From the 2 of 52 linked papers with an AI index.
10 papers · 2 filters
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,…
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…
Neural Augmentation of MIMO-OFDM Receivers for Universal LLR Reconstruction
Ory Eger, Nir Shlezinger
The growing demands for higher throughput and cost-efficient wireless communications drive the need for receivers that are both simple to deploy and robust to hardware impairments…
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…
Knowledge Distillation for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
Mengyuan Ma, Nhan Thanh Nguyen, Nir Shlezinger +3
Infrastructure-mounted sensors can capture rich environmental information to enhance communications and facilitate beamforming in millimeter-wave systems. This work presents an eff…
Unsupervised End-to-End Array Calibration for Multi-Target Integrated Sensing and Communication
José Miguel Mateos-Ramos, Baptiste Chatelier, Luc Le Magoarou +3
In this work, we consider end-to-end calibration of an integrated sensing and communication (ISAC) base station (BS) under gain-phase and antenna displacement impairments without c…