Mean-field game theoretic edge caching in ultra-dense networks

Hyesung Kim; Jihong Park; Mehdi Bennis; Seong Lyun Kim; Merouane Debbah

doi:10.1109/TVT.2019.2953132

Mean-field game theoretic edge caching in ultra-dense networks

Hyesung Kim, Jihong Park, Mehdi Bennis, Seong Lyun Kim, Merouane Debbah

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

14 Citations (Scopus)

Abstract

This paper investigates a cellular edge caching problem under a very large number of small base stations (SBSs) and users. In this ultra-dense edge caching network (UDCN), conventional caching algorithms are inapplicable as their computational complexity increases with the number of small base stations (SBSs). Furthermore, the performance of UDCN is highly sensitive to the dynamics of user demand. To overcome such difficulties, we propose a distributed caching algorithm under a stochastic geometric network model, as well as a spatio-temporal user demand model that characterizes the content popularity dynamics. By leveraging mean-field game (MFG) theory, the complexity of the proposed UDCN caching algorithm becomes independent of the number of SBSs. Numerical evaluations validate this consistent complexity of the proposed algorithm with respect to the number of SBSs. Also, it shows that the proposed caching algorithm reduces not only the long run average cost of the network but also the redundant cached data respectively by 24% and 42%, compared to a baseline caching algorithm. Additionally, the simulation results show that the proposed caching algorithm is robust to imperfect popularity information while ensuring a low computational complexity.

Original language	English
Article number	8896875
Pages (from-to)	935-947
Number of pages	13
Journal	IEEE Transactions on Vehicular Technology
Volume	69
Issue number	1
DOIs	https://doi.org/10.1109/TVT.2019.2953132
Publication status	Published - 2020 Jan

Bibliographical note

Funding Information:
Manuscript received March 10, 2019; revised July 29, 2019; accepted October 14, 2019. Date of publication November 12, 2019; date of current version January 15, 2020. This article was presented in part at IEEE International Conference on Communications 2017 [1]. This work was supported in part by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science and ICT (NRF-2017R1A2A2A05069810), in part by Institute for Information and communications Technology Planning and Evaluation grant funded by the MSIT (2018-0-00170, Virtual Presence in Moving Objects through 5G), in part by the Academy of Finland project CARMA, in part by the Academy of Finland Project MISSION, in part by the Academy of Finland Project SMARTER, in part by the INFOTECH project NOOR, and in part by the Nokia Bell-Labs project ELLIS. The review of this paper was coordinated by Dr. Yuanxiong Guo. Corresponding author: Seong-Lyun Kim.) H. Kim and S.-L. Kim are with the Radio Resource Management and Optimization Laboratory, Department of Electrical and Electronic Engineering, Yon-sei University, Seoul 03722, South Korea (e-mail: hskim@ramo.yonsei.ac.kr; slkim@ramo.yonsei.ac.kr).

Publisher Copyright:
© 1967-2012 IEEE.

All Science Journal Classification (ASJC) codes

Automotive Engineering
Aerospace Engineering
Electrical and Electronic Engineering
Applied Mathematics

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10.1109/TVT.2019.2953132

Cite this

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title = "Mean-field game theoretic edge caching in ultra-dense networks",

abstract = "This paper investigates a cellular edge caching problem under a very large number of small base stations (SBSs) and users. In this ultra-dense edge caching network (UDCN), conventional caching algorithms are inapplicable as their computational complexity increases with the number of small base stations (SBSs). Furthermore, the performance of UDCN is highly sensitive to the dynamics of user demand. To overcome such difficulties, we propose a distributed caching algorithm under a stochastic geometric network model, as well as a spatio-temporal user demand model that characterizes the content popularity dynamics. By leveraging mean-field game (MFG) theory, the complexity of the proposed UDCN caching algorithm becomes independent of the number of SBSs. Numerical evaluations validate this consistent complexity of the proposed algorithm with respect to the number of SBSs. Also, it shows that the proposed caching algorithm reduces not only the long run average cost of the network but also the redundant cached data respectively by 24% and 42%, compared to a baseline caching algorithm. Additionally, the simulation results show that the proposed caching algorithm is robust to imperfect popularity information while ensuring a low computational complexity.",

author = "Hyesung Kim and Jihong Park and Mehdi Bennis and Kim, {Seong Lyun} and Merouane Debbah",

note = "Funding Information: Manuscript received March 10, 2019; revised July 29, 2019; accepted October 14, 2019. Date of publication November 12, 2019; date of current version January 15, 2020. This article was presented in part at IEEE International Conference on Communications 2017 [1]. This work was supported in part by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science and ICT (NRF-2017R1A2A2A05069810), in part by Institute for Information and communications Technology Planning and Evaluation grant funded by the MSIT (2018-0-00170, Virtual Presence in Moving Objects through 5G), in part by the Academy of Finland project CARMA, in part by the Academy of Finland Project MISSION, in part by the Academy of Finland Project SMARTER, in part by the INFOTECH project NOOR, and in part by the Nokia Bell-Labs project ELLIS. The review of this paper was coordinated by Dr. Yuanxiong Guo. Corresponding author: Seong-Lyun Kim.) H. Kim and S.-L. Kim are with the Radio Resource Management and Optimization Laboratory, Department of Electrical and Electronic Engineering, Yon-sei University, Seoul 03722, South Korea (e-mail: hskim@ramo.yonsei.ac.kr; slkim@ramo.yonsei.ac.kr). Publisher Copyright: {\textcopyright} 1967-2012 IEEE.",

year = "2020",

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N1 - Funding Information: Manuscript received March 10, 2019; revised July 29, 2019; accepted October 14, 2019. Date of publication November 12, 2019; date of current version January 15, 2020. This article was presented in part at IEEE International Conference on Communications 2017 [1]. This work was supported in part by Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science and ICT (NRF-2017R1A2A2A05069810), in part by Institute for Information and communications Technology Planning and Evaluation grant funded by the MSIT (2018-0-00170, Virtual Presence in Moving Objects through 5G), in part by the Academy of Finland project CARMA, in part by the Academy of Finland Project MISSION, in part by the Academy of Finland Project SMARTER, in part by the INFOTECH project NOOR, and in part by the Nokia Bell-Labs project ELLIS. The review of this paper was coordinated by Dr. Yuanxiong Guo. Corresponding author: Seong-Lyun Kim.) H. Kim and S.-L. Kim are with the Radio Resource Management and Optimization Laboratory, Department of Electrical and Electronic Engineering, Yon-sei University, Seoul 03722, South Korea (e-mail: hskim@ramo.yonsei.ac.kr; slkim@ramo.yonsei.ac.kr). Publisher Copyright: © 1967-2012 IEEE.

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N2 - This paper investigates a cellular edge caching problem under a very large number of small base stations (SBSs) and users. In this ultra-dense edge caching network (UDCN), conventional caching algorithms are inapplicable as their computational complexity increases with the number of small base stations (SBSs). Furthermore, the performance of UDCN is highly sensitive to the dynamics of user demand. To overcome such difficulties, we propose a distributed caching algorithm under a stochastic geometric network model, as well as a spatio-temporal user demand model that characterizes the content popularity dynamics. By leveraging mean-field game (MFG) theory, the complexity of the proposed UDCN caching algorithm becomes independent of the number of SBSs. Numerical evaluations validate this consistent complexity of the proposed algorithm with respect to the number of SBSs. Also, it shows that the proposed caching algorithm reduces not only the long run average cost of the network but also the redundant cached data respectively by 24% and 42%, compared to a baseline caching algorithm. Additionally, the simulation results show that the proposed caching algorithm is robust to imperfect popularity information while ensuring a low computational complexity.

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Mean-field game theoretic edge caching in ultra-dense networks

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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