We propose finite-alphabet equalization, a new paradigm that restricts the entries of the spatial equalization matrix to low-resolution numbers, enabling high-throughput, low-power, and low-cost hardware equalizers. To minimize the performance loss of this paradigm, we introduce FAME, short for finite-alphabet minimum mean-square error (MMSE) equalization, which is able to significantly outperform a naïve quantization of the linear MMSE matrix. We develop efficient algorithms to approximately solve the NP-hard FAME problem and showcase that near-optimal performance can be achieved with equalization coefficients quantized to only 1–3 bits for massive multi-user multiple-input multiple-output (MU-MIMO) millimeter-wave (mmWave) systems. We provide very-large scale integration (VLSI) results that demonstrate a reduction in equalization power and area by at least a factor of $ {3.9} {\times }$ and $ {5.8} {\times }$ , respectively.

中文翻译：

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用于全数字大规模 MU-MIMO 毫米波通信的有限字母 MMSE 均衡

我们提出了有限字母均衡，这是一种新范式，将空间均衡矩阵的条目限制为低分辨率数字，从而实现高吞吐量、低功耗和低成本的硬件均衡器。为了最大限度地减少这种范式的性能损失，我们引入了 FAME，即有限字母表最小均方误差 (MMSE) 均衡化的缩写，它能够显着优于线性 MMSE 矩阵的朴素量化。我们开发了有效的算法来近似解决 NP-hard FAME 问题，并展示了对于大规模多用户多输入多输出 (MU-MIMO) 毫米，通过量化为仅 1-3 位的均衡系数可以实现接近最佳的性能波（毫米波）系统。 $ {3.9} {\times }$ 和 $ {5.8} {\times }$ ， 分别。