The Experts below are selected from a list of 68187 Experts worldwide ranked by ideXlab platform
Tianpeng Jiang - One of the best experts on this subject based on the ideXlab platform.
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d2d big data content deliveries over Wireless Device to Device sharing in large scale mobile networks
arXiv: Networking and Internet Architecture, 2018Co-Authors: Xiaofei Wang, Yuhua Zhang, Victor C M Leung, Nadra Guizani, Tianpeng JiangAbstract:Recently the topic of how to effectively offload cellular traffic onto Device-to-Device (D2D) sharing among users in proximity has been gaining more and more attention of global researchers and engineers. Users utilize Wireless short-range D2D communications for sharing contents locally, due to not only the rapid sharing experience and free cost, but also high accuracy on deliveries of interesting and popular contents, as well as strong social impacts among friends. Nevertheless, the existing related studies are mostly confined to small-scale datasets, limited dimensions of user features, or unrealistic assumptions and hypotheses on user behaviors. In this article, driven by emerging Big Data techniques, we propose to design a big data platform, named D2D Big Data, in order to encourage the Wireless D2D communications among users effectively, to promote contents for providers accurately, and to carry out offloading intelligence for operators efficiently. We deploy a big data platform and further utilize a large-scale dataset (3.56 TBytes) from a popular D2D sharing application (APP), which contains 866 million D2D sharing activities on 4.5 million files disseminated via nearly 850 million users in 13 weeks. By abstracting and analyzing multidimensional features, including online behaviors, content properties, location relations, structural characteristics, meeting dynamics, social arborescence, privacy preservation policies and so on, we verify and evaluate the D2D Big Data platform regarding predictive content propagating coverage. Finally, we discuss challenges and opportunities regarding D2D Big Data and propose to unveil a promising upcoming future of Wireless D2D communications.
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D2D Big Data: Content Deliveries over Wireless Device-to-Device Sharing in Large-Scale Mobile Networks
IEEE Wireless Communications, 2018Co-Authors: Xiaofei Wang, Yuhua Zhang, Victor C M Leung, Nadra Guizani, Tianpeng JiangAbstract:Recently the topic of how to effectively offload cellular traffic onto Device-to-Device sharing among users in proximity has been gaining more and more attention from global researchers and engineers. Users utilize Wireless short-range Device-to-Device communications for sharing contents locally, due to not only the rapid sharing experience and free cost, but also high accuracy on deliveries of interesting and popular contents, as well as strong social impact among friends. Nevertheless, the existing related studies are mostly confined to small-scale datasets, limited dimensions of user features, or unrealistic assumptions and hypotheses on user behaviors. In this article, driven by the emerging big data techniques, we propose to design a big data platform, named D2D big data, in order to encourage Wireless Device-to-Device communications among users effectively, to promote contents for providers accurately, and to carry out offloading intelligence for operators efficiently. We deploy a big data platform and further utilize a large-scale dataset (3.56 TB) from a popular Device-to-Device sharing application that contains 866 million Device-to-Device sharing activities on 4.5 million files disseminated via nearly 850 million users in 13 weeks. By abstracting and analyzing multi-dimensional features, including online behaviors, content properties, location relations, structural characteristics, meeting dynamics, social arborescence, privacy preservation policies, and so on, we verify and evaluate the D2D big data platform regarding predictive content propagating coverage. Finally, we discuss challenges and opportunities regarding D2D big data and unveil the promising upcoming future of Wireless Device-to-Device communications.
Hossein Saidi - One of the best experts on this subject based on the ideXlab platform.
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Social Community-Aware Content Placement in Wireless Device-to-Device Communication Networks
IEEE Transactions on Mobile Computing, 2019Co-Authors: Mehdi Naderi Soorki, Mohammad Hossein Manshaei, Walid Saad, Hossein SaidiAbstract: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 us to exploit the multi-community social context of users for improving the local offloading of cached content in a multi-hop 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 percent 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.
Andreas F. Molisch - One of the best experts on this subject based on the ideXlab platform.
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caching policy and cooperation distance design for base station assisted Wireless d2d caching networks throughput and energy efficiency optimization and tradeoff
IEEE Transactions on Wireless Communications, 2018Co-Authors: Mingchun Lee, Andreas F. MolischAbstract:This paper investigates the optimal caching policy and cooperation distance design from both throughput and energy efficiency (EE) perspectives in base station (BS)-assisted Wireless Device-to-Device (D2D) caching networks. By jointly considering the effects of the BS transmission, D2D-caching, and self-caching, and the impact of the cooperation distance, a clustering approach is proposed with specifically designed power control and resource reuse policies. The throughput and EE of two network structures are comprehensively analyzed and designs aiming to optimize the throughout and EE, respectively, are proposed. We also characterize the trade-off between the throughput and EE and provide corresponding designs. Simulations considering practical parameters are conducted to verify the analyses and evaluate the proposed designs; they demonstrate superior performance compared with state-of-the art.
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Wireless Device-to-Device Caching Networks: Basic Principles and System Performance
IEEE Journal on Selected Areas in Communications, 2016Co-Authors: Mingyue Ji, Giuseppe Caire, Andreas F. MolischAbstract:As Wireless video is the fastest growing form of data traffic, methods for spectrally efficient on-demand Wireless video streaming are essential to both service providers and users. A key property of video on-demand is the asynchronous content reuse, such that a few popular files account for a large part of the traffic but are viewed by users at different times. Caching of content on Wireless Devices in conjunction with Device-to-Device (D2D) communications allows to exploit this property, and provide a network throughput that is significantly in excess of both the conventional approach of unicasting from cellular base stations and the traditional D2D networks for “regular” data traffic. This paper presents in a tutorial and concise form some recent results on the throughput scaling laws of Wireless networks with caching and asynchronous content reuse, contrasting the D2D approach with other alternative approaches such as conventional unicasting, harmonic broadcasting, and a novel coded multicasting approach based on caching in the user Devices and network-coded transmission from the cellular base station only. Somehow surprisingly, the D2D scheme with spatial reuse and simple decentralized random caching achieves the same near-optimal throughput scaling law as coded multicasting. Both schemes achieve an unbounded throughput gain (in terms of scaling law) with respect to conventional unicasting and harmonic broadcasting, in the relevant regime where the number of video files in the library is smaller than the total size of the distributed cache capacity in the network. To better understand the relative merits of these competing approaches, we consider a holistic D2D system design incorporating traditional microwave (2 GHz) and millimeter-wave (mm-wave) D2D links; the direct connections to the base station can be used to provide those rare video requests that cannot be found in local caches. We provide extensive simulation results under a variety of system settings and compare our scheme with the systems that exploit transmission from the base station only. We show that, also in realistic conditions and nonasymptotic regimes, the proposed D2D approach offers very significant throughput gains.
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Wireless Device to Device caching networks basic principles and system performance
arXiv: Information Theory, 2013Co-Authors: Giuseppe Caire, Andreas F. MolischAbstract:As Wireless video transmission is the fastest-growing form of data traffic, methods for spectrally efficient video on-demand Wireless streaming are essential to service providers and users alike. A key property of video on-demand is the asynchronous content reuse, such that a few dominant videos account for a large part of the traffic, but are viewed by users at different times. Caching of content on Devices in conjunction with D2D communications allows to exploit this property, and provide a network throughput that is significantly in excess of both the conventional approach of unicasting from the base station and the traditional D2D networks for regular data traffic. This paper presents in a semi-tutorial concise form some recent results on the throughput scaling laws of Wireless networks with caching and asynchronous content reuse, contrasting the D2D approach with a competing approach based on combinatorial cache design and network coded transmission from the base station (BS) only, referred to as coded multicasting. Interestingly, the spatial reuse gain of the former and the coded multicasting gain of the latter yield, somehow surprisingly, the same near-optimal throughput behavior in the relevant regime where the number of video files in the library is smaller than the number of streaming users. Based on our recent theoretical results, we propose a holistic D2D system design that incorporates traditional microwave (2 GHz) as well as millimeter-wave D2D links; the direct connections to the base station can be used to provide those rare video requests that cannot be found in local caches. We provide extensive simulations under a variety of system settings, and compare our scheme with other existing schemes by the BS. We show that, despite the similar behavior of the scaling laws, the proposed D2D approach offers very significant throughput gains with respect to the BS-only schemes.
Mehdi Naderi Soorki - One of the best experts on this subject based on the ideXlab platform.
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Social Community-Aware Content Placement in Wireless Device-to-Device Communication Networks
IEEE Transactions on Mobile Computing, 2019Co-Authors: Mehdi Naderi Soorki, Mohammad Hossein Manshaei, Walid Saad, Hossein SaidiAbstract: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 us to exploit the multi-community social context of users for improving the local offloading of cached content in a multi-hop 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 percent 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.
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social community aware content placement in Wireless Device to Device communication networks
arXiv: Computer Science and Game Theory, 2018Co-Authors: Mehdi Naderi Soorki, Mohammad Hossein Manshaei, Walid Saad, Hossei SaidiAbstract: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.
Xiaofei Wang - One of the best experts on this subject based on the ideXlab platform.
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d2d big data content deliveries over Wireless Device to Device sharing in large scale mobile networks
arXiv: Networking and Internet Architecture, 2018Co-Authors: Xiaofei Wang, Yuhua Zhang, Victor C M Leung, Nadra Guizani, Tianpeng JiangAbstract:Recently the topic of how to effectively offload cellular traffic onto Device-to-Device (D2D) sharing among users in proximity has been gaining more and more attention of global researchers and engineers. Users utilize Wireless short-range D2D communications for sharing contents locally, due to not only the rapid sharing experience and free cost, but also high accuracy on deliveries of interesting and popular contents, as well as strong social impacts among friends. Nevertheless, the existing related studies are mostly confined to small-scale datasets, limited dimensions of user features, or unrealistic assumptions and hypotheses on user behaviors. In this article, driven by emerging Big Data techniques, we propose to design a big data platform, named D2D Big Data, in order to encourage the Wireless D2D communications among users effectively, to promote contents for providers accurately, and to carry out offloading intelligence for operators efficiently. We deploy a big data platform and further utilize a large-scale dataset (3.56 TBytes) from a popular D2D sharing application (APP), which contains 866 million D2D sharing activities on 4.5 million files disseminated via nearly 850 million users in 13 weeks. By abstracting and analyzing multidimensional features, including online behaviors, content properties, location relations, structural characteristics, meeting dynamics, social arborescence, privacy preservation policies and so on, we verify and evaluate the D2D Big Data platform regarding predictive content propagating coverage. Finally, we discuss challenges and opportunities regarding D2D Big Data and propose to unveil a promising upcoming future of Wireless D2D communications.
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D2D Big Data: Content Deliveries over Wireless Device-to-Device Sharing in Large-Scale Mobile Networks
IEEE Wireless Communications, 2018Co-Authors: Xiaofei Wang, Yuhua Zhang, Victor C M Leung, Nadra Guizani, Tianpeng JiangAbstract:Recently the topic of how to effectively offload cellular traffic onto Device-to-Device sharing among users in proximity has been gaining more and more attention from global researchers and engineers. Users utilize Wireless short-range Device-to-Device communications for sharing contents locally, due to not only the rapid sharing experience and free cost, but also high accuracy on deliveries of interesting and popular contents, as well as strong social impact among friends. Nevertheless, the existing related studies are mostly confined to small-scale datasets, limited dimensions of user features, or unrealistic assumptions and hypotheses on user behaviors. In this article, driven by the emerging big data techniques, we propose to design a big data platform, named D2D big data, in order to encourage Wireless Device-to-Device communications among users effectively, to promote contents for providers accurately, and to carry out offloading intelligence for operators efficiently. We deploy a big data platform and further utilize a large-scale dataset (3.56 TB) from a popular Device-to-Device sharing application that contains 866 million Device-to-Device sharing activities on 4.5 million files disseminated via nearly 850 million users in 13 weeks. By abstracting and analyzing multi-dimensional features, including online behaviors, content properties, location relations, structural characteristics, meeting dynamics, social arborescence, privacy preservation policies, and so on, we verify and evaluate the D2D big data platform regarding predictive content propagating coverage. Finally, we discuss challenges and opportunities regarding D2D big data and unveil the promising upcoming future of Wireless Device-to-Device communications.
