Joint User Scheduling and Beamforming Design for Multiuser MISO Downlink Systems

TL;DR

This paper introduces a novel joint user scheduling and beamforming optimization framework for multiuser MISO downlink systems, utilizing a successive convex approximation algorithm and a graph neural network approach to improve performance and efficiency.

Contribution

It proposes a new joint US-BF design method combining convex optimization and graph neural networks, addressing non-convexity and computational challenges in multiuser systems.

Findings

J-USBF achieves near-optimal performance

J-USBF offers higher computational efficiency

Method generalizes to dynamic wireless scenarios

Abstract

In multiuser communication systems, user scheduling and beamforming (US-BF) design are two fundamental problems that are usually studied separately in the existing literature. In this work, we focus on the joint US-BF design with the goal of maximizing the set cardinality of scheduled users, which is computationally challenging due to the non-convex objective function and the coupled constraints with discrete-continuous variables. To tackle these difficulties, a successive convex approximation based US-BF (SCA-USBF) optimization algorithm is firstly proposed. Then, inspired by wireless intelligent communication, a graph neural network based joint US-BF (J-USBF) learning algorithm is developed by combining the joint US and power allocation network model with the BF analytical solution. The effectiveness of SCA-USBF and J-USBF is verified by various numerical results, the latter achieves close performance and higher computational efficiency. Furthermore, the proposed J-USBF also enjoys the generalizability in dynamic wireless network scenarios.