Enhancing QoS and RF-EMF radiation using RSMA and AI in 6G networks: a comprehensive survey

Abstract

The sixth generation (6G) wireless networks are envisioned to deliver unprecedented levels of connectivity, ultra-reliable low-latency communication (URLLC), and energy efficiency while addressing public concerns about radiofrequency electromagnetic field (RF-EMF) exposure. Rate-Splitting Multiple Access (RSMA) has emerged as a promising multiaccess technique that flexibly manages interference and supports a broad range of Quality of Service (QoS) requirements. In parallel, the integration of artificial intelligence (AI), including machine learning (ML) and reinforcement learning (RL), has become essential for dynamic resource optimisation, proactive decision making, and real-time adaptation in complex and dense wireless environments. This survey presents a comprehensive overview of RSMA’s operational principles, with a focus on its encoding/decoding architecture using low-density parity-check (LDPC) and polar coding and how it enhances spectral and energy efficiency compared to traditional multiple access schemes. It also presents the mathematical framework for both the RSMA scheme and the RF-EMF evaluation. The survey also explores AI-driven strategies for optimising RSMA, including power control, beamforming, and user scheduling, while minimising RF-EMF exposure. We examine the regulatory thresholds provided by IEEE, FCC, and ICNIRP, focusing on exposure-aware RSMA models that balance RF safety with performance demands in relation to system constraints. Furthermore, we discuss system-level trade-offs between QoS and RF-EMF exposure and system performance metrics such as bit error rate (BER), latency, spectral efficiency, and specific absorption rate (SAR) levels. This survey identifies open challenges, including real-time AI deployment, scalability, and security, and discusses future research directions, such as hybrid multiple access models, privacy-preserving ML, and AI-RSMA-6G integration. Finally, this survey serves both as a reference guide and a basis for future research on the safe, intelligent, and high-performing deployment of RSMA in 6G networks, which optimises both user experience and environment.