STAR-RIS Integrated Non-Orthogonal Multiple Access and Over-the-Air Federated Learning: Framework, Analysis, and Optimization

Abstract

This paper integrates non-orthogonal multiple access (NOMA) and over-the-air federated learning (AirFL) into a unified framework using one simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). The STAR-RIS plays an important role in adjusting the decoding order of hybrid users for efficient interference mitigation and omni-directional coverage extension. To capture the impact of non-ideal wireless channels on AirFL, a closed-form expression for the optimality gap (a.k.a. convergence upper bound) between the actual loss and the optimal loss is derived. This analysis reveals that the learning performance is significantly affected by the active and passive beamforming schemes as well as wireless noise. Furthermore, when the learning rate diminishes as the training proceeds, the optimality gap is explicitly shown to converge with linear rate. To accelerate convergence while satisfying quality-of-service requirements, a mixed-integer non-linear programming (MINLP) problem is formulated by jointly designing the transmit power at users and the configuration mode of STAR-RIS. Next, a trust region-based successive convex approximation method and a penalty-based semidefinite relaxation approach are proposed to handle the decoupled non-convex subproblems iteratively. An alternating optimization algorithm is then developed to find a suboptimal solution for the original MINLP problem. Extensive simulation results show that i) the proposed framework can efficiently support NOMA and AirFL users via concurrent uplink communications, ii) our algorithms achieve faster convergence rate on IID and non-IID settings compared to existing baselines, and iii) both the spectrum efficiency and learning performance is significantly improved with the aid of the well-tuned STAR-RIS.

The paper has been accepted for publication in the IEEE Internet of Things Journal

References in corpus (6)

• STAR: Simultaneous Transmission And Reflection for 360° Coverage by Intelligent Surfaces
• Evolution of NOMA Toward Next Generation Multiple Access (NGMA) for 6G
• Federated Learning: A Signal Processing Perspective
• Integrating Over-the-Air Federated Learning and Non-Orthogonal Multiple Access: What Role can RIS Play?
• Federated Learning for 6G: Applications, Challenges, and Opportunities
• Optimized Power Control for Over-the-Air Federated Edge Learning

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• Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey
• On Secrecy Performance of RIS-Assisted MISO Systems over Rician Channels with Spatially Random Eavesdroppers
• Semi-Federated Learning for Collaborative Intelligence in Massive IoT Networks
• Balancing Accuracy and Integrity for Reconfigurable Intelligent Surface-aided Over-the-Air Federated Learning
• Simultaneously Transmitting and Reflecting Surfaces for Ubiquitous Next Generation Multiple Access in 6G and Beyond
• Federated Deep Reinforcement Learning for RIS-Assisted Indoor Multi-Robot Communication Systems
• Hybrid RIS-Assisted MIMO Dual-Function Radar-Communication System
• Spectral and Energy Efficiency Maximization of MISO STAR-RIS-assisted URLLC Systems
• Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework
• Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling