12 papers
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,…
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…
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…
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…
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…