Joint Sparse Beamforming and Power Control for Large-scale DAS with Network-Assisted Full Duplex

TL;DR

This paper proposes joint sparse beamforming and power control algorithms for network-assisted full duplex in large-scale distributed antenna systems, improving uplink and downlink rates by addressing interference and backhaul constraints.

Contribution

It introduces two novel optimization methods for maximizing data rates in NAFD-enabled L-DAS, handling interference and backhaul limitations effectively.

Findings

Proposed algorithms outperform traditional TDD schemes in rate gains.

Joint processing enables flexible duplex modes in large-scale DAS.

Simulation results validate the effectiveness of the algorithms.

Abstract

In this paper, we studied the joint sparse beamforming and power control for network-assisted full duplex (NAFD) in large-scale distributed antenna system (L-DAS), where the remote antenna units either be operating in half-duplex mode or full-duplex mode are all connected to the central processing unit (CPU) via high-speed backhaul links. With joint processing at CPU, NAFD could achieve truly flexible duplex, including flexible half-duplex and full-duplex. Cross-link interference and the finite-capacity backhaul are the main problems of NAFD in L-DAS. To solve these problems, we aim to maximize the aggregated rate of uplink and downlink subject to quality of service constraints and backhaul constraints. Two approaches have been proposed to solve the optimization problem, where the first approach converts the object function to the difference of two convex functions with semi-definite relax (SDR) and then an iterative SDR-block coordinate descent method is applied to solve the problem. The second method is based on sequential parametric convex approximation. Simulation results have shown that the proposed algorithms yield a higher rate gain compared to the traditional time-division duplex scheme.