AI for Optimizing Hardware Resources and Maintenance of Wireless Communication Devices

Abstract

This study develops an AI-based framework to optimize hardware resource usage and enhance predictive maintenance in wireless communication systems. It addresses key challenges, including noisy sensor data, unreliable anomaly detection, and inefficient maintenance practices, all of which impact reliability, cost, and sustainability. Using Kalman Filters, the system effectively estimates operational states despite noisy inputs, while autoencoders monitor integrity with reconstruction errors peaking at only 0.04, below the fault threshold. The ARIMA (1,0,1) model forecasts wear progression, with outputs rising from 1.0 to 1.6039, enabling proactive maintenance. Remaining Useful Life (RUL) estimations, based on a 10,000-hour lifespan, indicate a predictable decline from 8,000 to 2,000 hours, supporting condition-based planning. A health scoring system utilizing vibration, temperature, and voltage identified optimal and suboptimal states, yielding scores ranging from 19.68 to 22.22. Maintenance cost analysis revealed a linear increase: each 5-hour delay added $1,000, reaching $3,500, underscoring the financial risk of reactive strategies. Reinforcement learning optimized decision-making with rewards of −2 and −105, guiding low-cost, high-availability operations. The Weibull distribution and reliability modeling showed that system survival probabilities declined from 81.87% at 2000 hours to 36.79% at 10,000 hours, reflecting the aging trends in hardware. The study highlights policy implications, advocating for AI integration to reduce downtime by 25% and energy consumption by 18%. It supports federated learning and the development of secure, scalable AI models for 5G and future 6G networks. Conclusively, the framework promotes proactive maintenance, enhances uptime, and supports environmentally responsible wireless system operations.