Caching at Base Stations with Heterogeneous User Demands and Spatial Locality

TL;DR

This paper develops a caching policy framework for base stations that considers heterogeneous user preferences, activity levels, and spatial locality, demonstrating improved network performance and fairness through real data validation.

Contribution

It introduces a novel framework that optimizes caching by exploiting individual user behavior, moving beyond traditional assumptions of uniform preferences and activity levels.

Findings

Exploiting user heterogeneity improves network performance.

User fairness is enhanced by considering individual preferences.

Performance gains increase with spatial locality skewness.

Abstract

Existing proactive caching policies are designed by assuming that all users request contents with identical activity level at uniformly-distributed or known locations, among which most of the policies are optimized by assuming that user preference is identical to content popularity. However, these assumptions are not true based on recent data analysis. In this paper, we investigate what happens without these assumptions. To this end, we establish a framework to optimize caching policy for base stations exploiting heterogeneous user preference, activity level, and spatial locality. We derive success probability and average rate of each user as utility function, respectively, and obtain the optimal caching policy maximizing a weighted sum of average utility (reflecting network performance) and minimal utility of users (reflecting user fairness). To investigate the intertwined impact of individual user request behavior on caching, we provide an algorithm to synthesize user preference from given content popularity and activity level with controlled preference similarity, and validate the algorithm with real datasets. Analysis and simulation results show that exploiting individual user behavior can improve both network performance and user fairness, and the gain increases with the skewness of spatial locality, and the heterogeneity of user preference and activity level.