Enhancing Handover for 5G mmWave Mobile Networks Using Jump Markov Linear System and Deep Reinforcement Learning
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Chiputa, M
Zhang, M
Nawaz Ali, GGM
Chong, PHJ
Sabit, H
Kumar, A
Li, H
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MDPI
Abstract
The Fifth Generation (5G) mobile networks use millimeter waves (mmWaves) to offer gigabit data rates. However, unlike microwaves, mmWave links are prone to user and topographic dynamics. They easily get blocked and end up forming irregular cell patterns for 5G. This in turn causes too early, too late, or wrong handoffs (HOs). To mitigate HO challenges, sustain connectivity, and avert unnecessary HO, we propose an HO scheme based on a jump Markov linear system (JMLS) and deep reinforcement learning (DRL). JMLS is widely known to account for abrupt changes in system dynamics. DRL likewise emerges as an artificial intelligence technique for learning highly dimensional and time‐varying behaviors. We combine the two techniques to account for time‐varying, abrupt, and irregular changes in mmWave link behavior by predicting likely deterioration patterns of target links. The prediction is optimized by meta training techniques that also reduce training sample size. Thus, the JMLS–DRL platform formulates intelligent and versatile HO policies for 5G. When compared to a signal and interference noise ratio (SINR) and DRL‐based HO scheme, our HO scheme becomes more reliable in selecting reliable target links. In particular, our proposed scheme is able to reduce wasteful HO to less than 5% within 200 training episodes compared to the DRL‐based HO scheme that needs more than 200 training episodes to get to less than 5%. It supports longer dew time between HOs and high sum rates by ably averting unnecessary HOs with almost half the HOs compared to a DRL‐based HO scheme.
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millimeter bands, Fifth Generation, handover, deep reinforcement learning, jump Markov linear system, VEHICULAR NETWORKS, 4613 Theory Of Computation, 46 Information and Computing Sciences, 4006 Communications Engineering, 40 Engineering, Behavioral and Social Science, Machine Learning and Artificial Intelligence, 0301 Analytical Chemistry, 0502 Environmental Science and Management, 0602 Ecology, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering, Analytical Chemistry, 3103 Ecology, 4008 Electrical engineering, 4009 Electronics, sensors and digital hardware, 4104 Environmental management, 4606 Distributed computing and systems software
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Sensors, ISSN: 1424-8220 (Print); 1424-8220 (Online), MDPI, 22(3), 746-. doi: 10.3390/s22030746
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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Except where otherwise noted, this item's license is described as Creative Commons Attribution License