Low complexity distributed channel estimation for massive MIMO
The traditional channel estimation algorithm in massive multiple-input multiple-output(MIMO)system requires a large pilot overhead and has high computation complexity.To solve this problem,we propose a two-stage distributed chan-nel estimation scheme with low complexity.In the initial stage of the scheme,the traditional compressed sensing algorithm is used to recover the channel matrix at the base station side.In the second stage,the proposed scheme realizes continuous channel tracking by using the temporal correlation of the channel on the user side.The massive MIMO angle domain channel is divided into dense part and sparse part.The distributed adaptive weak matching pursuit(DAWMP)algorithm proposed in this paper is used for multi-dimensional reconstruction of sparse channel by utilizing the joint sparsity of sub-channels.Compared with the linear minimum mean square error(LMMSE)algorithm,the channel decomposition strategy effectively reduces the computational complexity of channel estimation on the user side.At the same time,simulation results show that compared with the classical compressed sensing channel estimation algorithm,the computational complexity of the proposed algorithm is reduced by about 33%,and the performance of the algorithm is improved by about 0.5 dB.
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