Nir Shlezinger

research

∙ 05/12/2023

Adaptive and Flexible Model-Based AI for Deep Receivers in Dynamic Channels

Artificial intelligence (AI) is envisioned to play a key role in future ...

0 Tomer Raviv, et al. ∙

research

∙ 03/03/2023

AI-Empowered Hybrid MIMO Beamforming

Hybrid multiple-input multiple-output (MIMO) is an attractive technology...

0 Nir Shlezinger, et al. ∙

research

∙ 02/23/2023

Deep Unfolding Hybrid Beamforming Designs for THz Massive MIMO Systems

Hybrid beamforming (HBF) is a key enabler for wideband terahertz (THz) m...

0 Nhan Thanh Nguyen, et al. ∙

research

∙ 02/10/2023

On the Tacit Linearity Assumption in Common Cascaded Models of RIS-Parametrized Wireless Channels

We analytically derive from first physical principles the functional dep...

0 Antonin Rabault, et al. ∙

research

∙ 02/05/2023

Modular Model-Based Bayesian Learning for Uncertainty-Aware and Reliable Deep MIMO Receivers

In the design of wireless receivers, DNNs can be combined with tradition...

0 Tomer Raviv, et al. ∙

research

∙ 01/15/2023

CRC-Aided Learned Ensembles of Belief-Propagation Polar Decoders

Polar codes have promising error-correction capabilities. Yet, decoding ...

0 Tomer Raviv, et al. ∙

research

∙ 10/23/2022

LQGNet: Hybrid Model-Based and Data-Driven Linear Quadratic Stochastic Control

Stochastic control deals with finding an optimal control signal for a dy...

0 Solomon Goldgraber Casspi, et al. ∙

research

∙ 10/18/2022

Split-KalmanNet: A Robust Model-Based Deep Learning Approach for SLAM

Simultaneous localization and mapping (SLAM) is a method that constructs...

0 Geon Choi, et al. ∙

research

∙ 10/12/2022

Outlier-Insensitive Kalman Filtering Using NUV Priors

The Kalman filter (KF) is a widely-used algorithm for tracking the laten...

0 Shunit Truzman, et al. ∙

research

∙ 09/03/2022

Data Augmentation for Deep Receivers

Deep neural networks (DNNs) allow digital receivers to learn to operate ...

0 Tomer Raviv, et al. ∙

research

∙ 08/23/2022

Joint Privacy Enhancement and Quantization in Federated Learning

Federated learning (FL) is an emerging paradigm for training machine lea...

0 Natalie Lang, et al. ∙

research

∙ 07/29/2022

Deep Learning Based Successive Interference Cancellation for the Non-Orthogonal Downlink

Non-orthogonal communications are expected to play a key role in future ...

0 Thien Van Luong, et al. ∙

research

∙ 06/24/2022

Deep-Learning-Aided Distributed Clock Synchronization for Wireless Networks

The proliferation of wireless communications networks over the past deca...

0 Emeka Abakasanga, et al. ∙

research

∙ 06/09/2022

Discriminative and Generative Learning for Linear Estimation of Random Signals [Lecture Notes]

Inference tasks in signal processing are often characterized by the avai...

0 Nir Shlezinger, et al. ∙

research

∙ 06/08/2022

Channel Estimation with Hybrid Reconfigurable Intelligent Metasurfaces

Reconfigurable Intelligent Surfaces (RISs) are envisioned to play a key ...

0 Haiyang Zhang, et al. ∙

research

∙ 06/07/2022

Decentralized Low-Latency Collaborative Inference via Ensembles on the Edge

The success of deep neural networks (DNNs) is heavily dependent on compu...

0 May Malka, et al. ∙

research

∙ 05/05/2022

Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization

Decision making algorithms are used in a multitude of different applicat...

0 Nir Shlezinger, et al. ∙

research

∙ 03/27/2022

Online Meta-Learning For Hybrid Model-Based Deep Receivers

Recent years have witnessed growing interest in the application of deep ...

0 Tomer Raviv, et al. ∙

research

∙ 02/21/2022

Composite Anomaly Detection via Hierarchical Dynamic Search

Anomaly detection among a large number of processes arises in many appli...

0 Benjamin Wolff, et al. ∙

research

∙ 02/16/2022

Deep-Learning-Assisted Configuration of Reconfigurable Intelligent Surfaces in Dynamic rich-scattering Environments

The integration of Reconfigurable Intelligent Surfaces (RISs) into wirel...

0 Kyriakos Stylianopoulos, et al. ∙

research

∙ 02/10/2022

On the Acquisition of Stationary Signals Using Uniform ADCs

In this work, we consider the acquisition of stationary signals using un...

0 Peter Neuhaus, et al. ∙

research

∙ 02/06/2022

PhysFad: Physics-Based End-to-End Channel Modeling of RIS-Parametrized Environments with Adjustable Fading

Programmable radio environments parametrized by reconfigurable intellige...

0 Rashid Faqiri, et al. ∙

research

∙ 02/04/2022

Wideband Multi-User MIMO Communications with Frequency Selective RISs: Element Response Modeling and Sum-Rate Maximization

Reconfigurable Intelligent Surfaces (RISs) are an emerging technology fo...

0 Konstantinos D. Katsanos, et al. ∙

research

∙ 10/28/2021

DeepNP: Deep Learning-Based Noise Prediction for Ultra-Reliable Low-Latency Communications

Closing the gap between high data rates and low delay in real-time strea...

0 Alejandro Cohen, et al. ∙

research

∙ 10/18/2021

Unsupervised Learned Kalman Filtering

In this paper we adapt KalmanNet, which is a recently pro-posed deep neu...

0 Guy Revach, et al. ∙

research

∙ 10/10/2021

Uncertainty in Data-Driven Kalman Filtering for Partially Known State-Space Models

Providing a metric of uncertainty alongside a state estimate is often cr...

0 Itzik Klein, et al. ∙

research

∙ 09/22/2021

Deep Augmented MUSIC Algorithm for Data-Driven DoA Estimation

Direction of arrival (DoA) estimation is a crucial task in sensor array ...

0 Julian P. Merkofer, et al. ∙

research

∙ 07/21/2021

KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics

Real-time state estimation of dynamical systems is a fundamental task in...

0 Guy Revach, et al. ∙

research

∙ 04/10/2021

Hybrid Reconfigurable Intelligent Metasurfaces: Enabling Simultaneous Tunable Reflections and Sensing for 6G Wireless Communications

Current discussions on the sixth Generation (6G) of wireless communicati...

0 George C. Alexandropoulos, et al. ∙

research

∙ 03/31/2021

Federated Learning: A Signal Processing Perspective

The dramatic success of deep learning is largely due to the availability...

0 Tomer Gafni, et al. ∙

research

∙ 03/24/2021

Meta-ViterbiNet: Online Meta-Learned Viterbi Equalization for Non-Stationary Channels

Deep neural networks (DNNs) based digital receivers can potentially oper...

0 Tomer Raviv, et al. ∙

research

∙ 03/08/2021

Reconfigurable Intelligent Surfaces for Rich Scattering Wireless Communications: Recent Experiments, Challenges, and Opportunities

Recent advances in the fabrication and experimentation of Reconfigurable...

0 George C. Alexandropoulos, et al. ∙

research

∙ 02/05/2021

LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receivers

The need to recover high-dimensional signals from their noisy low-resolu...

0 Shahin Khobahi, et al. ∙

research

∙ 01/12/2021

Model-Based Machine Learning for Communications

We present an introduction to model-based machine learning for communica...

0 Nir Shlezinger, et al. ∙

research

∙ 12/15/2020

Model-Based Deep Learning

Signal processing, communications, and control have traditionally relied...

86 Nir Shlezinger, et al. ∙

research

∙ 11/14/2020

FedRec: Federated Learning of Universal Receivers over Fading Channels

Wireless communications are often subject to fading conditions. Various ...

0 Mahdi Boloursaz Mashhadi, et al. ∙

research

∙ 10/12/2020

Multi-Level Group Testing with Application to One-Shot Pooled COVID-19 Tests

One of the main challenges in containing the Coronoavirus disease 2019 (...

0 Alejandro Cohen, et al. ∙

research

∙ 09/29/2020

Task-Based Analog-to-Digital Converters

Obtaining digital representations of multivariate continuous-time (CT) s...

0 Peter Neuhaus, et al. ∙

research

∙ 09/27/2020

Over-the-Air Federated Learning from Heterogeneous Data

Federated learning (FL) is a framework for distributed learning of centr...

0 Tomer Sery, et al. ∙

research

∙ 07/16/2020

eSampling: Energy Harvesting ADCs

Analog-to-digital converters (ADCs) allow physical signals to be process...

0 Neha Jain, et al. ∙

research

∙ 06/14/2020

Dynamic Metasurface Antennas for 6G Extreme Massive MIMO Communications

Next generation wireless base stations and access points will transmit a...

0 Nir Shlezinger, et al. ∙

research

∙ 06/05/2020

UVeQFed: Universal Vector Quantization for Federated Learning

Traditional deep learning models are trained at a centralized server usi...

0 Nir Shlezinger, et al. ∙

research

∙ 06/05/2020

Inference from Stationary Time Sequences via Learned Factor Graphs

The design of methods for inference from time sequences has traditionall...

0 Nir Shlezinger, et al. ∙

research

∙ 02/29/2020

The Rate Distortion Function of Asynchronously Sampled Memoryless Cyclostationary Gaussian Processes

Man-made communications signals are typically modelled as continuous-tim...

0 Emeka Abakasanga, et al. ∙

research

∙ 02/14/2020

Data-Driven Symbol Detection via Model-Based Machine Learning

The design of symbol detectors in digital communication systems has trad...

0 Nariman Farsad, et al. ∙

research

∙ 02/11/2020

Task-Based Quantization with Application to MIMO Receivers

Multiple-input multiple-output (MIMO) systems are required to communicat...

0 Nir Shlezinger, et al. ∙

research

∙ 02/08/2020

DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection

Digital receivers are required to recover the transmitted symbols from t...

0 Nir Shlezinger, et al. ∙

research

∙ 01/31/2020

Data-Driven Factor Graphs for Deep Symbol Detection

Many important schemes in signal processing and communications, ranging ...

0 Nir Shlezinger, et al. ∙

research

∙ 12/14/2019

Dynamic Metasurface Antennas for MIMO-OFDM Receivers with Bit-Limited ADCs

The combination of orthogonal frequency modulation (OFDM) and multiple-i...

0 Hanqing Wang, et al. ∙

research

∙ 10/21/2019

Distributed Quantization for Sparse Time Sequences

Analog signals processed in digital hardware are quantized into a discre...

0 Alejandro Cohen, et al. ∙

Nir Shlezinger

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