Research On The High Energy-efficient Wideband Compressive Spectrum Detection Methods Based On Distributed Compressive Sensing

Cognitive radio(CR)can significantly improve the spectral efficiency in the limited spectrum resource condition.Wideband spectrum sensing which takes primary user(PU)wideband spectrum as detection target has become emerging CR research direction.For cognitive radio network(CRN)wideband spectrum sensing,compressive sensing(CS)approach can reduce hardware requirements compared with conventional methods,which has the advantages of low power consumption,low sampling rate and low computational complexity.In addition,with the increase of CR secondary user(SU)density and the expansion of network coverage,CRN has higher requirements for energy efficiency.Hence,green CRN gradually becomes one of the research hot spots in future CRN.CS-based wideband spectrum sensing schemes in CRN are investigated in this dissertation.Wideband spectrum detection algorithm based on multi-task Bayesian compressive sensing(BCS)is mainly studied.On this basis of it,energy-efficient based BCS wideband spectrum detection algorithm and energy-efficient priority based distributed compressive sensing(DCS)spectrum detection and power allocation scheme are studied with the consideration of energy efficiency.Specific works are shown as follows:The research background and research significance are introduced in Chapter 1.In this chapter,CRN spectrum detection and high energy-efficient green CR are briefly introduced.In addition,the recent research progresses of CS theory and wideband spectrum detection based on CS are presented respectively.On the basis of it,this chapter illustrates the current research hotspot of energy-efficient based wideband compressive spectrum detection.The basic principles of CS theory are introduced in Chapter 2.It mainly focuses on three procedures in CS theory,including signal sparse transformation,measurement matrix design and signal reconstruction algorithm.Moreover,three joint sparse models of DCS and the corresponding convex relaxation methods as well as greedy pursuit reconstruction algorithms are presented in detail.Wideband spectrum detection algorithm based on BCS is studied in Chapter 3.Firstly,wideband spectrum detection and BCS model are introduced in CRN respectively.Then,wideband spectrum detection based on multi-task BCS is studied.Simulation results show that the proposed method achieves the fast convergence of reconstruction mean square error(MSE)and improve detection performance significantly.Meanwhile,energy efficiency based BCS wideband spectrum detection is further investigated in this chapter.Simulation results show that the proposed approach guarantees receiver operation characteristics(ROC)performance of wideband compressive spectrum detection with smaller sampling points,which ensures energy efficiency for CR SUs.A high energy-efficient spectrum detection and power allocation strategy based on DCS is investigated in Chapter 4.The trade-off between energy efficiency and spectral efficiency in CR network is considered in this strategy.Distributed compressive sensing-subspace pursuit(DCS-SP)algorithm is utilized for SU sensing signal reconstruction,and spectrum detection is performed in accordance with channel cumulative energy.Meanwhile,SU power allocation scheme based on rate adaptation(RA)criterion is further studied.Through the construction of a weighted energy consumption function considering energy consumption both in signal reconstruction phase and spectrum detection phase,the proposed method takes reconstruction MSE,detection probability,SU power allocation ratio as well as cognitive link spectral efficiency as the constrained conditions.The optimization problem is numerically resolved to obtain the minimum weighted energy consumption in different reconstruction energy consumption weights and different signal sparsity value scenarios.Numerical results indicate that,the weighted energy consumption of the proposed scheme achieves its minimum value in the low reconstruction energy consumption weight and low signal sparsity value scenarios.The minimum weighted energy consumption value can be achieved only when approximate equal power allocation is assigned for SUs.In addition,two tradeoffs exist in the proposed scheme,namely,the tradeoff between the weighted energy consumption and detection performance as well as the tradeoff between the weighted energy consumption and cognitive link spectral efficiency.Finally,conclusions and further work prospects are given in Chapter 5.This dissertation initially explores high energy-efficient wideband compressive spectrum detection methods based on DCS.Research results provide significant meanings for the further related works.