OFDM Channel Estimation Algorithm Based On Recovery Algorithm

Compressive sensing is a topic that has recently gained more and more attention in various areas. The theory broke the bounds of the Nyquist Sampling Theorem, which reduce the sampling rate and save system resources. As the central part of the next-generation mobile communications, the Orthogonal Frequency Division Multiplexing (OFDM) technology has been widely applied to wireless network. In communications, compressive sensing is being widely adopted for OFDM system channel estimation.In this paper, the principle of compressed sensing theory has been introduced firstly. The paper also elaborates the sparse character of signal, compression of signal and reconstruction of original signal. Based on the theory of OFDM system channel estimation, two novel algorithms, which are Orthogonal Matching Pursuit with Replacement (OMPR) and Bayesian algorithm, have been applied on channel estimation. OMPR, like the classic greedy algorithm OMP, adds exactly one coordinate to the support at each iteration, based on the correlation with the current residual. However, unlike OMP, OMPR also removes one coordinate from the support. Based on the different distribution of the sparse matrix, Bayesian gradually approached the cost function to get the optimal results. Finally, compared with performances of different methods, simulation results demonstrate that OMPR and Bayesian methods are more robust and faster than existing methods.