Performance Analysis of Digital Beamforming mmWave MIMO with Low-Resolution DACs/ADCs

TL;DR

The paper tackles the feasibility of fully digital beamforming at mmWave with low-resolution DACs/ADCs by modeling quantization via AQNM, exploiting TDD reciprocity and OFDM, and evaluating channel estimation approaches. It introduces a system model with a frequency-selective Rician channel and compares OOBA-MRC and OMP channel estimation against a baseline, analyzing SE and EE across antenna counts and quantization levels $n_b \in \{2,4,8\}$. The results show that $n_b=4$ often offers the best energy efficiency while preserving SE, especially at higher $K$-factors, highlighting important hardware-design trade-offs for practical fully digital mmWave MIMO. These findings inform hardware choices that balance data-rate performance against power consumption for latency-sensitive mmWave links.

Abstract

Future wireless communications will rely on multiple-input multiple-output (MIMO) beamforming operating at millimeter wave (mmWave) frequency bands to deliver high data rates. To support flexible spatial processing and meet the demands of latency critical applications, it is essential to use fully digital mmWave MIMO beamforming, which relies on accurate channel estimation. However, ensuring power efficiency in fully digital mmWave MIMO systems requires the use of low-resolution digital-to-analog converters (DACs) and analog-to-digital converters (ADCs). The reduced resolution of these quantizers introduces distortion in both transmitted and received signals, ultimately degrading system performance. In this paper, we investigate the channel estimation performance of mmWave MIMO systems employing fully digital beamforming with low-resolution quantization, under practical system constraints. We evaluate the system performance in terms of spectral efficiency (SE) and energy efficiency (EE). Simulation results demonstrate that a moderate quantization resolutions of 4-bit per DAC/ADC offers a favorable trade-off between energy consumption and achievable data rate.

Figures (1)

• Figure 1: With a low $K$-factor (left), lower resolution reduces SE for all methods, while 4-bit quantization yields the highest EE. At high $K$-factor (right), the OOBA-MRC is more robust to quantization, with 2-bit and 4-bit quantizers achieving high SE and EE.