Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks With Mobile Users
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In this paper, the problem of proactive caching is studied for cloud radio access networks (CRANs). In the studied model, the baseband units (BBUs) can predict the content request distribution and mobility pattern of each user and determine which content to cache at remote radio heads and the BBUs. This problem is formulated as an optimization problem, which jointly incorporates backhaul and fronthaul loads and content caching. To solve this problem, an algorithm that combines the machine learning framework of echo state networks (ESNs) with sublinear algorithms is proposed. Using ESNs, the BBUs can predict each user's content request distribution and mobility pattern while having only limited information on the network's and user's state. In order to predict each user's periodic mobility pattern with minimal complexity, the memory capacity of the corresponding ESN is derived for a periodic input. This memory capacity is shown to capture the maximum amount of user information needed for the proposed ESN model. Then, a sublinear algorithm is proposed to determine which content to cache while using limited content request distribution samples. Simulation results using real data from Youku and the Beijing University of Posts and Telecommunications show that the proposed approach yields significant gains, in terms of sum effective capacity, that reach up to 27.8% and 30.7%, respectively, compared with two baseline algorithms: random caching with clustering and random caching without clustering.
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An agent can be associated with each serving station in a cellular network to assist in learning optimal scheduling parameters to enhance network QoS.
“An agent can be associated with each serving station in a cellular network to assist in learning the optimal scheduling parameters to enhance the network Quality-of-Service (QoS) [78].”
From Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019) · cites this paper as [78]
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Deep Learning Model for Content Aware Caching at MEC Servers
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Deep Learning-based Predictive Caching in the Edge of a Network
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- Authors
- Chen, M., Saad, W., Yin, C., & Debbah, M.
- Affiliations
- Beijing University of Posts and Telecommunications (CN)
- Virginia Tech (US)
- Supélec (FR)
- Venue
- IEEE Transactions on Wireless Communications
- Published
- 2017-06
- Volume
- 16
- Issue
- 6
- Pages
- 3520-3535
- ISSN
- 1536-1276 (ISSN-L)
- OpenAlex
- W2962850461
- Cited by
- 176
- References
- 35
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 8 · 2023: 10 · 2022: 15 · 2021: 18 · 2020: 35 · 2019: 42 · 2018: 33 · 2017: 7
- FWCI
- 21.32
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Transactions on Wireless Communications (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (61671086, 61629101)
- ERC Starting through MORE (Advanced Mathematical Tools for Complex Network Engineering) (305123)
- U.S. National Science Foundation (IIS-1633363, CNS-1460316, CNS-1513697)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Caching and Content Delivery
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › Age of Information Optimization
- Keywords
- proactive caching, cloud radio access network, echo state networks, user mobility prediction, baseband unit, memory capacity, sublinear-time algorithms, effective capacity, remote radio heads
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