Joint power-time resource optimization for multi-tag RFID wireless power transfer via model predictive control and ADMM

Abstract

This paper presents a comprehensive optimization framework for long-range radio frequency identification (RFID)-based wireless power transfer (WPT) systems operating in the 915 MHz ISM band. Unlike conventional fixed-parameter designs utilizing reactive Proportional-Integral-Derivative (PID) controllers, we propose a joint power-time resource allocation strategy that maximizes end-to-end energy efficiency while ensuring quality-of-service (QoS) fairness among multiple IoT tags. Three key innovations are presented: (1) An Model Predictive Control (MPC)-based adaptive power control mechanism that replaces traditional PID controllers to proactively handle time-varying channel conditions; (2) A convex optimization framework for dynamic Time-Division Multiple Access (TDMA) slot allocation that balances energy harvesting fairness among multiple tags subject to non-linear rectifier constraints; (3) An Alternating Direction Method of Multipliers (ADMM)-based distributed algorithm that efficiently decouples the non-convex joint optimization problem into tractable sub-problems. Detailed system models including Friis path loss with impedance mismatch, 5-stage Dickson rectifier non-linearity, and storage capacitor dynamics are formulated. Simulation results demonstrate that the proposed Joint Power-Time Optimization (JPTO) framework achieves 37.6% peak efficiency (versus 28.4% for conventional PID control), reduces power fluctuations by 61.9%, and maintains a normalized fairness index of 0.94 for heterogeneous loads (temperature, IMU, vision sensors) operating over 0.5–5 m distances, validating the effectiveness for diverse mobile IoT applications.