1 citations · 1 across the 8 of their papers we have counts for
8 papers
AoI-Oriented Globally Optimal Joint Source and Update Scheduling in Fluid Antenna Systems
Xiaopeng Yuan, Paul Zheng, Anke Schmeink
As a promising technique, fluid antennas enable adaptive radio environment management and interference mitigation through reconfigurable fluid port selections. In this work, to exp…
Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness
Murilo Batista, Shirin Salehi, Saeed Mashdour +3
Building on recent advances in representation learning for wireless channels, this work investigates the cost-benefit trade-offs of high-dimensional channel embeddings in practical…
Analytically Characterized Optimal Power Control for Signal-Level-Integrated Sensing, Computing and Communication in Federated Learning
Paul Zheng, Yao Zhu, Xiaopeng Yuan +2
In the Internet-of-Things (IoT) era, efficient functionality integration is essential to address the growing demands of communication, computation, and sensing. Signal-level integr…
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…
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 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…
PENDANTSS: PEnalized Norm-ratios Disentangling Additive Noise, Trend and Sparse Spikes
Paul Zheng, Emilie Chouzenoux, Laurent Duval
Denoising, detrending, deconvolution: usual restoration tasks, traditionally decoupled. Coupled formulations entail complex ill-posed inverse problems. We propose PENDANTSS for joi…