Study On Channel Estimation Technologies Of MIMO-OFDM Systems Based On Compressed Sensing

Combining the dual advantages of multiple-input-multiple-output technology and orthogonal frequency division multiplexing,the MIMO-OFDM technology can multiply system capacity and resist frequency selective fading,and has been widely applied in the 4th generation of mobile communication.The traditional linear channel estimation methods do not consider the sparseness of the wireless channel,so a large number of pilots have to be used to obtain CSI,resulting in low system spectrum efficiency.Compressed sensing can recover the original signal from a small number of observations,offering a new idea for channel estimation.This paper focuses on compressed sensing channel estimation of MIMO-OFDM system.Pilot pattern designing,channel estimation under the condition of unknown channel sparsity and impulsive noise is studied.The main content contains:(1)The main principle and practical application of compressed sensing are briefly introduced.The feasibility of compressed sensing channel estimation of MIMO-OFDM system is analyzed.The OMP and DCS-SOMP channel estimation algorithm are introduced as an example.(2)A pilot optimization algorithm for MIMO-OFDM system compressed sensing is studied,and a pilot pattern based on simulated annealing algorithm and space-time block code is designed.Based on minimizing mutual coherence of the measurement matrix,aiming at the slow convergence rate and high cost of pilot subcarrier of the pilot pattern based on genetic algorithm and shift mechanism,simulated annealing algorithm with stronger local search capability is proposed for pilot optimizing.Moreover,space-time block code is employed in pilot symbols to allow the pilot subcarrier positions of different antennas to be overlapped by each other,thus a futher reduction of pilot overhead and higher system spectrum efficiency are obtained.The simulation result shows that the proposed scheme performs better with a shorter convergence time.(3)The channel estimation algorithm with unknown channel saprsity is studied in this paper.Considering the high computational complexity and the necessity of noise parameter estimation of SAMP channel estimation algorithm,an improved OMP channel estimation algorithm is proposed,which determines whether to stop the iteration by the second order difference of the residual.The proposed algorithm does not require noise parameter as a priori condition,and its complexity is lower than SAMP algorithm.The simulation result shows that the proposed channel estimation algorithm outperforms SAMP algorithm under the condition of high SNR,and its BER is close to ideal channel estimation.Furthermore,the proposed algorithm is combined with Distributed Compressed Sensing to improve the channel estimation performance under the condition of low SNR.(4)The channel estimation scheme in impulsive noise environment is studied.Since the computational complexity of the threshold in SPA-SAMP algorithm is too high,the improved DCS-SOMP algorithm is adopted to estimate the impulsive noise and channel impulse response.The improved DCS-SOMP algorithm does not require complicated threshold computation or Gaussian noise parameter estimation.Under the condition of high SNR,the proposed scheme outperforms SPA-SAMP algorithm slightly,and its BER is close to ideal channel estimation without impulsive noise.