Proactive Caching Scheme with Local Content Popularity Prediction
Jiazhi Ren, Hui Tian, Gao-feng Nie · 2020
Considering the problem that most works on content placement so far consider global popularity, neglecting the demand difference between base stations (BSs), a content placement scheme based on similarity between small base stations (SBSs) and local content popularity prediction considering popularities' geographical diversity are proposed. Firstly, SBSs that possess similar historical content requests is identified by similarity measurements. Then the probabilities of future requests are predicted for each similar SBS group utilizing linear regression method. Based on this local popularity, the sub-optimal content placement decision is made according to stochastic geometry and convex optimization. Thereafter, real data sets to verify our prediction algorithm and investigate system performance are used. It is shown that the proposed scheme outperforms the comparison schemes in terms of hit ratio.