Research On Channel Estimation Algorithm Based On Compressed Sensing

It's an important guarantee to get accurate channel estimation without delay for successful demodulation of received information in Orthogonal Frequency Division Multiplexing(OFDM)system.The signal between the base station and the mobile station is affected by Doppler shifts and multipath effect which caused by high-speed movement and multipath propagation environment.To obtain accurate CSI(Channel State Information),A large number of pilots are utilized which causes pilot overhead and leads to a decline in system spectrum utilization.Therefore,how to perform a good compromise between channel estimation performance and pilot overhead has become a key problem to solve for channel estimation.This paper pay attention to compressed sensing algorithm for channel estimation,the main contents are as follows:Firstly,the propagation characteristics of wireless communication and the knowledge of wireless channel modeling were studied.Then wireless channel's CE-BEM model was established,the basic coefficients of the CE-BEM model exhibit sparse characteristics;SISO-OFDM Simulation Link Platform was built in which the traditional channel estimation algorithm and the compressed sensing channel estimation algorithm were simulated and analyzed.Secondly,the feasibility of applying the compressed sensing theory to channel estimation was analyzed.Then the connection between compressed sensing theory and the OFDM system channel estimation algorithm was established;Research of compressed sensing recovery algorithm is one of the focuses of theoretical research,a variety of compressed sensing recovery algorithms were analyzed theoretically.Based on the established LTE link simulation platform,simulation was implemented and analyzed to verify the performance of compressed sensing channel estimation algorithm.Thirdly,considering that Orthogonal Match Pursuit recovery algorithm has the disadvantages of low channel estimation efficiency and requiring channel sparsity as priori information,the paper paid attention to algorithm optimization.Firstly,the channel estimation algorithm based on Sparsity Adaptive Matching Pursuit(CE-SAMP)algorithm was utilized.Simulation results show that the channel estimation with CE-SAMP algorithm does not need the sparsity as priori information and the number of iterations for the algorithm was reduced;To overcome the shortcoming of CE-SAMP algorithm,Alterable step Sparsity AdaptiveMatching Pursuit(CE-AsSAMP)was utilized;Secondly,combining the idea of stagewise matching pursuit and conjugate gradient direction updating,the stagewise conjugate gradient matching pursuit(CE-StCGP)algorithm was designed for channel estimation.Simulation results show that,in the case of achieving the same channel estimation mean square error performance,stagewise conjugate matching pursuit algorithm is fastest to recover CSI when comparing with other channel estimation algorithms.