基于射频能量采集的Underlay CRN的能效优化

提出一种基于射频(RF)能量采集的认知无线电网络(CRN)架构。次用户(SU)消耗的总能量必须等于或小于采集的总能量，以保护主用户(PU)不受干扰。在满足次用户的服务质量前提下，确定在射频能量采集认知无线网络中最大化能效的最优传输时间和功率分配。在能效最大化过程中，引入吞吐量约束，找到服务质量和能源消耗之间的平衡。能效优化是一个非线性分式规划问题，使用坐标上升将其分成2个子问题，即给定传输时间下的功率分配与给定功率分配下的传输时间选择，然后使用Charnes-Cooper变形方法将非凸问题转化为一个等价凹问题。仿真结果表明，该方案能够实现有效的能效优化。

Energy efficiency optimization for Underlay Cognitive Radio Networks with RF energy harvesting

A Radio Frequency(RF) energy harvesting based Cognitive Radio Network(CRN) is proposed, where a Secondary User(SU) first harvests energy from the RF signals of Primary User(PU) and then transmits data using the harvested energy in one slot. The total consumed energy by the SU must be no more than the total harvested energy, in order to protect the PU from interference. Under the satisfaction of Quality-of-Service(QoS) of SU, the goal is to determine the optimal transmitting time and power allocation that maximizes its Energy Efficiency(EE) in the RF Energy Harvesting CRN(EH-CRN). In the process of maximizing energy efficiency, a balance is found between QoS and energy consumption. Less energy consumption allows the cognitive energy harvesting system to run more steadily and continuously, which is important while throughput constraint ensures the QoS of the system. To solve EE optimization as a nonlinear fractional optimization problem, it is firstly decomposed into two sub-problems by using Coordinate Ascendant, and then the nonconvex problem is transformed into an equivalent concave problem by using Charnes-Cooper Transformation method. Simulation results show the proposed scheme achieves effective EE.