Content popularity prediction in Fog-RANs : a Bayesian learning approach
Tao, Yunwei; Jiang, Yanxiang; Zheng, Fu-Chun; Bennis, Mehdi; You, Xiaohu (2022-02-02)
Y. Tao, Y. Jiang, F. -C. Zheng, M. Bennis and X. You, "Content Popularity Prediction in Fog-RANs: A Bayesian Learning Approach," 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, 2021, pp. 1-6, doi: 10.1109/GLOBECOM46510.2021.9685947
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe2023040535117
Tiivistelmä
Abstract
In this paper, the content popularity prediction problem in cache-enabled fog radio access networks (F-RANs) is investigated. In order to predict the content popularity with high accuracy and low complexity, we propose a Gaussian process based Poisson regressor to model the content request pattern. Firstly, the relationship between content features and popularity is captured by our developed model. Then, we utilize Bayesian learning to learn the model parameters, which are robust to over-fitting. However, Bayesian methods are usually unable to find a closed-form expression of the posterior distribution. To tackle this issue, we apply a Stochastic Variance Reduced Gradient Hamiltonian Monte Carlo (SVRG-HMC) to approximate the posterior distribution. Two types of predictive content popularity are formulated for the requests of existing contents and newly-added contents. Simulation results show that the performance of our proposed policy outperforms the policy based on other Monte Carlo based method.
Kokoelmat
- Avoin saatavuus [32026]