Cooperative Content Caching for 5G and Beyond Mobile Wireless Networks

Abstract

: The increased interest in traffic-intensive applications such as High Definition (HD) video, augmented reality, and 3-D visualization is expected to result in higher network traffic. Such higher-fold traffic growth requires a significant paradigm shift in implementing upcoming 5G technology so that the user requests can be accommodated at the core network without causing a bottleneck. Emerging mobile content caching techniques can efficiently relieve overloaded network by caching popular content at intermediate nodes and user devices. Its efficacy, however, lies in the intelligent caching of popular files. To better deploy caching, a heterogeneous caching architecture is proposed that supports comprehensive cooperation. We propose three cooperative caching schemes in cellular networks, D2D networks, and cross-tier networks. Caching decisions are made by considering the content popularity, the device distribution, the transmission method, and the caching capability. Furthermore, we investigate a multi-association-based model in which a user associates with multiple caching entities to retrieve its requested content. We then present an agglomerative hierarchical clustering algorithm for setting up users' preferences and grouping them into the same clusters based on the similarity of their requests. Stochastic geometry has been used to model and analyze different coverage scenarios. Gains obtained are quantified in terms of coverage probability, cache hit probability, and delay through numerical and network simulations. Results show that the coverage probability achieved is 40% higher than the compared method. On the other hand, the cache hit probability increases to nearly 90% after clustering with the proposed method. The delay performance outperforms a popularity-based caching scheme and results in a 75% decrease in delay; however, the network's energy consumption is compromised for this purpose.