Spectrum Sensing and Signal Identiﬁcation with Deep

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Spectrum Sensing and Signal Identification withDeep Learning based on Spectral Correlation

FunctionKursat Tekbıyık, Graduate Student Member, IEEE, Ozkan Akbunar, Student Member, IEEE,

Ali Rıza Ekti, Senior Member, IEEE, Ali Gorcin, Senior Member, IEEE,Gunes Karabulut Kurt, Senior Member, IEEE, Khalid A. Qaraqe, Senior Member, IEEE

Abstract—Spectrum sensing is one of the means of utilizingthe scarce source of wireless spectrum efficiently. In this paper,a convolutional neural network (CNN) model employing spectralcorrelation function (SCF) which is an effective characterizationof cyclostationarity property, is proposed for wireless spectrumsensing and signal identification. The proposed method classifieswireless signals without a priori information and it is imple-mented 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, theproposed CNN method does not require a statistical decisionprocess and does not need to know the distinct features of signalsbeforehand. Implementation of the method on the measured over-the-air real-world signals in cellular bands indicates importantperformance gains when compared to the signal classifying deeplearning networks available in the literature and against classicalsensing methods. Even though the implementation herein isover cellular signals, the proposed approach can be extendedto the detection and classification of any signal that exhibitscyclostationary features. Finally, the measurement-based datasetwhich is utilized to validate the method is shared for thepurposes of reproduction of the results and further research anddevelopment.

Index Terms—Deep learning, spectrum sensing, cyclostation-arity, signal classification, spectral correlation function, convolu-tional neural networks.

I. INTRODUCTION

Today’s wireless communication systems have to bear anunprecedented increase in data transmission volume. It is

K. Tekbıyık, O. Akbunar, A.R. Ekti and A. Gorcin are with the Infor-matics and Information Security Research Center (BILGEM), TUBITAK,Kocaeli, Turkey. e-mail: {kursat.tekbiyik, ozkan.akbunar, aliriza.ekti,ali.gorcin}@tubitak.gov.tr

A.R. Ekti is with the Department of Electrical-Electronics Engineering,Balıkesir University, Balıkesir, Turkey. e-mail: [email protected]

A. Gorcin is with the Department of Electronics and Communica-tions Engineering, Yıldız Technical University, Istanbul, Turkey. e-mail:[email protected]

K. Tekbıyık and G.K. Kurt are with the Department of Electronics and Com-munications Engineering, Istanbul Technical University, Istanbul, Turkey. e-mail: {tekbiyik, gkurt}@itu.edu.tr

K. A. Qaraqe is with the Department of Electrical and ComputerEngineering, Texas A&M University at Qatar, Doha, Qatar. e-mail:[email protected]

This work has received funding from the European Commission Horizon2020 research and innovation programme BEYOND5 under grant agreementNo. 876124.

This study has been supported by NPRP12S-0225-190152 from the QatarNational Research Fund (a member of The Qatar Foundation).

essential for wireless communication networks to utilize thelimited source of spectrum as efficiently and effectively aspossible to meet the demand [1]. Furthermore, the effortsincluding the deployment of small cells, utilizing mmWavebands, effective spectrum usage algorithms, massive multiple-input multiple-output (MIMO) systems [2], and cognitive radionetworks target the same goal. Cognitive radios aim to attendthis purpose by sharing the spectrum dynamically amongusers; thus, spectrum sensing and signal identification becamemajor techniques for cognitive radio networks.Consideringjoint communications, sensing, and localization demanded by6G and beyond, efficient spectrum allocation will be morecrucial for heterogeneous networks. For instance, 5G NRRelease 16 introduces dynamic spectrum sharing which isnovel method enabling parallel operation of 5G and Long-Term Evolution (LTE) in the same band [3]. Furthermore, itis envisioned that radar and communications system will sharethe same frequency band [4].

When spectrum sensing and signal identification techniquesare considered, it is seen that sensing techniques of energy de-tection and matched filtering require a priori information suchas number of second order noise statistics, cyclic frequenciesand particular pulse shaping filter characteristics to operate.Moreover, after processing of the received signals, a statisticaldecision mechanism should be implemented to complete thesensing process [5]. Such cumbersome process can hamperthe agile decision making requirements of 5G and beyondnetworks, thus, classical sensing paradigm can not satisfythe requirements of fast changing operation environment ofcontemporary and future wireless communications networks.In this context, deep learning (DL) has been proposed as asolution to the parameter adaptation issues of classical tech-niques. This stems from the known ability of DL techniquesin extracting the intrinsic features of given inputs through aconvolutional process. The use of DL based approaches alsoeliminates the need for a statistical decision mechanism at theend of the identification process. Along this line, the recentstudy shows that DL methods outperform classical approachesin signal detection in the spectrum [6]. To achieve the require-ments for 5G and beyond wireless networks, an intelligentradio design for spectrum sensing and signal identification isrequired and such solution can be realized with the help ofmachine learning (ML) algorithms [7] utilizing features suchcyclostationarity of signals [8].

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A. Related Work

When the literature on the implementation of artificialintelligence techniques for spectrum sensing and signal iden-tification purposes are considered, it is initially seen thatconvolutional neural networks (CNNs) are trained with high-order statistics of single carrier signals for modulation classi-fication [9]. A CNN classifier is used for modulation and in-terference identification for industrial scientific medical (ISM)bands by utilizing fast Fourier transform (FFT), amplitude-phase representation (AP) and in-phase/quadrature (I/Q) fea-tures for training [10]. Another study [11] focused on theprotocol classification in ISM band by utilizing fully connectedneural networks. As another example of the application of DLto signal classification, long short term memory (LSTM) isdeployed for modulation classification and identification ofdigital video broadcast (DVB), Tetra, LTE, Global Systemfor 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 thereal channels, there are numerous phenomenons, which furthercomplicate the signal characteristics.

On the other hand, cyclostationarity signal analysis has beenexplored for modulation classification, parameter estimationand spectrum sensing for more than 20 years. In addition tobeing an established method for spectrum sensing in cognitiveradio domain, cyclostationary features detection (CFD) is alsoutilized to distinguish generic modulations such as M-PSK,M-FSK, and M-QAM [8, 13]. When the radio access tech-nology (RAT) identification [14] is considered, second ordercyclostationarity is employed for classification of LTE andGSM signals [15]. Later, a tree-based classification approachis proposed to identify GSM, cdma2000, universal mobiletelecommunications system (UMTS) and LTE signals [16].

B. The Contributions

1) Methodological novelty: CFD depends on extracting theunderlying features using likelihood-based techniques utiliz-ing statistical decision mechanisms and for CFD to operateunder the dynamically changing communication medium, anadditional mechanism to adaptively adjust decision parameterssuch as thresholds and the number samples is required [17].On the other hand, even though employment of DL techniquesfor the purposes of spectrum sensing and signal identificationimplies considerable advantages in terms of performance andcomplexity, utilization of FFT, AP and I/Q as input featuresto the intelligent networks do not lead stable and dependableresults due to the rapidly and significantly changing wirelesscommunications medium between the nodes. Therefore, thisstudy proposes application of SCF as input feature to CNNs forblind wireless signal identification. The problems of spectrumsensing and signal identification are framed into two particularcontexts which utilize a novel CNN model designed andtrained with spectral correlation function (SCF) of wirelesssignals without bi-frequency mapping. Therefore, the proposedmethod can be employed either to decide whether the signalis present or not in the spectrum or to distinguish signals

from each other. Sensing and identification performance of themethod is tested and validated utilizing real-life over-the-airsignal measurements of GSM, UMTS, and LTE signals.

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

2) Novelty in terms of numerical studies: In terms of per-formance analysis, first, a comparative analysis is conductedand superiority of SCF over the features of I/Q, AP andFFT is shown for the purpose of training of DL networks.Second, comparison with the existing DL methods such asconvolutional long short term memory fully connected deepneural network (CLDNN) [18], LSTM [12], DenseNet [19],ResNet [9] are given in terms of accuracy, memory consump-tion and computational complexity. Third, it is shown thatthe proposed method outperforms support vector machines(SVMs) trained with SCF, which is our previous study. Fourth,the performance of the proposed method is compared with theclassical spectrum sensing technique of CFD, which requiresthe cyclic frequencies as a priori information. The identifica-tion results indicate important performance improvements overthe aforementioned techniques.

3) Novelty in terms of experimental activities: Focusingon the valuable information in the dataset is an importantmetric for the proposed method; thus, it is denoted thatutilizing only the meaningful part of the input matrices im-proves the classification performance along with alleviation intraining time and complexity. On the other hand, the generaldataset, which has been developed from measurements takenthrough a comprehensive measurement campaign conductedin different locations and frequency bands, is shared publiclyin [20]. Therefore, the measurement-based dataset is open toresearchers as a comprehensive resource in the developmentand validation of their work.

4) Applicability for future research problems: Even thoughin this work the scope of implementation is focused on cellularsignals, the introduced identification system can be directlyused for detection and classification of any signal that exhibitcyclostationary features. All the analyses are based on the real-world measurements taken during an extensive measurementcampaign conducted at different locations with varying en-vironmental conditions such as channel fading statistics andsignal-to-noise ratio (SNR) levels. Finally, the measurementdata that this work is experimented on is also shared forreproducibility of this work and to support future research anddevelopment activities in this domain.

C. Organization of the Paper

The rest of the paper is structured as follows. Backgroundinformation on the system model, cyclostationary analysis andCNNs is presented in Section II. The problem statement is

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given in Section III. The proposed CNN model is described inSection IV. The details of the measurement setup and datasetutilized in this study are given in Section V. Section VIpresents measurement results and details the classificationperformance of the proposed method. The concluding remarksare provided in Section VII.

II. BACKGROUND

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

r(t) = ρ(t) ∗ x(t) + ω(t), (1)

where ω(t) denotes the complex additive white Gaussiannoise (AWGN) with CN (0, σ2

N ) in the form of ω(t) =ωI(t)+jωQ(t) as both ωI(t) and ωQ(t) beingN (0, σ2

N/2) andj =√−1; the complex baseband equivalent of the transmitted

signals is denoted as x(t); and ρ(t) stands for the impulseresponse for the time-invariant wireless channel because ofextremely short observation time for a signal.

Depending on the idle or busy state of the mobile propaga-tion channel in the radio frequency (RF) spectrum, the signaldetection process of deep learning methods can be modelledas a binary hypothesis test

r(t) =

{ρ(t)x(t) + ω(t), H1

ω(t), H0.(2)

H0 and H1 hypotheses stand for the presence of noise onlyand the unknown signal, respectively. Therefore, the problemstatement can be stated as identification of the presence of theunknown signal, x(t), and classification of the x(t).

A. Cyclostationarity

Cyclostationary signal processing leads to extracting hiddenperiodicities 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 providethe necessary information for identification. Thus, the un-known signals x(t) can be identified by using cyclostationaryfeatures to obtain the statistical characteristics of r(t) inthe presence of ω(t) and multipath fading without a prioriinformation. A nonlinear transformation, second-order cyclo-stationarity of a signal can be expressed as

sτ (t) = E {r(t+ τ/2)r∗(t− τ/2)} , (3)

where sτ (t) is the autocorrelation of r(t). Assuming that theautocorrelation function is periodic with T0 for second-ordercyclostationary signals, a Fourier series expansion of sτ (t) isgiven as

Rαr (τ) =1

T0

∫ T0/2

−T0/2

sτ (t)e−j2παtdt, (4)

where Rαr (τ) is the cyclic autocorrelation function (CAF) andα values denote the cyclic frequencies.

The Fourier transform of the CAF for a fixed α is givenwith the cyclic Wiener relation [8]

Sr(f) =

∫ T/2

−T/2Rαr (τ) e−j2πfτdτ, (5)

where Sr(f) is called as SCF which is equal to the powerspectral density (PSD) when α is zero.

The computational complexity of calculating SCF is rel-atively high. However, this complexity can be decreased byusing the FFT accumulation method (FAM) based on timesmoothing via FFT [21]. FAM estimates the SCF as

SrT =∑k

RT (kL, f)R∗T (kL, f)gc(n− k)e−i2πkq/P , (6)

where RT (n, f) denotes the complex demodulates which isthe N ′-point FFT of r(n) passed through a Hamming windowand can be computed by

RT (n, f) =

N ′/2∑k=−N ′/2

a(k)r(n− k)e−i2πf(n−k)Ts , (7)

where a(n) and gc(n) are both data tapering windows. Thesymbols N ′, Ts, and L denote the channelization length,sampling period, and sample size of hopping blocks, re-spectively. The ratio between the number of total samplesand L is employed as the length of second FFT, whoselength is denoted as P . The FAM has six implementationsteps. These steps are respectively channelization, windowing,N ′-point FFT, complex multiplication, P -point FFT and bi-frequency mapping. In the study, the unit rectangle and Ham-ming windows are employed as gc(n) and a(n), respectively.Fig. 1 illustrates SCFs results in bi-frequency plane, whichare estimated by FAM algorithm for GSM, UMTS, and LTEalong with the noise. Consequently, the input matrix, XSCF

k ,to be fed into classifier model is given as

XSCFk = |SrT (nL, f)|, (8)

B. Amplitude-Phase

The amplitude and phase values of time-domain I/Q datacan be used to establish a real-valued classification featurematrix, XAP

k . This feature matrix is composed of the ampli-tude and phase vectors of the received signal samples. So,XAPk is defined as

XAPk =

[xTAxTφ

], (9)

where xA = (rq2 + ri

2)12 and xφ = arctan(

rqri

) denote theamplitude and phase vectors, respectively.

C. Fast Fourier Transform

The characteristics of signals in frequency domain can beemployed as discriminating classification features. The FFT of

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(a) AWGN (b) GSM (c) UMTS (d) LTE

Fig. 1. FAM based SCF estimates of cellular signals in bi-frequency plane. It is easily observed that the signals show different cyclic characteristics. Thenoise 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.

the received signal is used to obtain a real-valued classificationfeature matrix XFFT

k as

f = F(r), XFFTk =

[fTrefTim

], (10)

where F(·) stands for the FFT of the received signals; fre andfim are real and imaginary parts of f , respectively.

D. The Convolutional Neural Networks

CNN is a class of deep neural networks which is mainlyemployed in image classification and recognition. CNN pro-cess inputs like a visual system in human. In other words,it extracts features in an input rather than fitting data [22]. Inthis study, we utilize the input matrices which resemble imageconsisting 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 apooling layer. In the convolution layer, the feature matrix isconvolved with different filters to obtain convolved featuremap as follows

h[i, j] =

m∑p=1

n∑l=1

wp,lXk[i+ p− 1, j + l − 1], (11)

where wp,l is the element at p-th row and l-th column ofthe m × n filter matrix, and Xk [·, ·] denotes the elementsof feature matrix convolved by wp,l. The convolution layeris followed by the pooling layer to reduce computationalcomplexity and training time, and control over-fitting due tothe fact that pooling layer makes the activation less sensitive tofeature locations [23]. The u× v maximum pooling operationis described as

g[i, j] = max {h[i+ a− 1, j + b− 1]} , (12)

where 1 ≤ a ≤ u and 1 ≤ b ≤ v. The output of the poolinglayer is a 3-D tensor. This output is then reshaped into a 1-Dvector. This vector is fed to the dense (fully-connected) layersfor the final classification decision.

III. PROBLEM STATEMENT

The dynamic communications environment of next genera-tion wireless networks require fast, robust and adaptive sensingand identification of the multi-dimensional communicationsmedium to utilize the resources quickly and efficiently [5].

In this context, spectrum sensing and signal identificationbecomes important means of achieving effective resourceutilization. To that end, we approach the problems of sensingand 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 andempty spectrum which can be referred to as AWGN only. Foreach signal the cyclic spectrum is constructed based on theprocedures described in Section II-A. The cyclic spectrum isthen fed to the CNN classifier, which is trained with fourpossible 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 isemployed for both detection and classification for the sake ofsimplicity) is utilized to decide whether a signal exists in thegiven band or not by training the CNN by two classes, firstcomprised of GSM, UMTS, and LTE signals and second partwith AWGN only. Thus, in the first stage a decision is madeabout whether a signal exists in the spectrum or not as in thecase of classical spectrum sensing. If the decision is madethat there is an information bearing signal in the given band,second stage is activated utilizing a CNN classifier, which istrained in our case with three classes (i.e., GSM, UMTS, andLTE) and finally a decision is made for the class of the signaloccupying the spectrum.

Please note that the classification refers to identification ofthe signals, and at the detection part of the approach H1 andH0 refers to the existence and non-existence of a signal overthe spectrum based on binary hypothesis testing. Both CASE1and CASE2 are illustrated in Fig. 2.

Firstly, we can define the accuracy for CASE1, PCASE1 as:

PCASE1 = P (χk = χk), k = 0, 1, 2, 3, (13)

where χk denotes the label array of the transmitted signals andk represents the label of the classes AWGN, GSM, UMTS,and LTE, respectively. χk is array for the predicted classesof the received signals. In a short, PCASE1 stands for theaccuracy of four-classes classification problem. For CASE2,it is required to define two independent accuracy functions:the sensing accuracy, P S

CASE2 and the classification accuracy,P CCASE2, which are defined as

P SCASE2 = P (χS = 1|H1) + P (χS = 0|H0), (14)P CCASE2 = P (χk = χk|H1), k = 1, 2, 3. (15)

χS is the prediction of χS regarding to the presence of asignal in the spectrum. χk stands for the predictions for the

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Fig. 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 ITHE PROPOSED CNN LAYOUT.

Layer Output Dimensions

Input 8193× 16Conv1 8193× 16× 64

Leaky ReLU1 8193× 16× 64Max Pool1 4097× 8× 64

Conv2 4097× 8× 128Leaky ReLU2 4097× 8× 128

Max Pool2 2049× 4× 128Conv3 2049× 4× 64

Leaky ReLU3 2049× 4× 64Max Pool3 1025× 2× 64

Flatten 131200Dense1 256Dense2 4

Trainable Par. 33, 736, 772

classification part of CASE2. χS is defined for the transmittedsignal as:

χS =

{0, k = 0,1, k = 1, 2, 3.

(16)

The overall accuracy for CASE2 can be introduced in termsof P S

CASE2 and P CCASE2 by

PCASE2 = P SCASE2P

CCASE2. (17)

IV. THE PROPOSED CNN MODEL

As indicated in Section III, the proposed method relies ona novel CNN model. Design and implementation of CNN forclassification of wireless mobile communication signals is con-ducted via an open source machine learning library, Keras [24].The proposed CNN model consists of three convolution andthree pooling layers sequentially. The convolution layers haverespectively 64, 128, and 64 filters. The network is terminatedby two fully connected layers. First hidden layer includes 256neurons. Second hidden layer consists of 4 and 3 neurons forCASE1 and CASE2, respectively. The leaky rectified linearunit (ReLU) activation function with an alpha value 0.1 is usedin 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 bya mapping line with a small slope. In each convolution layer,3 × 3 filters are used. 2 × 2 max pooling is used to reducethe dimension and training time. A fully connected layer isformed by 256 neurons and Leaky ReLU activation function.Following the fully connected layers, the probabilities for each

Convolution and Pooling Layers Dense Layers

256

N-class

Fig. 3. The proposed CNN model consists of three convolutional layers andtwo dense layers with Adam optimizer with learning rate of 10−5.

class are computed by the softmax activation function. Inaddition, the adaptive moment estimation (ADAM) optimizeris utilized when determining the model parameters. In thetraining phase, early stopping is employed to prevent themodel from over-fitting. The patience is chosen as 10 epochsfor early stopping function and validation loss is monitoredduring the training. If the validation loss converges a level andremain at this level during 10 epochs, the training is terminatedand the weights at the end of training are used in the test. Theimplementation layout for the proposed CNN model is givenin Table I. The input matrices, XAP

k , XFFTk , and XSCF

k areused at the beginning of the proposed model by convolvingwith filters. The overall block diagram for the proposed CNNmodel is depicted in Fig. 3.

When the motivation behind designing such a CNN modelis considered, it should be noted firstly that the informationabout changes in the local regions of the mapped output isextracted by using 3×3×64 filters in the first convolution layer.In this problem, because the SCF creates local differencesin frequency and cyclic frequency regions, the smaller filtersize 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 alllocal terms as a general process, the second layer examinesthe properties such as location and size related to thesecharacteristics. Here, it is aimed to deal with cyclic featuresin detail by increasing the number of filters to 128. In thelast layer, all properties are converted to an average of allinformation gathered and eventually sent to the decisive layerwhich is dense layer. For this reason, the number of filters inthe last layer should be chosen so that sufficient informationis obtained without overfitting. Therefore, the number offilters is selected as 64 in the last layer. It is customary toquantify the performance of a classifier model in terms of theprecision (Π), recall (Ψ), and F1-score performance metrics.The precision metric quantifies how much positive results areactually positive, the recall provides information on how muchtrue positives are identified correctly as positive, and F1-scoregives an overall measure for the accuracy of a classifier modelsince it is the harmonic average of precision and recall. Thesemetrics are given as

Π =ξ

ξ + υ, Ψ =

ξ

ξ + µ, F1-score = 2× Π×Ψ

Π + Ψ, (18)

where ξ, υ, and µ denote the numbers of true positive, falsepositive, and false negative, respectively.

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V. MEASUREMENT METHODOLOGY AND DATASETGENERATION

The dataset to test and evaluate the proposed method isdeveloped from the measurements taken through an extensivemeasurement campaign conducted at different locations andfrequency bands. In order to make the model robust againstenvironmental changes, measurements have been conductedin different locations as illustrated in Fig. 5. The locations oftransmitters and measurement points can be seen in Fig. 5. Itcan be seen that the signals propagate through the urban area,and then reach the receivers in sub-urban area. The measure-ment focuses on 800, 900, 1800, and 2100 MHz frequencybands that are allocated for cellular communications. RohdeSchwarz FSW26 spectrum analyzer and a set of Yagi-Udaantennas are employed at the receiver. The measurements areunified as follows: for each signal observed in the spectrum,16384 I/Q samples are taken. Measurements are conductedat 15 different SNR levels. Each level consists of the samenumber of signals which is 4000. Therefore, 60000 signalsin total are recorded and included in the dataset. Samplepower spectra of these signal types, obtained with the Welch’smethod, are shown in Fig. 4. When the proposed method isconsidered, the dataset is split into test and train data with theproportion of 0.4 and 0.6, respectively.

To better understand the effects of wireless communi-cations channels over the received signals, first, amplitudedistributions of four different recordings of all three signalsare given in Fig. 6. The figure indicates differing powerand amplitude levels. The distribution of the received powerchanges considerably since the measurements are taken atdifferent locations, times and frequency bands. Second, Fig. 7illustrates the phase distribution of four different recordings ofall three signals. It is seen that the phase of received signalsare distributed almost uniformly in between −π and π radians.This result implies Rayleigh-like fading behavior stemmingfrom the amplitude and phase distributions of received signals.This is an expected result when the measurement area andthe locations of transmitters and receivers are considered.Eventually the received power and phase of the signals areobviously affected by the shadowing, multipath fading andpath loss as depicted in Fig. 6 and Fig. 7.

The dataset is shared in [20] in the format of SCF. Thedataset covers 60000 SCF matrices with the dimensions of8193 × 16 corresponding to received I/Q samples of 16384for each signal.

VI. CLASSIFICATION PERFORMANCE ANALYSIS

We evaluate the performance of the proposed classificationmodel over the comprehensive dataset described in Section V.Therefore, the dataset is composed of GSM, wideband codedivision multiple access (WCDMA) for UMTS and LTEsignals which are recorded over-the-air at different locationswith unique conditions in terms of the number of channeltaps, and fading, again as noted in Section V. Training andtest sets contain 9000 and 6000 signals for each waveform.The I/Q signal length is 16384. CNN is trained and tested on

TABLE IICLASSIFICATION PERFORMANCE METRICS FOR THE PROPOSED CNN

MODEL WITH SCF, AP, AND FFT FEATURES FOR CASE2.

SNR Feature Signal Precision (Π) Recall (Ψ) F1-Score

1dB

I/Q

UMTS 0.33 1.00 0.49LTE 0.00 0.00 0.00GSM 0.00 0.00 0.00Average 0.11 0.33 0.16

AP


FFT


SCF


5dB

I/Q


AP


FFT


SCF


10dB

I/Q


AP


FFT


SCF


15dB

I/Q


AP


FFT


SCF


the graphics processing unit (GPU) server equipped with fourNVIDIA Tesla V100 GPUs.

First, we focus on the results for CASE1. As stated before,CASE1 refers to four-classes classification problem. As shown

7

910 915 920 925 930 935 940 945 950 955 960

Frequency (MHz)

-170

-160

-150

-140

-130

Po

we

r (d

B)

GSM signalsUMTS signals LTE signalGSM signals

Fig. 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.

Transmitter

Receiver

Fig. 5. An overview of the measurement area. The transmitters are locatedin the urban area, but the receivers are in a sub-urban area.

Fig. 6. Sample PDFs of the amplitude of received signals in the dataset. Theexample PDFs show the different channel and received power characteristics.

Fig. 7. Sample PDFs of the phase of received signals in the dataset. Theexample PDFs show uniform distribution characteristics.

in Fig. 8, the test accuracy of the model exceeds 90% at11dB SNR. It takes a maximum accuracy value of 92% at15dB. The confusion matrices related to CASE1 are depicted

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

SNR (dB)

50

60

70

80

90

100

Test A

ccura

cy (

%)

Fig. 8. Accuracy values with respect to SNR level of the received signals forboth cases.

in Fig. 9. Due to the low SNR values, the model mostly can notaccurately clasify the signals and identifies the signal as Noise.This case can be observed in Fig. 9(a). Therefore, dividingthe problem into two parts becomes a viable alternative:first sense, then classify. In this case, we analyse both CNNdetector and CNN classifier (see Fig. 2). For the sensing partof the architecture, noise signals are labeled as 0 and the restof the set is labeled as 1. The detection results are plotted againin Fig. 8 as P S

CASE2. The detection accuracy follows 96% atalmost all SNR values.

Following the steps above, assuming that the signal ispresent in the spectrum at the output of CNN detector ofCASE2 in Fig. 2, the performance of the CNN classifier canbe investigated. This stage is labeled as P C

CASE2 in Fig. 8 andit is observed that the classification accuracy exceeds 90% at3dB SNR. It gives the best performance, 98.5%, at 9dB andit is remained stable until 15dB. The Fig. 10(a) depicts theconfusion matrices related to CNN classifier of CASE2 andimplies that even at low SNR regime, the classifier can identifyGSM signals with high accuracy; however, overall precisionof the classifier is low i.e., in contrary to GSM signals, theclassifier has difficulty in recognition of UMTS and LTEsignals in low SNR regime. But the accuracy and precisionof the classifier enhance as SNR increases in Fig. 10(b) andFig. 10(c). This phenomena is observed due to the dominanceof characteristics in feature matrices which follow Gaussian

8

distribution. As known, GSM is associated with Gaussian min-imum shift keying (GMSK); therefore, GSM signals inherentlyshow characteristics defined by Gaussian distribution in thecase of high SNR. Decreasing in SNR leverages Gaussiancharacteristics in the received signal because of AWGN. Thatis to say, UMTS and LTE signals with lower SNR valuesbecome denoting Gaussian characteristics; thus, the model isprone to learn Gaussian characteristics to decrease its lossfunction. When the trained model is tested, it is expectedthat the model can identify the signals which have dominantGaussian characteristics. As a result, the model can identifyGSM signals, which inherently denote Gaussian attributes, inlower SNR regime where UMTS and LTE signals lose theirunique features. This statement shows parallelism with theresults given in CASE1 in Fig. 9(a). The model accuratelyidentifies AWGN at lower SNR regime as given in Fig. 9(a).

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 mis-detection in the sensing phase. Both the detection rate inthe sensing stage and the accuracy in the classification stageare high at 3dB and thereafter, so overall performance doesnot suffer a significant loss. As shown in Fig. 8, the overallperformance of CASE2 is far superior to that of CASE1.Especially at low SNR levels, the signals remaining after firstdetecting and separating noise from the signal set by the CNNdetector can be classified with much better performance. Inthis way, the performance is higher in CASE2. However, itshould be noted that CASE2 is more costly than CASE1 interms of training time and the number of models. Obviously,CASE2 can be predicted to perform better than CASE1 in thepresence of a jammers exhibiting Gaussian characteristics orother interfering signals.

Sensing performance can be considered that it is slightlylower than conventional spectrum sensing methods like energydetector and matched filter. However, it should be noted thatthis study employs real–world data rather than simulation orsynthetic data. For example, energy detectors can sense asignal in a spectrum with optimal performance; however, itneeds to know noise variance. But even with a slight erroron estimating the noise variance, the sensing performanceseriously decreases. Moreover, as the power of spread spec-trum signals (e.g. WCDMA in UMTS) is spreaded in a wideband, its power is very close to the noise floor. By takinginto this account, in a fading environment, it can be said thatED cannot perform a satisfactory detection rate for spreadspectrum signals as stated in [25]. It is worth noting that ourmeasurements follow Rayleigh distribution as seen in Fig. 6and Fig. 7. On the other hand, matched filters are waveform-specific solution and they require the perfect knowledge forsignals.

A. Investigation for the Impact of Different Features

In this section, we compare the performance of otherfeatures of I/Q, AP, and FFT which are frequently employedfor sensing purposes with SCF. The features are used asdetailed in Section II. The results of this test are presented

TABLE IIIPERFORMANCE COMPARISON BETWEEN THE EXISTING DL NETWORKS

AND THE PROPOSED SYSTEM FOR THE CLASSIFICATION STAGE OF CASE2AT SNR VALUE OF 15DB.

Network Signal Precision Recall F1-score

CLDNN [18]


LSTM [28]


Proposed CNN with SCF


in Table II. Unlike the modulation classification studies [9,26], I/Q cannot provide a meaningful input for the model dueto the severe fading effect on the phase of signal, which is seenin Fig. 7. The histograms of phase imply that the signal phaseis corrupted and the information on the phase is lost. That iswhy I/Q shows poor performance. The average performancesalso indicate that SCF outperforms I/Q, AP, and FFT for allSNR levels. Assuming that these two are used along with I/Qas the main features for training, these results show significantgains for real-world signals especially above 5dB SNR level.It is observed that AP performs better than FFT. The averagetraining time per epoch is approximately 60s for SCF featurewhere both FFT and AP take 7.5s per epoch; however,both FFT and AP cannot show an acceptable classificationperformance, PCCASE2. Although the cost of computing bothfeatures is far behind the SCF, they are far from delivering thedesired 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 notlinearly separable, SCF clusters the vectors in the space andallows almost linear separation as depicted in Fig. 11. Theanalysis based on t-SNE results show that SCF better separatessignal vectors in space. The results of this study are in linewith the previous analysis [27].

B. Comparison with Existing Deep Learning Networks

The existing DL networks are employed to classify thecellular communication signals. We utilize CLDNN [18] andLSTM [28] models. These models are originally used inmodulation classification. Without any change in the models,input matrix, and input vector as proposed in the papers areadopted in the study. CLDNN takes a 2×128 matrix which iscomposed of amplitude and phase values for each I/Q sample.On the other hand, LSTM model utilizes a vector reshapedversion of the matrix used in CLDNN. Therefore, the lengthof the vector is 256. Its first half includes in-phase componentswhile the rest of the vector is quadrature components. Otherdetails are found in [18, 28]. The precision, recall, and F1-score are given in Table III. It shows that CLDNN and LSTMdecide that the received signal is UMTS whatever it actuallyis. Even though LSTM and CLDNN can be trained in a short

9

Noise

UMTS LTE GS

M

Predicted Signal

Noise

UMTS

LTE

GSM

Real Signa

l

97.25% 0.00% 0.75% 2.00%

30.50% 26.50% 32.00% 11.00%

17.50% 9.00% 54.25% 19.25%

37.25% 6.25% 7.25% 49.25%

(a)

Noise

UMTS LTE GS

M

Predicted Signal

Noise

UMTS

LTE

GSM

Real Signa

l

97.50% 0.00% 0.25% 2.25%

0.00% 91.25% 8.75% 0.00%

0.00% 0.00% 97.50% 2.50%

0.00% 7.25% 2.50% 90.25%

(b)

Noise

UMTS LTE GS

M

Predicted Signal

Noise

UMTS

LTE

GSM

Real Signa

l

96.75% 0.25% 1.25% 1.75%

0.00% 96.50% 3.50% 0.00%

0.00% 0.00% 98.75% 1.25%

0.00% 0.00% 0.00% 100.00%

(c)Fig. 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 onlyone signal at low SNR level.

UMTS LTE GS

M

Predicted Signal

UMTS

LTE

GSM

Real Signa

l

42.00% 21.50% 36.50%

30.25% 45.50% 24.25%

1.50% 0.00% 98.50%

(a)

UMTS LTE GS

M

Predicted Signal

UMTS

LTE

GSM

Real Signa

l

92.00% 8.00% 0.00%

3.00% 95.25% 1.75%

0.00% 0.00% 100.00%

(b)

UMTS LTE GS

M

Predicted Signal

UMTS

LTE

GSM

Real Signa

l

96.00% 4.00% 0.00%

0.00% 99.50% 0.50%

0.00% 0.00% 100.00%

(c)Fig. 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 doesnot randomly choose only one signal at low SNR level.

GSM

LTE

WCDMA

(a) I/Q

GSM

LTE

WCDMA

(b) AP

GSM

LTE

WCDMA

(c) FFT

GSM

LTE

WCDMA

(d) SCF

Fig. 11. Two-dimensional demonstration of the features by the t-SNE algo-rithm. This illustration shows that in contrary to the other features, SCF canseparately cluster real-world signals in space successfully.

time by using I/Q vector and matrix, employing I/Q vectorand matrix give poor classification performance.

C. Comparison with SVM

In our previous work, we employed SVMs to identify real-world signals [27]. Even though utilization of SCF in SVMprovides good performance, training of SVM should be con-ducted for each SNR level separately i.e., at the end of thetraining, the more SNR values in the dataset, the more mod-els should be created. The real-world utilization of SVM re-quires an SNR estimator and loading of all pre-trained mod-els to memory during operation; thus, reducing the applica-bility of the method and making improvements a necessity.As seen in Fig. 12, the CNN-based classifier shows a superiorperformance compared to SVM-based classifier of [27], underthe conditions of the classification part of CASE2. To this end,while CNN-based classifier employs a less costly feature dueto elimination of mapping of bi-frequency spectrum, it stillperforms with higher accuracy. Therefore, producing a modelindependent of the SNR is an advantage of the proposed CNNbased method since the training set contains an equal numberof signals from each SNR. As a result, a single model wouldbe adequate for classification in a large SNR range at the teststage.

10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

SNR (dB)

40

50

60

70

80

90

100T

est A

ccura

cy (

%)

The Proposed CNN with SCF

SVM with SCF

Fig. 12. The classification performance comparison between SVM in [27]and the proposed CNN structure for P C

CASE2.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

SNR [dB]

0

10

20

30

40

50

60

70

80

90

100

Accu

racy [

%]

CNN-based Detector

CFAR Detector(Pf = 0.05)

CFAR Detector(Pf = 0.1)

Fig. 13. Spectrum sensing performances of CFAR detectors and CNN-baseddetector with respect to SNR.

D. Comparison with CFD

Besides signal classification, the proposed CNN model canbe used for spectrum sensing. We investigated the sensingperformance of the model by training a CNN-based spectrumoccupancy detector trained over 600 pure noise signals and 600noisy WCDMA signals for each SNR value. Then, the modelis tested with 400 pure noise signals and 400 noisy WCDMAsignals for each SNR level and sensing results are acquired.Furthermore, for comparison purposes, we implement a con-stant false alarm rate (CFAR) detector utilizing classical CFD[29] to identify WCDMA signals and the same dataset isalso used for CFAR detector. Please note that UMTS signalsare deliberately selected due to their known dominant SCFcharacteristics stemming from cyclic spreading codes. Theresults of this test are given in Fig. 13. In view of these results,it is clearly seen that the CNN-based detector outperforms theCFAR detector at all SNR regimes. For example, the sensingperformance of the CNN-based detector is 91.75% at 3dB

0

0.2

0.4

0.6

0.8

1

Test A

ccura

cy

CLDNN CNN DenseNet LSTM ResNet0

0.2

0.4

0.6

0.8

1

Norm

aliz

ed T

ime C

ons. and M

em

ory

Allo

c. Norm. Epoch Duration

Norm. Memory Alloc.

Maximum Test Acc.

Average Test Acc.

Fig. 14. Model comparison in terms of memory, complexity, and accuracy.The epoch time and memory allocation rate are normalized with their maxi-mum values observed in these models (maximum values for both are observedin LSTM). The average accuracy is the mean accuracy in the SNR rangebetween 1dB and 15dB.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

SNR (dB)

20

30

40

50

60

70

80

90

100

Test A

ccura

cy (

%)

The Proposed CNN

CLDNN

ResNet

LSTM

DenseNet

Fig. 15. Test accuracy with respect to SNR values for the proposed CNN,CLDNN, LSTM, ResNet, and DenseNet models.

while the probability of detection for the CFAR detector are45.6% and 59.4% for the selected false alarm rates as 0.05and 0.1, respectively.

E. Focusing on the Meaningful Region of Spectral CorrelationFunction

As seen in Fig. 1, the meaningful part of the features islocated in the middle of the matrices. In order to investigate thepossibility of accuracy improvement and the fair comparisonwith the existing DL networks, we employ a small partition inmiddle of the SCF matrix, where the cyclic characteristics aremainly observed. As stated in Section V, an SCF matrix hasthe dimension 8193× 16. Therefore, it is not possible to trainsuch a dense model in our server equipped with four NVIDIATesla V100 GPUs. To compare our proposed CNN architecturewith a more dense model, we decrease the dimensions of theSCF matrices by using only 16× 16 part in the middle ofthe matrices. Only in this way, we are able to train complex

11

Noise

UMTS LTE GS

M

Predicted Signal

Noise

UMTS

LTE

GSM

Real Signa

l

1.00 0.00 0.00 0.00

0.36 0.47 0.13 0.04

0.15 0.30 0.51 0.04

0.18 0.03 0.00 0.79

(a)

Noise

UMTS LTE GS

M

Predicted Signal

Noise

UMTS

LTE

GSM

Real Signa

l

0.99 0.00 0.00 0.01

0.00 0.82 0.16 0.02

0.00 0.07 0.93 0.01

0.00 0.00 0.04 0.96

(b)

Fig. 16. Confusion matrices for CASE1 at SNR levels of (a) 1dB and (b)5dB when 16× 16 inputs are employed.

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 worthsaying that, we conduct four-class classification (i.e., CASE1)in this study. The results depicted in Fig. 14 shows that theproposed CNN is favorable in terms of both low complex-ity (i.e., epoch time) and efficient memory allocation, as wellas high test accuracy. During this study, batch sizes are keptsame for all models. The memory allocation and training timehave been normalized by LSTM’s memory allocation rate andtraining time, respectively; thus, computer-independent resultsare provided in Fig. 14. It should be noted that early stop-ping is used during training of models and the minimum num-ber of epochs is required by the proposed CNN. Furthermore,Fig. 15 denotes the accuracy with respect to SNR levels foreach model. By considering results, it can be observed that theproposed CNN is more robust and efficient than the existingmodels. Moreover, it is seen that CNN gives better resultswith this smaller matrix than the complete matrix is used.By eliminating the region except for the meaningful part ofSCF, the input matrices become more distinct from each other.Fig. 1 implies that SCF matrices have similarities except forthe meaningful part. The confusion matrices in Fig. 16 for16× 16 inputs denote the improvement in the precision ofAWGN. This explains why the small portion of the matrixcan lead to higher accuracy.

It is also explored how the dimensions of the small partitionaffect the performance of the CNN model. The results showthat using 4 rows does not perform well enough. When usingrows between 8 and 128 (as power of two), the results aresatisfactory. The test accuracy with respect to input size isdemonstrated in Fig. 17. It is revealed that by considering theaccuracy at lower SNR regimes and the training time, 16× 16is the most suitable size for the CNN.

VII. CONCLUSION

In this study, a DL-based method utilizing SCF as aninput to a novel CNN model to achieve spectrum sensingor signal identification interchangeably or jointly without therequirement of any a priori information is proposed. Firstapproach investigates the joint sensing and classification ofwireless signals. Second, a sequential approach is adopted.The results show that sequential approach performs better than

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

SNR (dB)

30

40

50

60

70

80

90

100

Test A

ccura

cy (

%)

4 x 16

8 x 16

16 x 16

32 x 16

64 x 16

128 x 16

Fig. 17. The test accuracy of the proposed CNN architecture with respect toinput size.

the joint approach. Moreover, comparative analysis indicatedthe superiority of SCF as a distinctive feature when compareto the contemporary features utilized for currently availableDL-based detector models. The results also imply that understringent channel conditions, the CNN model of the proposedmethod provides better spectrum sensing performance thanother available DL models, SVMs, and classical CFD. Theseresults indicate that applicability of DL-based techniques inthe rapidly changing communications environment of con-temporary wireless communications networks. In subsequentstudies, the performance of the proposed method for sensingand identification of other wireless signals or modulationtechniques with cyclic features can be explored. Furthermore,the performance of the proposed method can be investigatedagainst adversarial attacks and efforts can be made to developvarious techniques to strengthen its resistance to these types ofintrusions. Although this study focuses on supervised learning,it is possible to improve the performance of the proposedmethod by supporting unsupervised learning methods in fea-ture extraction.

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[13] N. Han, S. Shon, J. H. Chung, and J. M. Kim, “Spectral correlation basedsignal detection method for spectrum sensing in IEEE 802.22 WRANsystems,” in 8th International Conference Advanced CommunicationTechnology, vol. 3, 2006, pp. 1765–1770.

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in spectrum sensing for cognitive radios,” in Thirty-Eighth AsilomarConference on Signals, Systems and Computers, vol. 1, 2004, pp. 772–776.

[26] K. Tekbiyik, A. R. Ekti, A. Gorcin, G. K. Kurt, and C. Kececi, “Robustand fast automatic modulation classification with CNN under multi-path fading channels,” in IEEE 91st Vehicular Technology Conference(VTC2020-Spring), 2020, pp. 1–6.

[27] K. Tekbıyık, O. Akbunar, A. R. Ekti, A. Gorcin, and G. K. Kurt, “Multi–dimensional wireless signal identification based on support vector ma-chines,” IEEE Access, vol. 7, pp. 138 890–138 903, 2019.

[28] S. Hu, Y. Pei, L. Pu, and Y.-C. Liang, “Robust modulation classificationunder uncertain noise condition using recurrent neural network,” in IEEEGlob. Commun. Conf., 2018, pp. 1–7.

[29] C. M. Spooner and A. N. Mody, “Wideband cyclostationary signalprocessing using sparse subsets of narrowband subchannels,” IEEETrans. on Cogn. Commun. Netw., vol. 4, no. 2, pp. 162–176, 2018.

Kursat Tekbıyık received his B.Sc. and M.Sc. de-grees (with high honors) in electronics and commu-nication engineering from Istanbul Technical Uni-versity, Istanbul, Turkey, in 2017 and 2019, respec-tively. He is currently pursuing his Ph.D. degree intelecommunications engineering in Istanbul Techni-cal University. He is also working as a senior re-searcher at the HISAR Lab. of TUBITAK BILGEM.His research interests include algorithm design forsignal intelligence, next-generation wireless commu-nication systems, terahertz wireless communications,

and machine learning.

Ozkan Akbunar received B.Sc. degree from Elec-trical and Electronics Engineering department inAnadolu University, Eskisehir, Turkey. He is pro-ceeding his M.Sc education in TelecommunicationEngineering department in Istanbul Technical Uni-versity, Istanbul, Turkey. He is working in the Scien-tific and Technological Research Council of Turkeyat Informatics and Information Security ResearchCenter as R&D engineer since December 2017.His main research areas are wireless communicationsystems, digital signal processing, machine learning

and radar.

Ali Rıza Ekti is from Tarsus, Turkey. He receivedB.Sc. degree in Electrical and Electronics Engi-neering from Mersin University, Mersin, Turkey,(September 2002-June 2006), also studied at Uni-versidad Politechnica de Valencia, Valencia, Spainin 2004-2005, received M.Sc. degree in ElectricalEngineering from the University of South Florida,Tampa, Florida (August 2008-December 2009) andreceived Ph.D. degree in Electrical Engineering fromDepartment of Electrical Engineering and ComputerScience at Texas A&M University (August 2010-

August 2015). He is currently an assistant professor at Balikesir UniversityElectrical and Electronics Engineering Department. He also holds the DivisionManager position of HISAR Lab. at TUBITAK BILGEM where he is respon-sible for R&D activities in the wireless communications and signal processing.His current research interests include statistical signal processing, convexoptimization, machine learning, resource allocation and traffic offloading inwireless communications in 5G and beyond systems.

Ali Gorcin graduated from Istanbul Technical University with B.Sc. inElectronics and Telecommunications Engineering and completed his master’sdegree on defense technologies at the same university. After working atTurkish Science Foundation (TUBITAK) on avionics projects for more thansix years, he moved to the U.S. to pursue PhD degree in University of SouthFlorida (USF) on wireless communications. He worked for Anritsu Companyduring his tenure in USF and worked for Reverb Networks and Viavi Solutionsafter his graduation. He is currently holding an assistant professorship positionat Yildiz Technical University in Istanbul and also serving as the interimpresident of TUBITAK BILGEM.

13

Gunes Karabulut Kurt received the B.S. degreewith high honors in electronics and electrical en-gineering from the Bogazici University, Istanbul,Turkey, in 2000 and the M.A.Sc. and the Ph.D.degrees in electrical engineering from the Univer-sity of Ottawa, ON, Canada, in 2002 and 2006,respectively. From 2000 to 2005, she was a ResearchAssistant with the CASP Group, University of Ot-tawa. Between 2005 and 2006, she was with TenXcWireless, Canada. From 2006 to 2008, she was withEdgewater Computer Systems Inc., Canada. From

2008 to 2010, she was with Turkcell Research and Development AppliedResearch and Technology, Istanbul. Since 2010, she has been with IstanbulTechnical University, where she currently works as a Professor. She is a MarieCurie Fellow. Since August 2019, she is at Carleton University as a visitingprofessor and also appointed as an adjunct research professor. She is currentlyserving an Associate Technical Editor of the IEEE Communications Magazine.

Khalid A. Qaraqe was born in Bethlehem andreceived the B.S. (Hons.) degree from the Universityof Technology, Bagdad, Iraq, in 1986, the M.S.degree from the University of Jordan, Amman, Jor-dan, in 1989, and the Ph.D. degree in from TexasA&M University, College Station, TX, in 1997, allin electrical engineering. From 1989 to 2004 he hasheld a variety positions in many companies and hehas over 12 years of experience in the telecom-munication industry. He has worked on numerousprojects and has experience in product development,

design, deployments, testing and integration. He joined the Department ofElectrical and Computer Engineering of Texas A&M University, Qatar, inJuly 2004, where he is now a Professor and Managing Director with theCenter for Remote Healthcare Technology, Qatar. He has been awarded morethan 20 research projects consisting of more than USD 13 M from localindustries in Qatar and the Qatar National Research Foundation (QNRF).He has published more than 131 journal papers in top IEEE journals, andpublished and presented 250 papers at prestigious international conferences.He has 20 book chapters published, four books, three patents, and presentedseveral tutorials and talks. His research interests include communication theoryand its application to design and performance, analysis of cellular systems andindoor communication systems. Particular interests are in mobile networks,broadband wireless access, cooperative networks, cognitive radio, diversitytechniques, index modulation, visible light communication, FSO, telehealthand noninvasive bio sensors.

Documents

Spectrum Sensing and Signal Identiﬁcation with Deep