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By: Nikat R Mulla and Dr. Kazi Kutubuddin Sayyad Liyakat.
1 UG Student, 2 Professor and Head,
1,2 Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur (MS), India.
The transition to Sixth Generation (6G) wireless infrastructure introduces unprecedented complexity, characterized by massive densification, reliance on the Terahertz (THz) spectrum, dynamic cell topologies driven by Reconfigurable Intelligent Surfaces (RIS), and stringent requirements for Ultra-Reliable Low-Latency Communication (URLLC). Traditional reactive or fixed-schedule maintenance protocols are fundamentally inadequate for sustaining the necessary service continuity and energy efficiency in this hyper-connected, heterogeneous environment. This paper explores the critical role of Artificial Intelligence (AI)—specifically Deep Learning (DL), Reinforcement Learning (RL), and Federated Learning (FL)—in pioneering an advanced Predictive Maintenance (PdM) paradigm for 6G. The proposed AI-driven PdM system leverages real-time, multi-modal sensor data streams (including thermal signatures, spectral anomalies, traffic load, and physical wear data) to establish high-fidelity Digital Twins of network components. AI algorithms function as the core engine, utilizing anomaly detection and complex pattern recognition to forecast impending hardware failures, spectral drift, or component degradation with high temporal accuracy. By shifting maintenance from scheduled guesswork to targeted, data-driven action, AI-PdM minimizes network downtime, significantly reduces Operational Expenditure (OPEX), and maximizes the lifespan and performance of expensive, energy-intensive THz equipment. This infrastructure is not merely managed; it becomes self- aware and autonomously reliable, an absolute prerequisite for realizing the ambitious goals of 6G.
AI, 6G, Predictive maintenance, Terahertz, Digital Twin
Citation:
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