Prioritized Uplink Resource Allocation in Smart Grid Backscatter Communication Networks via Deep Reinforcement Learning

Prioritized Uplink Resource Allocation in Smart Grid Backscatter Communication Networks via Deep Reinforcement Learning

With the rapid increase in the number of wireless sensor terminals in smart grids, backscattering has become a very promising green technology. By means of backscattering, wireless sensors can either reflect energy signals in the environment to exchange information with each other or capture the ene...

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Main Authors: Zhixiang Yang, Lei Feng, Zhengwei Chang, Jizhao Lu, Rongke Liu, Michel Kadoch, Mohamed Cheriet
Format: Article
Language:English
Published: MDPI AG 2020-04-01
Series:Electronics
Subjects:
backscatter
deep reinforcement learning
A3C
priority strategy
resource allocation
K-means clustering
Online Access:https://www.mdpi.com/2079-9292/9/4/622
id doaj-66af2103d5784b7d88c929d7858e80cd
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spelling doaj-66af2103d5784b7d88c929d7858e80cd2020-11-25T02:21:57ZengMDPI AGElectronics2079-92922020-04-01962262210.3390/electronics9040622Prioritized Uplink Resource Allocation in Smart Grid Backscatter Communication Networks via Deep Reinforcement LearningZhixiang Yang0Lei Feng1Zhengwei Chang2Jizhao Lu3Rongke Liu4Michel Kadoch5Mohamed Cheriet6State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaState Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaState Grid Sichuan Electric Power Company, Chengdu 610041, ChinaState Grid Henan Electric Power Company, Zhengzhou 475005, ChinaSchool of Electronic and Information Engineering, Beihang University, Beijing 100191, ChinaÉcole de Technologie Supérieure, University of Quebec, Montreal, QC H3C 1K3, CanadaÉcole de Technologie Supérieure, University of Quebec, Montreal, QC H3C 1K3, CanadaWith the rapid increase in the number of wireless sensor terminals in smart grids, backscattering has become a very promising green technology. By means of backscattering, wireless sensors can either reflect energy signals in the environment to exchange information with each other or capture the energy signals to recharge their batteries. However, the changing environment around wireless sensors, limited radio frequency and various service priorities in uplink communications bring great challenges in allocation resources. In this paper, we put forward a backscatter communication model based on business priority and cognitive network. In order to achieve optimal throughput of system, an asynchronous advantage actor-critic (A3C) algorithm is designed to tackle the problem of uplink resource allocation. The experimental results indicate that the presented scheme can significantly enhance overall system performance and ensure the business requirements of high-priority users.https://www.mdpi.com/2079-9292/9/4/622backscatterdeep reinforcement learningA3Cpriority strategyresource allocationK-means clustering
collection DOAJ
language English
format Article
sources DOAJ
author Zhixiang Yang
Lei Feng
Zhengwei Chang
Jizhao Lu
Rongke Liu
Michel Kadoch
Mohamed Cheriet
spellingShingle Zhixiang Yang
Lei Feng
Zhengwei Chang
Jizhao Lu
Rongke Liu
Michel Kadoch
Mohamed Cheriet
Prioritized Uplink Resource Allocation in Smart Grid Backscatter Communication Networks via Deep Reinforcement Learning
Electronics
backscatter
deep reinforcement learning
A3C
priority strategy
resource allocation
K-means clustering
author_facet Zhixiang Yang
Lei Feng
Zhengwei Chang
Jizhao Lu
Rongke Liu
Michel Kadoch
Mohamed Cheriet
author_sort Zhixiang Yang
title Prioritized Uplink Resource Allocation in Smart Grid Backscatter Communication Networks via Deep Reinforcement Learning
title_short Prioritized Uplink Resource Allocation in Smart Grid Backscatter Communication Networks via Deep Reinforcement Learning
title_full Prioritized Uplink Resource Allocation in Smart Grid Backscatter Communication Networks via Deep Reinforcement Learning
title_fullStr Prioritized Uplink Resource Allocation in Smart Grid Backscatter Communication Networks via Deep Reinforcement Learning
title_full_unstemmed Prioritized Uplink Resource Allocation in Smart Grid Backscatter Communication Networks via Deep Reinforcement Learning
title_sort prioritized uplink resource allocation in smart grid backscatter communication networks via deep reinforcement learning
publisher MDPI AG
series Electronics
issn 2079-9292
publishDate 2020-04-01
description With the rapid increase in the number of wireless sensor terminals in smart grids, backscattering has become a very promising green technology. By means of backscattering, wireless sensors can either reflect energy signals in the environment to exchange information with each other or capture the energy signals to recharge their batteries. However, the changing environment around wireless sensors, limited radio frequency and various service priorities in uplink communications bring great challenges in allocation resources. In this paper, we put forward a backscatter communication model based on business priority and cognitive network. In order to achieve optimal throughput of system, an asynchronous advantage actor-critic (A3C) algorithm is designed to tackle the problem of uplink resource allocation. The experimental results indicate that the presented scheme can significantly enhance overall system performance and ensure the business requirements of high-priority users.
topic backscatter
deep reinforcement learning
A3C
priority strategy
resource allocation
K-means clustering
url https://www.mdpi.com/2079-9292/9/4/622
work_keys_str_mv AT zhixiangyang prioritizeduplinkresourceallocationinsmartgridbackscattercommunicationnetworksviadeepreinforcementlearning
AT leifeng prioritizeduplinkresourceallocationinsmartgridbackscattercommunicationnetworksviadeepreinforcementlearning
AT zhengweichang prioritizeduplinkresourceallocationinsmartgridbackscattercommunicationnetworksviadeepreinforcementlearning
AT jizhaolu prioritizeduplinkresourceallocationinsmartgridbackscattercommunicationnetworksviadeepreinforcementlearning
AT rongkeliu prioritizeduplinkresourceallocationinsmartgridbackscattercommunicationnetworksviadeepreinforcementlearning
AT michelkadoch prioritizeduplinkresourceallocationinsmartgridbackscattercommunicationnetworksviadeepreinforcementlearning
AT mohamedcheriet prioritizeduplinkresourceallocationinsmartgridbackscattercommunicationnetworksviadeepreinforcementlearning
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