Low-Complexity Beamspace Channel Denoiser for mmWave Massive MIMO with Low-Resolution ADCs
Hanyoung Park, Eunho Kim, Ji-Woong Choi

TL;DR
This paper introduces a low-complexity beamspace channel denoising algorithm for mmWave massive MIMO systems with low-resolution ADCs, leveraging sparsity and Bayesian hypothesis testing.
Contribution
It proposes a computationally efficient denoising method with a hardware-friendly VLSI architecture, enabling practical deployment in large-scale antenna systems.
Findings
Achieves near-optimal denoising performance with reduced complexity
Develops a hardware-efficient FPGA implementation with lower latency
Demonstrates sublinear scaling with the number of antennas
Abstract
In this paper, we propose a low-complexity beamspace channel denoising algorithm for millimeter-wave (mmWave) massive multi-input multi-output (MIMO) systems with low-resolution analog-to-digital converters (ADCs). The proposed method exploits the inherent sparsity of mmWave channels in the beamspace domain and formulates the denoising problem as a Bayesian binary hypothesis testing under a Bernoulli-complex Gaussian prior. To capture the distortion induced by low-resolution ADCs in a complexity-efficient manner, thermal noise and quantization noise are jointly modeled as a composite noise. Based on this modeling, a closed-form threshold value and a hard-thresholding-based denoising rule are derived to distinguish signal-dominant and noise-dominant components. The resulting algorithm avoids computationally intensive operations such as matrix inversion, iterative optimization, and…
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