基于Tucker分解的MIMO信道子矩阵压缩：实现高效存储与降低SINR计算开销
Compressing MIMO Channel Submatrices with Tucker Decomposition: Enabling Efficient Storage and Reducing SINR Computation Overhead
AI总结：
本文提出基于张量Tucker分解的新型数据压缩框架，用于大规模MIMO信道矩阵，通过高效压缩信道表示并减少SINR估计开销，实现存储和计算效率的平衡。
摘要：
大规模多输入多输出（MIMO）系统通过使用大量天线来实现容量、频谱效率和能效的提升。然而，大天线阵列也带来了显著的存储和计算成本。本文提出了一种基于张量Tucker分解的新数据压缩框架，用于大规模MIMO信道矩阵。为解决大规模MIMO系统的存储和计算负担，我们将高维信道矩阵表示为张量，并提出了一种新的组内Tucker分解模型。该模型在高效压缩张量信道表示的同时减少了SINR估计开销。我们开发了交替更新算法和基于HOSVD的初始化方法来计算核心张量和因子矩阵。广泛的模拟显示，该方法在存储信道方面有显著节省，同时保持最小的SINR近似误差。通过利用张量技术，我们的方法在信道压缩和SINR计算复杂性之间取得了平衡，提供了一种有效的方法，同时解决大规模MIMO的存储和计算挑战。
英文摘要：
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.
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