Uncoordinated RIS-Aided Uplink IoT Cell-Free Massive MIMO-NOMA Systems

• Published in: IEEE Open Journal of the Communications Society
• Main Authors: Mohamad Ousseily, Joumana Farah, Matthieu Crussiere, Joseph Doumit, Charbel Abdel Nour, Eric Pierre Simon
• Format: Article
• Language: English
• Published: IEEE 2025-01-01
• Subjects: Cell-free massive MIMO; reconfigurable intelligent surfaces; non-orthogonal multiple access; grant-free access; resource allocation
• Online Access: https://ieeexplore.ieee.org/document/11207699/

Cell-free massive MIMO (CF-mMIMO) has emerged as a promising architecture for beyond 5G networks, offering enhanced coverage and spectral efficiency through distributed antenna arrays. This paper proposes a novel framework integrating reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) in CF-mMIMO for uplink grant-free Internet of Things (IoT) scenarios. In such scenarios, devices transmit without prior coordination with a central unit, which increases the risk of multi-user interference. NOMA is thus introduced to address this challenge through successive interference cancellation (SIC). IoT devices autonomously select subbands using the multi-armed bandit (MAB) framework. Based on the devices subband choices, RIS angles are configured so as to satisfy the quality-of-service (QoS) requirements of all devices while leveraging NOMA SIC ordering. The resulting optimization problem becomes infeasible in many practical scenarios. To address these cases, we introduce: (1) a classification approach to proactively identify certain infeasible constraints, and (2) an iterative constraint relaxation method to improve the likelihood of finding feasible solutions. Additionally, a dynamic SIC reordering strategy is proposed to further enhance system performance. An enhanced upper confidence bound (UCB) algorithm is also introduced where, upon convergence, devices maintain their subband choices to minimize unnecessary explorations and optimizations. Simulation results demonstrate that our framework achieves higher success rates while reducing the required optimizations compared to baseline approaches.