Abstract
The limited caching capacity of the local cache enabled Base station (BS) decreases the cache hit ratio (CHR) and user satisfaction ratio (USR). However, Cache enabled multi-tier cellular networks have been presented as a promising candidate for fifth generation networks to achieve higher CHR and USR through densification of networks. In addition to this, the cooperation among the BSs of various tiers for cached data transfer, intensify its significance many folds. Therefore, in this paper, we consider maximization of CHR and USR in a multi-tier cellular network. We formulate a CHR and USR problem for multi-tier cellular networks while putting major constraints on caching space of BSs of each tier. The unsupervised learning algorithms such as K-mean clustering and collaborative filtering have been used for clustering the similar BSs in each tier and estimating the content popularity respectively. A novel scheme such as cluster average popularity based collaborative filtering (CAP-CF) algorithm is employed to cache popular data and hence maximizing the CHR in each tier. Similarly, two novel methods such as intra-tier and cross-tier cooperation (ITCTC) and modified ITCTC algorithms have been employed in order to optimize the USR. Simulations results witness, that the proposed schemes yield significant performance in terms of average cache hit ratio and user satisfaction ratio compared to other conventional approaches.
Highlights
Introduction and BackgroundRecently, smart phone usage has enormously increased which results in exponential growth of mobile data traffic
We proposed a framework for content caching and user satisfaction ratio using cross-tier and intra-tier cooperation among the base stations
The contents which are not requested but most likely to be requested in future, are predicted using the collaborative filtering and the average cache
Summary
Smart phone usage has enormously increased which results in exponential growth of mobile data traffic. Different machine learning tool can extract information in the data traffic and cache the contents at the local base station [10]. Extreme learning machine predicts the popularity of the contents based on the different meta features of the contents [12] Unsupervised learning such as K-mean clustering and collaborative filtering methods are employed to cluster the users with similar crossest for the contents and cache the popular contents [11,13]. Proactive caching is done by creating clusters of base stations using the unsupervised K-mean clustering in each tier and fill half of the cache memory with the most popular contents in the cluster and rest of vacant cache is occupied by the contents that will be requested in future predicted by collaborative filtering C.F. Presenting two novel methods for the user satisfaction rate. It is shown that by using learning algorithms, caching the data on base station in the different tier, considerably improved the cache ratio and the user satisfaction ratio
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