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Coefficient adjustment matrix inversion approach and architecture for massive MIMO systems

机译：大规模MIMO系统的系数调整矩阵求逆方法和架构

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摘要

Thanks to hundreds of antennas, spectral efficiency of massive multiple-input multiple-output (MIMO) systems has drastically increased. However, the resulting huge dimension of matrices involved in massive MIMO MMSE detection causes prohibitive complexity. Although large scale matrix inversion with Neumann approximation achieves good tradeoff between complexity and accuracy for i.i.d. massive MIMO channel, its convergency speed degrades seriously for correlated massive MIMO channel. To this end, in this paper the matrix inversion approach based on coefficient adjustment (CA), which is more adaptable to correlated channel with higher throughput, is proposed. The corresponding hardware architecture is also given. FPGA results have shown that for 4 × 32 MIMO system, the proposed architecture can achieve 69.4% higher frequency with only 49.1% hardware cost compared to Cholesky decomposition method. CA approach can also achieve 37.9% higher throughput than Neumann scheme for correlated channel on average.
机译：多亏了数百根天线，大规模多输入多输出（MIMO）系统的频谱效率已大大提高。然而，大规模MIMO MMSE检测中所涉及的矩阵的巨大维度导致了令人望而却步的复杂性。尽管使用Neumann近似进行大规模矩阵求逆可以在i.i.d的复杂度和精度之间取得良好的折衷。大规模MIMO信道，其收敛速度对于相关的大规模MIMO信道会严重降低。为此，本文提出了一种基于系数调整（CA）的矩阵求逆方法，该方法更适合于具有较高吞吐量的相关信道。还给出了相应的硬件体系结构。 FPGA结果表明，对于4×32 MIMO系统，与Cholesky分解方法相比，所提出的体系结构可实现69.4％的更高频率，而硬件成本仅为49.1％。对于相关信道，CA方法平均还可以获得比Neumann方案高37.9％的吞吐量。

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