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.

该论文研究了基于异质用户喜好、活跃度和空间局部性的缓存策略优化框架，并提出了综合用户与内容请求行为的算法，结果表明该方法可提高网络性能和用户公平性。