Optimization for dynamic multi-RIS-assisted SWIPT-Enabled V2I networks: A deep learning approach

Abstract

Reconfigurable intelligent surfaces (RISs) have emerged as a highly promising technology in sixth-generation (6G) vehicular systems, offering the ability to dynamically control the wireless propagation environment. In this paper, we examine simultaneous wireless information and power transfer (SWIPT) by employing multiple RISs within a vehicle-to-infrastructure (V2I) communication system. The wireless environment exhibits high complexity due to fading and shadowing effects. To model this accurately, we adopt the double generalized Gamma (dGG) distribution. This comprehensive modeling approach enables a more realistic and insightful performance evaluation of RIS-assisted SWIPT systems under practical mobility and fading conditions. To reflect real-world vehicular dynamics, we incorporate a statistical Random Waypoint (RWP) mobility model, while also accounting for imperfections in channel state information (CSI) that arise due to high mobility and channel estimation errors. The study also integrates a non-linear energy harvesting (NL-EH) scheme to enhance performance via the power-splitting (PS) protocol. A unified objective function is proposed to jointly optimize transmit power and PS factors, aiming to maximize both the harvested energy and information rate. To address the non-convex nature of the problem, an iterative algorithm is utilized, supported by closed-form solutions derived from the Karush-Kuhn-Tucker (KKT) conditions and joint optimization (JO) method. Monte-Carlo simulations are conducted to verify the accuracy of the analytical results. Additionally, a deep neural network (DNN) framework is introduced for optimized value prediction, demonstrating superior SWIPT performance compared to single RIS configurations, with reduced complexity and faster execution. © 2025 Elsevier B.V., All rights reserved.