Double Reconfigurable Intelligent Surface-aided Green Internet of Things Edge Computing for Research on Computation Capacity

Chen Yan-Long; Zeng Xiang; Li Yu-Long; Wang Feng

doi:10.12052/gdutxb.230024

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Double Reconfigurable Intelligent Surface-aided Green Internet of Things Edge Computing for Research on Computation Capacity

更新时间：2026-05-27

- Double Reconfigurable Intelligent Surface-aided Green Internet of Things Edge Computing for Research on Computation Capacity
- Vol. 41, Issue 3, Pages: 110-118(2024)
- 作者机构：
  
  广东工业大学信息工程学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.230024
  CLC： F224.32
- Published：2024
- 稿件说明：
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陈彦龙, 曾祥, 李宇龙, 王丰. 双智能反射面辅助的绿色物联网边缘计算吞吐量研究[J]. 广东工业大学学报, 2024, 41(3): 110-118.

Chen Yan-Long, Zeng Xiang, Li Yu-Long, et al. Double Reconfigurable Intelligent Surface-aided Green Internet of Things Edge Computing for Research on Computation Capacity[J]. 2024, 41(3): 110-118.
陈彦龙, 曾祥, 李宇龙, 王丰. 双智能反射面辅助的绿色物联网边缘计算吞吐量研究[J]. 广东工业大学学报, 2024, 41(3): 110-118. DOI： 10.12052/gdutxb.230024.

Chen Yan-Long, Zeng Xiang, Li Yu-Long, et al. Double Reconfigurable Intelligent Surface-aided Green Internet of Things Edge Computing for Research on Computation Capacity[J]. 2024, 41(3): 110-118. DOI： 10.12052/gdutxb.230024.

摘要

为解决计算密集型应用的终端物联网用户设备微电池能量难题，研究绿色可再生能量收集技术和双重构智能反射面技术赋能边缘计算，构建双智能反射面辅助的绿色物联网边缘计算系统，有效延长终端物联网用户设备计算寿命，提高系统计算吞吐量。首先，建立双智能反射面辅助的多用户级联衰落信道模型，建立绿色可再生能量收集的多时隙随机到达模型，建模物联网终端设备的能量供需因果约束条件。其次，以系统计算吞吐量最大化为准则，建模终端设备本地计算速率、边缘计算卸载功率、智能反射面相位的联合优化设计问题；该设计问题隶属一类复杂的非凸优化问题。为此，采用轻量级的多阶段优化技术，快速迭代设计本地计算、计算卸载、智能反射面相移等变量，完成绿色物联网边缘计算系统设计。实验结果表明，在较少的系统计算时间下，本文所提方案与基于半定松弛算法的性能增益相当，且优于已有的基准方案。

Abstract

In order to solve the micro battery energy problem of terminal Internet of Things user devices in computation-intensive applications

green renewable energy harvesting technology and double reconfigurable intelligent surfaces(RIS) technology enabling edge computing were studied and a green Internet of Things edge computing system assisted by double-RIS was constructed

effectively extending the computing life of terminal Internet of Things user devices and improving system computation capacity. Firstly

a multi-user cascade fading channel model assisted by double-RIS was established

and a multi-slot random arrival model of green renewable energy harvesting was established to model the causal constraints of energy supply and demand of Internet of Things terminal devices. Secondly

based on the maximization of system computation capacity

the joint optimization design problem of terminal local computing rate

edge computing offloading power and phase shift of RISs was modeled. This design problem belongs to a class of complex non-convex optimization problem. To this end

lightweight multi-stage optimization technology was adopted to rapidly and iteratively design variables such as local computation

computation offloading and phase shift of RISs

etc

to complete the design of green Internet of Things edge computing system. The experimental results show that the performance gains of the proposed scheme are better than the existing benchmark schemes

and the proposed scheme is equivalent to the scheme based on semidefinite relaxation algorithm under less system computing time.

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references

WANG F, XING H, J XU. Real-time resource allocation for wireless powered multiuser mobile edge computing with energy and task causality [J]. IEEE Transactions on Communications, 2020, 68(11): 7140-7155.

WANG F, XU J, CUI S. Optimal energy allocation and task offloading policy for wireless powered mobile edge computing systems [J]. IEEE Transactions on Wireless Communications, 2020, 19(4): 2443-2459.

LIN Z, WANG F, LIU L. Computation rate maximization for multiuser mobile edge computing systems with dynamic energy arrivals[C]// 2021 IEEE/CIC International Conference on Communications in China (ICCC) , Xiamen: IEEE, 2021: 312-317.

XU Y, ZHANG T, LIU Y et al . Computation capacity enhancement by joint UAV and RIS design in IoT [J ] . IEEE Internet of Things Journal, 20 22, 9(20): 20590-20603.

YANG Y, HU Y, GURSOY M C. Energy efficiency analysis in RIS-aided MEC networks with finite blocklength codes[C]//2022 IEEE Wireless Communications and Networking Conference (WCNC) . Austin: IEEE, 2022: 423-428.

WU Q, ZHANG R. Intelligent reflecting surface enhanced wireless network: joint active and passive beamforming design[C]//2018 IEEE Global Communications Conference (GLOBECOM) . A-bu Dhabi: IEEE, 2018: 1-6.

CHEN G, WU Q. Computation rate maximization for IRS-aided wireless powered MEC systems [C]//2022 IEEE Wireless Communications and Networking Conference (WCNC) . Austin: IEEE, 2022: 417-422.

LI A, LIU Y, LI M. Joint scheduling design in wireless powered MEC IoT networks aided by reconfigurable intelligent surface[C]//2021 IEEE/CIC International Conference on Communications in China (ICCC Workshops) . Xiamen: IEEE, 2021: 159-164.

ZHANG D, CHEN Z, AWAD M K. Utility- optimal resource management and allocation algorithm for energy harvesting cognitive radio sensor networks [J]. IEEE Journal on Selected Areas in Communications, 2016, 34(12): 3552-3565.

LI H, XIONG K, DONG R, et al . Joint active and passive beamforming in IRS-enhanced wireless powered MEC networks [J ] . IEEE Wireless Communications Letters, 2022, 11(11): 2285-2289.

ARDAH K, GHEREKHLOO S, DEALMEIDA A L F, et al. Double-RIS versus single-RIS aided systems: tensor-based mimo channel estimation and design perspectives[C]// 2022 IEEE International Conference. Singapore: IEEE, 2022: 5183-5187.

GUO S, HOU Y, MAO J, et al. Double RIS-based hybrid beamforming design for MU-MISO mmWave communication systems[C]// 2022 IEEE/CIC International Conference on Communications. Foshan, China: IEEE, 2022: 220-225.

XIE W, LI B, XIONG Y, et al . Energy efficient collaborative computation for double-RIS assisted mobile edge networks [J ] . Physical Communication, 2022, 53: 101774.

LI B, LIU W S, XIE W C, et al . Partial computation offloading for double-RIS assisted multiuser mobile edge computing networks [J ] . Journal of Electronics Information Technology, 2022, 44(7): 2309-2316.

LONG W Y. Robust millimeter-wave wireless beamforming design assited by double Interlligent reflecting surfaces based on AO-CCP method [J]. Radio Communications Technology, 2022, 48(2): 276-283.

CHEN J F, CUI M, ZHANG G C, et al . Secure communication optimization for double-IRS assisted SWIPT system [J ] . Telecommunications Science, 2022, 38(1): 47-60.

LIU M, LI X, NING B, et al . Deep learning-based channel estimation for double-RIS aided massive MIMO system [J ] . IEEE Wireless Communications Letters, 2023, 12(1): 70-74.

LI Y, LI C, XU Z H. Channel estimation algorithm design for intelligent reflecting surface assisted wireless communication system [J]. Modern Information Technology, 2023, 7(1): 68-71.

SUDEVALAYAM S, KULKAMNI P. Energy harvesting sensor nodes: survey and implications [J]. IEEE Communications Surveys Tutorials, 2011, 13(3): 443-461.

NING B Y, CHEN Z, CHEN W J, et al . Beam-forming optimization for intelligent reflecting surface assisted MIMO: a Sum-Path-Gain maximization approach [J ] . IEEE Wireless Communications Letters, 2020, 9(7): 1105-1109.

XIE W C, LI B, DAI Y Y. PPO base task offloading scheme in aerial reconfigurable intelligent surface assited edge computing [J]. Computer Science, 2022, 49(6): 3-11.

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