Primary signal detection algorithms for spectrum sensing at low SNR over fading channels in cognitive radio

doi:10.1016/j.dsp.2019.07.016

Digital Signal Processing

Volume 93, October 2019, Pages 187-207

https://doi.org/10.1016/j.dsp.2019.07.016 Get rights and content

Abstract

The generalized detector (GD) can be implemented at the low signal-to-noise ratio (SNR) in cognitive radio (CR) systems to improve the spectrum sensing performance under correlated antenna array elements. The weighted GD (WGD) and the generalized likelihood ratio test for GD (GLRT-GD) are proposed to be used for coarse spectrum sensing when the noise power is known and unknown, respectively. The GD optimal detection threshold is defined based on the minimum probability of error criterion for various fading channels, namely, the additive white Gaussian noise (AWGN), Nakagami-m, and Rayleigh fading channels. The performance of the proposed algorithms are compared with the spectrum sensing performance of the energy detector (ED), weighted ED (WED), maximum-minimum eigenvalue (MME) detector, generalized likelihood ratio test for ED (GLRT-ED), matched filter (MF), arithmetic to geometric mean (AGM) detector, scaled largest eigenvalue (SLE) detector, moment based detector (MBD), covariance based detector (CBD), and others. The simulation results demonstrate superiority in the spectrum sensing performance of the proposed algorithms in comparison with the above-mentioned detectors. For example, the GLRT-GD achieves the SNR gain equal to 1.2 dB, 4.0 dB, and 4.5 dB in comparison with GLRT-ED, MME, and GM detectors, respectively, at the probability of false alarm $P_{FA} = 0.1$ . The WGD and GLRT-GD implementation allows us to achieve a considerable spectrum sensing performance improvement at small number of samples under the low SNR and the correlated antenna array elements.

Introduction

The spectrum scarcity problem under the rapid and huge growth of wireless sensor network service motivates many researchers to seek for various solutions. Approximately 70–80% band of the primary spectrum is already assigned and exclusively allocated to various types of wireless communications and sensor network technologies. New innovative techniques exploiting the available radio spectrum are required since only a part of the whole spectrum band is used at specified place and time [1]. Implementation of the cognitive radio (CR) systems allows us to alleviate the spectral congestion problem by opportunistic use of the frequency bands with the purpose to improve the efficiency of spectrum utilization. The CR principle encompasses several tasks such as the spectrum sensing, i.e., a detection of spectrum holes and interference avoidance, channel identification, i.e., the channel state estimation and capacity prediction, transmit power control, and dynamic spectrum management.

Spectrum sensing is needed to define the idle frequency bands, within the limits of which the entire CR operation is relied on. The radio spectrum awareness and existence of primary users (PUs) are obtained by performing the spectrum sensing at the secondary users (SUs) or secondary access nodes. As a result, the CR systems allow the SU to use the unutilized frequency bands without causing harmful interference to the PU. Many PU signal detection techniques can be applied in spectrum sensing [1], such as the energy detector (ED) [2], [3], generalized likelihood ratio test (GLRT) detector [4], matched filter [5], [6], cyclostationary detector [7], [8], and eigenvalue-based detection algorithms [9]. There is no identification for a specific spectrum sensing technique in the related CR system standards (IEEE 802.22, IEEE 802.11K).

The cyclostationary detector can exploit the cyclostationary features embedded in the PU signal even at the low signal-to-noise ratio (SNR) [7], [8]. In the MF case, a perfect knowledge about the PU signal parameters, namely, the bandwidth, operating frequency, modulation type and order, frame format, etc, is required to demodulate the received signal. The covariance-based detector (CBD) exploits a difference in the statistical covariance of the received signal, generally, in practice it is estimated through the sample covariance matrix, and noise [10]. The decision statistics of the arithmetic to geometric mean (AGM) detector, maximum-minimum eigenvalue (MME) detector [11], energy to minimum eigenvalue (EME) detector [12], and scaled largest eigenvalue (SLE) detector [13] depend on the sample covariance matrix eigenvalues. The AGM test statistics is the ratio between the arithmetic mean and geometric mean of eigenvalues. The maximum and minimum eigenvalues of the PU signal covariance matrix are used to define the MME test statistics [11]. In the case of EME detector, the average power of received signal and the minimum eigenvalue of sample covariance matrix are defined to formulate the test statistics. MME and EME algorithms are called the blind detection methods because they use only the received signal samples to perform detection, similar to ED. The SLE detector [13] is based on GLRT with the final test statistics as a ratio of the largest eigenvalue to the sum of the sample covariance matrix eigenvalues. No a priori knowledge concerning the noise variance is required and, consequently, it is robust to the noise power uncertainty. The test statistics of the moment based detector (MBD) is the ratio of the fourth absolute moment to the square second absolute moment of practically relevant signal constellations [14]. The probability of detection formula for MBD differs according to the constellations, i.e., binary phase shift keying (BPSK), quadrature phase shift keying (QPSK), etc. For the max-min SNR based detector [15], the received signal is oversampled and the linear combining vector α with the size L of oversampling factor is introduced assuming a known transmitter pulse shaping. The vector α is optimized to have two components with different SNRs for the combined signal. The ratio of the signal energy corresponding to the maximum and minimum SNR is considered as the test statistics.

Useful ED sensing performance analysis is presented in [16]. The ED spectrum sensing performance and signal detection as a function of the average noise power fluctuations within the limits of short time interval are investigated in [17] and a new ED signal detection algorithm based on the dynamic detection threshold is discussed. Two stages spectrum sensing architecture combining the ED and feature detector is presented in [18]. This idea was proposed by IEEE 802.22 working group. The ED with two-steps threshold [19] and the weighted ED (WED) [20] achieve the significant spectrum sensing performance improvement in comparison with the conventional ED.

The idea to employ the generalized detector (GD) for the coarse spectrum sensing in CR systems has been triggered by the purpose to improve the spectrum sensing performance at the low SNR. The GD based on the generalized approach to signal processing (GASP) in noise [21], [22], [23] represents a combination of the correlation detector and ED. A great difference between the GD and conventional ED is a presence of additional linear system, for example, the band pass filter, at the GD input. This filter can be considered as the source of reference noise, which does not contain the PU signal. The GD log-likelihood ratio test (log-LFT), based on which we can make a decision about the PU signal presence or absence in the process incoming at the SU input, demonstrates a definition of the jointly sufficient statistics of the mean and variance at the GD output and does not require any information about the PU signal and its parameters [21], [22, Chapter 3]. Note, that the conventional correlation detector makes a decision about the PU signal presence or absence in the incoming process based on a definition of the log-LRT mean only. The conventional ED defines a decision statistics with respect to PU signal presence or absence at the SU input based on determination of the log-LRT variance only. Definition of the jointly sufficient statistics of the GD log-LRT mean and variance allows us to make accurate decision about the PU signal presence or absence in comparison with the conventional MF, ED, correlation receiver and other modern signal detection algorithms. Theoretically, in the ideal case, the GD can be applied to detect any signal, i.e., the signal with known and unknown, deterministic or stochastic parameters under the very low SNR. The GD implementation in wireless communications and radar sensor systems is discussed in [24], [25], [26], [27], [28], [29], [30], [31] and [32], [33], [34], [35], [36], [37], [38], respectively. Investigation concerning the GD employment in CR systems has been discussed in [39], [40], [41].

In this work with, the objective of spectrum sensing performance improving, i.e., the PU signal detection, at the low SNR under the spatial correlated antenna array elements, we proposed two spectrum sensing algorithms, namely, the weighted GD (WGD) and the generalized likelihood ratio test for the GD (GLRT-GD) when the noise variance is known and unknown, respectively. Both new algorithms are the blind detection approaches because these algorithms use only the received signal samples for detection, and can be classified or considered as eigenvalue based detectors owing to their test statistics depend on the sample covariance matrix eigenvalues. The simulation results confirm the effectiveness of implementation of the proposed algorithms in CR systems in comparison with WED, generalized likelihood ratio test for the ED (GLRT-ED), AGM, MME, EME, SLE, MBD, CBD, and max-min SNR detectors.

The conventional GD detection performance over different fading scenarios is not available in the related modern literature. Thus, the optimal GD detection threshold at the low SNR over the additive white Gaussian noise (AWGN), Nakagami-m, and Rayleigh fading channels is derived based on criterion of the minimum probability of error.

The remainder of this paper is organized as follows. Section 2 presents a general system model discussed in this paper. Brief description of the conventional GD structure and decision statistics are delivered in Section 3. The moment generation function (MGF) of partial decision statistics at the GD output is defined in Section 4. The proposed WGD and GLRT-GD decision statistics are discussed in Section 5. Definition of the GD optimal detection threshold for various types of fading channels is presented in Section 6. The simulation results confirming a theoretical analysis are presented and discussed in Section 7. Finally, the conclusion remarks are made in Section 8.

Section snippets

System model for antenna array sensing

One example of the CR system is presented in Fig. 1. We assume that the SU or secondary sensor node is equipped by antenna array with the number of elements equal to M. Each antenna array element receives N samples during the sensing time. The coarse spectrum sensing dilemma at the k-th time instant can be described by the conventional binary hypothesis test as follows: ${\begin{matrix} H_{0} = z_{i} [k] = w_{i} [k], & i = 1, \dots, M; k = 0, \dots, N - 1, \\ H_{1} = z_{i} [k] = h_{i} [k] s [k] + w_{i} [k], & i = 1, \dots, M; k = 0, \dots, N - 1, \end{matrix}$ where $H_{1}$ is the hypothesis a “yes” PU signal; $H_{0}$

Conventional GD and related decision statistics

As we mentioned before, the GD is constructed in accordance with the GASP in noise [21], [22], [23]. The GD is considered as a linear combination of the correlation detector, which is optimal in the Neyman-Pearson criterion sense under detection of signals with a priori known parameters, and the ED, which is optimal under detection of signals with a priori unknown parameters or stochastic parameters. This GD feature allows us to obtain the better detection performance in comparison with other

MGF of the decision statistics at the GD output

The moment generating function (MGF) for the GD partial decision statistics $T_{GD} (X_{k})$ under the hypothesis $H_{1}$ given by $T_{GD} (X_{k}) = \sum_{i = 1}^{M} s_{i}^{2} [k] + \sum_{i = 1}^{M} η_{i}^{2} [k] - \sum_{i = 1}^{M} ζ_{i}^{2} [k]$ is required. This MGF can be presented in the following form (see Appendix 2): $M_{T_{GD} (X_{k})} (l) = \prod_{i = 1}^{M} {[1 - E_{s} σ_{h}^{2} α_{i} l]}^{- 1} \prod_{i = 1}^{M} M_{z_{1_{i}}} (l) \prod_{i = 1}^{M} M_{z_{2_{i}}} (- l) = \prod_{i = 1}^{M} {[1 - E_{s} σ_{h}^{2} α_{i} l]}^{- 1} \prod_{i = 1}^{M} {(1 - 2 σ_{w}^{2} l)}^{- 0.5} \prod_{i = 1}^{M} {(1 + 2 σ_{w}^{2} l)}^{- 0.5} = \prod_{i = 1}^{M} {[1 - E_{s} σ_{h}^{2} α_{i} l]}^{- 1} {(1 - 2 σ_{w}^{2} l)}^{- 0.5 M} {(1 + 2 σ_{w}^{2} l)}^{- 0.5 M} = {(1 - 4 σ_{w}^{4} l^{2})}^{- 0.5 M} \prod_{i = 1}^{M} {[1 - E_{s} σ_{h}^{2} α_{i} l]}^{- 1},$ where $α_{i}$ is the eigenvalue of the correlation matrix R given by

WGD and GLRT-GD: correlated antenna array elements

Finally, the weighted GD (WGD) decision statistics can be determined using the following form (see Appendix 1): $T_{WGD} (X) = \ln L_{GD} (X) = \sum_{k = 0}^{N - 1} \sum_{i = 1}^{M} \frac{E_{s} σ_{h}^{2} α_{i}}{2 σ_{w}^{2} (E_{s} σ_{h}^{2} α_{i} + σ_{w}^{2})} y_{i}^{2} [k] - \frac{N}{2 σ_{w}^{2}} [\sum_{i = 1}^{M} λ_{R_{x} i} - \sum_{i = 1}^{M} λ_{R_{η} i}] = \sum_{k = 0}^{N - 1} \sum_{i = 1}^{M} \frac{γ α_{i}}{2 σ_{w}^{2} (γ α_{i} + 1)} y_{i}^{2} [k] - \frac{N}{2 σ_{w}^{2}} [\sum_{i = 1}^{M} λ_{R_{x} i} - \sum_{i = 1}^{M} λ_{R_{η} i}] ≷_{H_{0}}^{H_{1}} {THR}_{WGD},$ where ${THR}_{WGD}$ is the decision statistics threshold, and $γ = \frac{E_{s} σ_{h}^{2}}{σ_{w}^{2}}$ is the SNR at the GD input. We see from (30) that the weighting coefficients $γ α_{i} / (γ α_{i} + 1)$ are actually similar to the Wiener filter weights in a transformed space

Optimal GD detection threshold: fading channels

The performance of any detector is evaluated by the probability of detection $P_{D}$ , bit error rate (BER), probability of false alarm $P_{FA}$ , probability of error $P_{error}$ , and probability of miss $P_{miss}$ . The detection threshold is the main parameter used to determine these probabilities. In general, it is desirable to achieve the high probability of detection $P_{D}$ keeping the probability of false alarm $P_{FA}$ as small as possible. A choice of the detection threshold can be carried out based on various

Simulation results

In this section, we verify the spectrum sensing performance of the conventional GD and the proposed WGD and GLRT-GD by simulation using MATLAB and compare it with other detectors under the uncorrelated and correlated antenna array elements. Simulation is performed using IEEE 802.22 system parameters [54]. The main simulation parameters are presented in Table 3.

Comparison of the spectrum sensing performance between the conventional GD, conventional ED, MF, and GLRT detector proposed in [13]

Conclusions

The spectrum sensing in the CR networks requires sensors that are able to detect the frequency holes in a reliable, robust, and computationally feasible manner. By these reasons, we suggest to employ the GD with the purpose to improve the spectrum sensing performance. Comparison of the conventional ED and GD spectrum sensing performances is carried out at the same initial conditions under the uncorrelated and spatially correlated antenna array elements. The conventional GD demonstrates the

Declaration of Competing Interest

There is no any conflict of interests between the authors.

Acknowledgements

This research has been supported by the National Council of Science and Technology in Mexico (Censejo Nacional Ciencia y Tecnology – CONACYT, research grant no 35022). Additionally, the authors would like to thank the anonymous reviewers for the comments and suggestions that helped to improve a quality of the present paper.

References (56)

V. Tuzlukov
A new approach to signal detection theory
Digit. Signal Process.
(1998)
V. Tuzlukov
DS-CDMA downlink systems with fading channel employing the generalized receiver
Digit. Signal Process.
(2011)
S. Sharma et al.
Cognitive radio techniques under practical imperfections: a survey
IEEE Commun. Surv. Tutor.
(July 2015)
J. Song et al.
Spectrum sensing in cognitive radios based on enhanced energy detector
IET Commun.
(May 2012)
S.M. Kay
Fundamentals of Statistical Signal Processing: Detection Theory, vol. 2
(1998)
T.J. Lim et al.
GLRT-based spectrum sensing for cognitive radio
H.S. Chen et al.
Signature based spectrum sensing algorithms for IEEE 802.22 WRAN
Q. Lv et al.
Matched filter based spectrum sensing and power level recognition with multiple antennas
W.M. Jang
Blind cyclostationary spectrum sensing in cognitive radios
IEEE Commun. Lett.
(March 2014)
P. Urriza et al.
Multiple antenna cyclostationary spectrum sensing based on the cyclic correlation significance test
IEEE J. Sel. Areas Commun.
(2013)

A. Kortun et al.

On the eigenvalue-based spectrum sensing and secondary user throughput

IEEE Trans. Veh. Technol.

(2014)

M. Jin et al.

On the performance of covariance based spectrum sensing for cognitive radio

IEEE Trans. Signal Process.

(July 2012)

Y. Zeng et al.

Maximum-minimum eigenvalue detection for cognitive radio

Y. Zeng et al.

Eigenvalue-based spectrum sensing algorithms for cognitive radio

IEEE Trans. Commun.

(June 2009)

P. Wang

Multiantenna-assisted spectrum sensing for cognitive radio

IEEE Trans. Veh. Technol.

(May 2010)

T. Bogale et al.

Wideband sensing and optimization for cognitive radio networks with noise variance uncertainty

IEEE Trans. Commun.

(April 2015)

T. Bogale et al.

Max-min SNR signal energy based spectrum sensing algorithms for cognitive radio net-works with noise variance uncertainty

IEEE Trans. Wirel. Commun.

(Jan. 2014)

A. Mariani et al.

Effects of noise power estimation on energy detection for cognitive radio applications

IEEE Trans. Commun.

(Dec. 2011)

G. Yu et al.

A novel energy detection scheme based on dynamic threshold in cognitive radio systems

J. Comput. Inf. Syst.

(2012)

P. Mane et al.

Spectrum sensing in cognitive radio using TSA

Int. J. Comput. Netw. Wirel. Commun.

(Aug. 2012)

H. Chen et al.

Cooperative spectrum sensing based on double threshold detection and Dempster-Shafer theory

L. Luo et al.

Spectrum sensing for cognitive radio networks with correlated multiple antennas

Electron. Lett.

(Nov. 2011)

V. Tuzlukov

Signal Detection Theory

(2001)

V. Tuzlukov

Signal Processing Noise

(2002)

V. Tuzlukov

Generalized approach to signal processing in wireless communications: the main aspects and some examples

V. Tuzlukov

Signal processing by generalized receiver in DS-CDMA wireless communication systems with frequency-selective channels

Circuits Syst. Signal Process.

(2011)

V. Tuzlukov

Signal processing by generalized receiver in DS-CDMA wireless communication systems with optimal combining and partial cancellation

EURASIP J. Adv. Signal Process.

(2011)

V. Tuzlukov

Bit error probability of quadriphase DS-CDMA wireless communication systems based on generalized approach to signal processing

Telecommun. Rev.

(2013)

Cited by (19)

Efficient Methods to Detect Atmospheric Concentration with Low Signal to Noise Ratio on a Sensor Network
2024, Atmospheric Environment
Accidental or malicious releases of materials into the air can occur surreptitiously and lead to low concentrations involving very limited changes in the atmospheric background noise. This may result in signal variations on sensors placed in the environment characterized by a low signal-to-noise ratio (SNR). In this work, we aim to provide a selection of efficient online detection methods adapted to low signal to noise ratio (SNR). These methods take into consideration signals that are monitored by a subset of a network of sensors and for a limited period of time. We show that it is possible to detect with a very reasonable false alarm rate only knowing the statistics of the background noise. Derived from the Cumulative Sum (CUSUM), the methods presented and tested here are generalizations to multivariate time series considering space and time sparsity of the exposure of the network of sensors. We show that the methods previously presented in a Gaussian context can easily be extended to a Poisson model which is consistent with particle counting sensors like those used in the frame of radioactivity detection. We demonstrate the validity of the different methods on toy example and then prove its interest on data from the twin experiment of a fictitious radioactive release in an urban area.
Sequential detection of a temporary change in multivariate time series
2022, Digital Signal Processing: A Review Journal
Citation Excerpt :
Early detection allows estimation techniques such as in [2] to start monitoring the data at the right moment and facilitates the convergence to a solution while decreasing the computational cost. It is also used for early seismic detection [3], or early detection of infected people during a pandemic [4], but can also be applied to many other fields such as [5] and [6]. The CUSUM (CUmulative SUM) technique [7] is a powerful univariate sequential change-point detection tool on which are based many detection techniques.
In this work, we aim to provide a new and efficient recursive detection method for temporarily monitored signals. Motivated by the case of the propagation of an event over a field of sensors, we assumed that the change in the statistical properties in the monitored signals can only be temporary. Unfortunately, to our best knowledge, existing recursive and simple detection techniques such as the ones based on the cumulative sum (CUSUM) do not consider the temporary aspect of the change in a multivariate time series. In this paper, we propose a novel simple and efficient sequential detection algorithm, named Temporary-Event-CUSUM (TE-CUSUM). By combining with a new adaptive way to aggregate local CUSUM variables from each data stream, we empirically show that the TE-CUSUM has a very good detection rate in the case of an event passing through a field of sensors in a very noisy environment.
Spectrum sensing for cognitive radio based on Kendall's tau in the presence of non-Gaussian impulsive noise
2022, Digital Signal Processing: A Review Journal
Citation Excerpt :
Specifically, CR finds out the spectrum hole by continuously monitoring the spectrum state, so that SUs can utilize the vacant spectrum if it is not occupied by the PUs. In CR system, spectrum sensing is a mandatory functionality to achieve the reliable data transmission by reliably and quickly detecting the existence of PU signals [7–10]. A number of spectrum sensing schemes have been proposed in the literature, including cyclostationarity-based detection [11], eigenvalue-based detection [12–14], goodness-of-fit testing based detection [15], energy detection (ED) [16,17].
Traditional spectrum sensing techniques optimized against Gaussian noise may suffer from severe performance deterioration when non-Gaussian noise is present. To overcome such drawback, this paper proposes a novel Kendall's tau (KT) based detector for detecting the primary signal in additive non-Gaussian noise characterized by contaminated Gaussian model (CGM). The proposed detector, which exploits the ordinary information rather than the cardinal information from the observed raw data, is both computationally efficient and robust against large-valued outliers. The analytic expressions concerning the expectation and variance of KT are firstly established under this specific CGM, which emulates a frequently encountered scenario in practice. Performance analyses are further conducted in terms of false alarm probability, detection probability and receiver operating characteristic (ROC) curve. Comparative results with the traditional energy detection (ED) and three other state-of-the-art detectors demonstrate the superiority of KT, including: 1) robustness against impulsive noise compared to ED, 2) accurate control of the false alarm probability without any prior information about the noise distribution, and 3) better detection performance over three popular detectors with either Gaussian or non-Gaussian background noise.
Zoom discrete spectral correlation function, with application to cyclostationary signal detection
2022, Digital Signal Processing: A Review Journal
Citation Excerpt :
Regulatory approaches along the EU (and worldwide) are being carried out to allow more rational use of RF spectrum, namely by leasing spectrum to other users (Secondary Users - SU) when not in use by the owner (Incumbent, Licensee or Primary User - PU) or opportunistic use by some SU after sensing the spectrum and verifying that the PU is idle, stopping transmitting when the PU becomes active. In this scenario, spectrum sensing becomes the central task to be performed by any SU, and several sensing methods have been proposed in the scientific literature, e.g., [12,13]. The presence of background noise that adds up to the received signal of interest makes using an energy detection decision scheme unreliable if little or no statistical information about the noise is known [14].
In today's world, the proliferation of wireless devices grows in an exponential form, demanding more and more data communication capacity over wireless links, which leads to a scarcity in electromagnetic spectrum availability. Nevertheless, spectral occupancy studies have shown a low utilization rate of some licensed frequency bands both in time and geographic location. Cognitive Radio (CR) is a promising technology to address spectrum sharing by selectively detecting bands not being in use by a Primary User (PU) and opportunistically allocating them for a Secondary User (SU) utilization. However, the detection of unused frequency bands, which is the most challenging problem in CR, can be overcome by exploiting cyclostationary features exhibited in the received signal. Most, if not all, communications signals have cyclostationary signatures, which can be used to detect the PU. However, detecting these signatures requires a fine resolution in cycle frequency, which can cause high computational complexity when computing the discrete Spectral Correlation Function (SCF).
This paper presents an innovative adaptation of the well-known FFT Accumulation Method (FAM) to efficiently obtain the SCF in a zoomed/local sub-band of the entire frequency and cycle frequency $(f; α)$ plane. Computational complexity comparison of the FAM and the proposed zoom FAM (zFAM) algorithms is addressed, and computer simulation results are presented to illustrate the zFAM superiority to detect the cyclostationary features in a small region of the $(f; α)$ plane.
Joint sub-Nyquist wideband spectrum sensing and reliable data transmission for cognitive radio networks over white space
2020, Digital Signal Processing: A Review Journal
Citation Excerpt :
It can be further extended to Rayleigh fading channels [71] and Nakagami-m channels [72].
The emerging cognitive radio technology resolves the spectrum scarcity problem by exploiting the unutilised licenced spectrum on an opportunistic basis. Meanwhile, future cognitive devices require advanced sensing techniques for rapid and active identification of spectrum holes over a wideband. Incorporation of prior information from the geolocation database enhances the sensing performance, reduces the computation complexity and maximizes the spectrum utilisation by white space devices. However, unlike the TV white space database, other wideband spectrum databases are not yet available. Therefore for dynamic and fragile cognitive networks, the geolocation database cannot assist the spectrum sensing process, which necessitated database-independent real-time spectrum sensing. Constant interference from primary users is yet another major challenge with cognitive radio networks which makes reliable communication extremely difficult for secondary users. Forward error correction codes ensure the reliability of transmitted data, among which low-density parity check codes are the most appropriate. Lengthy low-density parity check codes, however, have certain drawbacks specifically high encoding and decoding complexity. Parallel concatenated Gallager code addresses these issues. This paper presents a reliable data transmission scheme for secondary users using the proposed non-zero syndrome parallel concatenated Gallager codes with interleaver for cognitive radio networks performing real-time spectrum sensing over white space. The joint sensing scheme along with prior knowledge from the geolocation database improves the spectrum sensing performance with reduced complexity and is more suitable for low-power cognitive devices. Further, simulation results affirm that proposed PCGC outperform a dedicated LDPC in terms of coding gain and bit-error rate.
An Improved Wireless Spectrum Anomaly Detection Model with Adaptive Attention Mechanism
2024, ACM International Conference Proceeding Series

View all citing articles on Scopus

Modar Shbat graduated from Damascus University, Faculty of Mechanical and Electrical Engineering as an electronics engineer in 2003. In 2005, he got a post graduate diploma in telecommunications engineering from Damascus University. He received master degree (M.Sc) in information and communications engineering from Korea Advanced Institute of Science and Technology (KAIST)/South Korea in 2008. He obtained his PhD degree in electronics engineering from Kyungpook National University (KNU), South Korea (Signal Processing Lab) in February 2014. Currently, Dr. Modar works for Polytechnic University of San Luis Potosi (Mexico) as an Assistant Professor in Networks and Telecommunications Engineering (IRTEL) Department. His research interests include: signal detection and processing algorithms, spectrum sensing in cognitive radio, smart antennas and beam-forming algorithms, and others. Dr. Modar Shbat scientific work and research accomplishments are published in peer reviewed journals, books, and presented in international conferences.

Vyacheslav Tuzlukov received the MSc and PhD degrees in radio physics from the Belorussian State University, Minsk, Belarus in 1976 and 1990, respectively, and DSc degree from the Kotelnikov Institute of Radioengineering and Electronics of Russian Academy of Sciences in 1995. From 2000 to 2002 he was a Visiting Professor at the University of Aizu, Japan and from 2003 to 2007 served as an Invited Professor at the Ajou University, Suwon, South Korea, within the Department of Electrical and Computer Engineering. Since March 2008 to February 2009 he joined as Full Professor at the Yeungnam University, Gyeonsang, South Korea within the School of Electronic Engineering, Communication Engineering, and Computer Science. Starting from March 2009 to March 2016 he served as a Full Professor and Director of Signal Processing Lab at the Department of Communication and Information Technologies, School of Electronics Engineering, College of IT Engineering, Kyungpook National University, Daegu, South Korea. He joined to Belarussian State Air Force Academy in September 2016 and served as a Head of Department till February 2018. Currently he is a Full Professor of the Belarussian State Academy of Communications. His research emphasis is on signal processing in radar, wireless communications, wireless sensor networks, remote sensing, sonar, satellite communications, mobile communications, and other signal processing systems. He is the author over 260 journal and conference papers, seventeenth books in signal processing published by Springer-Verlag and CRC Press, some of them are Signal Detection Theory (2001), Signal Processing Noise (2002), Signal and Image Processing in Navigational Systems (2005), Signal Processing in Radar Systems (2012), Editor of the book Communication Systems: New Research (2013), Nova Science Publishers, Inc, USA, and has also contributed Chapters “Underwater Acoustical Signal Processing” and “Satellite Communications Systems: Applications” to Electrical Engineering Handbook: 3rd Edition, 2005, CRC Press; “Generalized Approach to Signal Processing in Wireless Communications: The Main Aspects and Some Examples” to Wireless Communications and Networks: Recent Advances, InTech, 2012; “Radar Sensor Detectors for Vehicle Safety Systems” to Electrical and Hybrid Vehicles: Advanced Systems, Automotive Technologies, and Environmental and Social Implications, Nova Science Publishers, Inc., USA, 2014; “Wireless Communications: Generalized Approach to Signal Processing” and “Radio Resource Management and Femtocell Employment in LTE Networks”, to Communication Systems: New Research, Nova Science Publishers, Inc., USA, 2013, and “Radar Sensor Detectors for Vehicle Safety Systems” to Autonomous Vehicles: Intelligent Transport Systems and Automotive Technologies, Publishing House, University of Pitesti, Romania, 2013. He serves as a Chair of International Conferences on Signal Processing, Keynote Speaker, Plenary Lecturer, Chair of Sessions, Tutorial Instructor and organizes Special Sections at the major International Conferences and Symposia on signal processing. Dr. Tuzlukov is the Editor-in-Chief of the SOP Transactions on Signal Processing journal and American Journal of Sensor Technology. Dr. Tuzlukov was highly recommended by U.S. experts of Defense Research and Engineering (DDR& E) of the United States Department of Defense as a recognized expert in the field of humanitarian demining and minefield sensing technologies and had been awarded by Special Prize of the United States Department of Defense in 1999. Dr. Tuzlukov is distinguished as one of the leading achievers from around the world by Marquis Who's Who and his name and biography have been included in the Who's Who in the World, 2006-2013; Who's Who in World, 25th Silver Anniversary Edition, 2008, Marquis Publisher, NJ, USA; Who's Who in Science and Engineering, 2006-2012 and Who's Who in Science and Engineering, 10th Anniversary Edition, 2008-2009, Marquis Publisher, NJ, USA; 2009-2010 Princeton Premier Business Leaders and Professionals Honors Edition, Princeton Premier Publisher, NY, USA; 2009 Strathmore's Who's Who Edition, Strathmore's Who's Who Publisher, NY, USA; 2009 Presidental Who's Who Edition, Presidental Who's Who Publisher, NY, USA; Who's Who among Executives and Professionals, 2010 Edition, Marquis Publisher, NJ, USA; Who's Who in Asia 2012, 2nd Edition, Marquis Publisher, NJ, USA; Top 100 Executives of 2013 Magazine, Super Network Publisher, New York, USA, 2013; 2013/2014 Edition of the Global Professional Network, Business Network Publisher, New York, USA, 2013; 2013/2014 Edition of the Who's Who Network Online, Business Network Publisher, New York, USA, 2014; Online Professional Gateway, 2014 Edition, Business Network Publisher, New York, USA, 2014; 2014 “Worldwide Who's Who”, Marquis Publisher, NJ, USA; “New 2014-Edition Executive Who's Who”, Marquis Publisher, NJ, USA; 2014 Membership in the “Exclusive Top 100”, USA; 2014 Strathmore Professional Biographies Edition, Strathmore's Who's Who Publisher, NY, USA; 2015 Who's Who of Executive and Professional Honors Edition, Marquis Publisher, NJ, USA; 2015 Worldwide Who's Who Membership, Marquis Publisher, NJ, USA; 2015-2016 Membership in the “Exclusive Top 100”, USA.

View full text

Primary signal detection algorithms for spectrum sensing at low SNR over fading channels in cognitive radio

Abstract

Introduction

Section snippets

System model for antenna array sensing

Conventional GD and related decision statistics

MGF of the decision statistics at the GD output

WGD and GLRT-GD: correlated antenna array elements

Optimal GD detection threshold: fading channels

Simulation results

Conclusions

Declaration of Competing Interest

Acknowledgements

Digit. Signal Process.

Digit. Signal Process.

Cognitive radio techniques under practical imperfections: a survey

IEEE Commun. Surv. Tutor.

Spectrum sensing in cognitive radios based on enhanced energy detector

IET Commun.

Fundamentals of Statistical Signal Processing: Detection Theory, vol. 2

GLRT-based spectrum sensing for cognitive radio

Signature based spectrum sensing algorithms for IEEE 802.22 WRAN

Matched filter based spectrum sensing and power level recognition with multiple antennas

Blind cyclostationary spectrum sensing in cognitive radios

IEEE Commun. Lett.

Multiple antenna cyclostationary spectrum sensing based on the cyclic correlation significance test

IEEE J. Sel. Areas Commun.

On the eigenvalue-based spectrum sensing and secondary user throughput

IEEE Trans. Veh. Technol.

On the performance of covariance based spectrum sensing for cognitive radio

IEEE Trans. Signal Process.

Maximum-minimum eigenvalue detection for cognitive radio

Eigenvalue-based spectrum sensing algorithms for cognitive radio

IEEE Trans. Commun.

Multiantenna-assisted spectrum sensing for cognitive radio

IEEE Trans. Veh. Technol.

Wideband sensing and optimization for cognitive radio networks with noise variance uncertainty

IEEE Trans. Commun.

Max-min SNR signal energy based spectrum sensing algorithms for cognitive radio net-works with noise variance uncertainty

IEEE Trans. Wirel. Commun.

Effects of noise power estimation on energy detection for cognitive radio applications

IEEE Trans. Commun.

A novel energy detection scheme based on dynamic threshold in cognitive radio systems

J. Comput. Inf. Syst.

Spectrum sensing in cognitive radio using TSA

Int. J. Comput. Netw. Wirel. Commun.

Cooperative spectrum sensing based on double threshold detection and Dempster-Shafer theory

Spectrum sensing for cognitive radio networks with correlated multiple antennas

Electron. Lett.

Signal Detection Theory

Signal Processing Noise

Generalized approach to signal processing in wireless communications: the main aspects and some examples

Signal processing by generalized receiver in DS-CDMA wireless communication systems with frequency-selective channels

Circuits Syst. Signal Process.

Signal processing by generalized receiver in DS-CDMA wireless communication systems with optimal combining and partial cancellation

EURASIP J. Adv. Signal Process.

Bit error probability of quadriphase DS-CDMA wireless communication systems based on generalized approach to signal processing

Telecommun. Rev.