activity
20202023
most citedEdge Intelligence for Autonomous Driving in 6G Wireless System: Design Challenges and Solutions

5 citations · 12 across the 13 of their papers we have counts for

collaborators
Showing cs.NIShow all

9 papers · 1 filter

cs.NI20231 cited

Massive Access of Static and Mobile Users via Reconfigurable Intelligent Surfaces: Protocol Design and Performance Analysis

Xuelin Cao, Bo Yang, Chongwen Huang +5

The envisioned wireless networks of the future entail the provisioning of massive numbers of connections, heterogeneous data traffic, ultra-high spectral efficiency, and low latenc…

cs.NI2022

Federated Spectrum Learning for Reconfigurable Intelligent Surfaces-Aided Wireless Edge Networks

Bo Yang, Xuelin Cao, Chongwen Huang +6

Increasing concerns on intelligent spectrum sensing call for efficient training and inference technologies. In this paper, we propose a novel federated learning (FL) framework, dub…

cs.NI2021

HAP-Reserved Communications in Space-Air-Ground Integrated Networks

Xuelin Cao, Bo Yang, Chau Yuen +1

Terrestrial communication networks have experienced significant development in recent years by providing emerging services for ground users. However, one critical challenge raised…

cs.NI20212 cited

Converged Reconfigurable Intelligent Surface and Mobile Edge Computing for Space Information Networks

Xuelin Cao, Bo Yang, Chongwen Huang +4

Space information networks (SIN) are facing an ever-increasing thirst for high-speed and high-capacity seamless data transmission due to the integration of ground, air, and space c…

cs.NI2021

AI-Assisted MAC for Reconfigurable Intelligent Surface-Aided Wireless Networks: Challenges and Opportunities

Xuelin Cao, Bo Yang, Chongwen Huang +6

Recently, significant research attention has been devoted to the study of reconfigurable intelligent surfaces (RISs), which are capable of reconfiguring the wireless propagation en…

cs.NI2021

A Joint Energy and Latency Framework for Transfer Learning over 5G Industrial Edge Networks

Bo Yang, Omobayode Fagbohungbe, Xuelin Cao +4

In this paper, we propose a transfer learning (TL)-enabled edge-CNN framework for 5G industrial edge networks with privacy-preserving characteristic. In particular, the edge server…