Wideband Spectrum Sensing Based on Advanced Sub-Nyquist Sampling Structure

Abstract As the bandwidth increases, the high-speed sampling rate becomes the bottleneck for the development of wideband spectrum sensing. Wideband spectrum sensing with sub-Nyquist sampling attracts more attention and modulated wideband converter (MWC) is an attractive sub-Nyquist sampling system. For the purpose of breaking the system structure limit, an advanced sub-Nyquist sampling framework is proposed to simplify the MWC system structure, adopting the single sampling channel structure with a frequency shifting module to acquire the sub-Nyquist sampling values. In order to recover the signal support information, the sensing matrix must be built according to the only one mixing function. Most existing support recovery methods rely on some prior knowledge about the spectrum sparsity, which is difficult to acquire in practical electromagnetic environment. To address this problem, we propose an adaptive residual energy detection algorithm (ARED), which bypasses the need for the above-mentioned prior knowledge. Simulation results show that, without requiring the aforementioned prior knowledge, the ARED algorithm, which is based on the advanced sub-Nyquist sampling framework, has the similar performance as MWC and even higher than MWC in some cases.

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Compressive Spectrum Sensing with Temporal-Correlated Prior Knowledge Mining

Wireless Communications and Mobile Computing ◽

10.1155/2021/5539697 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Jianfeng Liu ◽

Xin-Lin Huang ◽

Ping Wang

Keyword(s):

Compressive Sensing ◽

Prior Knowledge ◽

Spectrum Sensing ◽

High Speed ◽

Sampling Rate ◽

Temporal Correlation ◽

False Alarm Probability ◽

Spectrum Estimation ◽

Sensing Data ◽

Correlation Information

Cognitive radio (CR) has been proposed to mitigate the spectrum scarcity issue to support heavy wireless services on sub-3GHz. Recently, broadband spectrum sensing becomes a hot topic with the help of compressive sensing technology, which will reduce the high-speed sampling rate requirement of analog-to-digital converter. This paper considers sequential compressive spectrum sensing, where the temporal correlation information between neighboring compressive sensing data will be exploited. Different from conventional compressive sensing, the previous compressive sensing data will be fused into prior knowledge in current spectrum estimation. The simulation results show that the proposed scheme can achieve 98.7% detection probability under 3.5% false alarm probability and performs the best compared with the typical BPDN and OMP schemes.

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Spectrum sensing of wideband signals based on cyclostationary and compressive sensing

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v20.i3.pp1361-1368 ◽

2020 ◽

Vol 20 (3) ◽

pp. 1361

Author(s):

Ali Mohammad A. AL-Hussain ◽

Maher Khudair Mahmood Al Azawi

Keyword(s):

Compressive Sensing ◽

Spectrum Sensing ◽

Compression Ratio ◽

High Speed ◽

Processing Time ◽

Sampling Rate ◽

Probability Of Detection ◽

Signal Spectrum ◽

Sensing Matrix ◽

Recovery Algorithm

Compressive sensing is a powerful technique used to overcome the problem of high sampling rate when dealing with wideband signal spectrum sensing which leads to high speed analogue to digital convertor (ADC) accompanied with large hardware complexity, high processing time, long duration of signal spectrum acquisition and high consumption power. Cyclostationary based detection with compressive technique will be studied and discussed in this paper. To perform the compressive sensing technique, Discrete Cosine Transform (DCT) is used as sparse representation basis of received signal and Gaussian random matrix as a sensing matrix, and then 𝓁1- norm recovery algorithm is used to recover the original signal. This signal is used with cyclostationary detector. The probability of detection as a function of SNR with several compression ratio and processing time versus compression ratio are used as performance parameters. The effect of the recovery error of reconstruction algorithm is presented as a function of probability of detection.

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Adaptive Sub-Nyquist Spectrum Sensing for Ultra-Wideband Communication Systems

Symmetry ◽

10.3390/sym11030342 ◽

2019 ◽

Vol 11 (3) ◽

pp. 342

Author(s):

Yong Lu ◽

Shaohe Lv ◽

Xiaodong Wang

Keyword(s):

Spectrum Sensing ◽

Frequency Band ◽

Communication Systems ◽

High Speed ◽

Signal To Noise Ratio ◽

Sampling Rate ◽

Ultra Wideband ◽

Wideband Spectrum Sensing ◽

Adaptive Policy ◽

Ultra Wideband Communication

With the ever-increasing demand for high-speed wireless data transmission, ultra-wideband spectrum sensing is critical to support the cognitive communication over an ultra-wide frequency band for ultra-wideband communication systems. However, it is challenging for the analog-to-digital converter design to fulfill the Nyquist rate for an ultra-wideband frequency band. Therefore, we explore the spectrum sensing mechanism based on the sub-Nyquist sampling and conduct extensive experiments to investigate the influence of sampling rate, bandwidth resolution and the signal-to-noise ratio on the accuracy of sub-Nyquist spectrum sensing. Afterward, an adaptive policy is proposed to determine the optimal sampling rate, and bandwidth resolution when the spectrum occupation or the strength of the existing signals is changed. The performance of the policy is verified by simulations.

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An Active and Passive Reputation Method for Secure Wideband Spectrum Sensing Based on Blockchain

Electronics ◽

10.3390/electronics10111346 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1346

Author(s):

Xinyu Xie ◽

Zhuhua Hu ◽

Min Chen ◽

Yaochi Zhao ◽

Yong Bai

Keyword(s):

Cognitive Radio ◽

Spectrum Sensing ◽

Cognitive Radio Networks ◽

Cooperative Spectrum Sensing ◽

Sampling Rate ◽

Radio Networks ◽

Single Node ◽

Decision Algorithm ◽

Collusion Attacks ◽

Wideband Spectrum Sensing

Spectrum is a kind of non-reproducible scarce strategic resource. A secure wideband spectrum sensing technology provides the possibility for the next generation of ultra-dense, ultra-large-capacity communications to realize the shared utilization of spectrum resources. However, for the open collaborative sensing in cognitive radio networks, the collusion attacks of malicious users greatly affect the accuracy of the sensing results and the security of the entire network. To address this problem, this paper proposes a weighted fusion decision algorithm by using the blockchain technology. The proposed algorithm divides the single-node reputation into active reputation and passive reputation. Through the proposed token threshold concept, the active reputation is set to increase the malicious cost of the node; the passive reputation of the node is determined according to the historical data and recent performance of the blockchain. The final node weight is obtained by considering both kinds of reputation. The proposed scheme can build a trust-free platform for the cognitive radio collaborative networks. Compared with the traditional equal-gain combination algorithm and the centralized sensing algorithm based on the beta reputation system, the simulation results show that the proposed algorithm can obtain reliable sensing results with a lower number of assistants and sampling rate, and can effectively resist malicious users’ collusion attacks. Therefore, the security and the accuracy of cooperative spectrum sensing can be significantly improved in cognitive radio networks.

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SPECTRUM SENSING OF WIDE BAND SIGNALS BASED ON ENERGY DETECTION WITH COMPRESSIVE SENSING

Journal of Engineering and Sustainable Development ◽

10.31272/jeasd.24.6.7 ◽

2020 ◽

Vol 24 (06) ◽

pp. 83-90

Author(s):

Ali Mohammad A. AL-Hussain ◽

◽

Maher K. Mahmood ◽

Keyword(s):

False Alarm ◽

Compressive Sensing ◽

Spectrum Sensing ◽

High Speed ◽

Sampling Rate ◽

Wide Band ◽

Probability Of Detection ◽

Signal Spectrum ◽

Radio System ◽

Probability Of False Alarm

Compressive sensing (CS) technique is used to solve the problem of high sampling rate with wide band signal spectrum sensing where high speed analogue to digital converter is needed to do that. This leads to difficult hardware implementation, large time of sensing and detection with high consumptions power. The proposed approach combines energy-based detection, with CS compressive sensing and investigates the probability of detection, and the probability of false alarm as a function of the SNR, showing the effect of compression to spectrum sensing performance of cognitive radio system. The Discrete Cosine Transform (DCT) is used as a sparse representation basis of the received signal, and random matrix as a compressive matrix. The 𝓁1 norm algorithm is used to reconstruct the original signal. A closed form of probability of detection and probability of false alarm are derived. Computer simulation shows clearly that the compression ratio, recovery error and SNR level affect the probability of detection.

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A Self-Adaptive Progressive Support Selection Scheme for Collaborative Wideband Spectrum Sensing

Sensors ◽

10.3390/s18093011 ◽

2018 ◽

Vol 18 (9) ◽

pp. 3011 ◽

Cited By ~ 1

Author(s):

Zhuhua Hu ◽

Yong Bai ◽

Mengxing Huang ◽

Mingshan Xie ◽

Yaochi Zhao

Keyword(s):

Spectrum Sensing ◽

Noise Intensity ◽

Sampling Rate ◽

Fusion Center ◽

Hardware Complexity ◽

Selection Scheme ◽

Wideband Spectrum Sensing ◽

Support Recovery ◽

The Impact ◽

Self Adaptive

The sampling rate of wideband spectrum sensing for sparse signals can be reduced by sub-Nyquist sampling with a Modulated Wideband Converter (MWC). In collaborative spectrum sensing, the fusion center recovers the spectral support from observation and measurement matrices reported by a network of CRs, to improve the precision of spectrum sensing. However, the MWC has a very high hardware complexity due to its parallel structure; it sets a fixed threshold for a decision without considering the impact of noise intensity, and needs a priori information of signal sparsity order for signal support recovery. To address these shortcomings, we propose a progressive support selection based self-adaptive distributed MWC sensing scheme (PSS-SaDMWC). In the proposed scheme, the parallel hardware sensing channels are scattered on secondary users (SUs), and the PSS-SaDMWC scheme takes sparsity order estimation, noise intensity, and transmission loss into account in the fusion center. More importantly, the proposed scheme uses a support selection strategy based on a progressive operation to reduce missed detection probability under low SNR levels. Numerical simulations demonstrate that, compared with the traditional support selection schemes, our proposed scheme can achieve a higher support recovery success rate, lower sampling rate, and stronger time-varying support recovery ability without increasing hardware complexity.

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Adaptive and Blind Wideband Spectrum Sensing Scheme Using Singular Value Decomposition

Wireless Communications and Mobile Computing ◽

10.1155/2017/3279452 ◽

2017 ◽

Vol 2017 ◽

pp. 1-14 ◽

Cited By ~ 1

Author(s):

Zhuhua Hu ◽

Yong Bai ◽

Yaochi Zhao ◽

Chong Shen ◽

Mingshan Xie

Keyword(s):

Singular Value Decomposition ◽

Spectrum Sensing ◽

Noise Intensity ◽

Sampling Rate ◽

Approximation Error ◽

Singular Value ◽

Decision Threshold ◽

Wideband Spectrum Sensing ◽

Reconstruction Performance ◽

Value Decomposition

The Modulated Wideband Converter (MWC) can provide a sub-Nyquist sampling for continuous analog signal and reconstruct the spectral support. However, the existing reconstruction algorithms need a priori information of sparsity order, are not self-adaptive for SNR, and are not fault tolerant enough. These problems affect the reconstruction performance in practical sensing scenarios. In this paper, an Adaptive and Blind Reduced MMV (Multiple Measurement Vectors) Boost (ABRMB) scheme based on singular value decomposition (SVD) for wideband spectrum sensing is proposed. Firstly, the characteristics of singular values of signals are used to estimate the noise intensity and sparsity order, and an adaptive decision threshold can be determined. Secondly, optimal neighborhood selection strategy is employed to improve the fault tolerance in the solver of ABRMB. The experimental results demonstrate that, compared with ReMBo (Reduce MMV and Boost) and RPMB (Randomly Projecting MMV and Boost), ABRMB can significantly improve the success rate of reconstruction without the need to know noise intensity and sparsity order and can achieve high probability of reconstruction with fewer sampling channels, lower minimum sampling rate, and lower approximation error of the potential of spectral support.

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Efficient spectrum sensing technique based on energy detector, compressive sensing, and de-noising techniques

International Journal of Engineering & Technology ◽

10.14419/ijet.v6i1.6672 ◽

2016 ◽

Vol 6 (1) ◽

pp. 1 ◽

Cited By ~ 2

Author(s):

Amr Hussein ◽

Hossam Kasem ◽

Mohamed Adel

Keyword(s):

Compressive Sensing ◽

Spectrum Sensing ◽

Compression Ratio ◽

System Performance ◽

High Speed ◽

Sampling Rate ◽

Energy Detector ◽

Recent Theory ◽

Detector Performance ◽

Compression Sensing

Highdata rate cognitive radio (CR) systems require high speed Analog-to-Digital Converters (ADC). This requirement imposes many restrictions on the realization of the CR systems. The necessity of high sampling rate can be significantly alleviated by utilizing analog to information converter (AIC). AIC is inspired by the recent theory of Compressive Sensing (CS), which states that a discrete signal has a sparse representation in some dictionary, which can be recovered from a small number of linear projections of that signal. This paper proposes an efficient spectrum sensing technique based on energy detection, compression sensing, and de-noising techniques. De-noising filters are utilized to enhance the traditional Energy Detector performance through Signal-to-Noise (SNR) boosting. On the other hand, the ordinary sampling provides an ideal performance at a given conditions. A near optimal performance can be achieved by applying compression sensing. Compression sensing allows signal to be sampled at sampling rates much lower than the Nyquist rate. The system performance and ADC speed can be easily controlled by adjusting the compression ratio. In addition, a proposed energy detector technique is introduced by using an optimum compression ratio. The optimum compression ratio is determined using a Genetic Algorithm (GA) optimization tool. Simulation results revealed that the proposed techniques enhanced system performance.

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