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Massive multiple-input multiple-output (MIMO) is becoming a key technology for future wireless communications. Channel feedback for massive MIMO is a challenging task due to the increased dimension of MIMO channel matrix. By exploiting the channel sparsity, channel estimation based on compressive sensing (CS) aims to reduce the feedback overhead in massive MIMO systems. In this paper, various CS algorithms for channel estimation in massive MIMO systems are summarized, and a novel CS algorithm, i.e. modified sparsity adaptive matching pursuit (MSAMP), is proposed hereupon. Moreover, various measurement matrices are introduced in the CS scheme. The performances of channel estimation and recovery are simulated and compared. It is inferred from simulation that, SAMP is very appropriate for reconstructing sparse channel information in massive MIMO system, especially the modified SAMP can give higher reconstruction, and some measurement matrix is suitable for certain optimal CS algorithms.

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Massive multiple-input multiple-output (MIMO) is considered as the promising technique in next generation of wireless communication system. It has high spectral efficiency and energy efficiency. In addition, it can mitigate the interferences among users as the amount of antennas at base station (BS) grows. But, the high complexity and cost of hardware pose huge challenges to massive MIMO. In future, antenna selection (AS) is still a choice to decrease the burden of BS. In this letter, the AS algorithm based on K-means clustering is proposed to maximizing the capacity with low computational complexity. Finally, the simulated results are presented to validate the proposed method.