Wireless-Powered Machine-to-Machine Multicasting in Cellular Networks

Abstract

In future cellular networks, it is expected that data traffic will increase significantly due to deployments of large numbers of Internet of Things (IoT) objects. The IoT objects operate underlaying a cellular network, and they use Machine-to-Machine (M2M) communication to transmit multicst messages. We propose to use Radio Frequency (RF) Energy Transmitters (ET) to compensate the IoT objects with the energy consumed in forwarding multicast messages. Our goal is to support multicast service for IoT objects and transmit energy to them such that the total transferred energy by the ETs is minimized. We formulated the problem mathematically as a non-convex Mixed Integer Nonlinear Program (MINLP). Due to the difficulty of solving the problem optimally, we decompose the original problem into two sub-problems using Generalized Bender Decomposition with Successive Convex programming (GBD-SCP). Although this method facilitates finding a solution for the problem, the problem is still hard due to binary variables. Hence, we propose the Constraints Decomposition with SCP and Binary Variable Relaxation (CDR) algorithm to solve the problem more efficiently. Simulation results show that the proposed algorithm achieves a performance close to the GBD-SCP algorithm while the computation time is reduced significantly when the network size is larger.