Fractional Programming Strategy for Rate-Energy Optimization in RIS-assisted SWIPT IoT Networks

Abstract

This paper addresses the challenge of balancing conflicting goals, namely data rate and energy harvesting (EH) in Simultaneous Wireless Information and Power Transfer (SWIPT) systems, while incorporating Reconfigurable Intelligent Surface (RIS) technology. We formulate a weighted optimization objective to address this issue, seeking to simultaneously maximize data rate, EH, and minimize transmit power utilization. The proposed approach involves optimizing time switching (TS) ratios and transmit power using a practical phase-dependent amplitude model for each RIS element's reflectivity. To address this complex optimization problem involving ratio of concave-convex problem, the paper introduces fractional programming-based modified Dinkelbach Algorithm providing upper and lower bounds, which are then compared with Quadratic transform-related algorithms and solutions based on Karush-Kuhn-Tucker (KKT) conditions. Numerical findings highlight the effectiveness of the proposed algorithms in enhancing the overall performance of SWIPT systems with RIS technology. © 2024 IEEE.