Energy consumption optimization in UAV-assisted multi-layer mobile edge computing with active transmissive RIS

IF 4.3 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computer Communications Pub Date : 2025-09-19 DOI:10.1016/j.comcom.2025.108320

Kexin Yang, Yaxi Liu, Boxin He, Jiahao Huo, Wei Huangfu

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引用次数: 0

Abstract

Unmanned Aerial Vehicle (UAV)-assisted edge computing provides low-latency and low-energy consumption computing capabilities for sparsely distributed Internet of Things (IoT) networks. In addition, the assisted UAVs provide line-of-sight links to further improve communication quality. However, the existing offloading strategies have low efficiency and high costs. Motivated by this, we propose a novel UAV-assisted multi-layer mobile edge computing network with active transmissive reconfigurable intelligent surface (RIS). The introduced an active transmissive RIS not only receives data from UAVs but also performs computing functionality. We establish an optimization to minimize the total system energy consumption under delay constraints by jointly planning UAV positions and allocating computing bits, sub-carriers, time slots, transmission power, and RIS transmission coefficient. To tackle this problem, we first use the block coordinate descent (BCD) algorithm to decouple it into four sub-problems. Then, we solve them by adopting successive convex approximation (SCA), difference-convex (DC) programming, and introducing slack variables. Experimental results demonstrate that the proposed network is superior to the other five baselines concerning energy consumption reduction. Also, the influences of system parameters are verified, including the number of IoT devices, the number of RIS elements, and the delay threshold.

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基于主动传输RIS的无人机辅助多层移动边缘计算能耗优化

无人机（UAV）辅助边缘计算为稀疏分布的物联网（IoT）网络提供低延迟、低能耗的计算能力。此外，辅助无人机提供视距链接，进一步提高通信质量。然而，现有的卸载策略效率低，成本高。基于此，我们提出了一种具有主动传输可重构智能表面（RIS）的新型无人机辅助多层移动边缘计算网络。主动传输RIS不仅可以接收来自无人机的数据，还可以执行计算功能。通过联合规划无人机位置、分配计算位、子载波、时隙、发射功率和RIS传输系数，建立了在时延约束下最小化系统总能耗的优化算法。为了解决这个问题，我们首先使用块坐标下降（BCD）算法将其解耦为四个子问题。然后采用逐次凸逼近（SCA）、差分凸规划（DC）和引入松弛变量等方法求解。实验结果表明，该网络在降低能耗方面优于其他5个基线。并验证了系统参数的影响，包括物联网设备的数量、RIS元素的数量和延迟阈值。

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 Communications 工程技术-电信学

CiteScore

14.10

自引率

5.00%

发文量

397

审稿时长

66 days

期刊介绍： Computer and Communications networks are key infrastructures of the information society with high socio-economic value as they contribute to the correct operations of many critical services (from healthcare to finance and transportation). Internet is the core of today''s computer-communication infrastructures. This has transformed the Internet, from a robust network for data transfer between computers, to a global, content-rich, communication and information system where contents are increasingly generated by the users, and distributed according to human social relations. Next-generation network technologies, architectures and protocols are therefore required to overcome the limitations of the legacy Internet and add new capabilities and services. The future Internet should be ubiquitous, secure, resilient, and closer to human communication paradigms. Computer Communications is a peer-reviewed international journal that publishes high-quality scientific articles (both theory and practice) and survey papers covering all aspects of future computer communication networks (on all layers, except the physical layer), with a special attention to the evolution of the Internet architecture, protocols, services, and applications.