Mixed-Timescale Caching and Beamforming in Content Recommendation Aware Fog-RAN: A Latency Perspective

Xiaolong Yang, Yaru Fu, Wanli Wen, Tony Q.S. Quek, Zesong Fei

Research output: Contribution to journalArticlepeer-review


Content caching is recognized as a promising solution to release the heavy burden of backhaul links and decrease the content transmission latency in Fog radio access networks (Fog-RANs). However, the content caching design is still a challenging problem with considering the user request patterns, the content delivery strategies, and the limited caching capacity. Recommendation has the capability of reshaping users’ content requests for further prompting caching gain. The joint recommendation, caching, beamforming holds the potential to improve the system performance of Fog-RANs. In this paper, a joint recommendation, caching, and beamforming scheme is proposed for multi-cell multi-antenna recommendation aware Fog-RANs. Aiming at minimizing the content transmission latency, we formulate a joint recommendation, caching, and beamforming optimization problem. The minimization problem is a very challenging two-timescale mixed integer nonlinear programming problem, which is hard to solve in general. By exploring structural properties of the problem, we propose an alternative optimization algorithm with low complexity through decomposing the original problem into three sub-problems. Extensive simulations show that our proposed method can significantly reduce the content transmission delay.

Original languageEnglish
JournalIEEE Transactions on Communications
Publication statusAccepted/In press - 2020

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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