In order to extend the communication coverage and improve system performance, the applications of unmanned aerial vehicles (UAVs) in wireless communications have attracted a lot of attention in the industry. In this paper, we propose a power control algorithm in energy harvesting (EH)-based cognitive mobile relay networks where an UAV is equipped with a decode-and-forward (DF) relay to cooperate the communication of secondary user (SU). Assuming that the only power source for SU transmitter with EH is a battery with infinite capacity, we solve a throughput maximization problem to optimize the transmit powers of SU and the mobile relay, subject to the causality constraint of energy usage at SU transmitter, the maximum transmit power constraint of the mobile relay, and the interference temperature (IT) constraint to protect the communication of primary user (PU). When formulating this throughput maximization problem, we adopt an offline scheme with deterministic settings. For simplicity, the original multi-variable optimization problem is transformed into a single variable optimization problem via the optimal throughput principle of the DF relaying communication system. Furthermore, we solve this new optimization problem via the Lagrange dual method, and we derive the closed-form expressions of the optimal solutions. The simulation results illustrate the optimized system performance that the optimal throughput of the secondary system can be achieved by the proposed dynamic power control algorithm.

中文翻译：

---

无人机辅助认知移动中继网络中通过能量收集实现吞吐量最大化


为了扩大通信覆盖范围、提高系统性能，无人机在无线通信方面的应用引起了业界的广泛关注。在本文中，我们提出了一种基于能量收集（EH）的认知移动中继网络的功率控制算法，其中无人机配备了解码转发（DF）中继来配合次级用户（SU）的通信。假设具有 EH 的 SU 发射机的唯一电源是无限容量的电池，我们解决吞吐量最大化问题来优化 SU 和移动中继的发射功率，受到 SU 发射机能量使用的因果关系约束，最大移动中继的发射功率约束，以及保护主用户（PU）通信的干扰温度（IT）约束。在制定这个吞吐量最大化问题时，我们采用具有确定性设置的离线方案。为了简单起见，利用DF中继通信系统的最优吞吐量原理，将原来的多变量优化问题转化为单变量优化问题。此外，我们通过拉格朗日对偶方法解决了这个新的优化问题，并导出了最优解的封闭式表达式。仿真结果说明了优化的系统性能，即通过所提出的动态功率控制算法可以实现二次系统的最佳吞吐量。