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
Content popularity prediction in Fog-RANs : a Bayesian learning approach
|Author:||Tao, Yunwei1; Jiang, Yanxiang1,2; Zheng, Fu-Chun1,2;|
1National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China
2School of Electronic and Information Engineering, Harbin Institute of Technology, Shenzhen 518055, China
3Centre for Wireless Communications, University of Oulu, Oulu 90014, Finland
|Online Access:||PDF Full Text (PDF, 0.7 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023040535117
|Publish Date:|| 2023-04-05
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.
|Pages:||1 - 6|
2021 IEEE Global Communications Conference (GLOBECOM)
IEEE Global Communications Conference
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work was supported in part by the Natural Science Foundation of China under grant 61971129, the Natural Science Foundation of Jiangsu Province under grant BK20181264, the Shenzhen Science and Technology Program under Grant KQTD20190929172545139 and JCYJ20180306171815699, and the National Major Research and Development Program of China under Grant 2020YFB1805005.
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