Social Community-Aware Content Placement in Wireless Device-to-Device Communication Networks

TL;DR

This paper introduces a social community-aware framework for optimizing content caching in multi-hop D2D wireless networks, leveraging social context and cooperative game theory to enhance content offloading efficiency.

Contribution

It proposes a novel hypergraph-based model and a cooperative game approach using Shapley values to identify optimal cache locations considering social communities.

Findings

Achieves up to 21% improvement in content offloading over traditional centrality-based schemes.

Develops a hypergraph model capturing multi-community social connections.

Introduces a new offloading power metric for cache placement in D2D networks.

Abstract

In this paper, a novel framework for optimizing the caching of popular user content at the level of wireless user equipments (UEs) is proposed. The goal is to improve content offloading over wireless device-to-device (D2D) communication links. In the considered network, users belong to different social communities while their UEs form a single multi-hop D2D network. The proposed framework allows to exploit the multi-community social context of users for improving the local offloading of cached content in a multihop D2D network. To model the collaborative effect of a set of UEs on content offloading, a cooperative game between the UEs is formulated. For this game, it is shown that the Shapley value (SV) of each UE effectively captures the impact of this UE on the overall content offloading process. To capture the presence of multiple social communities that connect the UEs, a hypergraph model is proposed. Two line graphs, an influence-weighted graph, and a connectivity-weighted graph, are developed for analyzing the proposed hypergaph model. Using the developed line graphs along with the SV of the cooperative game, a precise offloading power metric is derived for each UE within a multi-community, multi-hop D2D network. Then, UEs with high offloading power are chosen as the optimal locations for caching the popular content. Simulation results show that, on the average, the proposed cache placement framework achieves 12%, 19%, and 21% improvements in terms of the number of UEs that received offloaded popular content compared to the schemes based on betweenness, degree, and closeness centrality, respectively.