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Mobility-Aware Caching in D2D Networks

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TLDR
In this article, a mobility-aware caching placement strategy is proposed to maximize the data offloading ratio, which is defined as the percentage of the requested data that can be delivered via D2D links rather than through base stations.
Abstract
Caching at mobile devices can facilitate device-to-device (D2D) communications, which may significantly improve spectrum efficiency and alleviate the heavy burden on backhaul links. However, most previous works ignored user mobility, thus having limited practical applications. In this paper, we take advantage of the user mobility pattern by the inter-contact times between different users, and propose a mobility-aware caching placement strategy to maximize the data offloading ratio , which is defined as the percentage of the requested data that can be delivered via D2D links rather than through base stations. Given the NP-hard caching placement problem, we first propose an optimal dynamic programming algorithm to obtain a performance benchmark with much lower complexity than exhaustive search. We then prove that the problem falls in the category of monotone submodular maximization over a matroid constraint, and propose a time-efficient greedy algorithm, which achieves an approximation ratio as $\frac {1}{2}$ . Simulation results with real-life data sets will validate the effectiveness of our proposed mobility-aware caching placement strategy. We observe that users moving at either a very low or very high speed should cache the most popular files, while users moving at a medium speed should cache less popular files to avoid duplication.

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Citations
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Journal ArticleDOI

A Survey of Device-to-Device Communications: Research Issues and Challenges

TL;DR: This work reviews recently proposed solutions in over explored and under explored areas in D2D, and provides new insights on open issues in these areas and discusses potential future research directions.
Journal ArticleDOI

Mobility-Aware Edge Caching and Computing in Vehicle Networks: A Deep Reinforcement Learning

TL;DR: A deep reinforcement learning with the multi-timescale framework to tackle the grand challenges of the vehicular networks and proposes the mobility-aware reward estimation for the large timescale model to mitigate the complexity due to the large action space.
Journal ArticleDOI

Mobile Edge Intelligence and Computing for the Internet of Vehicles

TL;DR: Key design issues, methodologies, and hardware platforms are introduced, including edge-assisted perception, mapping, and localization for intelligent IoV, and typical use cases for intelligent vehicles are illustrated.
Journal ArticleDOI

Green and Mobility-Aware Caching in 5G Networks

TL;DR: Simulation results prove that the caching placement on SBS and on mobile devices leveraging user mobility is more efficient than other existing caching strategies in terms of both cache hit ratio and energy efficiency.
Journal ArticleDOI

On Mobile Edge Caching

TL;DR: This paper presents a detailed and in-depth discussion on the caching process, which can be delineated into four phases including content request, exploration, delivery, and update and identifies different issues and review related works in addressing these issues.
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