Structure-based learning via graph neural networks for multi-group multicast beamforming

In this thesis, we consider a downlink multi-antenna multi-group multicasting problem, where users in the same group request the same content. Even though existing optimization-based algorithms can obtain a sub-optimal multicast beamforming solution, they are designed under perfect channel state information (CSI) and need to be performed each time channel state information is updated. Due to the complexity involved in solving the problem, they lead to high computational run-time for largescale antenna-array systems. To overcome this challenge, we propose a robust efficient learning-based approach for a scalable beamformer design under imperfect CSI. We mainly focus on the design of the base station (BS) multicast beamformer to maximize the weighted signal-to-interference-plus-noise ratio (SINR) among all users, subject to a transmit power budget constraint, also known as the max-min fair (MMF) problem. We propose to solve the MMF problem by developing a graph neural network (GNN) model and explore the available optimal multicast beamforming structure to speed up the training process and improve performance. A scalable GNN-based beamformer architecture critically depends on the design of the hidden layers. By exploring the interactions between user groups, we construct the hidden layers of our proposed GNN to effectively capture intra-group and inter-group interaction patterns, enabling the network to learn the beamforming solution effectively. Our GNN via design can also generalize different numbers of users and groups without re-training. Simulation results show that the proposed GNN model achieves a near-optimal performance. We also show that our GNN-based model is significantly faster compared to conventional optimization-based methods and outperforms other existing learning-based models. It can be well generalized to different numbers of users and groups with excellent accuracy.

Name thesis:degree_name

Master of Applied Science (MASc)

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• SoleimaniMajd, Reza

Advisor dc:contributor.advisor

• Dong, Min

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1944

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1944