Abstract
Next generation cellular systems need efficient content-distribution schemes. Content-sharing via Device-to-Device (D2D) clustered networks has emerged as a popular approach for alleviating the burden on the cellular network. In this article, we utilize Content-Centric Networking and Network Virtualization to propose a distributed architecture, that supports efficient content delivery. We propose to use clustering at the user level for content-distribution. A weighted multifactor clustering algorithm is proposed for grouping the D2D User Equipment (DUEs) sharing a common interest. The proposed algorithm is evaluated in terms of energy efficiency, area spectral efficiency, and throughput. The effect of the number of clusters on these performance parameters is also discussed. The proposed algorithm has been further modified to allow for a tradeoff between fairness and other performance parameters. A comprehensive simulation study demonstrates that the proposed clustering algorithm is more flexible and outperforms several classical and state-of-the-art algorithms.
Highlights
Unprecedented demand for multicast applications is driving a move towards content-centric cellular networks
Our study proposes a network architecture that combines the concepts of Centric Networking (CCN) and Network Virtualization (NV)
As the ratio of the variances given in Equation (12) increases, user segregation becomes more precise which leads to the optimal number of clusters
Summary
Unprecedented demand for multicast applications is driving a move towards content-centric cellular networks. To realize clustering that supports content-sharing via D2D, a suitable architecture is necessary that conforms to the standards of future cellular networks but is distributive in nature. It should be capable of handling a high user density. A distributed architecture is proposed that is effectively supported by hash functions to identify the socially connected users This is in contrast to the majority of the published works on D2D multicasting that do not consider distributed architecture along with content-identification. To the best of the author’s knowledge, reported work in the literature considers either the spatial distribution of users or users’ social ties for their respective clustering algorithms. The findings of this research work are summarized in the Conclusion section while discussing the future directions of the proposed study
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