LLM-Driven Adaptive 6G-Ready Wireless Body Area Networks: Survey and Framework

• URL: http://arxiv.org/abs/2508.08535v2
• Date: Thu, 14 Aug 2025 02:38:22 GMT
• Title: LLM-Driven Adaptive 6G-Ready Wireless Body Area Networks: Survey and Framework
• Authors: Mohammad Jalili Torkamani, Negin Mahmoudi, Kiana Kiashemshaki,
• Abstract summary: 6G communications, post-quantum cryptography, and energy harvesting have the potential to enhance WBAN performance.<n>We propose a novel Large Language Model-driven adaptive WBAN framework in which a Large Language Model acts as a cognitive control plane.<n>This approach aims to enable ultra-reliable, secure, and self-optimizing WBANs for next-generation mobile health applications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Wireless Body Area Networks (WBANs) enable continuous monitoring of physiological signals for applications ranging from chronic disease management to emergency response. Recent advances in 6G communications, post-quantum cryptography, and energy harvesting have the potential to enhance WBAN performance. However, integrating these technologies into a unified, adaptive system remains a challenge. This paper surveys some of the most well-known Wireless Body Area Network (WBAN) architectures, routing strategies, and security mechanisms, identifying key gaps in adaptability, energy efficiency, and quantum-resistant security. We propose a novel Large Language Model-driven adaptive WBAN framework in which a Large Language Model acts as a cognitive control plane, coordinating routing, physical layer selection, micro-energy harvesting, and post-quantum security in real time. Our review highlights the limitations of current heuristic-based designs and outlines a research agenda for resource-constrained, 6G-ready medical systems. This approach aims to enable ultra-reliable, secure, and self-optimizing WBANs for next-generation mobile health applications.

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