Open AccessJournal Article
Mobility-Aware Caching in D2D Networks
Vikas Wasade,Nilesh Bodhane +1 more
TLDR
The results show that the proposed policy achieves higher offloading and lower content-retrieval delays than existing state-of-the-art approaches.Abstract:
In this paper, we propose a novel policy for device caching that facilitates popular content exchange through highrate device-to-device (D2D) millimeter-wave (mmWave) communication.The D2D aware caching (DAC) policy splits the cacheable content into two content groups and distributes it randomly to the user equipment devices (UEs), with the goal to enable D2D connections. By exploiting the high-bandwidth availability and the directionality of mmWaves, we ensure high rates for the D2D transmissions, while mitigating the co-channel interference that limits the D2D-communication potentials in the sub-6 GHz bands. Furthermore, based on a stochasticgeometry approach for the modeling of the network topology, we analytically derive the offloading gain that is achieved by the proposed policy and the distribution of the content retrieval delay considering both half- and full-duplex mode for the D2D communication. The accuracy of the proposed analytical framework is validated through Monte-Carlo simulations. In addition, for a wide range of a content popularity indicator the results show that the proposed policy achieves higher offloading and lower content-retrieval delays than existing state-of-the-art approaches.read more
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
Le Thanh Tan,Rose Qingyang Hu +1 more
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
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.
Journal ArticleDOI
Twin-Timescale Artificial Intelligence Aided Mobility-Aware Edge Caching and Computing in Vehicular Networks
TL;DR: A joint communication, caching and computing strategy for achieving cost efficiency in vehicular networks is proposed and it is proposed to maximize a carefully constructed mobility-aware reward function using the classic particle swarm optimization scheme at the associated large timescale level.
Posted Content
Mobile Edge Intelligence and Computing for the Internet of Vehicles
Jun Zhang,Khaled Ben Letaief +1 more
TL;DR: In this paper, the edge information system (EIS), including edge caching, edge computing, and edge AI, will play a key role in the future intelligent IoV, which will provide not only low-latency content delivery and computation services, but also localized data acquisition, aggregation and processing.
References
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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
Le Thanh Tan,Rose Qingyang Hu +1 more
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
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.
Journal ArticleDOI
Twin-Timescale Artificial Intelligence Aided Mobility-Aware Edge Caching and Computing in Vehicular Networks
TL;DR: A joint communication, caching and computing strategy for achieving cost efficiency in vehicular networks is proposed and it is proposed to maximize a carefully constructed mobility-aware reward function using the classic particle swarm optimization scheme at the associated large timescale level.