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

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3011 ◽  
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.

scholarly journals Adaptive and Blind Wideband Spectrum Sensing Scheme Using Singular Value Decomposition

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
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.

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

Electronics ◽  
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.

scholarly journals Subsampled Circulant Matrix Based Wideband Spectrum Sensing Using Fusion Based Recovery Algorithm

2021 ◽  
Vol 38 (4) ◽  
pp. 1201-1208
Author(s):  
T V N L Aswini ◽  
Padma K Raju ◽  
Leela B Kumari
Keyword(s):  
Spectrum Sensing ◽  
Sampling Technique ◽  
Circulant Matrix ◽  
Hardware Complexity ◽  
Secondary Users ◽  
Recovery Algorithm ◽  
Wideband Spectrum Sensing ◽  
Modulated Wideband Converter ◽  
Added Benefit ◽  
Perfect Recovery

This paper reflects the problem of wideband spectrum recovery. The demand for spectrum usage is increasing exponentially as the wireless technologies rules the world. To meet these needs, Cognitive Radio is one of the emerging technologies, which intelligently allots the spectrum to the secondary users. Since the spectrum is wideband, the capability of spectrum sensing is improved by introducing sub-nyquist sampling under compressive sensing framework. In this paper, a sub-nyquist sampling technique of Modulated Wideband Converter (MWC) is used as it possesses m-parallel channels providing fast sensing and robust structure. A circulant matrix method is used to improve the hardware complexity of MWC. Also at the reconstruction of MWC, a fusion based recovery algorithm is proposed which became an added benefit for perfect recovery of the support. The results are compared with conventional MWC in terms of support recovery, mean square error and SNR gain. Simulations proved that the proposed algorithm performs superior at low as well as high SNR with increased gain.

scholarly journals An Effective Adaptive Threshold Based Compressive Spectrum Sensing in Cognitive Radio Networks

2021 ◽  
Vol 9 (1) ◽  
pp. 1220-1224
Author(s):  
S. Varalakshmi, K. Senthil Kumar, A. K. Gnanasekar, S. Sureshkrishna
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Sampling Rate ◽  
Adaptive Threshold ◽  
Radio Networks ◽  
Parametric Estimation ◽  
Noise Uncertainty ◽  
Low Snr ◽  
Wideband Spectrum Sensing

Spectrum sensing is playing a vital role in Cognitive Radio networks. Wideband spectrum sensing increases the speed of sensing but which in turn requires higher sampling rate and also increases the complexity of hardware and also power consumption. Compression based sensing reduces the sampling rate by using Sub-Nyquist sampling but the compression and the reconstruction problem exists. In compression based spectrum sensing, noise uncertainty is one of the major performance degradation factor. To reduce this degradation, compressive measurements based sensing with adaptive threshold is proposed. In this technique compressed signal is sensed without any reconstruction of the signal. When the nodes are mobile in the low SNR region, the noise uncertainty degrades the performance of spectrum sensing. To conquer this problem, noise variance is estimated using parametric estimation technique and the threshold is varied adaptively. In the low SNR region, this proposed technique reduces the effect of noise and improves the spectrum sensing performance.

scholarly journals Mixed-Signal Parallel Compressive Spectrum Sensing for Cognitive Radios

2010 ◽  
Vol 2010 ◽  
pp. 1-10 ◽  
Author(s):  
Zhuizhuan Yu ◽  
Xi Chen ◽  
Sebastian Hoyos ◽  
Brian M. Sadler ◽  
Jingxuan Gong ◽  
...  
Keyword(s):  
Spectrum Sensing ◽  
Integrated Circuit ◽  
Dynamic Range ◽  
Cognitive Radios ◽  
Experimental System ◽  
Basis Expansion ◽  
Current Frequency ◽  
Mixed Signal ◽  
Wideband Spectrum Sensing ◽  
The Impact

Wideband spectrum sensing for cognitive radios requires very demanding analog-to-digital conversion (ADC) speed and dynamic range. In this paper, a mixed-signal parallel compressive sensing architecture is developed to realize wideband spectrum sensing for cognitive radios at sub-Nqyuist rates by exploiting the sparsity in current frequency usage. Overlapping windowed integrators are used for analog basis expansion, that provides flexible filter nulls for clock leakage spur rejection. A low-speed experimental system, built with off-the-shelf components, is presented. The impact of circuit nonidealities is considered in detail, providing insight for a future integrated circuit implementation.

scholarly journals A Sequential Compressed Spectrum Sensing Algorithm against SSDH Attack in Cognitive Radio Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Zhuhua Hu ◽  
Yong Bai ◽  
Lu Cao ◽  
Mengxing Huang ◽  
Mingshan Xie
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Detection Probability ◽  
Signal Reconstruction ◽  
Sampling Rate ◽  
Radio Networks ◽  
Wideband Spectrum Sensing ◽  
Sensing Technology ◽  
Key Technologies ◽  
Simulation Results

Spectrum sensing is one of the key technologies in wireless wideband communication. There are still challenges in respect of how to realize fast and robust wideband spectrum sensing technology. In this paper, a novel nonreconstructed sequential compressed wideband spectrum sensing algorithm (NSCWSS) is proposed. Firstly, the algorithm uses a sequential spectrum sensing method based on history memory and reputation to ensure the robustness of the algorithm. Secondly, the algorithm uses the strategy of compressed sensing without reconstruction, which thus ensures the sensing agility of the algorithm. The algorithm is simulated and analyzed by using the centralized cooperative sensing. The theoretical analysis and simulation results reveal that, under the condition of ensuring the certain detection probability, the proposed algorithm effectively reduces complex computation of signal reconstruction, significantly reducing the wideband spectrum sampling rate. At the same time, in the cognitive wideband communication scenarios, the algorithm also achieves a better defense against the SSDF attack in spectrum sensing.

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

Symmetry ◽  
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.

scholarly journals Wideband Spectrum Sensing Based on Advanced Sub-Nyquist Sampling Structure

2021 ◽  
Author(s):  
Xue Wang ◽  
Qian Chen ◽  
Min Jia ◽  
Xuemai Gu
Keyword(s):  
Prior Knowledge ◽  
Spectrum Sensing ◽  
High Speed ◽  
Sampling Rate ◽  
Detection Algorithm ◽  
Channel Structure ◽  
System Structure ◽  
Sensing Matrix ◽  
Sampling System ◽  
Wideband Spectrum Sensing

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.

A Novel Distributed Compressive Wideband Spectrum Sensing Method in Cognitive Radio Networks

2014 ◽  
Vol 667 ◽  
pp. 311-317
Author(s):  
Chang Lin ◽  
Qi Zhu ◽  
Chang Shu
Keyword(s):  
Spectrum Sensing ◽  
Radio Networks ◽  
Detection Performance ◽  
Detection Sensitivity ◽  
Fusion Center ◽  
Recovery Algorithm ◽  
Wideband Spectrum Sensing ◽  
Sensing Technology ◽  
System Robustness ◽  
Distributed Compressive Sensing

In this paper, we present an optimum weighted approach for wideband spectrum sensing. Distributed compressive sensing technology is exploited to obtain dramatic rate reductions while differential procedure is deduced to extremely enhance the detection sensitivity. The measurements are collected from each SU at a fusion center, where a C-out-of-J method is proposed to dramatically heighten the detection performance. SCSMP recovery algorithm is utilized to reconstruct the signals, which are then weighted by the estimated SNRs. Corroborating simulation results show that the raised algorithm can effectively reduce sampling rates at each SU, substantially raise the detection performance and saliently improve system robustness against noise.

An Energy Efficient Spectrum Aware Routing for Cognitive Radio Networks (CRNs)

2020 ◽  
Vol 10 ◽  
Author(s):  
Dileep Reddy Bolla ◽  
Jijesh J J ◽  
Mahaveer Penna ◽  
Shiva Shankar
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Energy Efficient ◽  
Research Work ◽  
Spectrum Management ◽  
Residual Energy ◽  
Radio Networks ◽  
Traffic Load ◽  
The Impact

Back Ground/ Aims:: Now-a-days in the Wireless Communications some of the spectrum bands are underutilized or unutilized; the spectrum can be utilized properly by using the Cognitive Radio Techniques using the Spectrum Sensing mechanisms. Objectives:: The prime objective of the research work carried out is to achieve the energy efficiency and to use the spectrum effectively by using the spectrum management concept and achieve better throughput, end to end delay etc., Methods:: The detection of the spectrum hole plays a vital role in the routing of Cognitive Radio Networks (CRNs). While detecting the spectrum holes and the routing, sensing is impacted by the hidden node issues and exposed node issues. The impact of sensing is improved by incorporating the Cooperative Spectrum Sensing (CSS) techniques. Along with these issues the spectrum resources changes time to time in the routing. Results:: All the issues are addressed with An Energy Efficient Spectrum aware Routing (EESR) protocol which improves the timeslot and the routing schemes. The overall network life time is improved with the aid of residual energy concepts and the overall network performance is improved. Conclusion:: The proposed protocol (EESR) is an integrated system with spectrum management and the routing is successfully established to communication in the network and further traffic load is observed to be balanced in the protocol based on the residual energy in a node and further it improves the Network Lifetime of the Overall Network and the Individual CR user, along with this the performance of the proposed protocol outperforms the conventional state of art routing protocols.

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