Proactive Content Caching by Exploiting Transfer Learning for Mobile Edge Computing
Abstract
To address the vast multimedia traffic volume and requirements of user Quality of Experience (QoE) in the next generation mobile communication system (5G), it is imperative to develop efficient content caching strategy at mobile network edges, which is deemed as a key technique for 5G. Recent advances in edge/cloud computing and machine learning facilitate efficient content caching for 5G, where mobile edge computing (MEC) can be exploited to reduce service latency by equipping computation and storage capacity at the edge network. In this paper, we propose a proactive caching mechanism named Learning based Cooperative Caching (LECC) strategy based on MEC architecture to reduce transmission cost while improving user QoE for future mobile networks. In LECC, we exploit a Transfer Learning (TL)-based approach for estimating content popularity, and then formulate the proactive caching optimization model. As the optimization problem is NP- hard, we resort to a greedy algorithm for solving the cache content placement problem. Performance evaluation reveals that LECC can apparently improve content cache hit rate, decrease content transmission cost in comparison with known existing caching strategies.
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ML-based proactive content caching at the edge node can avoid backhaul congestion and further reduce service latency.
“Moreover, ML-based proactive content caching at the edge node can also be exploited to avoid backhaul congestion and further reduce the service latency [66,67].”
From Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019) · cites this paper as [67]
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- Authors
- Hou, T., Feng, G., Qin, S., & Jiang, W.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Venue
- GLOBECOM 2017 - 2017 IEEE Global Communications Conference
- Publisher
- IEEE
- Published
- 2017-12
- Pages
- 1-6
- ISSN
- 1930-529X (ISSN-L)
- OpenAlex
- W2783206954
- Cited by
- 85
- References
- 19
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 4 · 2023: 9 · 2022: 10 · 2021: 15 · 2020: 18 · 2019: 21 · 2018: 5
- FWCI
- 9.59
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Globecom (publishedVersion)
- 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 content caching, mobile edge computing, transfer learning, 5G, quality of experience, greedy algorithm, transmission cost reduction, cache hit rate, multimedia traffic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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