@inproceedings{d7eef2d1f9124cd9991cf6c033f856ac,
title = "Learning-Based Content Caching and User Clustering: A Deep Deterministic Policy Gradient Approach",
abstract = "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.",
keywords = "content caching, deep learning, Reinforcement learning, small cell network, user clustering",
author = "Chan, {Kun Lin} and Feng-Tsun Chien",
year = "2020",
month = may,
doi = "10.1109/ICASSP40776.2020.9053382",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5055--5059",
booktitle = "2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings",
address = "美國",
note = "2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 ; Conference date: 04-05-2020 Through 08-05-2020",
}
