Finite-Alphabet MMSE Equalization for All-Digital Massive MU-MIMO mmWave Communication

Abstract

We propose finite-alphabet equalization, a new paradigm that restricts the entries of the spatial equalization matrix to low-resolution numbers, enabling high-throughput, lowpower, and low-cost hardware equalizers. To minimize the performance loss of this paradigm, we introduce FAME, short for finitealphabet 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 multiuser multipleinput 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× and 5.8×, respectively.

Soft-Output Finite Alphabet Equalization for mmWave Massive MIMO

“…The present work extends its journal version [1] by providing an unbiased equalizer with soft-output capabilities as well as results for a coded mmWave massive MU-MIMO system.…”

“…Here, v H u ∈ C 1×B and x H u ∈ X 1×B are the uth rows of V H and X H , respectively. Although unbiased equalization as in (7) differs from biased equalization β * u x H u y as originally proposed in [1], we emphasize that (7), as its biased counterpart, also reduces hardware complexity. Concretely, the inner product x H u y (formed by B scalar products) can be computed with low-resolution multipliers and adders.…”

“…After estimating the channel, the all-digital BS uses spatial equalization to recover the UEs' individual signals. Finite-alphabet equalization [1] consists of using low-resolution spatial equalization matrices.…”

“…As defined in [1], a finite-alphabet equalization matrix is a U × B matrix that has the following form:…”

“…To compute finite-alphabet equalization matrices that minimize the post-equalization MSE as in (1), the work in [1] For a fixed β u , the FAME problem in (8) is NP-hard [17,18]. To develop practical algorithms, reference [1] reformulates the problem in (8) using a two-step procedure: First, compute…”