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Deep reinforcement learning online offloading for SWIPT multiple access edge computing network

More computation-intensive and low latency applications are emerging recently, and they are constrained by the computing power and battery life of internet of things (IoT). Simultaneous wireless information and power transfer (SWIPT) with mobile-edge computing (MEC) can improve the data processing c...

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Main Authors: Teckchai, Tiong, Ismail Saad, Tze, Kenneth Kin Teo, Herwansyah Lago
Format: Proceedings
Language:English
English
Published: Institute of Electrical and Electronics Engineers 2021
Subjects:
QA76.75-76.765 Computer software
Online Access:https://eprints.ums.edu.my/id/eprint/32718/1/Deep%20reinforcement%20learning%20online%20offloading%20for%20SWIPT%20multiple%20access%20edge%20computing%20network.ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/32718/2/Deep%20Reinforcement%20Learning%20Online%20Offloading%20for%20SWIPT%20Multiple%20Access%20Edge%20Computing%20Network.pdf
https://eprints.ums.edu.my/id/eprint/32718/
https://ieeexplore.ieee.org/document/9612551
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spelling my.ums.eprints.327182022-06-08T01:28:18Z https://eprints.ums.edu.my/id/eprint/32718/ Deep reinforcement learning online offloading for SWIPT multiple access edge computing network Teckchai, Tiong Ismail Saad Tze, Kenneth Kin Teo Herwansyah Lago QA76.75-76.765 Computer software More computation-intensive and low latency applications are emerging recently, and they are constrained by the computing power and battery life of internet of things (IoT). Simultaneous wireless information and power transfer (SWIPT) with mobile-edge computing (MEC) can improve the data processing capability of energy constrained networks. In this paper, a SWIPT-based MEC system is proposed, comprising a multi-antenna access point (AP), multiple single antenna low power IoT devices and a MEC server. The IoT devices exploit the harvested energy for either locally computing or offloading the tasks to the MEC server. Conventional numerical optimization methods are not able to solve combinatorial problems within the limit of the wireless channel coherence time. Thus, Online Offloading with Deep Reinforcement learning (OODRL) is proposed. The proposed algorithm jointly optimizes the offloading decisions, the time slots devoted to energy harvesting (EH), and local computation/offloading to maximize the MEC computation rate. Deep Q network (DQN) is used to learn the binary offloading decisions from the learning experience. This method no longer needs to solve combinatorial problems. Simulation results are presented to demonstrate that the proposed algorithm is able to approach near-optimal performance and superior in decreasing tasks computation time compared with existing optimization methods, enabling real time optimal resource allocation and offloading achievable in a fast-fading wireless environment. Institute of Electrical and Electronics Engineers 2021-11-30 Proceedings PeerReviewed text en https://eprints.ums.edu.my/id/eprint/32718/1/Deep%20reinforcement%20learning%20online%20offloading%20for%20SWIPT%20multiple%20access%20edge%20computing%20network.ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/32718/2/Deep%20Reinforcement%20Learning%20Online%20Offloading%20for%20SWIPT%20Multiple%20Access%20Edge%20Computing%20Network.pdf Teckchai, Tiong and Ismail Saad and Tze, Kenneth Kin Teo and Herwansyah Lago (2021) Deep reinforcement learning online offloading for SWIPT multiple access edge computing network. https://ieeexplore.ieee.org/document/9612551
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic QA76.75-76.765 Computer software
spellingShingle QA76.75-76.765 Computer software
Teckchai, Tiong
Ismail Saad
Tze, Kenneth Kin Teo
Herwansyah Lago
Deep reinforcement learning online offloading for SWIPT multiple access edge computing network
description More computation-intensive and low latency applications are emerging recently, and they are constrained by the computing power and battery life of internet of things (IoT). Simultaneous wireless information and power transfer (SWIPT) with mobile-edge computing (MEC) can improve the data processing capability of energy constrained networks. In this paper, a SWIPT-based MEC system is proposed, comprising a multi-antenna access point (AP), multiple single antenna low power IoT devices and a MEC server. The IoT devices exploit the harvested energy for either locally computing or offloading the tasks to the MEC server. Conventional numerical optimization methods are not able to solve combinatorial problems within the limit of the wireless channel coherence time. Thus, Online Offloading with Deep Reinforcement learning (OODRL) is proposed. The proposed algorithm jointly optimizes the offloading decisions, the time slots devoted to energy harvesting (EH), and local computation/offloading to maximize the MEC computation rate. Deep Q network (DQN) is used to learn the binary offloading decisions from the learning experience. This method no longer needs to solve combinatorial problems. Simulation results are presented to demonstrate that the proposed algorithm is able to approach near-optimal performance and superior in decreasing tasks computation time compared with existing optimization methods, enabling real time optimal resource allocation and offloading achievable in a fast-fading wireless environment.
format Proceedings
author Teckchai, Tiong
Ismail Saad
Tze, Kenneth Kin Teo
Herwansyah Lago
author_facet Teckchai, Tiong
Ismail Saad
Tze, Kenneth Kin Teo
Herwansyah Lago
author_sort Teckchai, Tiong
title Deep reinforcement learning online offloading for SWIPT multiple access edge computing network
title_short Deep reinforcement learning online offloading for SWIPT multiple access edge computing network
title_full Deep reinforcement learning online offloading for SWIPT multiple access edge computing network
title_fullStr Deep reinforcement learning online offloading for SWIPT multiple access edge computing network
title_full_unstemmed Deep reinforcement learning online offloading for SWIPT multiple access edge computing network
title_sort deep reinforcement learning online offloading for swipt multiple access edge computing network
publisher Institute of Electrical and Electronics Engineers
publishDate 2021
url https://eprints.ums.edu.my/id/eprint/32718/1/Deep%20reinforcement%20learning%20online%20offloading%20for%20SWIPT%20multiple%20access%20edge%20computing%20network.ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/32718/2/Deep%20Reinforcement%20Learning%20Online%20Offloading%20for%20SWIPT%20Multiple%20Access%20Edge%20Computing%20Network.pdf
https://eprints.ums.edu.my/id/eprint/32718/
https://ieeexplore.ieee.org/document/9612551
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score 13.211869

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