Energy-Efficient Ultra-Dense 5G Networks: Recent Advances, Taxonomy and Future Research Directions

The global surge of connected devices and multimedia services necessitates increased capacity and coverage of communication networks. One approach to address the unprecedented rise in capacity and coverage requirement is deploying several small cells to create ultra-dense networks. This, however, ex...

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Autores principales:	Amna Mughees, Mohammad Tahir, Muhammad Aman Sheikh, Abdul Ahad
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	5G energy efficiency ultra-dense networks game theory machine learning resource allocation Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/a800871e6c7744e0b92112641bf7608c
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spelling	oai:doaj.org-article:a800871e6c7744e0b92112641bf7608c2021-11-18T00:07:40ZEnergy-Efficient Ultra-Dense 5G Networks: Recent Advances, Taxonomy and Future Research Directions2169-353610.1109/ACCESS.2021.3123577https://doaj.org/article/a800871e6c7744e0b92112641bf7608c2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9591614/https://doaj.org/toc/2169-3536The global surge of connected devices and multimedia services necessitates increased capacity and coverage of communication networks. One approach to address the unprecedented rise in capacity and coverage requirement is deploying several small cells to create ultra-dense networks. This, however, exacerbates problems with energy consumption and network management due to the density and unplanned nature of the deployment. This review discusses various approaches to solving energy efficiency problems in ultra-dense networks, ranging from deployment to optimisation. Based on the review, we propose a taxonomy, summarise key findings, and discuss operational and implementation details of past research contributions. In particular, we focus on popular approaches such as machine learning, game theory, stochastic and heuristic techniques in the ultra-dense network from an energy perspective due to their promise in addressing the issue in future networks. Furthermore, we identify several challenges for improving energy efficiency in an ultra-dense network. Finally, future research directions are outlined for improving energy efficiency in ultra-dense networks in 5G and beyond 5G networks.Amna MugheesMohammad TahirMuhammad Aman SheikhAbdul AhadIEEEarticle5Genergy efficiencyultra-dense networksgame theorymachine learningresource allocationElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 147692-147716 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	5G energy efficiency ultra-dense networks game theory machine learning resource allocation Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	5G energy efficiency ultra-dense networks game theory machine learning resource allocation Electrical engineering. Electronics. Nuclear engineering TK1-9971 Amna Mughees Mohammad Tahir Muhammad Aman Sheikh Abdul Ahad Energy-Efficient Ultra-Dense 5G Networks: Recent Advances, Taxonomy and Future Research Directions
description	The global surge of connected devices and multimedia services necessitates increased capacity and coverage of communication networks. One approach to address the unprecedented rise in capacity and coverage requirement is deploying several small cells to create ultra-dense networks. This, however, exacerbates problems with energy consumption and network management due to the density and unplanned nature of the deployment. This review discusses various approaches to solving energy efficiency problems in ultra-dense networks, ranging from deployment to optimisation. Based on the review, we propose a taxonomy, summarise key findings, and discuss operational and implementation details of past research contributions. In particular, we focus on popular approaches such as machine learning, game theory, stochastic and heuristic techniques in the ultra-dense network from an energy perspective due to their promise in addressing the issue in future networks. Furthermore, we identify several challenges for improving energy efficiency in an ultra-dense network. Finally, future research directions are outlined for improving energy efficiency in ultra-dense networks in 5G and beyond 5G networks.
format	article
author	Amna Mughees Mohammad Tahir Muhammad Aman Sheikh Abdul Ahad
author_facet	Amna Mughees Mohammad Tahir Muhammad Aman Sheikh Abdul Ahad
author_sort	Amna Mughees
title	Energy-Efficient Ultra-Dense 5G Networks: Recent Advances, Taxonomy and Future Research Directions
title_short	Energy-Efficient Ultra-Dense 5G Networks: Recent Advances, Taxonomy and Future Research Directions
title_full	Energy-Efficient Ultra-Dense 5G Networks: Recent Advances, Taxonomy and Future Research Directions
title_fullStr	Energy-Efficient Ultra-Dense 5G Networks: Recent Advances, Taxonomy and Future Research Directions
title_full_unstemmed	Energy-Efficient Ultra-Dense 5G Networks: Recent Advances, Taxonomy and Future Research Directions
title_sort	energy-efficient ultra-dense 5g networks: recent advances, taxonomy and future research directions
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/a800871e6c7744e0b92112641bf7608c
work_keys_str_mv	AT amnamughees energyefficientultradense5gnetworksrecentadvancestaxonomyandfutureresearchdirections AT mohammadtahir energyefficientultradense5gnetworksrecentadvancestaxonomyandfutureresearchdirections AT muhammadamansheikh energyefficientultradense5gnetworksrecentadvancestaxonomyandfutureresearchdirections AT abdulahad energyefficientultradense5gnetworksrecentadvancestaxonomyandfutureresearchdirections
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Energy-Efficient Ultra-Dense 5G Networks: Recent Advances, Taxonomy and Future Research Directions

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