Energy efficient resource allocation in delay-aware UAV-based cognitive radio networks with energy harvesting

In this paper, we study an UAV-based cognitive radio network with energy harvesting (EH-UAV-CRN) in delay-aware scenarios. First, based on the sensing results of the primary user, the energy harvesting-enabled UAV dynamically adapts the transmission power to obtain spectrum efficiency for data transmission. In addition, the process of UAV collecting renewable energy is described by compound Poisson distribution approximately. Our goal is to obtain the maximum energy efficiency (EE) of EH-UAV-CRN under the constraints of average energy, multiple minimum rate outage probability (MMROP), average transmission power, and average interference power. As the EE maximization is a non-tractable optimal problem with multiple complex constraints, one so-called delay-related approximate optimization strategy (De-AOS) is proposed. Specifically, the primitive optimal problem is firstly transformed into a tractable counterpart by the proposed De-AOS. Moreover, the optimal resource allocation solution is obtained by taking advantage of the Lagrange duality method and one-dimensional linear programming. Simulation results demonstrate that our strategy can be used flexibly in various delay-related scenarios by setting different MMROP thresholds. Moreover, compared with the traditional quality of service-based minimum rate constraint scheme, our algorithm can obtain significant EE gains under the same constraints of energy, interference and transmission power. © 2021 Elsevier Ltd