Channel Estimation Method of OFDM System based on Compressed Sensing

Abstract

Aiming at the time-domain sparsity and unknown sparsity of wireless channels, compressed sensing technology is applied to the channel estimation of Orthogonal Frequency Division Multiplexing (OFDM) system. This paper proposes a sparsity adaptive matching pursuit channel estimation algorithm. It uses the Discrete Fourier Transform (DFT) channel estimation algorithm to process the noise inside and outside the cyclic prefix. The estimated channel frequency response is used to terminate the sparse iteration of the Orthogonal Matching Pursuit (OMP) algorithm and realize the sparsity adaptive signal reconstruction. At the same time, in the atomic preselection stage, the Dice coefficient criterion is used instead of the inner product criterion as the correlation measurement criterion to achieve better estimation performance. The simulation results show that the algorithm has better performance than the traditional compressed sensing channel estimation algorithm, and can improve the system’s Normalized Mean Square Error (NMSE) and Bit Error Rate (BER) performance.

Keywords

• compressed sensing
• channel estimation
• sparsity adaptive