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

Abstract

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%.

Social and Economic Contribution of 5G and Blockchain With Green Computing: Taxonomy, Challenges, and Opportunities

“…A major problem is the successful integration of solutions within SE structures [38]. In the 5G age, energy usage is a challenging concern to combat many challenges such as reactive mode of operation, high latency wakes up times, incorrect cell user association, multiple Self-Organizing Networks (SON) cross-functional operation [39].…”

“…The primary study gap is that current articles have not included working of 5G with BC and GC based methodology, which needs more study shortly. [58] Elsevier Computers & Industrial Engineering [30] Conference IEEE Wireless Communications [57] Conference IEEE 86th Vehicular Technology Conference (VTC-Fall) [59] MDPI Signals [39] Elsevier Computer Communications [51] MDPI Information…”

Edge Intelligence in Private Mobile Networks for Next-Generation Railway Systems

“…If these travel delay issues are not addressed, on average, a passenger can waste up to two and a half days per year waiting for a congested train. Identification of passenger movement in underground train networks is also a persisting challenge that contributes to high energy consumption and subsequent failure of effective optimization measures for mobile networks (Asad et al, 2020a;Asad et al, 2020b). AI can fuel numerous traffic management applications residing at the edge of the PMN while keeping a closer look at the traffic patterns and inferring decisions in smarter ways.…”

A Lightweight Cell Switching and Traffic Offloading Scheme for Energy Optimization in Ultra-Dense Heterogeneous Networks

“…A Q-learning-based cell switching framework was proposed to reduce the energy consumption and CO 2 emission levels of the network. In [30], a mobility management based energy optimization framework for HetNets was proposed using both supervised and Q-learning algorithms. The proposed framework uses supervised learning alongside historical data set of bus passengers passing through the HetNet to predict the traffic loads of the BSs while Q-learning was used to determine the cell switching and traffic offloading strategy that would minimize both the energy consumption and CO 2 emission level.…”