most citedSpatio-temporal Modeling for Large-scale Vehicular Networks Using Graph Convolutional Networks

1 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.NI20221 cited

Life-long Learning for Reasoning-based Semantic Communication

Jingming Liang, Yong Xiao, Yingyu Li +2

Semantic communication is an emerging paradigm that focuses on understanding and delivering semantics, or meaning of messages. Most existing semantic communication solutions define…

cs.NI2022

Reasoning on the Air: An Implicit Semantic Communication Architecture

Yong Xiao, Yingyu Li, Guangming Shi +1

Semantic communication is a novel communication paradigm which draws inspiration from human communication focusing on the delivery of the meaning of a message to the intended users…

cs.AI20221 cited

ModulE: Module Embedding for Knowledge Graphs

Jingxuan Chai, Guangming Shi

Knowledge graph embedding (KGE) has been shown to be a powerful tool for predicting missing links of a knowledge graph. However, existing methods mainly focus on modeling relation…

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…

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…