@Article{CSIAM-AM-5-3,
author = {Zhang, Yuanwei and Zhu, Ya-Nan and Zhang, Xiaoqun},
title = {Compressing MIMO Channel Submatrices with Tucker Decomposition: Enabling Efficient Storage and Reducing SINR Computation Overhead},
journal = {CSIAM Transactions on Applied Mathematics},
year = {2024},
volume = {5},
number = {3},
pages = {615--635},
abstract = {<p style="text-align: justify;">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.</p>},
issn = {2708-0579},
doi = {https://doi.org/10.4208/csiam-am.SO-2023-0051},
url = {https://global-sci.com/article/91029/compressing-mimo-channel-submatrices-with-tucker-decomposition-enabling-efficient-storage-and-reducing-sinr-computation-overhead}
}