Spatial correlation based channel compression feedback algorithm for massive MIMO systems

Abstract Aiming at the non-linear structure of massive multiple-input multiple-output (MIMO) channel data, this paper proposes a channel state information (CSI) compression feedback algorithm based on Laplacian Eigenmaps (LE) non-linear processing for massive MIMO uniform linear array. The spatial correlation of the channel array determines the Laplacian matrix, and the channel compression matrix is obtained by Laplacian matrix eigenvalue decomposition. The simulation results show that the proposed LE algorithm can reduce the feedback overhead, and its bit error rate (BER) performance is better than that of the discrete cosine transform (DCT) sparse compression algorithm. In addition, the proposed LE algorithm computational complexity is higher than DCT, and lower than principal component analysis (PCA) and Karhunen-Loeve transform (KLT) algorithms, but the LE algorithm can achieve higher feedback accuracy when the feedback overhead is slightly lower than DCT.

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