595 citations · 3.6k across the 131 of their papers we have counts for
16 papers · 1 filter
Model-Based Deep Learning
Nir Shlezinger, Jay Whang, Yonina C. Eldar +1
Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Such model-based methods utilize mathematical formulations th…
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…
Multi-Level Group Testing with Application to One-Shot Pooled COVID-19 Tests
Amit Solomon, Alejandro Cohen, Nir Shlezinger +2
A key requirement in containing contagious diseases, such as the Coronavirus disease 2019 (COVID-19) pandemic, is the ability to efficiently carry out mass diagnosis over large pop…
BiLiMO: Bit-Limited MIMO Radar via Task-Based Quantization
Feng Xi, Nir Shlezinger, Yonina C. Eldar
Recent years have witnessed growing interest in reduced cost radar systems operating with low power. Multiple-input multiple-output (MIMO) radar technology is known to achieve high…
Task-Based Analog-to-Digital Converters
Peter Neuhaus, Nir Shlezinger, Meik Dörpinghaus +2
Obtaining digital representations of multivariate continuous-time (CT) signals is a challenge encountered in many signal processing systems. In practice, these signals are often ac…
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…
Learned Factor Graphs for Inference from Stationary Time Sequences
Nir Shlezinger, Nariman Farsad, Yonina C. Eldar +1
The design of methods for inference from time sequences has traditionally relied on statistical models that describe the relation between a latent desired sequence and the observed…
Spatial Modulation for Joint Radar-Communications Systems: Design, Analysis, and Hardware Prototype
Dingyou Ma, Nir Shlezinger, Tianyao Huang +4
Dual-function radar-communications (DFRC) systems implement radar and communication functionalities on a single platform. Jointly designing these subsystems can lead to substantial…
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…
Data-Driven Symbol Detection via Model-Based Machine Learning
Nariman Farsad, Nir Shlezinger, Andrea J. Goldsmith +1
The design of symbol detectors in digital communication systems has traditionally relied on statistical channel models that describe the relation between the transmitted symbols an…
Task-Based Quantization with Application to MIMO Receivers
Nir Shlezinger, Yonina C. Eldar
Multiple-input multiple-output (MIMO) systems are required to communicate reliably at high spectral bands using a large number of antennas, while operating under strict power and c…
DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection
Nir Shlezinger, Rong Fu, Yonina C. Eldar
Digital receivers are required to recover the transmitted symbols from their observed channel output. In multiuser multiple-input multiple-output (MIMO) setups, where multiple symb…
Data-Driven Factor Graphs for Deep Symbol Detection
Nir Shlezinger, Nariman Farsad, Yonina C. Eldar +1
Many important schemes in signal processing and communications, ranging from the BCJR algorithm to the Kalman filter, are instances of factor graph methods. This family of algorith…