Algorithm-based adaptive channel allocation and power splitting strategy for SWIPT systems

In practical mobile communication systems, terminal users face challenges such as insufficient computational power, stringent battery life constraints, and scarcity of wireless resources, exacerbated by limitations in communication environments and battery technology. This paper investigates a Simultaneous Wireless Information and Power Transfer (SWIPT) scenario and proposes a Genetic Algorithm-based Adaptive Channel Configuration and Power Splitting (GA-EH) strategy. The strategy aims to maximize the total energy harvested by all users while guaranteeing their quality of service (QoS). It employs a binary channel allocation scheme alongside a cooperative transmission mode to configure communication and cooperative links for users, thereby enhancing energy harvesting. Simulation results demonstrate that, compared to a standard genetic algorithm without elitism, the proposed GA-EH strategy achieves faster convergence and greater stability. It effectively increases the total harvested energy and extends the operational lifetime of user devices.