
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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RF Chain Reduction for MIMO Systems: A Hardware Prototype
RF chain circuits play a major role in digital receiver architectures, a...
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A Block Sparsity Based Estimator for mmWave Massive MIMO Channels with Beam Squint
Multipleinput multipleoutput (MIMO) millimeter wave (mmWave) communica...
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Coordinated Nonorthogonal Pilot Design for Massive MIMO
Pilot contamination caused by the nonorthogonality of pilots is a main l...
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The Capacity of Memoryless Channels with Sampled Cyclostationary Gaussian Noise
Nonorthogonal communications play an important role in future communica...
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An Algorithm Unrolling Approach to Deep Blind Image Deblurring
Blind image deblurring remains a topic of enduring interest. Learning ba...
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An Algorithm Unrolling Approach to Deep Image Deblurring
While neural networks have achieved vastly enhanced performance over tra...
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On Multiterminal Communication over MIMO Channels with Onebit ADCs at the Receivers
The fundamental limits of communication over multipleinput multipleout...
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Tradeoff Between Delay and High SNR Capacity in Quantized MIMO Systems
Analogtodigital converters (ADCs) are a major contributor to the power...
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Semisupervised Learning in NetworkStructured Data via Total Variation Minimization
We propose and analyze a method for semisupervised learning from partia...
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Fast Deep Learning for Automatic Modulation Classification
In this work, we investigate the feasibility and effectiveness of employ...
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Dynamic Metasurface Antennas for Uplink Massive MIMO Systems
Massive multipleinput multipleoutput (MIMO) communications are the foc...
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Deep Signal Recovery with OneBit Quantization
Machine learning, and more specifically deep learning, have shown remark...
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Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound
Contrast enhanced ultrasound is a radiationfree imaging modality which ...
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Functional Nonlinear Sparse Models
Signal processing in inherently continuous and often nonlinear applicati...
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Analysis of Frequency Agile Radar via Compressed Sensing
Frequency agile radar (FAR) is known to have excellent electronic counte...
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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