595 citations · 3.6k across the 131 of their papers we have counts for
21 papers · 1 filter
Wideband THz Multi-User Downlink Communications with Leaky Wave Antennas
Natalie Lang, Yaela Gabay, Nir Shlezinger +4
Future wireless systems are envisioned to utilize the large spectra available at THz bands for wireless communications. Extremely massive multiple-input multiple-output (MIMO) ante…
GSP-KalmanNet: Tracking Graph Signals via Neural-Aided Kalman Filtering
Itay Buchnik, Guy Sagi, Nimrod Leinwand +3
Dynamic systems of graph signals are encountered in various applications, including social networks, power grids, and transportation. While such systems can often be described as s…
Limited Communications Distributed Optimization via Deep Unfolded Distributed ADMM
Yoav Noah, Nir Shlezinger
Distributed optimization is a fundamental framework for collaborative inference and decision making in decentralized multi-agent systems. The operation is modeled as the joint mini…
Outlier-Insensitive Kalman Filtering: Theory and Applications
Shunit Truzman, Guy Revach, Nir Shlezinger +1
State estimation of dynamical systems from noisy observations is a fundamental task in many applications. It is commonly addressed using the linear Kalman filter (KF), whose perfor…
Adaptive KalmanNet: Data-Driven Kalman Filter with Fast Adaptation
Xiaoyong Ni, Guy Revach, Nir Shlezinger
Combining the classical Kalman filter (KF) with a deep neural network (DNN) enables tracking in partially known state space (SS) models. A major limitation of current DNN-aided des…
Deep Learning-Aided Subspace-Based DOA Recovery for Sparse Arrays
Yoav Amiel, Dor H. Shmuel, Nir Shlezinger +1
Sparse arrays enable resolving more direction of arrivals (DoAs) than antenna elements using non-uniform arrays. This is typically achieved by reconstructing the covariance of a vi…
NUV-DoA: NUV Prior-based Bayesian Sparse Reconstruction with Spatial Filtering for Super-Resolution DoA Estimation
Mengyuan Zhao, Guy Revach, Tirza Routtenberg +1
Achieving high-resolution Direction of Arrival (DoA) recovery typically requires high Signal to Noise Ratio (SNR) and a sufficiently large number of snapshots. This paper presents…
Bayesian KalmanNet: Quantifying Uncertainty in Deep Learning Augmented Kalman Filter
Yehonatan Dahan, Guy Revach, Jindrich Dunik +1
Recent years have witnessed a growing interest in tracking algorithms that augment Kalman Filters (KFs) with Deep Neural Networks (DNNs). By transforming KFs into trainable deep le…
Compressed Private Aggregation for Scalable and Robust Federated Learning over Massive Networks
Natalie Lang, Nir Shlezinger, Rafael G. L. D'Oliveira +1
Federated learning (FL) is an emerging paradigm that allows a central server to train machine learning models using remote users' data. Despite its growing popularity, FL faces cha…
Joint Communications and Sensing Hybrid Beamforming Design via Deep Unfolding
Nhan Thanh Nguyen, Ly V. Nguyen, Nir Shlezinger +3
Joint communications and sensing (JCAS) is envisioned as a key feature in future wireless communications networks. In massive MIMO-JCAS systems, hybrid beamforming (HBF) is typical…
Joint Communications and Sensing Design for Multi-Carrier MIMO Systems
Nhan Thanh Nguyen, Nir Shlezinger, Khac-Hoang Ngo +2
In conventional joint communications and sensing (JCAS) designs for multi-carrier multiple-input multiple-output (MIMO) systems, the dual-functional waveforms are often optimized f…
Model-Based Deep Learning
Nir Shlezinger, Yonina C. Eldar
Signal processing traditionally relies on classical statistical modeling techniques. Such model-based methods utilize mathematical formulations that represent the underlying physic…
SubspaceNet: Deep Learning-Aided Subspace Methods for DoA Estimation
Dor H. Shmuel, Julian P. Merkofer, Guy Revach +2
Direction of arrival (DoA) estimation is a fundamental task in array processing. A popular family of DoA estimation algorithms are subspace methods, which operate by dividing the m…
Adaptive and Flexible Model-Based AI for Deep Receivers in Dynamic Channels
Tomer Raviv, Sangwoo Park, Osvaldo Simeone +2
Artificial intelligence (AI) is envisioned to play a key role in future wireless technologies, with deep neural networks (DNNs) enabling digital receivers to learn to operate in ch…
Latent-KalmanNet: Learned Kalman Filtering for Tracking from High-Dimensional Signals
Itay Buchnik, Damiano Steger, Guy Revach +3
The Kalman filter (KF) is a widely-used algorithm for tracking dynamic systems that are captured by state space (SS) models. The need to fully describe a SS model limits its applic…
AI-Empowered Hybrid MIMO Beamforming
Nir Shlezinger, Mengyuan Ma, Ortal Lavi +3
Hybrid multiple-input multiple-output (MIMO) is an attractive technology for realizing extreme massive MIMO systems envisioned for future wireless communications in a scalable and…
Deep Unfolding Hybrid Beamforming Designs for THz Massive MIMO Systems
Nhan Thanh Nguyen, Mengyuan Ma, Nir Shlezinger +3
Hybrid beamforming (HBF) is a key enabler for wideband terahertz (THz) massive multiple-input multiple-output (mMIMO) communications systems. A core challenge with designing HBF sy…
CRC-Aided Learned Ensembles of Belief-Propagation Polar Decoders
Tomer Raviv, Alon Goldman, Ofek Vayner +2
Polar codes have promising error-correction capabilities. Yet, decoding polar codes is often challenging, particularly with large blocks, with recently proposed decoders based on l…
On the Tacit Linearity Assumption in Common Cascaded Models of RIS-Parametrized Wireless Channels
Antonin Rabault, Luc Le Magoarou, Jérôme Sol +4
We analytically derive from first physical principles the functional dependence of wireless channels on the RIS configuration for generic (i.e., potentially complex-scattering) RIS…
Uncertainty-Aware and Reliable Neural MIMO Receivers via Modular Bayesian Deep Learning
Tomer Raviv, Sangwoo Park, Osvaldo Simeone +1
Deep learning is envisioned to play a key role in the design of future wireless receivers. A popular approach to design learning-aided receivers combines deep neural networks (DNNs…
Learn to Rapidly and Robustly Optimize Hybrid Precoding
Ortal Lavi, Nir Shlezinger
Hybrid precoding plays a key role in realizing massive multiple-input multiple-output (MIMO) transmitters with controllable cost. MIMO precoders are required to frequently adapt ba…