  Massive multiple-input multiple-output (MIMO) systems employ a large number
of antennas to achieve gains in capacity, spectral efficiency, and energy
efficiency. However, the large antenna array also incurs substantial storage
and computational costs. This paper proposes a novel data compression framework
for massive MIMO channel matrices based on tensor Tucker decomposition. To
address the substantial storage and computational burdens of massive MIMO
systems, we formulate the high-dimensional channel matrices as tensors and
propose a novel groupwise Tucker decomposition model. This model efficiently
compresses the tensorial channel representations while reducing SINR estimation
overhead. We develop an alternating update algorithm and HOSVD-based
initialization to compute the core tensors and factor matrices. Extensive
simulations demonstrate significant channel storage savings with minimal SINR
approximation errors. By exploiting tensor techniques, our approach balances
channel compression against SINR computation complexity, providing an efficient
means to simultaneously address the storage and computational challenges of
massive MIMO.
