Minimization of Energy Consumption in TDMA-Based Wireless-Powered Multi-Access Edge Computing Networks

Xi CHEN; Guodong JIANG; Kaikai CHI; Shubin ZHANG; Gang CHEN; Jiang LIU

doi:10.1587/transfun.2023EAP1035

IEICE TRANSACTIONS on Fundamentals

Minimization of Energy Consumption in TDMA-Based Wireless-Powered Multi-Access Edge Computing Networks

Xi CHEN, Guodong JIANG, Kaikai CHI, Shubin ZHANG, Gang CHEN, Jiang LIU

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Many nodes in Internet of Things (IoT) rely on batteries for power. Additionally, the demand for executing compute-intensive and latency-sensitive tasks is increasing for IoT nodes. In some practical scenarios, the computation tasks of WDs have the non-separable characteristic, that is, binary offloading strategies should be used. In this paper, we focus on the design of an efficient binary offloading algorithm that minimizes system energy consumption (EC) for TDMA-based wireless-powered multi-access edge computing networks, where WDs either compute tasks locally or offload them to hybrid access points (H-APs). We formulate the EC minimization problem which is a non-convex problem and decompose it into a master problem optimizing binary offloading decision and a subproblem optimizing WPT duration and task offloading transmission durations. For the master problem, a DRL based method is applied to obtain the near-optimal offloading decision. For the subproblem, we firstly consider the scenario where the nodes do not have completion time constraints and obtain the optimal analytical solution. Then we consider the scenario with the constraints. By jointly using the Golden Section Method and bisection method, the optimal solution can be obtained due to the convexity of the constraint function. Simulation results show that the proposed offloading algorithm based on DRL can achieve the near-minimal EC.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E106-A No.12 pp.1544-1554

Publication Date: 2023/12/01

Publicized: 2023/06/19

Online ISSN: 1745-1337

DOI: 10.1587/transfun.2023EAP1035

Type of Manuscript: PAPER

Category: Communication Theory and Signals

Authors

Xi CHEN
  Zhejiang University of Technology
Guodong JIANG
  Zhejiang University of Technology
Kaikai CHI
  Zhejiang University of Technology
Shubin ZHANG
  Zhejiang University of Technology
Gang CHEN
  Zhejiang Institute of Mechanical and Electrical Engineering
Jiang LIU
  Waseda University

Keyword

deep reinforcement learning, wireless power transfer, mobile edge computing, time division multiple access

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Xi CHEN, Guodong JIANG, Kaikai CHI, Shubin ZHANG, Gang CHEN, Jiang LIU, "Minimization of Energy Consumption in TDMA-Based Wireless-Powered Multi-Access Edge Computing Networks" in IEICE TRANSACTIONS on Fundamentals, vol. E106-A, no. 12, pp. 1544-1554, December 2023, doi: 10.1587/transfun.2023EAP1035.
Abstract: Many nodes in Internet of Things (IoT) rely on batteries for power. Additionally, the demand for executing compute-intensive and latency-sensitive tasks is increasing for IoT nodes. In some practical scenarios, the computation tasks of WDs have the non-separable characteristic, that is, binary offloading strategies should be used. In this paper, we focus on the design of an efficient binary offloading algorithm that minimizes system energy consumption (EC) for TDMA-based wireless-powered multi-access edge computing networks, where WDs either compute tasks locally or offload them to hybrid access points (H-APs). We formulate the EC minimization problem which is a non-convex problem and decompose it into a master problem optimizing binary offloading decision and a subproblem optimizing WPT duration and task offloading transmission durations. For the master problem, a DRL based method is applied to obtain the near-optimal offloading decision. For the subproblem, we firstly consider the scenario where the nodes do not have completion time constraints and obtain the optimal analytical solution. Then we consider the scenario with the constraints. By jointly using the Golden Section Method and bisection method, the optimal solution can be obtained due to the convexity of the constraint function. Simulation results show that the proposed offloading algorithm based on DRL can achieve the near-minimal EC.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2023EAP1035/_p

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@ARTICLE{e106-a_12_1544,
author={Xi CHEN, Guodong JIANG, Kaikai CHI, Shubin ZHANG, Gang CHEN, Jiang LIU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Minimization of Energy Consumption in TDMA-Based Wireless-Powered Multi-Access Edge Computing Networks},
year={2023},
volume={E106-A},
number={12},
pages={1544-1554},
abstract={Many nodes in Internet of Things (IoT) rely on batteries for power. Additionally, the demand for executing compute-intensive and latency-sensitive tasks is increasing for IoT nodes. In some practical scenarios, the computation tasks of WDs have the non-separable characteristic, that is, binary offloading strategies should be used. In this paper, we focus on the design of an efficient binary offloading algorithm that minimizes system energy consumption (EC) for TDMA-based wireless-powered multi-access edge computing networks, where WDs either compute tasks locally or offload them to hybrid access points (H-APs). We formulate the EC minimization problem which is a non-convex problem and decompose it into a master problem optimizing binary offloading decision and a subproblem optimizing WPT duration and task offloading transmission durations. For the master problem, a DRL based method is applied to obtain the near-optimal offloading decision. For the subproblem, we firstly consider the scenario where the nodes do not have completion time constraints and obtain the optimal analytical solution. Then we consider the scenario with the constraints. By jointly using the Golden Section Method and bisection method, the optimal solution can be obtained due to the convexity of the constraint function. Simulation results show that the proposed offloading algorithm based on DRL can achieve the near-minimal EC.},
keywords={},
doi={10.1587/transfun.2023EAP1035},
ISSN={1745-1337},
month={December},}

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TY - JOUR
TI - Minimization of Energy Consumption in TDMA-Based Wireless-Powered Multi-Access Edge Computing Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1544
EP - 1554
AU - Xi CHEN
AU - Guodong JIANG
AU - Kaikai CHI
AU - Shubin ZHANG
AU - Gang CHEN
AU - Jiang LIU
PY - 2023
DO - 10.1587/transfun.2023EAP1035
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E106-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2023
AB - Many nodes in Internet of Things (IoT) rely on batteries for power. Additionally, the demand for executing compute-intensive and latency-sensitive tasks is increasing for IoT nodes. In some practical scenarios, the computation tasks of WDs have the non-separable characteristic, that is, binary offloading strategies should be used. In this paper, we focus on the design of an efficient binary offloading algorithm that minimizes system energy consumption (EC) for TDMA-based wireless-powered multi-access edge computing networks, where WDs either compute tasks locally or offload them to hybrid access points (H-APs). We formulate the EC minimization problem which is a non-convex problem and decompose it into a master problem optimizing binary offloading decision and a subproblem optimizing WPT duration and task offloading transmission durations. For the master problem, a DRL based method is applied to obtain the near-optimal offloading decision. For the subproblem, we firstly consider the scenario where the nodes do not have completion time constraints and obtain the optimal analytical solution. Then we consider the scenario with the constraints. By jointly using the Golden Section Method and bisection method, the optimal solution can be obtained due to the convexity of the constraint function. Simulation results show that the proposed offloading algorithm based on DRL can achieve the near-minimal EC.
ER -

IEICE TRANSACTIONS on Fundamentals