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Learning-Based Resource Allocation Scheme for TDD-Based 5G CRAN System

Published: 13 November 2016 Publication History

Abstract

Provision of high data rates with always-on connectivity 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.

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  • (2022)Resource allocation schemes in 5G: survey and challengesi-manager's Journal on Communication Engineering and Systems10.26634/jcs.11.2.1898411:2(25)Online publication date: 2022
  • (2021)Resource Allocation Schemes for 5G Network: A Systematic ReviewSensors10.3390/s2119658821:19(6588)Online publication date: 2-Oct-2021
  • (2021)From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A SurveyApplied Sciences10.3390/app1117811711:17(8117)Online publication date: 31-Aug-2021
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        cover image ACM Conferences
        MSWiM '16: Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems
        November 2016
        370 pages
        ISBN:9781450345026
        DOI:10.1145/2988287
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        Published: 13 November 2016

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        Author Tags

        1. 5g
        2. cran
        3. machine learning.
        4. resource allocation
        5. tdd

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        MSWiM '16 Paper Acceptance Rate 36 of 138 submissions, 26%;
        Overall Acceptance Rate 398 of 1,577 submissions, 25%

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        View all
        • (2022)Resource allocation schemes in 5G: survey and challengesi-manager's Journal on Communication Engineering and Systems10.26634/jcs.11.2.1898411:2(25)Online publication date: 2022
        • (2021)Resource Allocation Schemes for 5G Network: A Systematic ReviewSensors10.3390/s2119658821:19(6588)Online publication date: 2-Oct-2021
        • (2021)From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A SurveyApplied Sciences10.3390/app1117811711:17(8117)Online publication date: 31-Aug-2021
        • (2020)Energy Efficiency in 5G Cellular Network SystemsIEEE Design & Test10.1109/MDAT.2019.296034237:1(64-78)Online publication date: Feb-2020
        • (2020)5G Scheduling using Reinforcement Learning*2020 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon)10.1109/FarEastCon50210.2020.9271421(1-5)Online publication date: 6-Oct-2020
        • (2020)Machine Learning Meets Communication Networks: Current Trends and Future ChallengesIEEE Access10.1109/ACCESS.2020.30417658(223418-223460)Online publication date: 2020
        • (2019)Resource Allocation in Pre-5G Cellular SystemsProceedings of the 2nd International Conference on Networking, Information Systems & Security10.1145/3320326.3320339(1-6)Online publication date: 27-Mar-2019
        • (2018)Random forests for resource allocation in 5G cloud radio access networks based on position informationEURASIP Journal on Wireless Communications and Networking10.1186/s13638-018-1149-72018:1Online publication date: 7-Jun-2018
        • (2018)Random forests resource allocation for 5G systems: Performance and robustness study2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)10.1109/WCNCW.2018.8369028(326-331)Online publication date: Apr-2018
        • (2018)RBF-SVM Based Resource Allocation Scheme for 5G CRAN Networks2018 3rd International Conference and Workshops on Recent Advances and Innovations in Engineering (ICRAIE)10.1109/ICRAIE.2018.8710423(1-6)Online publication date: Nov-2018

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