Research On Wideband Spectrum Compressed Sensing Algorithms For Cognitive Radio

Spectrum is a kind of non-renewable scarce resource.With the rapid development of 5G technology and next generation wireless communication network,the contradiction between low utilization rate of spectrum resources and shortage of spectrum resources caused by fixed spectrum allocation strategy becomes increasingly acute.In recent years,with the rapid development of cognitive radio technology and compressed sensing technology,the wideband compressed spectrum sensing technology based on Modulated Wideband Converter(MWC)has been widely studied.These studies provide a feasible technical solution for idle spectrum detection in an extremely wide frequency range,which makes it possible for ultra-dense and ultra-high-capacity communication networks to share spectrum resources and meet the needs of more emerging wideband services.Based on the compressed sensing theory,this paper starts from several research points,such as spectrum sensing accuracy,prior information mining,hardware complexity,channel modeling,and reliable sensing.The main research work of this thesis is summarized as follows.(1)MWC can perform a sub-Nyquist sampling for sparse multi-band analog signals,and achieve an accurate reconstruction of the support set.However,the existing SOMP reconstruction algorithms need a priori information of signal sparsity,and can be improved in required channel number and reconstruction performance.For these issues,this paper applies the SwOMP algorithm to the CTF(Continuous-To-Finite)block of MWC.On the one hand,the algorithm constructs the atom selection criterion by weakness parameter and the inner product maximum value.Multiple atoms can be selected at one time,thus improving the accuracy of selecting the most relevant atoms.On the other hand,the algorithm does not need to know the sparsity information of the signal,and can reconstruct the spectral support of the signal in a semi-blind state.The simulation results demonstrate that,compared with SOMP algorithm,the SwSOMP-based MWC sensing system can realize high reconstruction probability of signal support set with fewer channels and lower SNR,and can also improve the reconfigurable frequency band number of signal.(2)The reconstruction framework based on MWC can realize the reconstruction of signal spectral support.However,the existing reconstruction algorithms need 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 Boost(ABRMB)scheme based on SVD(Singular Value Decomposition)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 when the number of hardware channels is the same,the reconfigurable frequency band of signal is more.In addition,the proposed scheme has a lower minimum sampling rate and a lower approximation error of the potential of spectral support.(3)In collaborative spectrum sensing,in order to improve the precision of spectrum sensing,the fusion center recovers the spectral support from observation and measurement matrices reported by a network of CRs.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 prosressive 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)land 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.(4)How to realize fast and robust wideband spectrum sensing technology is still facing challenges.In this paper,a novel non-reconstructed sequential compressed wideband spectrum sensing algorithm(NSCWSS)is proposed.Firstly,the algorithm models the sensing channel and considers the influence of fading on spectrum sensing.Secondly,the algorithm uses a sequential spectrum sensing method based on history memory and reputation to ensure the robustness of the algorithm.Thirdly,the algorithm adopts the compressed sensing strategy without reconstruction,which reduces the computational complexity.The simulation analysis is carried out for the centralized cooperative spectrum sensing model.The simulation results show that under the condition of certain detection probability,the algorithm can effectively reduce the computational complexity of signal reconstruction and the sampling times of wideband spectrum sensing.At the same time,in the cognitive wideband colmunication scenarios,the algorithm also achieves a better defense against the SSDF attack in spectrum sensing.In summary,this paper focuses on the key challenging issues in the study of wideband spectrum sensing.In the actual sensing environment,the proposed algorithms basically achieve the goal of wideband spectrum sensing with higher sensing accuracy,more reconfigurable frequency bands,fewer sampling channels,lower system sampling rate and more security.