60 citations · 130 across the 27 of their papers we have counts for
10 papers · 1 filter
FedRec: Federated Learning of Universal Receivers over Fading Channels
Mahdi Boloursaz Mashhadi, Nir Shlezinger, Yonina C. Eldar +1
Wireless communications is often subject to channel fading. Various statistical models have been proposed to capture the inherent randomness in fading, and conventional model-based…
Over-the-Air Federated Learning from Heterogeneous Data
Tomer Sery, Nir Shlezinger, Kobi Cohen +1
Federated learning (FL) is a framework for distributed learning of centralized models. In FL, a set of edge devices train a model using their local data, while repeatedly exchangin…
eSampling: Energy Harvesting ADCs
Neha Jain, Nir Shlezinger, Bhawna Tiwari +4
Analog-to-digital converters (ADCs) allow physical signals to be processed using digital hardware. The power consumed in conversion grows with the sampling rate and quantization re…
Dynamic Metasurface Antennas for 6G Extreme Massive MIMO Communications
Nir Shlezinger, George C. Alexandropoulos, Mohammadreza F. Imani +2
Next generation wireless base stations and access points will transmit and receive using extremely massive numbers of antennas. A promising technology for realizing such massive ar…
UVeQFed: Universal Vector Quantization for Federated Learning
Nir Shlezinger, Mingzhe Chen, Yonina C. Eldar +2
Traditional deep learning models are trained at a centralized server using labeled data samples collected from end devices or users. Such data samples often include private informa…
The Rate Distortion Function of Asynchronously Sampled Memoryless Cyclostationary Gaussian Processes
Emeka Abakasanga, Nir Shlezinger, Ron Dabora
Man-made communications signals are typically modelled as continuous-time (CT) wide-sense cyclostationary (WSCS) processes. As modern processing is digital, it operates on sampled…