1 citations · 1 across the 17 of their papers we have counts for
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Non-Coherent Over-the-Air Federated Learning: Protocol, Convergence, and Device Scheduling
Haifeng Wen, Nicolò Michelusi, Osvaldo Simeone +2
To mitigate the scalability bottleneck in the radio access network (RAN) in federated edge learning (FEEL), over-the-air federated learning (AirFL) exploits waveform superposition…
SCAN-BEST: Sub-6GHz-Aided Near-field Beam Selection with Formal Reliability Guarantees
Weicao Deng, Binpu Shi, Min Li +1
As millimeter-wave (mmWave) MIMO systems adopt larger antenna arrays, near-field propagation becomes increasingly prominent, especially for users close to the transmitter. Traditio…
NCAirFL: CSI-Free Over-the-Air Federated Learning Based on Non-Coherent Detection
Haifeng Wen, Nicolò Michelusi, Osvaldo Simeone +1
Over-the-air federated learning (FL), i.e., AirFL, leverages computing primitively over multiple access channels. A long-standing challenge in AirFL is to achieve coherent signal a…
Statistically Valid Information Bottleneck via Multiple Hypothesis Testing
Amirmohammad Farzaneh, Osvaldo Simeone
The information bottleneck (IB) problem is a widely studied framework in machine learning for extracting compressed features that are informative for downstream tasks. However, cur…
Semi-Supervised Learning via Cross-Prediction-Powered Inference for Wireless Systems
Houssem Sifaou, Osvaldo Simeone
In many wireless application scenarios, acquiring labeled data can be prohibitively costly, requiring complex optimization processes or measurement campaigns. Semi-supervised learn…
CSI Transfer From Sub-6G to mmWave: Reduced-Overhead Multi-User Hybrid Beamforming
Weicao Deng, Min Li, Ming-Min Zhao +2
Hybrid beamforming is vital in modern wireless systems, especially for massive MIMO and millimeter-wave (mmWave) deployments, offering efficient directional transmission with reduc…