[论文解读] Throughput Maximization for Wireless Communication systems with Backscatter- and Cache-assisted UAV Technology

Dinh Hieu Tran, Sumit Gautam+2 more|arXiv (Cornell University)|Nov 16, 2020

Energy Harvesting in Wireless Networks参考文献 44被引用 12

一句话总结

本文提出了一种背向散射与缓存协同的无人机（UB）系统，通过联合优化时间分割比例、背向散射系数和无人机轨迹，在线性与非线性能量采集模型下最大化无线网络吞吐量。作者采用KKT条件与连续凸逼近技术，设计了一种高效的交替算法，在基准方案上实现了显著的吞吐量增益。

ABSTRACT

Unmanned aerial vehicle (UAV) has been widely adopted in wireless systems due to its flexibility, mobility, and agility. Nevertheless, a limited onboard battery greatly hinders UAV to prolong the serving time from communication tasks that need a high power consumption in active RF communications. Fortunately, caching and backscatter communication (BackCom) are appealing technology for energy efficient communication systems. This motivates us to investigate a wireless communication network with backscatter- and cache-assisted UAV technology. We assume a UAV with a cache memory is deployed as a flying backscatter device (BD), term the UAV-enabled BD (UB), to relay the source's signals to the destination. Besides, the UAV can harvest energy from the source's RF signals and then utilizes it for backscattering information to the destination. In this context, we aim to maximize the total throughput by jointly optimizing the dynamic time splitting (DTS) ratio, backscatter coefficient, and the UB's trajectory with caching capability at the UB corresponding to linear energy harvesting (LEH) and non-linear energy harvesting (NLEH) models. These formulations are troublesome to directly solve since they are mixed-integer non-convex problems. To find solutions, we decompose the original problem into three subproblems, whereas we first optimize the DTS ratio for a given backscatter coefficient and UB's trajectory, followed by the backscatter coefficient optimization for a given DTS ratio and UB's trajectory, and the UB's trajectory is finally optimized for a given DTS ratio and backscatter coefficient. Finally, the intensive numerical results demonstrate that our proposed schemes achieve significant throughput gain in comparison to the benchmark schemes.

研究动机与目标

• 解决高功率主动射频通信系统中无人机电池寿命有限的问题。
• 通过在无人机中集成背向散射通信与缓存技术，提升频谱与能量效率。
• 在实际能量采集约束下，通过动态时间分割与轨迹优化，实现系统吞吐量最大化。
• 为无人机辅助网络中的非凸混合整数优化问题开发低复杂度算法。

提出的方法

• 针对线性与非线性能量采集（LEH与NLEH）模型，构建了用于吞吐量最大化的混合整数非凸优化问题。
• 将问题分解为三个子问题：时间分割比例、背向散射系数与无人机轨迹优化，并以交替方式求解。
• 利用Karush-Kuhn-Tucker（KKT）条件，推导出时间分割比例与背向散射系数的闭式最优解。
• 采用连续凸逼近（SCA）技术求解非凸的无人机轨迹子问题。
• 应用块坐标下降（BCD）算法，以协调方式迭代优化三个变量。
• 在真实信道与能量约束下，对线性与非线性能量采集模型均验证了该方法的有效性。

实验结果

研究问题

• RQ1 在高功率射频传输的无线通信系统中，如何提升无人机的能量效率？
• RQ2 如何实现时间分割、背向散射系数与无人机轨迹的最优联合设计，以最大化系统吞吐量？
• RQ3 线性与非线性能量采集模型对背向散射辅助无人机网络性能有何影响？
• RQ4 能否为关键系统参数推导出闭式解，以降低计算复杂度？
• RQ5 在无人机网络中集成缓存与背向散射通信可实现多大的吞吐量增益？

主要发现

• 所提算法在线性与非线性能量采集场景下，均显著优于基准方案，实现吞吐量增益。
• 推导出最优时间分割比例与背向散射系数的闭式表达式，显著降低计算时间。
• 连续凸逼近（SCA）技术可有效实现非凸约束下的轨迹优化。
• 时间分割、背向散射系数与无人机轨迹的联合优化，显著提升了频谱与能量效率。
• 与线性模型相比，非线性能量采集模型下系统性能明显提升。
• 仿真结果验证了所提交替算法在实际无人机部署场景中的收敛性与有效性。