803 citations · 3.5k across the 244 of their papers we have counts for
10 papers · 2 filters
Information Bottleneck-Inspired Type Based Multiple Access for Remote Estimation in IoT Systems
Meiyi Zhu, Chunyan Feng, Caili Guo +2
Type-based multiple access (TBMA) is a semantics-aware multiple access protocol for remote inference. In TBMA, codewords are reused across transmitting sensors, with each codeword…
A Bayesian Framework for Digital Twin-Based Control, Monitoring, and Data Collection in Wireless Systems
Clement Ruah, Osvaldo Simeone, Bashir Al-Hashimi
Commonly adopted in the manufacturing and aerospace sectors, digital twin (DT) platforms are increasingly seen as a promising paradigm to control, monitor, and analyze software-bas…
Channel-driven Decentralized Bayesian Federated Learning for Trustworthy Decision Making in D2D Networks
Luca Barbieri, Osvaldo Simeone, Monica Nicoli
Bayesian Federated Learning (FL) offers a principled framework to account for the uncertainty caused by limitations in the data available at the nodes implementing collaborative tr…
Network Topology Inference based on Timing Meta-Data
Wenbo Du, Tao Tan, Haijun Zhang +3
Consider a processor having access only to meta-data consisting of the timings of data packets and acknowledgment (ACK) packets from all nodes in a network. The meta-data report th…
Calibrating AI Models for Few-Shot Demodulation via Conformal Prediction
Kfir M. Cohen, Sangwoo Park, Osvaldo Simeone +1
AI tools can be useful to address model deficits in the design of communication systems. However, conventional learning-based AI algorithms yield poorly calibrated decisions, unabl…
Digital Twin-Based Multiple Access Optimization and Monitoring via Model-Driven Bayesian Learning
Clement Ruah, Osvaldo Simeone, Bashir Al-Hashimi
Commonly adopted in the manufacturing and aerospace sectors, digital twin (DT) platforms are increasingly seen as a promising paradigm to control and monitor software-based, "open"…