Ac-computing enabled SWIPT communications for multi-user iot networks

Abstract

One of the major challenges in Internet of Things (IoT) networks is the limited battery life of the sensors. Simultaneous Wireless Information and Power Transfer (SWIPT) technology can help overcome this challenge and enable battery-less IoT communication. An attractive solution to further enhance energy harvesting in IoT devices is to enable alternating current (AC) computing, which directly supplies the computing block of the receiver with the required power. In this paper, we consider a general and realistic structure of an AC-computing-enabled IoT network, with several access points (APs) serving multiple IoT devices. We study rate maximization while ensuring the harvesting of necessary power for both the operation and battery charging of the IoT devices. A sum-rate maximization problem is formulated, jointly designing the beamforming vectors and power splitting factors, subject to constraints on the total harvested energy and transmit power budget. The formulated problem is inherently non-convex. To address this non-convexity, semidefinite relaxation (SDR) and successive convex approximation (SCA) methods are employed. In addition, we employ an algorithm that gradually relaxes the rank-one constraint, referred to as Sequential Rank-One Constraint Relaxation (SROCR). The simulation results demonstrate the improved performance of the proposed scheme in terms of energy efficiency, compared to conventional schemes in the literature.