124 citations · 146 across the 22 of their papers we have counts for
6 papers · 1 filter
Joint Link Adaptation and Device Scheduling Approach for URLLC Industrial IoT Network: A DRL-based Method with Bayesian Optimization
Wei Gao, Paul Zheng, Peng Wu +2
In this article, we consider an industrial internet of things (IIoT) network supporting multi-device dynamic ultra-reliable low-latency communication (URLLC) while the channel stat…
UAV-Enabled ISAC: Towards On-Demand Sensing Services and Enhanced Communication
Xiaopeng Yuan, Peng Wu, Xinran Wang +2
In this paper, we investigate an integrated sensing-and-communication (ISAC) network enabled by an unmanned aerial vehicle (UAV). The UAV is supposed to fly along a periodical circ…
On Signal Peak Power Constraint of Over-the-Air Federated Learning
Lorenz Bielefeld, Paul Zheng, Oner Hanay +3
Federated learning (FL) has been considered a promising privacy preserving distributed edge learning framework. Over-the-air computation (AirComp) leveraging analog transmission en…
Joint Optimization for Security and Reliability in Round-Trip Transmissions for URLLC services
Xinyan Le, Yao Zhu, Yulin Hu +1
Physical layer security (PLS) is a potential solution for secure and reliable transmissions in future Ultra-Reliable and Low-Latency Communications (URLLC). This work jointly optim…
Joint Communication Scheduling and Resource Allocation for Distributed Edge Learning: Seamless Integration in Next-Generation Wireless Networks
Paul Zheng, Navid Keshtiarast, Pradyumna Kumar Bishoyi +4
Distributed edge learning (DL) is considered a cornerstone of intelligence enablers, since it allows for collaborative training without the necessity for local clients to share raw…
Efficient Integration of Distributed Learning Services in Next-Generation Wireless Networks
Paul Zheng, Navid Keshtiarast, Pradyumna Kumar Bishoyi +4
Distributed learning (DL) is considered a cornerstone of intelligence enabler, since it allows for collaborative training without the necessity for local clients to share raw data…