1 citations · 1 across the 12 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…
Efficient Joint Resource Allocation for Wireless Powered ISAC with Target Localization
Boyao Li, Qinwei He, Boao Zhang +2
Wireless powered integrated sensing and communication (ISAC) faces a fundamental tradeoff between energy supply, communication throughput, and sensing accuracy. This paper investig…
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…
Physical Layer Deception as a Stackelberg Game: Strategy Regimes, Equilibrium, and Robust Design
Wenwen Chen, Bin Han, Yao Zhu +3
Physical layer deception (PLD) combines physical layer security (PLS) with deception: the transmitter actively misleads the eavesdropper with falsified information. We model the tr…
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…