Optimal Energy Allocation and Multiuser Scheduling in SWIPT Systems with Hybrid Power Supply

This paper studies the utilization and transfer of renewable green energy in a multiuser downlink communication network. In the considered multiuser system, the base station (BS) is powered by both harvested energy and grid. When the BS transmits data to one user terminal, other terminals can replenish energy opportunistically from received radio-frequency (RF) signals, which is called simultaneous wireless information and power transfer (SWIPT). Our objective is to maximize the average throughput by multiuser scheduling and energy allocation utilizing causal channel state information while satisfying the requirement for harvested energy and the average power constraint of the grid. With channel dynamics and energy arrival modeled as Markov processes, we characterize the problem as a Markov decision process (MDP). The standard reinforcement learning framework is considered as an effective solution to MDP. If the transition probability of MDP is known, the policy iteration (PI) algorithm is used to solve the problem, otherwise, the R-learning algorithm is adopted. Simulation results show that the proposed algorithm can improve the average throughput of the system and increase the energy harvested by idle user terminals compared with the existing work. And R-learning can achieve performance close to the PI algorithm under the condition that the channel transition probability is unknown.

I. Introduction

With the development of 5G and the Internet of Things, more and more complex wireless communication networks, which include hundreds of battery-powered nodes, have been built. Unfortunately, these wireless terminals have limited battery capacity and require manual charging and replacement, resulting in increased battery recycling costs and unreliable communication quality. With the increasing awareness of human environmental protection, people pay more attention to the use of green energy in wireless communication. Energy harvesting (EH) is a promising approach to reduce carbon emissions, and prior studies on sensor networks have shown that harvesting energy from the environment significantly extends the life of networks ([1], [2]).