Compressed Sensing Estimation of Underwater Acoustic MIMO Channels Based on Temporal Joint Sparse Recovery
ZHOU Yuehai WU Yanyi CHEN Dongsheng TONG Feng
(Key Laboratory of Underwater Acoustic Communication and Marine Information Technology of the Ministry of Education, Xiamen University, Xiamen 361005, China)
Multiple-Input-Multiple-Output (MIMO) under water acoustic communication is capable of improving the channel capacity in extremely limited bandwidth. However, the performance of traditional channel estimation algorithms, such as Least Squares (LS) method, Compressed Sensing (CS) method decreases rapidly because of the simultaneous presence of the Co-channel Interference (CoI) and multipath. As the sparse multipath structures between adjacent data blocks exhibit temporal correlation features, in this paper, the temporal correlation of sparse multipath structures is exploited to establish temporal joint sparse MIMO channel estimation model, and the Simultaneous Orthogonal Matching Pursuit (SOMP) algorithm is utilized for compressed sensing estimation of MIMO channels. Simulation and sea trial results validate the effectiveness of the proposed method.
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