Mobility management-based autonomous energy-aware framework using machine learning approach in dense mobile networks

Asad, S. M., Ansari, S. , Öztürk, M., Dashtipour, K., Rais, R. N. B., Hussain, S. , Abbasi, Q. H. and Imran, M. A. (2020) Mobility management-based autonomous energy-aware framework using machine learning approach in dense mobile networks. Signals, 1(2), pp. 170-187. (doi: 10.3390/signals1020010)

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A paramount challenge of prohibiting increased CO2 emissions for network densification is to deliver the Fifth Generation (5G) cellular capacity and connectivity demands, while maintaining a greener, healthier and prosperous environment. Energy consumption is a demanding consideration in the 5G era to combat several challenges such as reactive mode of operation, high latency wake up times, incorrect user association with the cells, multiple cross-functional operation of Self-Organising Networks (SON), etc. To address this challenge, we propose a novel Mobility Management-Based Autonomous Energy-Aware Framework for analysing bus passengers ridership through statistical Machine Learning (ML) and proactive energy savings coupled with CO2 emissions in Heterogeneous Network (HetNet) architecture using Reinforcement Learning (RL). Furthermore, we compare and report various ML algorithms using bus passengers ridership obtained from London Overground (LO) dataset. Extensive spatiotemporal simulations show that our proposed framework can achieve up to 98.82% prediction accuracy and CO2 reduction gains of up to 31.83%.

Item Type:Articles
Additional Information:This work was partially funded by Deanship of Graduate Studies and Research (DGSR), Ajman University under the grant number 2020-IRG-ENIT-10 and 2019-IRG-ENIT-8.
Glasgow Author(s) Enlighten ID:Ansari, Dr Shuja and Imran, Professor Muhammad and Öztürk, Metin and Asad, Syed and Hussain, Dr Sajjad and Abbasi, Dr Qammer and Dashtipour, Dr Kia
Creator Roles:
Asad, S. M.Conceptualization, Data curation, Formal analysis, Methodology, Software, Visualization, Writing – original draft
Ansari, S.Conceptualization, Formal analysis, Project administration, Visualization, Writing – review and editing
Ozturk, M.Conceptualization, Data curation, Software, Writing – review and editing
Dashtipour, K.Software
Hussain, S.Formal analysis, Project administration, Supervision
Abbasi, Q. H.Project administration, Supervision, Validation, Visualization
Imran, M. A.Funding acquisition, Project administration, Supervision, Validation
Authors: Asad, S. M., Ansari, S., Öztürk, M., Dashtipour, K., Rais, R. N. B., Hussain, S., Abbasi, Q. H., and Imran, M. A.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Signals
ISSN (Online):2624-6120
Copyright Holders:Copyright © 2020 by the authors
First Published:First published in Signals 1(2):170-187
Publisher Policy:Reproduced under a Creative Commons license

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