Outage Energy Efficiency Maximization for UAV-Assisted Energy Harvesting Cognitive Radio Networks

Abstract

The UAV-assisted energy harvesting cognitive wireless network has the feature of fast layout, remarkable spectrum efficiency (SE) and energy efficiency (EE), thereby being envisioned as one significant cutting-edge communication technology in the 5G/6G era. In this study, we consider an UAV-assisted energy harvesting cognitive radio network (UAV-EH-CRN), where an UAV is employed as one cognitive user (CU), hovering in the air to perform spectrum sensing and communicating with the dedicated receiver (DR) on the ground. Based on the sensing results of the primary user (PU), the UAV can adaptively adjust power to transmit with the DR. Moreover, by harvesting renewable energy, the UAV can replenish energy actively for transmission. By introducing outage rate (OR) for guaranteeing the UAVs SE requirements, our goal is to maximize the outage energy efficiency (OEE) for the UAV-EH-CRN, subject to constraints of energy, transmission power, and interference power. To make the non-convex OEE maximization problem solvable, one resource allocation policy portrayed as outage related convex approximation strategy (OR-CAS) is presented. Specifically, the OEE problem is firstly converted into a tractable counterpart by the proposed OR-CAS. Then, making use of the Lagrange duality method and one-dimensional linear programming, the optimal resource allocation solution for the OEE maximization is obtained. Simulation results show that our scheme enable the UAV-EH-CRN to adapt to various on-demand outage SE scenarios by setting different OR thresholds. Moreover, our algorithm can obtain significant EE gains and less time expenditure in comparison with the traditional exhaustive search approach.