Task offloading and resource allocation in hybrid-powered WPT MEC system: An enhanced deep reinforcement learning method

Abstract

Recently, the integration of mobile edge computing (MEC) and wireless power transfer (WPT) technologies presents a transformative approach to overcoming the energy limitations of wireless devices (WDs), thereby enhancing both the sustainability and operational efficiency of mobile networks. This paper introduces a novel green-prioritized hybrid energy supply system that harnesses both renewable and grid energy, which aims at optimizing energy use and computational power in mobile networks under dynamic conditions. Specifically, we formulate a long-term average grid energy minimization problem (LAGEMP) to reduce grid energy consumption while maintaining robust and efficient network operations. To solve the complex and dynamic LAGEMP, we propose an action space reduction scheme and an enhanced deep deterministic policy gradient (EDDPG) algorithm, which incorporates the cross-entropy method (CEM). These introduced enhanced approaches not only reduce the computational load but also expedite the convergence of network training, thereby optimizing both energy usage and task offloading strategies. Simulation results reveal that the EDDPG algorithm significantly outperforms existing strategies and algorithms, and achieves near-optimal task offloading efficiency with reduced grid energy.