activity
20172021
most citedDistributed Optimization for Energy-efficient Fog Computing in the Tactile Internet

123 citations · 366 across the 18 of their papers we have counts for

collaborators

20 papers

cs.NI2021

AdaptiveFog: A Modelling and Optimization Framework for Fog Computing in Intelligent Transportation Systems

Yong Xiao, Marwan Krunz

Fog computing has been advocated as an enabling technology for computationally intensive services in smart connected vehicles. Most existing works focus on analyzing the queueing a…

cs.NI2021

Optimizing Intelligent Reflecting Surface-Base Station Association for Mobile Networks

Dongzi Jin, Yong Xiao, Yingyu Li +2

This paper studies a multi-Intelligent Reflecting Surfaces (IRSs)-assisted wireless network consisting of multiple base stations (BSs) serving a set of mobile users. We focus on th…

cs.LG2021

Federated Traffic Synthesizing and Classification Using Generative Adversarial Networks

Chenxin Xu, Rong Xia, Yong Xiao +3

With the fast growing demand on new services and applications as well as the increasing awareness of data protection, traditional centralized traffic classification approaches are…

eess.SP20211 cited

Spatio-temporal Modeling for Large-scale Vehicular Networks Using Graph Convolutional Networks

Juntong Liu, Yong Xiao, Yingyu Li +3

The effective deployment of connected vehicular networks is contingent upon maintaining a desired performance across spatial and temporal domains. In this paper, a graph-based fram…

cs.NI2020

From Semantic Communication to Semantic-aware Networking: Model, Architecture, and Open Problems

Guangming Shi, Yong Xiao, Yingyu Li +1

Existing communication systems are mainly built based on Shannon's information theory which deliberately ignores the semantic aspects of communication. The recent iteration of wire…

cs.DC20201 cited

Optimizing Resource-Efficiency for Federated Edge Intelligence in IoT Networks

Yong Xiao, Yingyu Li, Guangming Shi +1

This paper studies an edge intelligence-based IoT network in which a set of edge servers learn a shared model using federated learning (FL) based on the datasets uploaded from a mu…