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Intelligent user-centric networks: learning-based downlink CoMP region breathing

Intelligent user-centric networks: learning-based downlink CoMP region breathing
Intelligent user-centric networks: learning-based downlink CoMP region breathing

In the presence of irregular transmission/reception point (TRP) topologies and non-uniform user distribution, the user-to-node association optimization is a rather challenging process in real user-centric networks, especially for the joint transmission aided coordinated multipoint (CoMP) technique. The grade of challenge further escalates, when taking the dynamic user scheduling process into account in order to enhance the system capacity attained. To tackle the above-mentioned problem, we holistically optimize the system by conceiving joint user scheduling and user-to-node association. Then, for the sake of striking a significantly better balance between the network capacity and coverage quality, we propose a generalized reinforcement learning assisted framework intrinsically amalgamated both with neural-fitted Q-iteration as well as with ensemble learning and transfer learning techniques. Consequently, a powerful policy can be found for dynamically adjusting the set of TRPs participating in the joint transmission, thus allowing the CoMP-region to breathe, depending on both the temporal and geographical distribution of the tele-traffic load across the network. To facilitate the prompt learning of the global policy supporting flexible scalability, the overall network optimization process is decoupled into multiple local optimization phases associated with a number of TRP clusters relying on iterative information exchange among them. Our simulation results show that the proposed scheme is capable of producing a policy achieving a network-edge throughput gain of up to 140% and a network capacity gain of up to 190% under the challenging scenario of having a non-uniform geographical UE distribution and bursty traffic.

CoMP, machine learning, Single frequency network, user-centric
0018-9545
5583-5597
Wang, Li
9903905a-a55b-456a-a8bd-71108364e156
Peters, Gunnar
02307514-59d7-4c24-a3e2-cfc28f6de310
Liang, Ying Chang
fdd98095-8a2f-466c-a155-e51f18c07bb5
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1
Wang, Li
9903905a-a55b-456a-a8bd-71108364e156
Peters, Gunnar
02307514-59d7-4c24-a3e2-cfc28f6de310
Liang, Ying Chang
fdd98095-8a2f-466c-a155-e51f18c07bb5
Hanzo, Lajos
66e7266f-3066-4fc0-8391-e000acce71a1

Wang, Li, Peters, Gunnar, Liang, Ying Chang and Hanzo, Lajos (2020) Intelligent user-centric networks: learning-based downlink CoMP region breathing. IEEE Transactions on Vehicular Technology, 69 (5), 5583-5597, [9043678]. (doi:10.1109/TVT.2020.2982319).

Record type: Article

Abstract

In the presence of irregular transmission/reception point (TRP) topologies and non-uniform user distribution, the user-to-node association optimization is a rather challenging process in real user-centric networks, especially for the joint transmission aided coordinated multipoint (CoMP) technique. The grade of challenge further escalates, when taking the dynamic user scheduling process into account in order to enhance the system capacity attained. To tackle the above-mentioned problem, we holistically optimize the system by conceiving joint user scheduling and user-to-node association. Then, for the sake of striking a significantly better balance between the network capacity and coverage quality, we propose a generalized reinforcement learning assisted framework intrinsically amalgamated both with neural-fitted Q-iteration as well as with ensemble learning and transfer learning techniques. Consequently, a powerful policy can be found for dynamically adjusting the set of TRPs participating in the joint transmission, thus allowing the CoMP-region to breathe, depending on both the temporal and geographical distribution of the tele-traffic load across the network. To facilitate the prompt learning of the global policy supporting flexible scalability, the overall network optimization process is decoupled into multiple local optimization phases associated with a number of TRP clusters relying on iterative information exchange among them. Our simulation results show that the proposed scheme is capable of producing a policy achieving a network-edge throughput gain of up to 140% and a network capacity gain of up to 190% under the challenging scenario of having a non-uniform geographical UE distribution and bursty traffic.

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Accepted/In Press date: 16 March 2020
e-pub ahead of print date: 20 March 2020
Published date: 1 May 2020
Additional Information: Funding Information: Manuscript received January 8, 2019; revised July 28, 2019, January 28, 2020, and March 12, 2020; accepted March 16, 2020. Date of publication March 20, 2020; date of current version May 14, 2020. The work of Y. C. Liang was supported in part by the National Natural Science Foundation of China under Grants 61631005 and U1801261, and in part by the 111 Project under Grant B20064. The work of L. Hanzo was supported in part by Engineering and Physical Sciences Research Council Projects EP/N004558/1, EP/P034284/1, EP/P034284/1, and EP/P003990/1 (COALESCE), and in part by the Royal Society’s Global Challenges Research Fund Grant as well as of the European Research Council’s Advanced Fellow Grant QuantCom. The review of this article was coordinated by Prof. Y. Qian. (Corresponding author: Lajos Hanzo.) Lajos Hanzo is with the Electronics and Computer Science, University of Southampton, Southampton SO17 3AR, U.K. (e-mail: lh@ecs.soton.ac.uk). Publisher Copyright: © 1967-2012 IEEE.
Keywords: CoMP, machine learning, Single frequency network, user-centric

Identifiers

Local EPrints ID: 438885
URI: http://eprints.soton.ac.uk/id/eprint/438885
ISSN: 0018-9545
PURE UUID: 753453cf-da83-4a4b-b3d3-9c1cedfbf25c
ORCID for Lajos Hanzo: ORCID iD orcid.org/0000-0002-2636-5214

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Date deposited: 26 Mar 2020 17:30
Last modified: 18 Mar 2024 02:36

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Contributors

Author: Li Wang
Author: Gunnar Peters
Author: Ying Chang Liang
Author: Lajos Hanzo ORCID iD

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