Throughput Maximization for Underlay Cognitive Radio Networks with RF Energy Harvesting

Abstract

This paper presents a green cognitive radio networks (CRNs) framework with radio frequency (RF) energy harvesting, namely RF-powered cognitive radio networks (RF-CRNs), where secondary users (SUs) first harvests energy from the RF signals of primary users and then transmits data using the harvested energy in one slot. The total consumed energy by the SU must be equal or less than the total harvested energy referred to as energy causality constraint, while the transmission power of SU must be restricted in order to protect the primary user from interference, namely collision constraint. Finally, under the satisfaction of quality-of-service (QoS) of SU (namely throughput constraint), our goal is to determine an optimal transmitting time and power allocation that maximizes its throughput in the RF-CRNs. We achieved the optimal result by transforming optimization problem into convex optimization and then applying Lagrange multiplier methods. Extensive performance evaluations conducted show the efficiency of the proposed algorithm.