595 citations · 3.6k across the 131 of their papers we have counts for
11 papers · 2 filters
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…
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…
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…
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…
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…