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A Comprehensive Survey on AI-Assisted Multiple Access Enablers for 6G and beyond Wireless Networks

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Vydáno v:Computer Modeling in Engineering & Sciences vol. 145, no. 2 (2025), p. 1575-1665
Hlavní autor: Kinzah Noor
Další autoři: Agbotiname, Lucky Imoize, Adelabu, Michael Adedosu, Cheng-Chi, Lee
Vydáno:
Tech Science Press
Témata:
Wireless networks
Frequency division multiple access
Wireless communications
Artificial intelligence
6G mobile communication
5G mobile communication
Privacy
Optimization
Resource allocation
Network latency
Energy efficiency
Time Division Multiple Access
Multiagent systems
Deep learning
Machine learning
Real time
Federated learning
Explainable artificial intelligence
Heuristic methods
Nonorthogonal multiple access
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Abstrakt:The envisioned 6G wireless networks demand advanced Multiple Access (MA) schemes capable of supporting ultra-low latency, massive connectivity, high spectral efficiency, and energy efficiency (EE), especially as the current 5G networks have not achieved the promised 5G goals, including the projected 2000 times EE improvement over the legacy 4G Long Term Evolution (LTE) networks. This paper provides a comprehensive survey of Artificial Intelligence (AI)-enabled MA techniques, emphasizing their roles in Spectrum Sensing (SS), Dynamic Resource Allocation (DRA), user scheduling, interference mitigation, and protocol adaptation. In particular, we systematically analyze the progression of traditional and modern MA schemes, from Orthogonal Multiple Access (OMA)-based approaches like Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) to advanced Non-Orthogonal Multiple Access (NOMA) methods, including power domain-NOMA, Sparse Code Multiple Access (SCMA), and Rate Splitting Multiple Access (RSMA). The study further categorizes AI techniques—such as Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Federated Learning (FL), and Explainable AI (XAI)—and maps them to practical challenges in Dynamic Spectrum Management (DSM), protocol optimization, and real-time distributed decision-making. Optimization strategies, including metaheuristics and multi-agent learning frameworks, are reviewed to illustrate the potential of AI in enhancing energy efficiency, system responsiveness, and cross-layer RA. Additionally, the review addresses security, privacy, and trust concerns, highlighting solutions like privacy-preserving ML, FL, and XAI in 6G and beyond. By identifying research gaps, challenges, and future directions, this work offers a structured resource for researchers and practitioners aiming to integrate AI into 6G MA systems for intelligent, scalable, and secure wireless communications.
ISSN:1526-1492
1526-1506
DOI:10.32604/cmes.2025.073200
Zdroj:Advanced Technologies & Aerospace Database