TY - JOUR
T1 - On recommendation-aware content caching for 6G
T2 - An artificial intelligence and optimization empowered paradigm
AU - Fu, Yaru
AU - Doan, Khai Nguyen
AU - Quek, Tony Q.S.
N1 - Publisher Copyright:
© 2020 Chongqing University of Posts and Telecommunications
PY - 2020/8
Y1 - 2020/8
N2 - 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.
AB - 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.
KW - 6G
KW - Artificial intelligence
KW - Content caching
KW - Optimization techniques
KW - Recommendation
UR - http://www.scopus.com/inward/record.url?scp=85088392468&partnerID=8YFLogxK
U2 - 10.1016/j.dcan.2020.06.005
DO - 10.1016/j.dcan.2020.06.005
M3 - Article
AN - SCOPUS:85088392468
SN - 2468-5925
VL - 6
SP - 304
EP - 311
JO - Digital Communications and Networks
JF - Digital Communications and Networks
IS - 3
ER -