Research Article
Content-Aware Proactive Caching and Energy-Efficient Design in Clustered Small Cell Networks
@INPROCEEDINGS{10.1007/978-3-030-67720-6_20, author={Xiang Yu and Huiting Luo and Long Teng and Ting Liu}, title={Content-Aware Proactive Caching and Energy-Efficient Design in Clustered Small Cell Networks}, proceedings={Communications and Networking. 15th EAI International Conference, ChinaCom 2020, Shanghai, China, November 20-21, 2020, Proceedings}, proceedings_a={CHINACOM}, year={2021}, month={2}, keywords={Clustered SCNs Popularity prediction CWFM EE Proactive caching}, doi={10.1007/978-3-030-67720-6_20} }- Xiang Yu
Huiting Luo
Long Teng
Ting Liu
Year: 2021
Content-Aware Proactive Caching and Energy-Efficient Design in Clustered Small Cell Networks
CHINACOM
Springer
DOI: 10.1007/978-3-030-67720-6_20
Abstract
This paper considers clustered small cell networks (SCNs) with combined design of cooperative caching and energy-efficient policy in the Coordinated Multi-Point (CoMP)-enabled cellular network. Small base stations (SBSs) with cache storage are grouped into associative clusters which can communicate with each other. This paper focus on movie on-demand streaming from Internet-based servers and proposed combined caching mode, where every SBS utilizes parts of cache space to cache the most popular contents (MPC), while the remaining is used for cooperatively caching different partitions of the less popular contents (LPC). Instead of the known content popularity, we constructs a content-aware weighted feature matrix (CWFM) in terms of spatiotemporal variation. Based on estimated content popularity and transmission design, we propose a caching scheme that makes a caching decision to maximize the energy efficiency (EE). To tackle this problem, A two-step stepwise optimization method is adopted. First, EE conditioning is optimized with a approach of linear programming and variable recovery. Then, the optimal proportion of cache space for MPC is analyzed by comparing the energy-efficient gain from the MPC with the energy-efficient loss from the discarded contents. Extensive simulation results confirm that our algorithm outperforms state-of-the-art algorithms based on MovieLens data set.
