Performance Optimization for Intelligent Reflecting Surface and Wireless Power Transfer-Aided Unmanned Aerial Vehicle Edge Computing Network
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摘要:
为解决无人机(unmanned aerial vehicle, UAV)边缘计算网络在地面无线基础设施失效时，因通信条件差、设备能量受限而导致通信质量、任务卸载性能与计算效率不佳的问题，以智能反射面(intelligent reflecting surface, IRS)和无线能量传输(wireless power transfer, WPT)技术为支撑，研究UAV边缘计算网络并提出相应优化方案. 一方面，采用 IRS 技术，通过实时调控反射信号相位，使信号指向目标方向以优化无线链路通信质量，进而增强计算任务卸载能力. 另一方面，利用 WPT 技术向无线设备供给临时能量，为设备执行计算任务处理提供能源支撑. 在该网络下，通过迭代优化的方法，对频谱资源、无线设备的CPU频率和发射功率、WPT和计算任务卸载之间的时间分配、IRS反射系数以及UAV飞行轨迹进行分析和推导，并对网络的计算速率进行优化设计. 仿真实验结果充分验证了所提方案对网络整体计算性能的有效提升. 在计算速率指标上，本文所提方案较各类UAV飞行方案提升至少34.3%，较多种任务处理机制提升至少15.3%，较不同优化方案提升至少15.9%. 上述结果表明，该方案能够显著改善UAV场景的边缘计算性能，可为UAV边缘计算网络提供高效可靠的无线设备能量保障与计算任务协同处理方案.
Abstract:To address the issues of degraded communication quality, reduced task offloading performance, and constrained computational efficiency in unmanned aerial vehicle (UAV) edge computing networks caused by poor communication conditions and limited wireless device energy when terrestrial wireless infrastructure fails, supported by intelligent reflecting surface (IRS) and wireless power transfer (WPT) technologies, UAV edge computing networks were studied, and corresponding optimization schemes were proposed. On the one hand, by adopting IRS technology, the communication quality of wireless links was optimized through real-time adjustment of the reflected signal phase to direct signals toward the target, thereby improving the task offloading capability. On the other hand, WPT technology was used to supply temporary energy to wireless devices, providing power support for them to execute computation tasks. Under this network, through an iterative optimization method, the spectrum resources, CPU frequencies and transmitting power of wireless devices, time allocation between WPT and task offloading, IRS coefficients, and UAV flying trajectory were analyzed and derived, and the network’s computation rate was optimized. The simulation results verify that the proposed scheme effectively improves the overall computing performance of the network. In terms of computation rate, the proposed scheme outperforms various UAV flying schemes by at least 34.3%, various task processing mechanisms by at least 15.3%, and different optimization schemes by at least 15.9%. The above results demonstrate that the proposed scheme can significantly improve the edge computing performance in UAV scenarios and can provide an efficient and reliable solution for wireless device energy supply and collaborative computation task processing in UAV edge computing network.
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图 1 IRS 和 WPT 辅助的 UAV 边缘计算网络模型
Figure 1. Model of IRS and WPT-aided UAV edge computing network
图 5 不同的飞行要求下优化目标的函数值
Figure 5. Values of optimization objective under different flying requirements
图 6 不同 UAV 发射功率下的总获取能量和总计算速率
Figure 6. Total harvested energy and total computation rate under different UAV transmission power
图 7 不同计算强度和频谱资源下优化目标的函数值
Figure 7. Values of optimization objective under different computation intensity and spectrum resource conditions
图 10 不同计算任务处理方案的性能比较
Figure 10. Performance comparison of different computing task processing schemes
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