
Federated Learning: A Signal Processing Perspective
The dramatic success of deep learning is largely due to the availability...
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MetaViterbiNet: Online MetaLearned Viterbi Equalization for NonStationary Channels
Deep neural networks (DNNs) based digital receivers can potentially oper...
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Deep Unfolded Recovery of SubNyquist Sampled Ultrasound Image
The most common technique for generating Bmode ultrasound (US) images i...
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Adaptive Quantization of Model Updates for CommunicationEfficient Federated Learning
Communication of model updates between client nodes and the central aggr...
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LoRDNet: Unfolded Deep Detection Network with LowResolution Receivers
The need to recover highdimensional signals from their noisy lowresolu...
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Image Restoration by Deep Projected GSURE
Illposed inverse problems appear in many image processing applications,...
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A Coding Theory Perspective on Multiplexed Molecular Profiling of Biological Tissues
Highthroughput and quantitative experimental technologies are experienc...
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ModelBased Machine Learning for Communications
We present an introduction to modelbased machine learning for communica...
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Bayesian Federated Learning over Wireless Networks
Federated learning is a privacypreserving and distributed training meth...
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ModelBased Deep Learning
Signal processing, communications, and control have traditionally relied...
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FlowStep3D: Model Unrolling for SelfSupervised Scene Flow Estimation
Estimating the 3D motion of points in a scene, known as scene flow, is a...
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Statistical modelbased evaluation of neural networks
Using a statistical modelbased data generation, we develop an experimen...
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Deep Networks for DirectionofArrival Estimation in Low SNR
In this work, we consider directionofarrival (DoA) estimation in the p...
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FedRec: Federated Learning of Universal Receivers over Fading Channels
Wireless communications are often subject to fading conditions. Various ...
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Point of Care Image Analysis for COVID19
Early detection of COVID19 is key in containing the pandemic. Disease d...
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Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution
We propose a learnedstructured unfolding neural network for the problem...
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Unrolling of Deep Graph Total Variation for Image Denoising
While deep learning (DL) architectures like convolutional neural network...
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Automotive Radar Interference Mitigation with Unfolded Robust PCA based on Residual Overcomplete AutoEncoder Blocks
Deep learning methods for automotive radar interference mitigation can s...
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MultiLevel Group Testing with Application to OneShot Pooled COVID19 Tests
One of the main challenges in containing the Coronoavirus disease 2019 (...
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COVID19 Classification of Xray Images Using Deep Neural Networks
In the midst of the coronavirus disease 2019 (COVID19) outbreak, chest ...
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A DeepUnfolded ReferenceBased RPCA Network For Video ForegroundBackground Separation
Deep unfolded neural networks are designed by unrolling the iterations o...
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TaskBased AnalogtoDigital Converters
Obtaining digital representations of multivariate continuoustime (CT) s...
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OvertheAir Federated Learning from Heterogeneous Data
Federated learning (FL) is a framework for distributed learning of centr...
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On the Error Exponent of Approximate Sufficient Statistics for Mary Hypothesis Testing
Consider the problem of detecting one of M i.i.d. Gaussian signals corru...
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eSampling: Energy Harvesting ADCs
Analogtodigital converters (ADCs) allow physical signals to be process...
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Dynamic Metasurface Antennas for 6G Extreme Massive MIMO Communications
Next generation wireless base stations and access points will transmit a...
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UVeQFed: Universal Vector Quantization for Federated Learning
Traditional deep learning models are trained at a centralized server usi...
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Inference from Stationary Time Sequences via Learned Factor Graphs
The design of methods for inference from time sequences has traditionall...
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Ensemble Wrapper Subsampling for Deep Modulation Classification
Subsampling of received wireless signals is important for relaxing hardw...
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RaSSteR: Random Sparse StepFrequency Radar
We propose a method for synthesizing high range resolution profiles (HRR...
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Sampling on Graphs: From Theory to Applications
The study of sampling signals on graphs, with the goal of building an an...
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DataDriven Symbol Detection via ModelBased Machine Learning
The design of symbol detectors in digital communication systems has trad...
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TaskBased Quantization with Application to MIMO Receivers
Multipleinput multipleoutput (MIMO) systems are required to communicat...
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On Throughput of Millimeter Wave MIMO Systems with Low Resolution ADCs
Use of low resolution analog to digital converters (ADCs) is an effectiv...
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DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection
Digital receivers are required to recover the transmitted symbols from t...
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DataDriven Factor Graphs for Deep Symbol Detection
Many important schemes in signal processing and communications, ranging ...
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Identifiability Conditions for Compressive Multichannel Blind Deconvolution
In applications such as multireceiver radars and ultrasound array syste...
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Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing
Deep neural networks provide unprecedented performance gains in many rea...
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Dynamic Metasurface Antennas for MIMOOFDM Receivers with BitLimited ADCs
The combination of orthogonal frequency modulation (OFDM) and multiplei...
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Distributed Quantization for Sparse Time Sequences
Analog signals processed in digital hardware are quantized into a discre...
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MAJoRCom: A DualFunction Radar Communication System Using Index Modulation
Dualfunction radar communication (DFRC) systems implement both sensing ...
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Joint RadarCommunications Strategies for Autonomous Vehicles
Selfdriving cars constantly asses their environment in order to choose ...
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Deep TaskBased Quantization
Quantizers play a critical role in digital signal processing systems. Re...
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Deep Neural Network Symbol Detection for Millimeter Wave Communications
This paper proposes to use a deep neural network (DNN)based symbol dete...
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Deep learning in ultrasound imaging
We consider deep learning strategies in ultrasound systems, from the fro...
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Serial Quantization for Representing Sparse Signals
Sparse signals are encountered in a broad range of applications. In orde...
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MultiCarrier Agile Phased Array Radar
Modern radar systems are expected to operate reliably in congested envir...
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ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection
Symbol detection plays an important role in the implementation of digita...
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Deep Learning for Interference Identification: Band, Training SNR, and Sample Selection
We study the problem of interference source identification, through the ...
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Sample Efficient Toeplitz Covariance Estimation
We study the query complexity of estimating the covariance matrix T of a...
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Yonina C. Eldar
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Professor of Electrical Engineering at Technion  Israel Institute of Technology, Adjunt Professor at Duke University