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Learning-based resource allocation scheme for TDD-based 5G CRAN system
KTH, School of Electrical Engineering (EES), Information Science and Engineering.ORCID iD: 0000-0002-6864-6970
KTH, School of Electrical Engineering (EES), Communication Theory. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-9442-671X
KTH, School of Electrical Engineering (EES), Communication Theory.
KTH, School of Electrical Engineering (EES), Communication Networks. Huawei Technologies Sweden R&D Center.ORCID iD: 0000-0002-7372-5139
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2016 (English)In: MSWiM 2016 - Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, ACM Press, 2016, 176-185 p.Conference paper, Published paper (Refereed)
Abstract [en]

Provision of high data rates with always-onconnectivity to high mobility users is one of the motivations for design of fifth generation (5G) systems. High system capacity can be achieved by coordination between large number of antennas, which is done using the cloud radio access network (CRAN) design in 5G systems. In terms of baseband processing, allocation of appropriate resources to the users is necessary to achieve high system capacity, for which the state of the art uses the users' channel state information (CSI); however, they do not take into account the associated overhead, which poses a major bottleneck for the effective system performance. In contrast to this approach, this paper proposes the use of machine learning for allocating resources to high mobility users using only their position estimates. Specifically, the 'random forest' algorithm, a supervised machine learning technique, is used to design a learning-based resource allocation scheme by exploiting the relationships between the system parameters and the users' position estimates. In this way, the overhead for CSI acquisition is avoided by using the position estimates instead, with better spectrum utilization. While the initial numerical investigations, with minimum number of users in the system, show that the proposed learning-based scheme achieves 86% of the efficiency achieved by the perfect CSI-based scheme, if the effect of overhead is factored in, the proposed scheme performs better than the CSI-based approach. In a realistic scenario, with multiple users in the system, the significant increase in overhead for the CSI-based scheme leads to a performance gain of 100%, or more, by using the proposed scheme, and thus proving the proposed scheme to be more efficient in terms of system performance.

Place, publisher, year, edition, pages
ACM Press, 2016. 176-185 p.
Keyword [en]
5G, CRAN, Machine learning, Resource allocation, TDD, Artificial intelligence, Decision trees, Learning systems, Supervised learning, Base-band processing, Learning based schemes, Numerical investigations, Radio access networks, Resource allocation schemes, Spectrum utilization, Supervised machine learning, Channel state information
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-202132DOI: 10.1145/2988287.2989158Scopus ID: 2-s2.0-85007008102ISBN: 9781450345026 (print)OAI: oai:DiVA.org:kth-202132DiVA: diva2:1081683
Conference
19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, MSWiM 2016, 13 November 2016 through 17 November 2016
Note

QC 20170314

Available from: 2017-03-14 Created: 2017-03-14 Last updated: 2017-03-28Bibliographically approved

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Gross, James

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Imtiaz, SaharGhauch, HadiUr Rahman, M. M.Koudouridis, GeorgiosGross, James
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