具有QoS保证的RF能量采集认知无线网络高能效资源分配技术

考虑了一种基于射频能量采集的认知无线网络系统。其中，次用户发射机（ST，secondary transmitter）首先从主用户（PU，primary user）发射的射频信号中收集能量，然后利用所收集能量与次用户通信。此外，ST保留有可能来自之前传输块的剩余能量作为初始能量。目标是通过传输时间和发射功率联合优化，达到次用户网络能量效率最大化。为保证次用户网络服务质量（QoS，quality of service），在能量效率最大化过程中对ST施加最小吞吐量需求约束。由于能量效率最大化是非线性分数规划问题，提出了一种基于Dinkelbach方法的快速迭代算法来实现资源的最优分配。仿真结果表明，该算法收敛速度快，可以在保证QoS约束的同时显著提高系统的能量效率。

Energy-efficient resource allocation technique of RF energy harvesting-based cognitive radio network with quality of service assurance

In this paper a research is carried out into an RF energy harvesting-based cognitive radio network (RF EH-CRN), aimming at the maximization of energy efficiency of secondary user networks by jointly optimizing transmission time and transmission power. The secondary transmitter (ST) first harvests energy from the radio frequency (RF) signals of primary user (PU) and then communicates with SU. Besides, ST maintains possible remaining energy from previous transmission blocks as initial energy. To ensure the quality of service (QoS) of secondary network, we impose a minimum throughput requirement constraint on ST in the process of energy offciency (EE) maximization. As EE maximization is a nonlinear fractional programming problem, we propose a fast iteration algorithm based on Dinkelbach method to achieve optimal resource allocation. Simulation results demonstrate that with fast convergence speed this algorithm can significantly improve the EE of the system while guaranteeing QoS constraints.