Performance Analysis and Optimization of Network-Assisted Full-Duplex Systems under Low-Resolution ADCs

TL;DR

This paper evaluates the effects of low-resolution ADCs on network-assisted full-duplex distributed MIMO systems, proposing optimization algorithms and beamforming techniques to enhance spectral and energy efficiency.

Contribution

It introduces a novel bit allocation algorithm, beamforming training mechanism, and multi-objective optimization approach for low-resolution ADCs in NAFD distributed M-MIMO.

Findings

Closed-form spectral and energy efficiency expressions derived.

Deep Q network and genetic algorithm effectively optimize ADC configurations.

Simulation results confirm theoretical analysis and system improvements.

Abstract

Network-assisted full-duplex (NAFD) distributed massive multiple input multiple output (M-MIMO) enables the in-band full-duplex with existing half-duplex devices at the network level, which exceptionally improves spectral efficiency. This paper analyzes the impact of low-resolution analog-to-digital converters (ADCs) on NAFD distributed M-MIMO and designs an efficient bit allocation algorithm for low-resolution ADCs. The beamforming training mechanism relieves the heavy pilot overhead for channel estimation, which remarkably enhances system performance by guiding the interference cancellation and coherence detection. Furthermore, closed-form expressions for spectral and energy efficiency with low-resolution ADCs are derived. The multi-objective optimization problem (MOOP) for spectral and energy efficiency is solved by the deep Q network and the non-dominated sorting genetic algorithm II. The simulation results corroborate the theoretical derivation and verify the effectiveness of introducing low-resolution ADCs in NAFD distributed M-MIMO systems. Meanwhile, a set of Pareto-optimal solutions for ADC accuracy flexibly provide guidelines for deploying in a practical NAFD distributed M-MIMO system.