Deep RL-Based Time Scheduling and Power Allocation in EH Relay Communication Networks

Abstract

Powering relays with harvested renewable ambient energy has been emerging as a promising solution to reduce the on-grid energy consumption and greenhouse gas emissions in green relaying communication networks. In this paper, we study the joint time scheduling and power allocation problem for the Decode-and-Forward energy-harvesting relay communication network. Particularly, our goal is to maximize the end-to-end throughput by a deadline subject to the finite data and energy storage. Due to the multi-slot optimization, the traditional deep reinforcement learning (RL) framework cannot be directly applied to obtain the optimal solution of maximizing the end-to-end throughput by a deadline in the online manner. To this end, we explore a novel deep reinforcement learning framework consisting of multiple computation units to obtain the online time scheduling and power allocation based on the current causal knowledge of energy arrivals and channel fading at each time slot. Simulation results show that the proposed deep reinforcement learning based algorithm can achieve more than 90% of maximum end-to-end throughput.