almost linear separation as depicted in Fig. 11. The
analysis based on t-SNE results show that SCF better separates signal vectors
in space. The results of this study are in line with the previous analysis
[27].

(a) I/Q

(b) AP

(c) FFT

(d) SCF

Figure 11: Two-dimensional demonstration of the features by the t-SNE
algorithm. This illustration shows that in contrary to the other features, SCF
can separately cluster real-world signals in space successfully.

###  VI-B Comparison with Existing Deep Learning Networks

TABLE III: Performance comparison between the existing DL networks and the proposed system for the classification stage of CASE2 at SNR value of 15dB. Network | Signal | Precision | Recall |  $F_{1}$-score
---|---|---|---|---
CLDNN [18] | UMTS | 0.33 | 1.00 | 0.50
LTE | 0.00 | 0.00 | 0.00
GSM | 0.00 | 0.00 | 0.00
Average | 0.11 | 0.33 | 0.17
LSTM [28] | UMTS | 0.33 | 1.00 | 0.50
LTE | 0.00 | 0.00 | 0.00
GSM | 0.00 | 0.00 | 0.00
Average | 0.11 | 0.33 | 0.17
Proposed CNN with SCF | UMTS | 0.79 | 1.00 | 0.88
LTE | 1.00 | 0.72 | 0.84
GSM | 0.99 | 1.00 | 0.99
Average | 0.93 | 0.91 | 0.91

The existing DL networks are employed to classify the cellular communication
signals. We utilize CLDNN [18] and LSTM [28] models. These models are
originally used in modulation classification. Without any change in the
models, input matrix, and input vector as proposed in the papers are adopted
in the study. CLDNN takes a $2\times 128$ matrix which is composed of
amplitude and phase values for each I/Q sample. On the other hand, LSTM model
utilizes a vector reshaped version of the matrix used in CLDNN. Therefore, the
length of the vector is $256$. Its first half includes in-phase components
while the rest of the vector is quadrature components. Other details are found
in [18, 28]. The precision, recall, and $F_{1}$-score are given in Table III.
It shows that CLDNN and LSTM decide that the received signal is UMTS whatever
it actually is. Even though LSTM and CLDNN can be trained in a short time by
using I/Q vector and matrix, employing I/Q vector and matrix give poor
classification performance.

###  VI-C Comparison with SVM

In our previous work, we employed SVMs to identify real-world signals [27].
Even though utilization of SCF in SVM provides good performance, training of
SVM should be conducted for each SNR level separately i.e., at the end of the
training, the more SNR values in the dataset, the more models should be
created. The real-world utilization of SVM requires an SNR estimator and
loading of all pre-trained models to memory during operation; thus, reducing
the applicability of the method and making improvements a necessity. As seen
in Fig. 12, the CNN-based classifier shows a superior performance compared to
SVM-based classifier of [27], under the conditions of the classification part
of CASE2. To this end, while CNN-based classifier employs a less costly
feature due to elimination of mapping of bi-frequency spectrum, it still
performs with higher accuracy. Therefore, producing a model independent of the
SNR is an advantage of the proposed CNN based method since the training set
contains an equal number of signals from each SNR. As a result, a single model
would be adequate for classification in a large SNR range at the test stage.

Figure 12: The classification performance comparison between SVM in [27] and
the proposed CNN structure for $P_{\texttt{CASE2}}^{\texttt{C}}$. Figure 13:
Spectrum sensing performances of CFAR detectors and CNN-based detector with
respect to SNR.

###  VI-D Comparison with CFD

Besides signal classification, the proposed CNN model can be used for spectrum
sensing. We investigated the sensing performance of the model by training a
CNN-based spectrum occupancy detector trained over 600 pure noise signals and
600 noisy WCDMA signals for each SNR value. Then, the model is tested with 400
pure noise signals and 400 noisy WCDMA signals for each SNR level and sensing
results are acquired. Furthermore, for comparison purposes, we implement a
constant false alarm rate (CFAR) detector utilizing classical CFD [29] to
identify WCDMA signals and the same dataset is also used for CFAR detector.
Please note that UMTS signals are deliberately selected due to their known
dominant SCF characteristics stemming from cyclic spreading codes. The results
of this test are given in Fig. 13. In view of these results, it is clearly
seen that the CNN-based detector outperforms the CFAR detector at all SNR
regimes. For example, the sensing performance of the CNN-based detector is
$91.75\%$ at $3$dB while the probability of detection for the CFAR detector
are $45.6\%$ and $59.4\%$ for the selected false alarm rates as $0.05$ and
$0.1$, respectively.

###  VI-E Focusing on the Meaningful Region of Spectral Correlation Function

Figure 14: Model comparison in terms of memory, complexity, and accuracy. The
epoch time and memory allocation rate are normalized with their maximum values
observed in these models (maximum values for both are observed in LSTM). The
average accuracy is the mean accuracy in the SNR range between 1dB and 15dB.
Figure 15: Test accuracy with respect to SNR values for the proposed CNN,
CLDNN, LSTM, ResNet, and DenseNet models.

As seen in Fig. 1, the meaningful part of the features is located in the
middle of the matrices. In order to investigate the possibility of accuracy
improvement and the fair comparison with the existing DL networks, we employ a
small partition in middle of the SCF matrix, where the cyclic characteristics
are mainly observed. As stated in Section V, an SCF matrix has the dimension
$8193\times 16$. Therefore, it is not possible to train such a dense model in
our server equipped with four NVIDIA Tesla V100 GPUs. To compare our proposed
CNN architecture with a more dense model, we decrease the dimensions of the
SCF matrices by using only $16\times 16$ part in the middle of the matrices.
Only in this way, we are able to train complex models such as LSTM [12] and
DenseNet [19] with SCF. Moreover, the proposed CNN, CLDNN [18], and ResNet [9]
are also trained with the shrunken SCF matrices. It is worth saying that, we
conduct four-class classification (i.e., CASE1) in this study. The results
depicted in Fig. 14 shows that the proposed CNN is favorable in terms of both
low complexity (i.e., epoch time) and efficient memory allocation, as well as
high test accuracy. During this study, batch sizes are kept same for all
models. The memory allocation and training time have been normalized by LSTM’s
memory allocation rate and training time, respectively; thus, computer-
independent results are provided in Fig. 14. It should be noted that early
stopping is used during training of models and the minimum number of epochs is
required by the proposed CNN. Furthermore, Fig. 15 denotes the accuracy with
respect to SNR levels for each model. By considering results, it can be
observed that the proposed CNN is more robust and efficient than the existing
models. Moreover, it is seen that CNN gives better results with this smaller
matrix than the complete matrix is used. By eliminating the region except for
the meaningful part of SCF, the input matrices become more distinct from each
other. Fig. 1 implies that SCF matrices have similarities except for the
meaningful part. The confusion matrices in Fig. 16 for $16\times 16$ inputs
denote the improvement in the precision of AWGN. This explains why the small
portion of the matrix can lead to higher accuracy.

It is also explored how the dimensions of the small partition affect the
performance of the CNN model. The results show that using 4 rows does not
perform well enough. When using rows between 8 and 128 (as power of two), the
results are satisfactory. The test accuracy with respect to input size is
demonstrated in Fig. 17. It is revealed that by considering the accuracy at
lower SNR regimes and the training time, $16\times 16$ is the most suitable
size for the CNN.

Figure 16: Confusion matrices for CASE1 at SNR levels of (a) 1dB and (b) 5dB
when $16\times 16$ inputs are employed. Figure 17: The test accuracy of the
proposed CNN architecture with respect to input size.

##  VII Conclusion

In this study, a DL-based method utilizing SCF as an input to a novel CNN
model to achieve spectrum sensing or signal identification interchangeably or
jointly without the requirement of any a priori information is proposed. First
approach investigates the joint sensing and classification of wireless
signals. Second, a sequential approach is adopted. The results show that
sequential approach performs better than the joint approach. Moreover,
comparative analysis indicated the superiority of SCF as a distinctive feature
when compare to the contemporary features utilized for currently available DL-
based detector models. The results also imply that under stringent channel
conditions, the CNN model of the proposed method provides better spectrum
sensing performance than other available DL models, SVMs, and classical CFD.
These results indicate that applicability of DL-based techniques in the
rapidly changing communications environment of contemporary wireless
communications networks. In subsequent studies, the performance of the
proposed method for sensing and identification of other wireless signals or
modulation techniques with cyclic features can be explored. Furthermore, the
performance of the proposed method can be investigated against adversarial
attacks and efforts can be made to develop various techniques to strengthen
its resistance to these types of intrusions. Although this study focuses on
supervised learning, it is possible to improve the performance of the proposed
method by supporting unsupervised learning methods in feature extraction.

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|  Kürşat Tekbıyık received his B.Sc. and M.Sc. degrees (with high honors) in
electronics and communication engineering from Istanbul Technical University,
Istanbul, Turkey, in 2017 and 2019, respectively. He is currently pursuing his
Ph.D. degree in telecommunications engineering in Istanbul Technical
University. He is also working as a senior researcher at the HISAR Lab. of
TUBITAK BILGEM. His research interests include algorithm design for signal
intelligence, next-generation wireless communication systems, terahertz
wireless communications, and machine learning.
---|---
|  Özkan Akbunar received B.Sc. degree from Electrical and Electronics
Engineering department in Anadolu University, Eskişehir, Turkey. He is
proceeding his M.Sc education in Telecommunication Engineering department in
Istanbul Technical University, Istanbul, Turkey. He is working in the
Scientific and Technological Research Council of Turkey at Informatics and
Information Security Research Center as R&D engineer since December 2017. His
main research areas are wireless communication systems, digital signal
processing, machine learning and radar.
---|---
|  Ali Rıza Ekti is from Tarsus, Turkey. He received B.Sc. degree in
Electrical and Electronics Engineering from Mersin University, Mersin, Turkey,
(September 2002-June 2006), also studied at Universidad Politechnica de
Valencia, Valencia, Spain in 2004-2005, received M.Sc. degree in Electrical
Engineering from the University of South Florida, Tampa, Florida (August
2008-December 2009) and received Ph.D. degree in Electrical Engineering from
Department of Electrical Engineering and Computer Science at Texas A&M
University (August 2010-August 2015). He is currently an assistant professor
at Balikesir University Electrical and Electronics Engineering Department. He
also holds the Division Manager position of HISAR Lab. at TUBITAK BILGEM where
he is responsible for R&D activities in the wireless communications and signal
processing. His current research interests include statistical signal
processing, convex optimization, machine learning, resource allocation and
traffic offloading in wireless communications in 5G and beyond systems.
---|---
Ali Görçin graduated from Istanbul Technical University with B.Sc. in
Electronics and Telecommunications Engineering and completed his master’s
degree on defense technologies at the same university. After working at
Turkish Science Foundation (TUBITAK) on avionics projects for more than six
years, he moved to the U.S. to pursue PhD degree in University of South
Florida (USF) on wireless communications. He worked for Anritsu Company during
his tenure in USF and worked for Reverb Networks and Viavi Solutions after his
graduation. He is currently holding an assistant professorship position at
Yildiz Technical University in Istanbul and also serving as the interim
president of TUBITAK BILGEM.
---
|  Güneş Karabulut Kurt received the B.S. degree with high honors in
electronics and electrical engineering from the Bogazici University, Istanbul,
Turkey, in 2000 and the M.A.Sc. and the Ph.D. degrees in electrical
engineering from the University of Ottawa, ON, Canada, in 2002 and 2006,
respectively. From 2000 to 2005, she was a Research Assistant with the CASP
Group, University of Ottawa. Between 2005 and 2006, she was with TenXc
Wireless, Canada. From 2006 to 2008, she was with Edgewater Computer Systems
Inc., Canada. From 2008 to 2010, she was with Turkcell Research and
Development Applied Research and Technology, Istanbul. Since 2010, she has
been with Istanbul Technical University, where she currently works as a
Professor. She is a Marie Curie Fellow. Since August 2019, she is at Carleton
University as a visiting professor and also appointed as an adjunct research
professor. She is currently serving an Associate Technical Editor of the IEEE
Communications Magazine.
---|---
|  Khalid A. Qaraqe was born in Bethlehem and received the B.S. (Hons.) degree
from the University of Technology, Bagdad, Iraq, in 1986, the M.S. degree from
the University of Jordan, Amman, Jordan, in 1989, and the Ph.D. degree in from
Texas A&M University, College Station, TX, in 1997, all in electrical
engineering. From 1989 to 2004 he has held a variety positions in many
companies and he has over 12 years of experience in the telecommunication
industry. He has worked on numerous projects and has experience in product
development, design, deployments, testing and integration. He joined the
Department of Electrical and Computer Engineering of Texas A&M University,
Qatar, in July 2004, where he is now a Professor and Managing Director with
the Center for Remote Healthcare Technology, Qatar. He has been awarded more
than 20 research projects consisting of more than USD 13 M from local
industries in Qatar and the Qatar National Research Foundation (QNRF). He has
published more than 131 journal papers in top IEEE journals, and published and
presented 250 papers at prestigious international conferences. He has 20 book
chapters published, four books, three patents, and presented several tutorials
and talks. His research interests include communication theory and its
application to design and performance, analysis of cellular systems and indoor
communication systems. Particular interests are in mobile networks, broadband
wireless access, cooperative networks, cognitive radio, diversity techniques,
index modulation, visible light communication, FSO, telehealth and noninvasive
bio sensors.
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  *[MIMO]: multiple-input multiple-output
  *[LTE]: Long-Term Evolution
  *[DL]: deep learning
  *[ML]: machine learning
  *[CNN]: convolutional neural network
  *[ISM]: industrial scientific medical
  *[FFT]: fast Fourier transform
  *[AP]: amplitude-phase representation
  *[I/Q]: in-phase/quadrature
  *[LSTM]: long short term memory
  *[DVB]: digital video broadcast
  *[GSM]: Global System for Mobile communications
  *[WFM]: wide-band FM
  *[CFD]: cyclostationary features detection
  *[RAT]: radio access technology
  *[UMTS]: universal mobile telecommunications system
  *[SCF]: spectral correlation function
  *[CLDNN]: convolutional long short term memory fully connected deep neural network
  *[SNR]: signal-to-noise ratio
  *[CNNs]: convolutional neural network
  *[AWGN]: additive white Gaussian noise
  *[RF]: radio frequency
  *[CAF]: cyclic autocorrelation function
  *[PSD]: power spectral density
  *[]:
  *[FAM]:   () accumulation method
  *[SCFs]: spectral correlation function
  *[ReLU]: rectified linear unit
  *[ADAM]: adaptive moment estimation
  *[WCDMA]: wideband code division multiple access
  *[GPU]: graphics processing unit
  *[GPUs]: graphics processing unit
  *[SVM]: support vector machine
  *[CFAR]: constant false alarm rate