Learning-Based Content Caching and User Clustering: A Deep Deterministic Policy Gradient Approach

Kun Lin Chan, Feng-Tsun Chien

研究成果: Conference contribution同行評審

摘要

The joint design of content caching and user clustering (JCC) in cache-enabled heterogeneous networks is challenging, due to various unknown, possibly time-varying, system parameters which potentially give rise to various design tradeoffs in practice. This paper presents the first study that investigates the problem of JCC using the deep deterministic policy gradient (DDPG)-based reinforcement learning, with the purpose of balancing both the energy efficiency (EE) and content hit probability (CHP), while satisfying the cluster size constraint (CSC). We propose a new learning structure, termed multiDDPG (MDDPG), that demonstrates better EE performance while providing a comparable CHP to the caching scheme with known content popularity.

原文English
主出版物標題2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
頁面5055-5059
頁數5
ISBN(電子)9781509066315
DOIs
出版狀態Published - 五月 2020
事件2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Barcelona, Spain
持續時間: 4 五月 20208 五月 2020

出版系列

名字ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2020-May
ISSN(列印)1520-6149

Conference

Conference2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020
國家Spain
城市Barcelona
期間4/05/208/05/20

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