Paper Info
Title
On Recommendation-Aware Content Caching For 6g: An Artificial Intelligence And Optimization Empowered Paradigm
Abstract
Recommendation-aware Content Caching (RCC) at the edge enables a significant reduction of the network latency and the backhaul load, thereby invigorating ubiquitous latency-sensitive innovative services. However, the effectiveness of RCC strategies is highly dependent on explicit information as regards subscribers' content request patterns, the sophisticated caching placement policy, and the personalized recommendation tactics. In this article, we investigate how the potentials of Artificial Intelligence (AI) and optimization techniques can be harnessed to address those core issues and facilitate the full implementation of RCC for the upcoming intelligent 6G era. Towards this end, we first elaborate on the hierarchical RCC network architecture. Then, the devised AI and optimization empowered paradigm is introduced, whereas AI and optimization techniques are leveraged to predict the users' content preferences in real-time situations with the assistance of their historical behavior data and determine the cache pushing and recommendation decision, respectively. Through extensive case studies, we validate the effectiveness of AI-based predictors in estimating users' content preference and the superiority of optimized RCC policies over the conventional benchmarks. At last, we shed light on the opportunities and challenges in the future.
Year
DOI
Venue
2020
10.1016/j.dcan.2020.06.005
DIGITAL COMMUNICATIONS AND NETWORKS
Keywords
DocType
Volume
Artificial intelligence, Content caching, Optimization techniques, Recommendation, 6G
Journal
6
Issue
ISSN
Citations 
3
2468-5925
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Yaru Fu18710.53
Khai Nguyen Doan2121.94
Tony Q. S. Quek33621276.75