132 citations · 411 across the 38 of their papers we have counts for
41 papers
Online Learning via Learned Latent Bayesian Tracking
Guy Gerson, Tomer Raviv, Nir Shlezinger +2
Online learning in non-stationary environments requires models to adapt rapidly from streaming data under strict computational constraints. A principled approach casts online learn…
EM-KalmanNet: Learned Expectation-Maximization for Adaptive Tracking in Partially Known, Block-Wise Time-Varying State-Space Models
Ori Cohen, Nir Shlezinger, Tirza Routtenberg
State estimation in partially known state space (SS) models is challenging when the dynamics or observation model varies across short data blocks. Classical model-based approaches,…
Revisiting the Misspecified Cramér-Rao Bound
Malaak Khatib, Nadav Harel, Joseph Tabrikian +1
Estimation under model misspecification arises in many signal processing problems, where the assumed observation model deviates from the true data-generating mechanism due to error…
Deep Unfolding: Recent Developments, Theory, and Design Guidelines
Nir Shlezinger, Santiago Segarra, Yi Zhang +4
Optimization methods play a central role in signal processing, serving as the mathematical foundation for inference, estimation, and control. While classical iterative optimization…
Online Learning of Modular Bayesian Deep Receivers: Single-Step Adaptation with Streaming Data
Yakov Gusakov, Osvaldo Simeone, Tirza Routtenberg +1
Deep neural network (DNN)-based receivers offer a powerful alternative to classical model-based designs for wireless communication, especially in complex and nonlinear propagation…
Leaky Wave Antennas for Next Generation Wireless Applications in sub-THz Frequencies: Current Status and Research Challenges
Natalie Lang, Atsutse K. Kludze, Nir Shlezinger +4
The ever-growing demand for ultra-high data rates, massive connectivity, and joint communication-sensing capabilities in future wireless networks is driving research into sub-terah…
Deep Unfolding with Approximated Computations for Rapid Optimization
Dvir Avrahami, Amit Milstein, Caroline Chaux +2
Optimization-based solvers play a central role in a wide range of signal processing and communication tasks. However, their applicability in latency-sensitive systems is limited by…
Weighted Bayesian Cramr-Rao Bound for Mixed-Resolution Parameter Estimation
Yaniv Mazor, Tirza Routtenberg
Mixed-resolution architectures, combining high-resolution (analog) data with coarsely quantized (e.g., 1-bit) data, are widely employed in emerging communication and radar systems…
Efficient Sampling Allocation Strategies for General Graph-Filter-Based Signal Recovery
Lital Dabush, Tirza Routtenberg
Sensor placement plays a crucial role in graph signal recovery in underdetermined systems. In this paper, we present the graph-filtered regularized maximum likelihood (GFR-ML) esti…
Efficient Recovery of Sparse Graph Signals from Graph Filter Outputs
Gal Morgenstern, Tirza Routtenberg
This paper investigates the recovery of a node-domain sparse graph signal from the output of a graph filter. This problem, which is often referred to as the identification of the s…
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…
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…
Non-Bayesian Post-Model-Selection Estimation as Estimation Under Model Misspecification
Nadav Harel, Tirza Routtenberg
In many parameter estimation problems, the exact model is unknown and is assumed to belong to a set of candidate models. In such cases, a predetermined data-based selection rule se…
Verifying the Smoothness of Graph Signals: A Graph Signal Processing Approach
Lital Dabush, Tirza Routtenberg
Graph signal processing (GSP) deals with the representation, analysis, and processing of structured data, i.e. graph signals that are defined on the vertex set of a generic graph.…
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…
Protection Against Graph-Based False Data Injection Attacks on Power Systems
Gal Morgenstern, Jip Kim, James Anderson +2
Graph signal processing (GSP) has emerged as a powerful tool for practical network applications, including power system monitoring. Recent research has focused on developing GSP-ba…
Barankin-Type Bound for Constrained Parameter Estimation
Eyal Nitzan, Tirza Routtenberg, Joseph Tabrikian
In constrained parameter estimation, the classical constrained Cramer-Rao bound (CCRB) and the recent Lehmann-unbiased CCRB (LU-CCRB) are lower bounds on the performance of mean-un…
Quickest Inference of Susceptible-Infected Cascades in Sparse Networks
Anirudh Sridhar, Tirza Routtenberg, H. Vincent Poor
We consider the task of estimating a network cascade as fast as possible. The cascade is assumed to spread according to a general Susceptible-Infected process with heterogeneous tr…
Efficient Graph Laplacian Estimation by Proximal Newton
Yakov Medvedovsky, Eran Treister, Tirza Routtenberg
The Laplacian-constrained Gaussian Markov Random Field (LGMRF) is a common multivariate statistical model for learning a weighted sparse dependency graph from given data. This grap…
GSP-Based MAP Estimation of Graph Signals
Guy Sagi, Tirza Routtenberg
In this paper, we consider the problem of recovering random graph signals from nonlinear measurements. We formulate the maximum a-posteriori probability (MAP) estimator, which resu…
Widely-Linear MMSE Estimation of Complex-Valued Graph Signals
Alon Amar, Tirza Routtenberg
In this paper, we consider the problem of recovering random graph signals with complex values. For general Bayesian estimation of complex-valued vectors, it is known that the widel…
Discriminative and Generative Learning for Linear Estimation of Random Signals [Lecture Notes]
Nir Shlezinger, Tirza Routtenberg
Inference tasks in signal processing are often characterized by the availability of reliable statistical modeling with some missing instance-specific parameters. One conventional a…
DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm
Julian P. Merkofer, Guy Revach, Nir Shlezinger +2
Direction of arrival (DoA) estimation of multiple signals is pivotal in sensor array signal processing. A popular multi-signal DoA estimation method is the multiple signal classifi…