AI-Based Power Control for Solar-Powered OFDM Systems

Abstract

This paper explores the integration of renewable energy and artificial intelligence (AI) into next-generation wireless communication networks. Using orthogonal frequency-division multiplexing (OFDM) over Rayleigh fading channels, we simulate and evaluate four scenarios: traditional wireless systems, renewable-powered systems, AI-assisted systems, and intelligent renewable-powered systems. Key performance metrics such as Bit Error Rate (BER), Spectral Efficiency (SE), and Energy Efficiency (EE) are analyzed under varying signal-to-noise ratio (SNR) conditions. A Q-learning-based AI algorithm is employed for dynamic power allocation, aiming to maximize energy efficiency while preserving communication reliability. Simulation results show that AI-assisted renewable-powered systems - especially those powered by solar energy - offer significant improvements in energy efficiency without degrading signal performance. The findings underscore the potential of combining AI and renewable energy to build sustainable, efficient, and reliable wireless networks. This study supports the vision of intelligent, green 6G and beyond communication systems, where environmental sustainability and high performance are jointly achieved through advanced optimization and clean energy integration.