Fractional Programming Based Optimization Techniques for RIS-Assisted SWIPT-IoT System

This letter addresses two distinctly poised objectives, i.e., data rate and energy harvesting (EH) in Simultaneous Wireless Information and Power Transfer (SWIPT) systems with Reconfigurable Intelligent Surface (RIS) by tackling a weighted objective to maximize data rate, EH, and transmit power utilization for multi-antenna BS and multiple RIS-User scenarios. This approach optimizes power splitting (PS) ratio at the end-user and transmit power using an optimized practical phase-dependent amplitude model for each RIS element reflectivity. Fractional programming-based Dinkelbach and Quadratic transform-related algorithms are proposed and compared with Karush-Kuhn-Tucker (KKT) conditions based solutions. Optimized discrete phase shift (DPS) level has been sought. Numerical results show that deploying more RIS elements and placing them closer together enhances both information rate and EH, whereas it nearly saturates with increasing DPS levels.