University of Oulu

Y. Chen, Y. Jiang, F. -C. Zheng, M. Bennis and X. You, "Coded Caching via Federated Deep Reinforcement Learning in Fog Radio Access Networks," 2022 IEEE International Conference on Communications Workshops (ICC Workshops), Seoul, Korea, Republic of, 2022, pp. 403-408, doi: 10.1109/ICCWorkshops53468.2022.9814524

Coded caching via federated deep reinforcement learning in fog radio access networks

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Author: Chen, Yingqi1; Jiang, Yanxiang1,2; Zheng, Fu-Chun1,2;
Organizations: 1National Mobile Communications Research Laboratory, Southeast University, Nanjing, China
2School of Electronic and Information Engineering, Harbin Institute of Technology, Shenzhen, China
3Centre for Wireless Communications, University of Oulu, Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.4 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2023021026806
Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2023-02-10
Description:

Abstract

In this paper, the placement strategy design of coded caching in fog-radio access networks (F -RANs) is investigated. By considering time-variant content popularity, federated deep re-inforcement learning is exploited to learn the placement strategy for our coded caching scheme. Initially, the placement problem is modeled as a Markov decision process (MDP) to capture the popularity variations and minimize the long-term content access delay. The reformulated sequential decision problem is solved by dueling double deep Q-learning (dueling DDQL). Then, federated learning is applied to learn the relatively low-dimensional local decision models and aggregate the global decision model, which alleviates over-consumption of bandwidth resources and avoids direct learning of a complex coded caching decision model with high-dimensional state space. Simulation results show that our proposed scheme outperforms the benchmarks in reducing the content access delay, keeping the performance stable, and trading off between the local caching gain and the global multicasting gain.

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Series: IEEE International Conference on Communications workshop
ISSN: 2164-7038
ISSN-E: 2694-2941
ISSN-L: 2164-7038
ISBN: 978-1-6654-2671-8
ISBN Print: 978-1-6654-2672-5
Pages: 403 - 408
DOI: 10.1109/iccworkshops53468.2022.9814524
OADOI: https://oadoi.org/10.1109/iccworkshops53468.2022.9814524
Host publication: 2022 IEEE International Conference on Communications Workshops (ICC Workshops)
Conference: IEEE International Conference on Communications Workshops
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: This work was supported in part by the National Key Research and Development Program under Grant 2021 YFB2900300, the National Natural Science Foundation of China under grant 61971129, and the Shenzhen Science and Technology Program under Grant KQTD20190929172545139.
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