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Massive multiple-input multiple-output (MIMO) systems employ a large numberof antennas to achieve gains in capacity, spectral efficiency, and energyefficiency. However, the large antenna array also incurs substantial storageand computational costs. This paper proposes a novel data compression frameworkfor massive MIMO channel matrices based on tensor Tucker decomposition. Toaddress the substantial storage and computational burdens of massive MIMOsystems, we formulate the high-dimensional channel matrices as tensors andpropose a novel groupwise Tucker decomposition model. This model efficientlycompresses the tensorial channel representations while reducing SINR estimationoverhead. We develop an alternating update algorithm and HOSVD-basedinitialization to compute the core tensors and factor matrices. Extensivesimulations demonstrate significant channel storage savings with minimal SINRapproximation errors. By exploiting tensor techniques, our approach balanceschannel compression against SINR computation complexity, providing an efficientmeans to simultaneously address the storage and computational challenges ofmassive MIMO.

Compressing MIMO Channel Submatrices with Tucker Decomposition: Enabling Efficient Storage and Reducing SINR Computation Overhead