Channel access and power control for energy-efficient delay-aware heterogeneous cellular networks for smart grid communications using deep reinforcement learning

Asuhaimi, F. A., Bu, S. , Klaine, P. V. and Imran, M. A. (2019) Channel access and power control for energy-efficient delay-aware heterogeneous cellular networks for smart grid communications using deep reinforcement learning. IEEE Access, 7, pp. 133474-133484. (doi:10.1109/ACCESS.2019.2939827)

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Abstract

Cellular technology with long-term evolution (LTE)-based standards is a preferable choice for smart grid neighborhood area networks due to its high availability and scalability. However, the integration of cellular networks and smart grid communications puts forth a significant challenge due to the simultaneous transmission of real-time smart grid data which could cause radio access network (RAN) congestions. Heterogeneous cellular networks (HetNets) have been proposed to improve the performance of LTE because HetNets can alleviate RAN congestions by off-loading access attempts from a macrocell to small cells. In this paper, we study energy efficiency and delay problems in HetNets for transmitting smart grid data with different delay requirements. We propose a distributed channel access and power control scheme, and develop a learning-based approach for the phasor measurement units (PMUs) to transmit data successfully by considering interference and signal-to-interference-plus-noise ratio (SINR) constraints. In particular, we exploit a deep reinforcement learning(DRL)-based method to train the PMUs to learn an optimal policy that maximizes the earned reward of successful transmissions without having knowledge on the system dynamics. Results show that the DRL approach obtains good performance without knowing the system dynamic beforehand and outperforms the Gittin index policy in different normal ratios, minimum SINR requirements and number of users in the cell.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Imran, Professor Muhammad and Valente Klaine, Mr Paulo and Bu, Dr Shengrong and Abdullah Asuhaimi, Fauzun Binti
Authors: Asuhaimi, F. A., Bu, S., Klaine, P. V., and Imran, M. A.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:IEEE Access
Publisher:IEEE
ISSN:2169-3536
ISSN (Online):2169-3536
Published Online:06 September 2019
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in IEEE Access 7:133474-133484
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
3007250Distributed Autonomous Resilient Emergency Management System (DARE)Muhammad ImranEngineering and Physical Sciences Research Council (EPSRC)EP/P028764/1ENG - Systems Power & Energy

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