关键词: 压缩感知, 正交频分复用, 稀疏信道估计, 压缩采样匹配追踪

Abstract: In combination of CS theory, the compressing sampling matching pursuit algorithm for Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing（MIMO_OFDM） system channel estimation requires the signal sparsity as a priori information, while in actual situation the sparsity is difficult to obtain, for this question it proposes a signal sparsity adaptive Compressive Modifying Sampling Matching Pursuit algorithm（CoMSaMP）. The algorithm adopts the atomic weak selection criteria with theoretical support as a pre-selection scheme, and sets the first clipping threshold to reduce the algorithm extra iteration, then reduces the computational complexity, the improve of crop mode on channel estimation ensures the improvement of the reconstruction accuracy, and ultimately realizes adaptive recovery on MIMO-OFDM sparse channel estimation . Simulation results show that, compared with the original algorithm, under the same SNR conditions, the CoMSaMP  algorithm has better performance on channel estimation, improves the spectral efficiency, reduces the complexity. When the sparsity level is high, the proposed algorithm has the better performance than the CoSaMP algorithm on anti-interference ability.

Key words: compressed sensing, Orthogonal Frequency Division Multiplexing（OFDM）, sparse channel estimation, Compressive Sampling Matching Pursuit（CoSaMP）