scispace - formally typeset
Y

Yong Li

Researcher at Tsinghua University

Publications -  752
Citations -  19342

Yong Li is an academic researcher from Tsinghua University. The author has contributed to research in topics: Computer science & Cellular network. The author has an hindex of 54, co-authored 646 publications receiving 13752 citations. Previous affiliations of Yong Li include Beijing University of Posts and Telecommunications & Huazhong University of Science and Technology.

Papers
More filters
Journal ArticleDOI

A survey of millimeter wave communications (mmWave) for 5G: opportunities and challenges

TL;DR: 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.
Journal ArticleDOI

Vehicular Fog Computing: A Viewpoint of Vehicles as the Infrastructures

TL;DR: 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.
Journal ArticleDOI

System architecture and key technologies for 5G heterogeneous cloud radio access networks

TL;DR: A H-CRAN is presented in this article as the advanced wireless access network paradigm, where cloud computing is used to fulfill the centralized large-scale cooperative processing for suppressing co-channel interferences.
Journal ArticleDOI

Software-Defined Network Function Virtualization: A Survey

TL;DR: 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.
Proceedings ArticleDOI

DeepMove: Predicting Human Mobility with Attentional Recurrent Networks

TL;DR: 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.