Paper Info
Title
Effective Cache-Enabled Wireless Networks: An Artificial Intelligence- and Recommendation-Oriented Framework
Abstract
Caching at the network edge can significantly reduce users' perceived latency and relieve backhaul pressure, hence invigorating a new set of innovations toward latency-sensitive applications. Nevertheless, the efficacy of caching policies relies on the users' content preference to be 1) known a priori and 2) highly homogeneous, which is not always the case in the real world. In this article, we explore how artificial intelligence (AI) techniques and recommendation can be leveraged to address those core issues and reap the potentials of cache-enabled wireless networks. Specifically, we present the hierarchical, cache-enabled wireless network architecture, in which AI techniques and recommendation are utilized, respectively, to estimate users' content requests in real time using historical data and to reshape users' content preference. Through case studies, we further demonstrate the effectiveness of an AI-based predictor in estimating users' content requests as well as the superiority of joint recommendation and caching policies over conventional caching policies without recommendation.
Year
DOI
Venue
2021
10.1109/MVT.2020.3033934
IEEE Vehicular Technology Magazine
Keywords
DocType
Volume
effective cache-enabled wireless networks,artificial intelligence,recommendation-oriented framework,network edge,backhaul pressure,latency-sensitive applications,intelligence techniques,cache-enabled wireless network architecture,AI techniques,caching policies
Journal
16
Issue
ISSN
Citations 
1
1556-6072
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Yaru Fu18710.53
Howard H. Yang200.34
Khai Nguyen Doan3121.94
Chenxi Liu49912.86
Xijun Wang5739.77
Tony Q. S. Quek63621276.75