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DeepMove: Predicting Human Mobility with Attentional Recurrent Networks
TLDR
In DeepMove, an attentional recurrent network for mobility prediction from lengthy and sparse trajectories, a multi-modal embedding recurrent neural network is designed to capture the complicated sequential transitions by jointly embedding the multiple factors that govern the human mobility.
Software-Defined Network Function Virtualization: A Survey
TLDR
This survey presents a thorough investigation of the development of NFV under the software-defined NFV architecture, with an emphasis on service chaining as its application.
Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures
TLDR
An interesting relationship among the communication capability, connectivity, and mobility of vehicles is unveiled, and the characteristics about the pattern of parking behavior are found, which benefits from the understanding of utilizing the vehicular resources.
A survey of millimeter wave communications (mmWave) for 5G: opportunities and challenges
TLDR
A survey of existing solutions and standards is carried out, and design guidelines in architectures and protocols for mmWave communications are proposed, to facilitate the deployment of mmWave communication systems in the future 5G networks.
Social-aware D2D communications: qualitative insights and quantitative analysis
TLDR
A social-aware enhanced D2D communication architecture that exploits social networking characteristics for system design is proposed that improves spectral reuse, bring hop gains, and enhance system capacity.
A Survey of Millimeter Wave (mmWave) Communications for 5G: Opportunities and Challenges
TLDR
A survey of existing solutions and standards is carried out, and design guidelines in architectures and protocols for mmWave communications are proposed, which should be further investigated to facilitate the deployment of mmWave communication systems in the future 5G networks.
Improve Service Chaining Performance with Optimized Middlebox Placement
TLDR
The formulation and proposed algorithms have no special assumption on network topology or policy specifications, therefore, they have broad range of applications in various types of networks such as enterprise, data center and broadband access networks.
Coalitional Games for Resource Allocation in the Device-to-Device Uplink Underlaying Cellular Networks
TLDR
This paper addresses the uplink resource allocation problem for multiple D2D and cellular users from a game theory point of view and proposes a coalition formation game based scheme, which achieves the close optimum solution obtained by the centralized exhaustive algorithm and enhances the system sum rate by about 20%-65% without sacrifice of resource sharing fairness.
DeepSTN+: Context-Aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis
TLDR
DeepSTN+ is proposed, a deep learning-based convolutional model to predict crowd flows in the metropolis that reduces the error of the crowd flow prediction by approximately 8%∼13% compared with the state-of-the-art baselines.
Exploiting Device-to-Device Communications in Joint Scheduling of Access and Backhaul for mmWave Small Cells
TLDR
A joint transmission scheduling scheme for the radio access and backhaul of small cells in the mmWave band, termed D2DMAC, where a path selection criterion is designed to enable device-to-device transmissions for performance improvement and a concurrent transmission scheduling algorithm is proposed to fully exploit spatial reuse in mmWave networks.
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