Mobility Management-Based Autonomous Energy-Aware Framework Using Machine Learning Approach in Dense Mobile Networks

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...

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Bibliographic Details
Published in:Signals (Basel) 2020-12, Vol.1 (2), p.170-187
Main Authors: Asad, Syed Muhammad, Ansari, Shuja, Ozturk, Metin, Rais, Rao Naveed Bin, Dashtipour, Kia, Hussain, Sajjad, Abbasi, Qammer H., Imran, Muhammad Ali
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Classification
Cost control
Discriminant analysis
Energy consumption
Global positioning systems
GPS
Machine learning
Neural networks
Passengers
Smart cities
Telecommunications towers
Traffic congestion
Traffic flow
Wireless networks
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Summary: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%.
ISSN:2624-6120
2624-6120
DOI:10.3390/signals1020010