UAV-mounted IRS assisted wireless powered mobile edge computing systems: Joint beamforming design, resource allocation and position optimization

IF 4.4 2区计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

Computer Networks Pub Date : 2024-10-09 DOI:10.1016/j.comnet.2024.110846

Majid Hadi, Reza Ghazizadeh

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引用次数: 0

Abstract

Intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have been recently used in wireless-powered mobile edge computing (MEC) systems to enhance the computation bits and energy harvesting performance. However, in the conventional IRS- and UAV-aided MEC systems, the IRS is installed at fixed locations on a building, which restricts the computation performance. UAV-mounted IRS (UAV-IRS), as a promising technology, combines the advantages of UAV and IRS. Hence, in this work, we study a UAV-IRS wireless-powered MEC system, where multiple UAV-IRSs are considered between Internet of Things (IoT) devices and the base station to improve the computation bits and energy harvesting. The multi-antenna base station first charges the IoT devices via radio frequency signals, and then IoT devices offload their computation tasks to the base station via UAV-IRSs. We formulate a computation bits maximization problem for all IoT devices by jointly determining detection beamforming at IoT devices, active energy beamforming at the base station, power allocation, time slot assignment, CPU frequency, the phase shifts design in the wireless energy transfer (WET) and task offloading, and UAV-IRSs positions. A block coordinate descent (BCD) algorithm by decomposing the introduced problem into four blocks is proposed, while the detection beamforming, active energy beamforming, transmit power, time slot assignment, CPU frequency, and the phase shifts design in the task offloading are derived in closed-form results. Also, the successive convex approximation and semidefinite relaxation (SDR) are adopted to obtain the UAV-IRS positions and the phase shifts in the WET, respectively. The simulation results verify the effectiveness of the presented BCD method compared with the different benchmark schemes.

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无人机安装的 IRS 辅助无线供电移动边缘计算系统：联合波束成形设计、资源分配和位置优化

智能反射面（IRS）和无人飞行器（UAV）最近被用于无线供电的移动边缘计算（MEC）系统，以提高计算位数和能量收集性能。然而，在传统的 IRS 和无人机辅助 MEC 系统中，IRS 安装在建筑物上的固定位置，这限制了计算性能。无人机安装的 IRS（UAV-IRS）是一项很有前途的技术，它结合了无人机和 IRS 的优点。因此，在这项工作中，我们研究了一种 UAV-IRS 无线供电 MEC 系统，其中考虑在物联网（IoT）设备和基站之间安装多个 UAV-IRS，以提高计算位数和能量收集。多天线基站首先通过射频信号为物联网设备充电，然后物联网设备通过无人机-IRS将其计算任务卸载到基站。我们通过共同确定物联网设备的检测波束成形、基站的主动能量波束成形、功率分配、时隙分配、CPU 频率、无线能量传输（WET）和任务卸载中的相移设计以及 UAV-IRS 的位置，为所有物联网设备提出了一个计算位最大化问题。提出了一种块坐标下降（BCD）算法，将引入的问题分解为四个块，并以闭式结果推导出检测波束成形、主动能量波束成形、发射功率、时隙分配、CPU 频率和任务卸载中的相移设计。此外，还采用了连续凸近似和半定量松弛（SDR）方法，分别求出了无人机-红外系统在 WET 中的位置和相移。仿真结果验证了所提出的 BCD 方法与不同基准方案相比的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computer Networks 工程技术-电信学

CiteScore

10.80

自引率

3.60%

发文量

434

审稿时长

8.6 months

期刊介绍： Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.