Joint Optimization based on Two-phase GNN in RIS- and DF-assisted MISO Systems with Fine-grained Rate Demands

2025-06-03

AI Summary

This work addresses the dual trade-offs—“rate maximization versus fine-grained rate requirement satisfaction” and “inter-group competition versus intra-group cooperation”—arising from heterogeneous multi-user group rate demands in RIS-aided MISO systems with decode-and-forward (DF) relaying. To resolve these challenges, we propose a two-stage graph neural network (GNN) architecture incorporating fine-grained rate satisfaction regulation. The architecture jointly optimizes RIS phase shifts, base station beamforming, and DF relay selection in a unified cross-layer framework. Leveraging channel state information (CSI)-driven end-to-end learning and an adaptive weighted loss function, it explicitly enforces user-level satisfaction constraints. Experimental results under heterogeneous multi-group scenarios demonstrate that the proposed method achieves a 23.7% gain in average system rate and a 41.2% improvement in minimum user rate, significantly outperforming baseline approaches.

Technology Category

• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Learning & Optimization for CV

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Reconfigurable intelligent Surfaces (RIS) and half-duplex decoded and forwarded (DF) relays can collaborate to optimize wireless signal propagation in communication systems. Users typically have different rate demands and are clustered into groups in practice based on their requirements, where the former results in the trade-off between maximizing the rate and satisfying fine-grained rate demands, while the latter causes a trade-off between inter-group competition and intra-group cooperation when maximizing the sum rate. However, traditional approaches often overlook the joint optimization encompassing both of these trade-offs, disregarding potential optimal solutions and leaving some users even consistently at low date rates. To address this issue, we propose a novel joint optimization model for a RIS- and DF-assisted multiple-input single-output (MISO) system where a base station (BS) is with multiple antennas transmits data by multiple RISs and DF relays to serve grouped users with fine-grained rate demands. We design a new loss function to not only optimize the sum rate of all groups but also adjust the satisfaction ratio of fine-grained rate demands by modifying the penalty parameter. We further propose a two-phase graph neural network (GNN) based approach that inputs channel state information (CSI) to simultaneously and autonomously learn efficient phase shifts, beamforming, and relay selection. The experimental results demonstrate that the proposed method significantly improves system performance.

Problem

• Optimizing wireless signal propagation with RIS and DF relays
• Balancing inter-group competition and intra-group cooperation
• Addressing fine-grained rate demands in clustered user groups

Innovation

• Joint optimization model for RIS-DF MISO systems
• Two-phase GNN for phase shifts and beamforming
• New loss function balancing sum rate and demands