Acta Polytechnica https://doi.org/10.14311/AP.2022.62.0228 Acta Polytechnica 62(2):228–237, 2022 © 2022 The Author(s). Licensed under a CC-BY 4.0 licence Published by the Czech Technical University in Prague IMPROVEMENT OF SPECTRUM SENSING PERFORMANCE IN COGNITIVE RADIO USING MODIFIED HYBRID SENSING METHOD Hadeel S. Abed∗, Hikmat N. Abdullah Al-Nahrain University, College of Information Engineering, Department of Information and Communication Engineering, Jadriah, 10001 Baghdad, Iraq ∗ corresponding author: hadeel_sami@coie.nahrainuniv.edu.iq Abstract. Cognitive radio (CR) is a wireless technology for increasing the bandwidth usage. Spectrum sensing (SS) is the first step in CR. There are three basic techniques in SS, energy detection (ED), matched filter (MF), and cyclostationary detection (CFD). These techniques have many challenges in performance detection (Pd) and computational complexity (CC). In this paper, we propose a hybrid sensing method that consists of MF and CFD to exploit their merits and overcome their challenges. The proposed method aims to improve Pd and reduce CC. When MF hasn’t had enough information about PU, it switches to CFD with a reduction of CC in both MF and CFD. The proposed method is simulated under fading with cooperative and non-cooperative scenarios, measured using Pd and CC ratio Cratio, and evaluated by comparing it with traditional and hybrid methods in the literature. The simulation results show that the proposed method outperforms other methods in Pd and Cratio. For example, at Eb/No equal to 0 dB under the Rayleigh fading channel, the Pd in the proposed method increased by 38 %, 28 %, 28 %, and 18 % as compared with the modified hybrid method, traditional hybrid method, traditional CFD method, and traditional MF method in the literature, respectively. Keywords: Cognitive radio, spectrum sensing, matched filter, cyclostationary, energy detection, hybrid sensing method. 1. Introduction Due to the large number and diversity of wireless devices and applications, the emergence of new appli- cations, and the continuous demand for higher data rates, the Radio Frequency (RF) spectrum is becom- ing increasingly crowded [1, 2]. Cognitive radio (CR) has been proposed as a promising technique that pro- vides a solution to the spectrum scarcity problem by dynamically exploiting the unused part of the spec- trum band [3, 4]. A cognitive radio was defined as a radio or system that senses, and is aware of its op- erational environment and can dynamically adjust its radio operating parameters accordingly [5]. Cognitive radio is a wireless technology that provides the ability to share the spectrum while avoiding any imposed harmful interference to the PU [6]. The CR aims to exploit the natural resources efficiently, including frequency, time, etc. [7]. Spectrum sensing is the first step to implementing a CR system. The basic com- ponent of spectrum sensing is a primary user (PU) signal or license band, and a secondary user (SU) or cognitive user (CU) that senses the PU band to detect the activity of PU and can use its spectrum when the PU is absent [8]. The SU must not interfere in any way with the PU to succeed the cognitive ra- dio networks [9]. Spectrum sensing techniques can be classified into two scenarios, non-cooperative and cooperative. Three basic techniques are used for spec- trum sensing, these are energy detection ED, matched filter MF, and cyclostationary feature detection CFD. The ED spectrum sensing technique is more used as compared to others due to its simplicity and minimal computational complexity. However, at low signal-to- noise ratio (SNR) values, and bad channel conditions, the ED cannot differentiate between the PU signal and the noise. The matched filter (MF) maximizes the received SNR in communication systems, so it can be considered as the best detector [10]. MF has a chal- lenge that it must know the information about the PU signal properties, i.e., packet format, pulse shaping, and the type of modulation. If the CR has incomplete information about the PU signal, then the MF cannot be used as an optimum detector. A cyclostationary detector can be used as a sub-optimal detector. CFD can distinguish between the PU signal and the noise. It has a good performance in low SNR conditions be- cause of its noise rejection characteristic [11]. However, a cyclostationary detector has a high computational complexity since it has a long sensing time, which is not favourable in some situations [12]. To improve the performance detection, CSS (cooperative spectrum sensing) is applied. CSS could overcome fading and shadow in wireless channels. There are two basic structures of CSS, centralised and distributed [13, 14]. In CSS, SUs sense the spectrum separately and trans- mit their local decisions to a fusion centre (FC). By applying some fusion logic scheme, FC is responsible for the overall decision [11]. The decision fusion rules can be either hard or soft. In a hard fusion rule, every 228 https://doi.org/10.14311/AP.2022.62.0228 https://creativecommons.org/licenses/by/4.0/ https://www.cvut.cz/en vol. 62 no. 2/2022 Improvement of Spectrum Sensing Performance in Cognitive . . . SU makes the local binary decision independently of the activity of PU, while in the soft fusion rule, the SUs send their sensing information to the fusion cen- tre without making local decisions. The decision is made at FC by using one of the combining rules [15– 17]. The rest of the paper is organized as follows: Section 2 presents the literature review of the related works. Section 3 displays the theoretical background of spectrum sensing techniques. Section 4 explains the procedures of the proposed hybrid method. Section 5 shows the computational complexity of the proposed method. Section 6 illustrates the simulation results and discussions, and finally, the conclusions of the paper are drawn. 2. Related works Several works related to the spectrum sensing tech- nique are proposed to improve its performance. In [11] traditional hybrid method based on energy and cyclostationary detectors, the cooperative scenario is proposed to improve the detection performance with- out taking into consideration the computational com- plexity. In this method, the PU signal is first scanned by ED to detect whether the PU is present or not. If ED is not certain about the detection of PU, then the PU signal is sensed by a cyclostationary detector. In [12], the reduction of the computational complex- ity in CFD is done by choosing optimum parameters. In [18], the hybrid method consists of two parallel paths of detectors. The first path is created from two sequential detector stages; in the first phase, ED is used to identify the PU signal existence where the signal has not been detected. Maximum–Minimum Eigenvalue (MME) is used as a second stage to detect the PU signal presence. In [19], the hybrid method is done by artificial neural networks (ANN). In [20], the hybrid method consists of five types of detec- tors, each one having its special functions to detect the spectrum whether it is free or occupied. In [21], the hybrid sensing method is proposed based on ED and cyclostationary detector with a reduced compu- tational complexity and an improved detection per- formance. In [22], the idea of the proposed method is similar to [12], it reduced the computational com- plexity with a good performance, its process is based on the optimal parameter selection strategy for choos- ing detection parameters of the cyclic frequency and lag. To improve the performance of spectrum sens- ing techniques and solve its complexity problem, we proposed a hybrid spectrum sensing method based on matched filter and cyclostationary feature detection. This method improves the performance detection of the matched filter when it does not have sufficient information about a PU signal or at very low SNR values, and reduces the computational complexity of the cyclostationary process with an excellent perfor- mance detection. The proposed method is measured using the probability of detection (Pd) and computa- tional complexity ratio under the Rayleigh multipath fading channel with cooperative and non-cooperative scenarios, and evaluated by comparing it with tradi- tional sensing techniques (cyclostationary and MF), the traditional hybrid method in reference [11] and improved hybrid method in reference [21]. 3. Spectrum sensing techniques There are three basic techniques used for spectrum sensing, which are energy detection, matched filter, and cyclostationary feature detection. Each technique is explained in the following sections. 3.1. Energy detector Energy detection (ED) is the simplest sensing tech- nique that does not require any knowledge about the PU signal to operate. It performs the detection by comparing the accumulated energy of the received signal with a predefined threshold. The threshold depends only on the noise power [1]. The received samples at the CU receiver are shown in the following Equation [23]: y(n) = Hθ x(n) + N oi(n), (1) where y(n) is the received sensed signal by the CU, x(n) is the PU signal, N oi(n) is the Additive White Gaussian Noise (AWGN) and H is the gain of the channel, and θ is the activity pointer and has one of two values as shown in Equation (2), θ = ® 0 for H0 hypothesis 1 for H1 hypothesis. (2) When PU is present, it is represented by hypothesis H1, while when the PU is absent, it is represented by hypothesis H0. The probabilities of false alarm and detection are measured by comparing the energy com- puted from the sensed signal on observation window W with a pre-defined threshold λ. The accumulated energy Enj can be written as shown in Equation (3). Enj = 1 N N∑ n=1 |y(n)|2, (3) where N is the total number of sensed samples N = W Fs, where Fs is the frequency sampling. The probabilities of false alarm Pf and detection Pd are shown in Equations (4) and (5), respectively: Pf = pr(Enj > λ | H0), (4) Pd = pr(Enj > λ | H1). (5) Numerically, the threshold value can be computed for a constant Pf value, which is shown in the following Equation (6) [24]. λ = (Q−1(Pf ) + √ N )2 √ N (N )2 (6) 229 Hadeel S. Abed, Hikmat N. Abdullah Acta Polytechnica 3.2. Cyclostationary feature detection Cyclostationary feature detection is a spectrum sens- ing technique for detecting the PU signals by exploit- ing the cyclostationary features of the received sig- nals, these features are the periodicity, number of signals, their modulation type, symbol rate, and pres- ence of interferer [25]. This method is achieved by the autocorrelation process. The autocorrelation can be computed by multiplying the received signal y(n) with its delay version. The sum of autocorrelation is compared with a pre-defined threshold to detect the activity of the PU signal. If the summation is larger than the threshold, it means that the PU is present, otherwise, it is absent [11, 26]. This technique can distinguish between the signal and the noise, so it has a better performance as compared to ED. How- ever, it has a high computational complexity, since it consumes a long sensing time. A signal is called a cyclostationary if its autocorrelation is a periodic function of time t with a given period. This type of cyclostationary detector is called a 2nd order cyclosta- tionary detector [25]. A discrete cyclic autocorrelation function of a discrete-time signal y(n) with a fixed lag l is defined in Equation (7) [21]. Rαyy (l) = lim N →∞ 1 N N −1∑ n=0 y[n]y∗[n + l]e−j2παn∆n, (7) where N is the number of samples of a signal y[n] and ∆n is the sampling interval. By applying the discrete Fourier transform to Rαyy (l), the cyclic spectrum (CS) is given as [21]: Sαyy (f ) = ∞∑ l=−∞ Rαyy (l)e −j2πf l∆l. (8) The detection of the PU signal is achieved by sensing the (cyclic frequency) of its cyclic spectrum or cyclic autocorrelation function (CAF). If the CAF is larger than the pre-defend threshold, the signal is present, otherwise, the signal is absent [25]. 3.3. Matched filter The matched filter is a coherent detection technique. This technique requires prior information about the PU signals at SU. Assuming that the PU transmitter sends a pilot stream simultaneously with the data, the SU receives the signal and the pilot stream. Matched filter detection is performed by projecting the received signal in the direction of the pilot [1]. The test statistic can be written as: TM F D = ∑ N y(n)x∗p(n), (9) where xp represents the PU signal, y represents the SU received signal. The test statistics, TM F D, are then compared with a pre-defined threshold to de- tect the activity of PU, as shown in the following Equation (10).® If TM F D ≥ λ, PU signal present If TM F D < λ, PU signal absent (10) 4. The proposed method In this method, the design is based on the matched filter and cyclostationary techniques with an improve- ment in detection performance and reduction in com- putational complexity in both of them. The process of this method is that the matched filter receives the PU signal and senses the half number of samples by selecting one and skipping another to reduce the computational complexity in the convolution process between the incoming received signal (PU signal) and its impulse response, which is stored in the matched filter of the spectrum sensing technique. When the de- tector does not have a better knowledge about the PU or when the received signal is distorted due to the channel effect, it switches to the cyclostationary tech- nique to overcome the degradation of performance detection. In the cyclostationary stage, it also senses the PU signal by using the half number of samples by sensing one and skipping one to reduce the computa- tional complexity in the autocorrelation process. So, in this proposed method, we gain a high-performance detection with a reduction in computational complex- ity. Figure 1 shows the flowchart that explains the procedures of the proposed method. Figure 2 shows the proposed system model using the centralised co- operative network. According to [11] and [21], the probability of detection of the proposed method can be written as: Pd,proposedi = 1 − (1 − Pd,M F i)(1 − Pd,cycoi) i = 1, 2, . . . k, (11) where k is the number of SUs in the cooperative scenario, Pd,proposedi is the probability of detection of the proposed method, Pd,M F i is the probability of detection in matched filter stage, and Pd,cycoi is the probability of detection in cyclostationary stage. 5. Computational complexity of the proposed method In this section, we compute the computational com- plexity in two stages (MF and CFD). Since the MF is based on the convolution process between the re- ceived and previous information of the PU signal, the computational complexity in the convolution process based on the frequency domain equals to a multiplica- tion between two signals and we need to compute the frequency domain transformation of both the received PU signal and its impulse, then, we need to compute the multiplication between them. The computational complexity of FFT for N samples is o(N log2N ) ac- cording to [21], while for multiplying two signals, each 230 vol. 62 no. 2/2022 Improvement of Spectrum Sensing Performance in Cognitive . . . Figure 1. Procedures of the proposed method. with N samples, it is o(N ). So, the computational complexity of a traditional MF becomes: CconF F T = 2o(N log2N ) + o(N ), (12) where N is the number of samples. In the proposed method, we select a half of the samples by choosing one and skipping one, so the Equation (12) becomes: CconpropoF F T = 2o Å N 2 log2 N 2 ã + o Å N 2 ã . (13) In the second stage, the cyclostationary process is based on the autocorrelation process and its compu- tational complexity is [22, 27]: Cauto = N o. of real multiplications + N o. of real additions Cauto = 4N + 4N − 2 (14) The complexity of a traditional cyclostationary pro- cess is written as shown below: Ccycl = 4N + 4N − 2 + o(N log2N ). (15) Since, in the proposed method, only a half of the samples was chosen for the cyclostationary process by selecting one and skipping one, the Equation (15) reduces to: Ccyclproposed = 2N + 2N − 2 + o Å N 2 log N 2 ã = 4N − 2 + o Å N 2 log N 2 ã (16) The total computational complexity of the proposed method is the addition of Equations (13) and (16), as shown in Equation (17). CT otalproposed = 4N − 2 + 3o Å N 2 log2 N 2 ã + o Å N 2 ã (17) 231 Hadeel S. Abed, Hikmat N. Abdullah Acta Polytechnica Figure 2. The proposed system model using the centralised cooperative network. Method Computational complexity Proposed method CT otalproposed = 4N − 2 + 3o ( N 2 log2 N 2 ) + o ( N 2 ) Hybrid method in [21] Chybrid = 2N + 2N − 2 + O (N log2(N )) Traditional hybrid [11] Chybridtradi = 4N + 4N − 2 + O (N log2(N )) Traditional Cyclostationary [21] Ccycl = 4N + 4N − 2 + o (N log2N ) Traditional MF CconF F T = 2o (N log2N ) + o(N ) Table 1. Comparison of computational complexity. The computational complexity ratio is defined as the ratio of computational complexity in the proposed method to the maximum computational complexity (in the traditional Cyclostiationary method). Cratio = CT otalproposed Ccycl (18) Table 1 displays the summary of the computational complexity of the proposed method, the traditional hybrid method in [11], the hybrid method in [21], traditional cyclostationary, and traditional MF. It can be noted that the complexity of the traditional hybrid method is the same as the one of the traditional cyclostationary method. 6. Simulation results and discussion This section shows the simulation results of the pro- posed method in both the cooperative and the non- cooperative scenarios. The performance is tested un- der AWGN and Rayleigh multipath fading channels. The results have been achieved using MATLAB 2018 on Windows 10. The performance results of the pro- posed method are measured using the probability of detection and computational complexity ratio and evaluated by comparing it with: hybrid methods in Parameters Values PU signal QPSK Carrier frequency Fc 200 Hz Sampling frequency Fs 4000 Hz Pf 0.001 Table 2. Simulation results. references [11] and [21], and with traditional methods (cyclostationary feature detection (CFD) and matched filter method MF). The simulation parameters used are presented in Table 2. The multipath fading used is “ITU indoor channel model (A)” with the specification shown in Table 3 [28]. Figure 3 shows the performance curves of Pd vs Eb/No for traditional sensing methods (energy detec- tion, cyclostationary, and matched filter) in AWGN using the non-cooperative scenario. It can be seen from this figure that the matched filter has a better performance as compared to the en- ergy detection and cyclostationary methods, especially at a low value of Eb/No, since it has a good knowledge of the PU signal. For example, at Eb/No equal to 0 dB, the probability of detection in the matched filter is increased by 36 % and 91 % as compared to cyclo- 232 vol. 62 no. 2/2022 Improvement of Spectrum Sensing Performance in Cognitive . . . Figure 3. Performance comparison between traditional sensing techniques under AWGN and non-cooperative scenarios. Figure 4. Performance comparison between traditional sensing techniques under Rayleigh fading and non-cooperative scenarios. Tap Relative Average Doppler delay power spectrum [ns] [dB] 1 0 0 flat 2 50 -3.0 flat 3 110 -10.0 flat 4 170 -18.0 flat 5 290 -26.0 flat 6 310 -32.0 flat Table 3. Multipath fading properties of ITU indoor channel model (A). stationary and energy detection, respectively. But the performance of matched filter becomes very bad when the knowledge of PU signal becomes poor and the cyclostationary technique becomes the best technique in the detection performance. The calculation of per- centage improvement in this and all bellow results are as shown below: percentage = (high value − low value) ∗ 100 %. When comparing two curves at the same Eb/No or N , we take the values from the curves and make sure that one curve has a value lower than the other, which is computed as shown in the above formula. Figure 4 presents the same performance as in Fig- ure 3, but in Rayleigh multipath fading, it can be noted that all techniques have the same detection performance as compared with Figure 3, but with a degradation in the probability of detection due to multipath fading, and the matched filter also outper- forms other technique in the case of a good knowledge of PU. 233 Hadeel S. Abed, Hikmat N. Abdullah Acta Polytechnica Figure 5. Pd versus Eb/No of proposed sensing methods under Rayleigh fading and non-cooperative scenario. Figure 6. Performance comparison between the cooperative and non-cooperative scenarios in the proposed and traditional methods. Figure 5 illustrates Pd vs Eb/No of the pro- posed method in the non-cooperative scenario under Rayleigh multipath fading as compared with hybrid methods in [11] and [21] and traditional methods (cy- clostationary feature detection (CFD), and matched filter detection). It can be observed that the probabil- ity of detection of the proposed method outperforms the other methods especially at low Eb/No values, since the matched filter gives an excellent performance detection when it has the best knowledge about the PU signal. When it has a poor knowledge, it switches to the cyclostationary technique, which is a blind technique (does not need information about the PU signal) and gives a very good performance detection especially at low values of Eb/No. So, the overall detection performance of the proposed method gives an excellent detection performance with a low com- putational complexity. For example, at Eb/No equal to 0 dB, the proposed method achieves an increase in detection probability of 38 %, 28 %, 28 %, and 18 % as compared with the traditional hybrid method in [11], the hybrid method in [21], traditional CFD method, and traditional MF method, respectively. Figure 6 displays the performance curves of the average Pd vs Eb/No of the proposed method in co- operative and non-cooperative scenarios as compared to the traditional hybrid method in [11]. In the co- operative scenario, we assumed 3 CUs do the sensing and one of them is suffering from multipath fading. It can be noted that the detection performance of the 234 vol. 62 no. 2/2022 Improvement of Spectrum Sensing Performance in Cognitive . . . Figure 7. Computational complexity ratio versus the number of samples. Method Performance detection Computational complexity Proposed method Excellent Moderate Hybrid method in [21] Good Low Traditional hybrid method in [11] Good High CFD Good High MF Very good (in best PU information) Moderate ED Low Low Table 4. Summary of performance measurement. cooperative scenario has a larger improvement than non-cooperative in both methods, since the effect of fading is reduced. For instance, at Eb/No equal to 0 dB in the proposed method, the performance detec- tion is increased by 26 % as compared with a single CU in multipath fading and increased by 20 % as com- pared with the traditional hybrid also with a single CU in multipath fading. In all cases, the proposed method has a better performance than the traditional hybrid method. Figure 7 shows the computational complexity ratio versus the number of samples. It can be seen that the proposed method has a lower computational com- plexity than the hybrid method in [11], traditional cyclostationary method, and MF, since it computes the convolution process in the MF stage or autocorrela- tion process in CFD with a half of the samples, and it is slightly greater than the hybrid method in [21], since this method uses an ED in the first stage. However, the proposed method outperforms the hybrid method in [21] and others in the probability of detection. For example, at N equal to 100, the computational com- plexity ratio in the proposed method decreased by 14 %, 14 %, and 12 % as compared to CFD, tradi- tional hybrid in [11], and MF, respectively. So, we conclude that the proposed method has an excellent probability of detection and a very good reduction in computational complexity. Table 4 summarizes the performance of the proposed method, hybrid meth- ods in [11] and [21], and traditional methods (ED, cyclostationary, and MF). This table shows that at very low SNR values, the proposed method is a per- fect choice for spectrum sensing in terms of detection performance and computational complexity and for a very good channel environment, the ED become the best choice, but since in most cases, the channel environment is bad, the proposed method is more appropriate than others. 7. Conclusions In this paper, we proposed a modified hybrid sensing method to overcome the problems of the traditional spectrum sensing technique. The proposed method is based on a combination of MF and CFD to improve the detection performance and reduce the computa- tional complexity. The proposed method is simulated using MATLAB under Rayleigh multipath fading with two scenarios: cooperative and non-cooperative, mea- sured using Pd and Cratio,and evaluated by a com- parison with traditional and hybrid sensing methods in the literature. The simulation results show that the proposed method outperforms other methods in 235 Hadeel S. Abed, Hikmat N. Abdullah Acta Polytechnica the literature in terms of probability of detection and computational complexity in both channels. In future work, this method can be tested under other types of fading channels. List of symbols y(n) Received sensed signal by the CU x(n) PU signal Noi(n) Additive White Gaussian Noise H The gain of the channel θ Activity pointer H1 Hypothesis when the PU is present H0 Hypothesis when the PU is absent W Observation window λ Pre-defined threshold Enj Accumulated energy N Number of sensed samples Fs Sampling frequency Pf Probability of false alarm Pd Probability of detection Rαyy (l) The discrete cyclic autocorrelation function ∆n Sampling interval S αyy (f ) Cyclic spectrum xp Previous information of PU signal TMFD The test statistic of MF Pd,proposedi Probability of detection of the proposed method Pd,MFi Probability of detection of MF stage Pd,cycoi Probability of detection of CFD stage k Number of SU CconFFT The computational complexity of traditional MF Ccycl The computational complexity of traditional CFD CTotalproposed The computational complexity of the pro- posed method Cratio The ratio of computational complexity Eb/No Signal to noise ratio per bit List of abbreviations CR Cognitive Radio SS Spectrum Sensing ED Energy Detection MF Matched Filter CFD Cyclostationary Feature Detection CC Computational Complexity RF Radio Frequency PU Primary User SU Secondary User CU Cognitive User SNR Signal to Noise Ratio CSS Cooperative Spectrum Sensing FC Fusion Center SUs Secondary Users MME Maximum–Minimum Eigenvalue ANN Artificial Neural Networks CAF Cyclic Autocorrelation Function CS Cyclic Spectrum FFT Fast Fourier Transform ITU International Telecommunication Union AWGN Additive White Gaussian Noise References [1] F. 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John Wiley & Sons, Ltd, 2002. https://doi.org/10.1002/0470847808.fmatter. 237 https://elmnet.ir/article/605357-22041/A-Low-Complexity-Cyclostationary-Based-Detection-Method-for-Cooperative-Spectrum-Sensing-in-Cognitive-Radio-Networks https://elmnet.ir/article/605357-22041/A-Low-Complexity-Cyclostationary-Based-Detection-Method-for-Cooperative-Spectrum-Sensing-in-Cognitive-Radio-Networks https://elmnet.ir/article/605357-22041/A-Low-Complexity-Cyclostationary-Based-Detection-Method-for-Cooperative-Spectrum-Sensing-in-Cognitive-Radio-Networks https://elmnet.ir/article/605357-22041/A-Low-Complexity-Cyclostationary-Based-Detection-Method-for-Cooperative-Spectrum-Sensing-in-Cognitive-Radio-Networks https://doi.org/10.1109/CCIntelS.2015.7437891 https://doi.org/10.1007/978-981-15-8335-3_41 https://doi.org/10.33103/uot.ijccce.19.4.1 https://doi.org/10.1109/LCOMM.2016.2602266 https://doi.org/10.26636/jtit.2021.144420 https://doi.org/10.1007/s13369-020-05281-0 https://doi.org/10.1007/s11277-020-08013-7 https://doi.org/10.11591/ijece.v7i5.pp2683-2695 https://doi.org/10.14311/AP.2020.60.0279 https://doi.org/10.1007/s12204-012-1222-z https://doi.org/10.1007/s11277-015-2962-5 http://www.ece.ualberta.ca/~chintha/pdf/thesis/phd_saman.pdf http://www.ece.ualberta.ca/~chintha/pdf/thesis/phd_saman.pdf https://doi.org/10.18685/EJSR(4)3_EJSR-16-010 https://doi.org/10.1587/comex.2016XBL0211 https://doi.org/10.1002/0470847808.fmatter Acta Polytechnica 62(2):228–237, 2022 1 Introduction 2 Related works 3 Spectrum sensing techniques 3.1 Energy detector 3.2 Cyclostationary feature detection 3.3 Matched filter 4 The proposed method 5 Computational complexity of the proposed method 6 Simulation results and discussion 7 Conclusions List of symbols References