Network Optimisation in 5G Networks: A Radio Environment Map Approach

Suarez Rodriguez, AC; Haider, N; He, Y; Dutkiewicz, E

Network Optimisation in 5G Networks: A Radio Environment Map Approach

Suarez Rodriguez, AC Haider, N

He, Y

Dutkiewicz, E

Permalink

Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Vehicular Technology, 2020, 69, (10), pp. 12043-12057
Issue Date:: 2020-10-01

Closed Access

	Filename	Description	Size
	09146281.pdf	Published version	1.99 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Suarez Rodriguez, AC
dc.contributor.author	Haider, N https://orcid.org/0000-0002-7099-0252
dc.contributor.author	He, Y https://orcid.org/0000-0003-1603-9375
dc.contributor.author	Dutkiewicz, E https://orcid.org/0000-0002-4268-9286
dc.date.accessioned	2021-02-16T02:12:55Z
dc.date.available	2021-02-16T02:12:55Z
dc.date.issued	2020-10-01
dc.identifier.citation	IEEE Transactions on Vehicular Technology, 2020, 69, (10), pp. 12043-12057
dc.identifier.issn	0018-9545
dc.identifier.issn	1939-9359
dc.identifier.uri	http://hdl.handle.net/10453/146132
dc.description.abstract	© 1967-2012 IEEE. Network densification has been a driven force for boosting capacity throughout the different generation of cellular networks. In the current 5G networks, extreme densification is a core technology to meet the ever-increasing capacity demand by reusing the spectrum over the spatial domain. As the cell density increases, so does the handover rate, diminishing the user throughput that could potentially undermine the prospective capacity gains. Furthermore, advanced interference mitigation techniques are needed to cope with the co-channel interference, adding extra backhaul traffic. In this paper, we propose a cell-association strategy based on radio environment maps for dense 5G networks that maximises the average user throughput across the network. By exploiting the stored channel states and user location, we connect cellular users to the optimal tier dynamically. Based on stochastic geometry, the association probability and the coverage probability have been derived for dense 5G networks, where the base stations' locations follow Poisson Point Processes. Moreover, the user throughput and backhaul traffic are evaluated to assess its performance in dense 5G networks. The proposed technique is validated via Monte Carlo simulations and compared to different cell-association techniques. Results show that our scheme finds a balance between the coverage probability and the handover rate by numerical optimisation. In particular, it increases the average user throughput by 65% over the traditional approach, and it reduces the amount of overhead up to 68% over the other cell-association strategies.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Vehicular Technology
dc.relation.isbasedon	10.1109/TVT.2020.3011147
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.subject.classification	Automobile Design & Engineering
dc.title	Network Optimisation in 5G Networks: A Radio Environment Map Approach
dc.type	Journal Article
utslib.citation.volume	69
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
utslib.copyright.status	closed_access	*
dc.date.updated	2021-02-16T02:12:23Z
pubs.issue	10
pubs.publication-status	Published
pubs.volume	69
utslib.citation.issue	10

Abstract:

© 1967-2012 IEEE. Network densification has been a driven force for boosting capacity throughout the different generation of cellular networks. In the current 5G networks, extreme densification is a core technology to meet the ever-increasing capacity demand by reusing the spectrum over the spatial domain. As the cell density increases, so does the handover rate, diminishing the user throughput that could potentially undermine the prospective capacity gains. Furthermore, advanced interference mitigation techniques are needed to cope with the co-channel interference, adding extra backhaul traffic. In this paper, we propose a cell-association strategy based on radio environment maps for dense 5G networks that maximises the average user throughput across the network. By exploiting the stored channel states and user location, we connect cellular users to the optimal tier dynamically. Based on stochastic geometry, the association probability and the coverage probability have been derived for dense 5G networks, where the base stations' locations follow Poisson Point Processes. Moreover, the user throughput and backhaul traffic are evaluated to assess its performance in dense 5G networks. The proposed technique is validated via Monte Carlo simulations and compared to different cell-association techniques. Results show that our scheme finds a balance between the coverage probability and the handover rate by numerical optimisation. In particular, it increases the average user throughput by 65% over the traditional approach, and it reduces the amount of overhead up to 68% over the other cell-association strategies.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/146132