Full-Duplex Multiuser MISO Under Coarse Quantization: Per-Antenna SQNR Analysis and Beamforming Design
Seunghyeong Yoo, Jaehyun Kim, Seokjun Park, Mintaek Oh, Namyoon Lee, Jinseok Choi
TL;DR
This work tackles full-duplex MU-MISO in the presence of coarse quantization, revealing that residual analog self-interference (SI) drives the necessary ADC resolution and complicates quantization distortion. It develops an SI-aware beamforming framework that alternates between a generalized eigenvalue-based precoder design and a quantization-aware MMSE combiner to balance SI suppression and downlink signaling. The analysis yields per-antenna SQNR bounds and ADC-bit requirements, showing a logarithmic increase in needed bits with residual SI; simulations confirm that the proposed method achieves higher spectral efficiency and energy efficiency than benchmarks, with 6–7 bits often optimal. Overall, the paper provides practical guidelines for FD MU-MISO operation under coarse quantization and demonstrates substantial performance gains from SI-aware beamforming under hardware-imposed quantization constraints.
Abstract
We investigate full-duplex (FD) multi-user multiple input single-output systems with coarse quantization, aiming to characterize the impact of employing low-resolution analog-to-digital converters (ADCs) on self-interference (SI) and to develop a quantization- and SI-aware beamforming method that alleviates quantization-induced performance degradation in the FD systems. We first present an analysis on the perantenna signal-to-quantization noise ratio for conventional linear beamformers to provide the desired range of the number of analog-to-digital converter (ADC) bits, providing system insights for reliable FD operation in regard to the ADC resolution and beamforming strategy. Motivated by the insights, we then propose an SI-aware beamforming method that mitigates residual SI and quantization distortion. The resulting spectral efficiency (SE) maximization problem is decomposed into two tractable subproblems solved via alternating optimization: precoder and combiner design. The precoder optimization is formulated as a generalized eigenvalue problem, where the dominant eigenvector yields the best stationary solution through power iteration, while the combiner is derived as a quantization-aware minimum meansquared error (MMSE) filter. Numerical studies show that the number of required ADC bits with the proposed beamforming falls within the derived theoretical range while achieving the highest SE compared to benchmarks.