Online Learning for Joint Energy Harvesting and Information Decoding Optimization in IoT-enabled Smart City

Abstract

In this study, we first present a framework that jointly optimize energy harvesting and information decoding for Internet of Things (IoT) devices, which are capable of simultaneous wireless information and power reception, in a smarty city. In particular, a generalized power splitting receiver for IoT devices is designed, where each antenna in the receiver has an independent power splitter, unlike the existing works in which only one power splitter is employed regardless of the number of antennas in the receiver. Such a receiver design can provide a great degree of freedom to improve the network performance. Based on the presented framework, for each IoT device, we formulate an optimization problem whose objective is to maximize the harvested energy of each IoT device while satisfying its data rate requirement. To solve this problem, we propose a double-deep deterministic policy gradient based online learning algorithm which enables each IoT device to jointly determine receive beamforming and power splitting ratio vectors in real-time. Further, each IoT device can implement the proposed algorithm in a distributed manner using only its local channel state information. As such, cooperation and information exchange among the base stations and IoT devices are not necessary when performing the proposed algorithm at IoT devices. The extensive simulation results show the validity of the proposed algorithm. IEEE