Trajectory Optimization Research of Wireless Power Communication Networks Assisted by Aerial Intelligent Reflecting Surface
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摘要: 由于无人机(UAV)良好的机动性、可靠性和快速部署等特性，无人机搭载智能反射面(IRS)可以有效解决复杂无线场景中混合接入点和节点之间由于障碍物遮挡导致信息传输和能量传输效率低的问题。该文提出一种基于时间划分的空中智能反射面辅助无线供能通信网络架构，充分利用空中智能反射面的灵活性提高网络性能。该架构针对每一个时隙，采用先收集能量后传输信息方案实现能量和数据的分时传输。在满足节点能量收集阈值的前提下，建立一个联合空中智能反射面飞行轨迹、节点选择关联变量、时隙分配比率和智能反射面相位的多变量耦合优化问题。采用块坐标下降算法把原始优化问题分解为4个子问题分别进行求解。首先根据波束对齐原理求解出智能反射面最优相位的闭式解，然后通过引入辅助变量并采用连续凸近似方法使非凸问题转变为凸问题，最后利用交替优化算法迭代求解。仿真结果表明，该文提出的联合优化方案具有很好的收敛性能并可以显著提高系统平均吞吐量。Abstract: Unmanned Aerial Vehicle (UAV) equipped with Intelligent Reflecting Surface (IRS) can effectively solve the problem of inefficient information and energy transmission between the hybrid access point and nodes in complex wireless scenarios due to obstacle occlusion. A novel framework for aerial IRS-assisted wireless powered communication networks is proposed that exploits the flexibility of aerial IRS to improve the performance of the network. The architecture achieves the transmission of energy and data for each time slot employing the harvest-then-transmit scheme. A multi-variable coupled optimization problem that combines the flight trajectory, node selection association variable, time slot allocation ratio, and the phase is established while satisfying the node energy harvesting threshold. Thus, the block coordinate descent algorithm is utilized to decompose the optimization problem into four sub-problems to be solved separately. Firstly, the closed-form solution for the optimal phase of the intelligent reflecting surface is derived based on the beam alignment theory. Secondly, the non-convex problem is transformed into a convex problem by introducing auxiliary variables and employing a successive convex approximation algorithm. Finally, the solution is iteratively solved utilizing the block coordinate descent algorithm. Simulation results show that the proposed scheme has excellent convergence performance and significantly improve the average throughput.
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1 联合优化算法
初始化$ {{\boldsymbol{U}}_0} $, $ {{\boldsymbol{\varTheta}} _0} $, $ {{{\alpha}} _0} $和$ {{{\tau}} _0} $；设置最大迭代回合$ {L_{{\mathrm{MAX}}}} $和精度$ \vartheta $； 根据初始化的$ {{\boldsymbol{U}}_0} $计算$ {{\boldsymbol{\varTheta}} _0} $； While: (1)设置迭代回合$ l = l + 1 $； (2)给定$ {{\boldsymbol{U}}_{l - 1}} $, $ {{\boldsymbol{\varTheta}} _{l - 1}} $和$ {{{\tau}} _{l - 1}} $，通过求解问题P2更新$ {\alpha _l} $； (3)给定$ {{{\alpha}} _{l - 1}} $, $ {{\boldsymbol{\varTheta}} _{l - 1}} $和$ {{{\tau }}_{l - 1}} $，通过求解问题P4更新$ {{\boldsymbol{U}}_l} $； (4)根据求解的$ {{\boldsymbol{U}}_l} $更新$ {{\boldsymbol{\varTheta}} _l} $； (5)给定$ {\alpha _l} $, $ {{\boldsymbol{U}}_l} $和$ {{\boldsymbol{\varTheta}} _l} $，通过求解P5优化$ {{{\tau}} _l} $； (6)给定$ {\alpha _l} $, $ {{\boldsymbol{U}}_l} $和$ {{\boldsymbol{\varTheta}} _l} $，计算 $ {F_l} = \dfrac{1}{N}\displaystyle\sum\limits_{n = 1}^N {\displaystyle\sum\limits_{k = 1}^K {{\alpha _k}[n]{R_k}[n]} } $； Until: $ ({F_l} - {F_{l - 1}})/{F_l} < \vartheta $或$ l \ge {L_{{\mathrm{MAX}}}} $；
结束并输出最优的$ {\boldsymbol{U}} $, ${\boldsymbol{ \varTheta}} $, $ \alpha $和$ {{\tau}} $；
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表 1 仿真参数
仿真参数 数值 仿真参数 数值 UAV飞行高度($ {H_{\mathrm{U}}} $) 10 m HAP高度($ {H_{\mathrm{B}}} $) 5 m 环境参数(a, b) 0.6, 0.11 参考信道增益($ {\beta _0} $) –0.054 6 高斯白噪声($ {\sigma ^2} $) –80 dBm UAV最大飞行速度($ {V_{\max }} $) 10 m/s 每一个时隙长度($ {\delta _t} $) 1 s 节点获取能量阈值($ {E_{{\mathrm{thr}}}} $) 5×10–3 J
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