STAR-RIS Enabled Heterogeneous Networks: Ubiquitous NOMA Communication and Pervasive Federated Learning

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 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 characterized to converge with a linear rate. To accelerate convergence while satisfying QoS 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 can achieve a faster convergence rate on IID and non-IID settings as compared to existing baselines, and iii) both the spectrum efficiency and learning performance can be significantly improved with the aid of the well-tuned STAR-RIS.

STAR-RIS Aided NOMA in Multicell Networks: A General Analytical Framework With Gamma Distributed Channel Modeling

“…However, all these works adopted conventional reflecting-only RISs, and the research on STAR-RIS aided NOMA networks is scarce. In STAR-RIS enhanced NOMA transmissions, optimization problems focused on sum rate maximization [33] and optimality gap minimization [34] were considered. For theoretical analysis, a recent work [16] first evaluated three STAR-RIS operating protocols in a NOMA single-cell network.…”

Intelligent Omni-Surfaces: Simultaneous Refraction and Reflection for Full-Dimensional Wireless Communications

“…The authors in [158] considered the mode switching protocol for an IOS-aided NOMA system, and proposed an algorithm to partition the IOS among the available users, aiming to determine the proper number of reflective or refractive elements for the system sumrate maximization while guaranteeing QoS requirements for individual users. The work in [159] integrated NOMA and over-the-air federated learning into a unified framework with the assistance of an IOS for coverage extension. The transmit power at users and the configuration of the IOS are optimized jointly to accelerate the convergence while satisfying QoS requirements for individual users.…”

Hybrid Reinforcement Learning for STAR-RISs: A Coupled Phase-Shift Model Based Beamformer

“…In [27], a proximal policy optimization (PPO) algorithm was developed for minimizing the expected Age-of-Information (AoI) for an aerial RIS. Specifically for the STAR-RIS scenario, a federated learning algorithm was proposed in [28] for maximizing the achievable data rate of a STAR-RIS assisted heterogeneous NOMA network.…”

“…k , but the difference is that the action space is reduced to a c o,t = {a w t , a ΘR t , a β t }. Hence, for any TS t, the optimized value {w t , Θ R,t , β t } can be completely covered by a c o,t using the mapping approach described in (26) ( 27) and (28).…”