Long Range Microwave Power Transfer Systems: Optimizing Beam Alignment using an AI Driven IoT Feedback Framework

Abstract

Wireless Power Transfer (WPT) via Far Field Microwave Power Transmission (MPT) is a critical technology for energy distribution in remote and barren terrains. However, the practical implementation of long range MPT is hindered by significant efficiency drops caused by beam misalignment, atmospheric scintillation, and mechanical tower jitter. This paper proposes a novel, closed loop feedback architecture that integrates an out of band IoT link with a heuristic AI optimization algorithm. By utilizing the ESP NOW protocol in Long Range (LR) mode, real time telemetry data is transmitted from the receiver station to a high precision transmitter hub. A Gradient Descent based beam steering logic to dynamically compensate for environmental interference has been simulated. The simulation is done over a 1 km terrestrial link. The proposed AI-driven framework achieves a 93.3% pointing-error reduction (from ±1.5° to ±0.1°). It features a feedback loop latency of 20 ms and a total system response time of under 65 ms. Compared to static open-loop systems and traditional mechanical gimbals (the baselines), the closed-loop architecture prevents an 80-85% permanent power drop during 0.8° wind gust misalignments, achieving an efficiency improvement of over 80% relative to the degraded baseline and restoring peak transmission efficiency to 98.4%. Furthermore, it yields a 34% higher cumulative energy output over a 24-hour period.