Research On Signal Reconstruction Methods Based On DCS In LEO Micro Satellite Cognitive Radio System

Low earth orbit(LEO) micro satellite operates at low orbit.It moves fast and switches frequently between satellites.It has a series of characteristics such as low signal-to-noise ratio(SNR)at ground gateway station,the limited spectral resource and energy constraint,large transmission delay at ground gateway station,etc.Cognitive radio(CR)opportunistically and dynamically utilizes the idle spectral resources via spectrum sensing,which becomes a hot research spot in wireless communications in the recent years.The traditional CR signal reconstruction and narrow band spectrum detection require heavy spectrum demand,so that signal reconstruction based on the Nyquist sampling theorem is not feasible.Noisy sensing signal recovery and wideband spectrum detection based on compressed sensing(CS)theory can overcome the above problem.It also breaks through traditional Nyquist sampling theorem with sampling rate constraint,and the sampling cost is greatly reduced with wide application prospects.This dissertation investigates LEO signal reconstruction methods via convex optimization based on distributed CS(DCS)in micro satellite cognitive radio(LEO-CR)system.Least angle regression(Lars)algorithm is studied in low SNR scenario.On the basis of that,aimed to the characteristics of low SNR,energy-efficient and delay tolerant for LEO-CR,the paper mainly considers three convex optimization signal reconstruction algorithms.Namely,energy-efficient and delay tolerant DCS-Lars,DCS dynamic updating(DCS-DX),unknown sparsity level DCS-Lars blind(DCS-Lars-B).The following are the specific research work of the thesis:The first chapter introduces the research background and significance.Related theory and technology of LEO micro satellite communication system and CR,CS theory and DCS,as well as joint sparsity model(JSM)and CS convex optimization signal reconstruction algorithm are briefly introduced.Meanwhile,the main work and the arrangement of the paper are also given in this chapter.The second chapter introduces DCS signal reconstruction algorithm.DCS JSM model,DCS reconstruction algorithms with evaluation methods are described in detail.The theory of CS framework,JSM-1 and JSM-2,the greedy algorithm,convex relaxation method and Bayesian compressive sensing(BCS)are given respectively.In the third chapter,signal reconstruction methods based on DCS convex optimization are investigated.Channel transmission model of LEO-CR system is introduced and JSM-2 model is given.According to the characteristics of LEO-CR system,the reconstruction mean square error(MSE)and the complexity performance are investigated for different convex recovery schemes in low SNR region respectively.Namely,basis pursuit de-noising(BPDN),homotopy method and least angle regression(Lars)algorithm.Aimed to sensing signal reconstruction in DCS,Lars and homotopy algorithms are extended.DCS-Lars algorithm and DCS-DX algorithm are proposed respectively in the JSM-2 model.Due to the fact that the unknown sparsity at ground gateway station,an improved DCS signal reconstruction algorithm based on sparsity adaptation is proposed.Simulation and performance analysis of the above algorithms are presented.Simulation results show that,the improved algorithm can effectively achieve low reconstruction MSE in low SNR region with better spectrum detection performance,and reconstruction complexity can be reduced simultaneously.On the basis of that,it makes a foundation for further research on energy-efficient delay tolerant signal reconstruction in LEO-CR system.In the fourth chapter,energy-efficient delay tolerant signal reconstruction methods are studied in LEO-CR system.Aimed to the characteristics of large transmission delay between LEO micro satellite and ground gateway station,LEO-CR delay tolerant asynchronous transfer model is proposed based on the theory of tapped delay line channel model.In the case of low SNR scenario,the closed expression of energy consumption in signal reconstruction phase and spectrum detection phase for DCS-Lars-B and DCS-DX methods are presented respectively.The weighted energy consumption function consists of signal reconstruction energy and spectrum detection energy.Energy-efficient optimization model is constructed with the objective of maximizing information transmission rate per unit energy.Numerical results of the optimization problem are presented to obtain the best LEO satellite and its transmission power,as well as its maximum energy efficiency in different signal reconstruction energy weight coefficient scenarios.In the case of delay tolerant LEO-CR,the proposed DCS signal reconstruction optimization scheme takes energy efficiency maximization as its objective.Finally,simulation and performance analysis are presented.Simulation results show that,the proposed scheme achieves higher energy efficiency with lower reconstruction energy weight in the delay tolerant LEO-CR.The fifth chapter is the summary of the dissertation and the prospect of future work.In this thesis,the problem of energy-efficient delay tolerant low SNR sensing signal reconstruction based on DCS is studied in LEO-CR system.The research achievements have significance for the future research work.