Deep Reinforcement Learning-Based Edge Caching in Heterogeneous Networks
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
With the increasing number of mobile device users worldwide, utilizing mobile edge computing (MEC) devicesclose to users for content caching can reduce transmission latency than receiving content from a server or cloud. However, because MEC has limited storage capacity, it is necessary to determine the content types and sizesto be cached. In this study, we investigate a caching strategy that increases the hit ratio from small base stations(SBSs) for mobile users in a heterogeneous network consisting of one macro base station (MBS) and multipleSBSs. If there are several SBSs that users can access, the hit ratio can be improved by reducing duplicate contentand increasing the diversity of content in SBSs. We propose a Deep Q-Network (DQN)-based caching strategythat considers time-varying content popularity and content redundancy in multiple SBSs. Content is stored inthe SBS in a divided form using maximum distance separable (MDS) codes to enhance the diversity of thecontent. Experiments in various environments show that the proposed caching strategy outperforms the othermethods in terms of hit ratio.
Abstract
With the increasing number of mobile device users worldwide, utilizing mobile edge computing (MEC) devicesclose to users for content caching can reduce transmission latency than receiving content from a server or cloud. However, because MEC has limited storage capacity, it is necessary to determine the content types and sizesto be cached. In this study, we investigate a caching strategy that increases the hit ratio from small base stations(SBSs) for mobile users in a heterogeneous network consisting of one macro base station (MBS) and multipleSBSs. If there are several SBSs that users can access, the hit ratio can be improved by reducing duplicate contentand increasing the diversity of content in SBSs. We propose a Deep Q-Network (DQN)-based caching strategythat considers time-varying content popularity and content redundancy in multiple SBSs. Content is stored inthe SBS in a divided form using maximum distance separable (MDS) codes to enhance the diversity of thecontent. Experiments in various environments show that the proposed caching strategy outperforms the othermethods in terms of hit ratio.
참고문헌 (17)
- "Cisco Annual Internet Report (2018–2023)" (2022)[URL]
- N. Abbas, "Mobile edge computing : a survey", IEEE Internet of Things Journal, 5(1), pp.450~465 (2018)
- P. Lin, "Multidimensional cooperative caching in CoMP-integrated ultra-dense cellular networks", IEEE Transactions on Wireless Communications, 19(3), pp.1977~1989 (2020)
- J. Yao, "On mobile edge caching", IEEE Communications Surveys & Tutorials, 21(3), pp.2525~2553 (2019)
- J. Chen, "Cooperative edge caching with location-based and popular contents for vehicular networks", IEEE Transactions on Vehicular Technology, 69(9), pp.10291~10305 (2020)
- C. Song, "QoE-driven edge caching in vehicle networks based on deep reinforcement learning", IEEE Transactions on Vehicular Technology, 70(6), pp.5286~5295 (2021)
- R. Wang, "Cooperative caching strategy with content request prediction in Internet of vehicles", IEEE Internet of Things Journal, 8(11), pp.8964~8975 (2021)
- G. Qiao, "Deep reinforcement learning for cooperative content caching in vehicular edge computing and networks", IEEE Internet of Things Journal, 7(1), pp.247~257 (2020)
- L. T. Tan, "Mobility-aware edge caching and computing in vehicle networks : a deep rein"forcement learning", IEEE Transactions on Vehicular Technology, 67(11), pp.10190~10203 (2018)
- L. T. Tan, "Twin-timescale artificial intelligence aided mobility-aware edge caching and computing in vehicular networks", IEEE Transactions on Vehicular Technology, 68(4), pp.3086~3099 (2019)
- F. Rezaei, "Performance analysis of heterogeneous cellular caching networks with overlapping small cells", IEEE Transactions on Vehicular Technology, 71(2), pp.1941~1951 (2022)
- I. Keshavarzian, "Energy-efficient mobility-aware caching algorithms for clustered small cells in ultra-dense networks", IEEE Transactions on Vehicular Technology, 68(7), pp.6833~6846 (2019)
- S. Gao, "Reinforcement learning based cooperative coded caching under dynamic popularities in ultra-dense networks", IEEE Transactions on Vehicular Technology, 69(5), pp.5442~5456 (2020)
- L. Breslau, "Web caching and Zipf-like distributions : evidence and implications", Proceedings of IEEE Conference on Computer Communications(INFOCOM’99), pp.126~134 (1999)
- K. Shanmugam, "Femtocaching : wireless content delivery through distributed caching helpers", IEEE Transactions on Information Theory, 59(12), pp.8402~8413 (2013)
- "Playing Atari with deep reinforcement learning" (2013)[URL]
- M. Kamel, "Ultra-dense networks : a survey", IEEE Communications Surveys & Tutorials, 18(4), pp.2522~2545 (2016)