595 citations · 3.6k across the 131 of their papers we have counts for
14 papers · 2 filters
Task-Based Graph Signal Compression
Pei Li, Nir Shlezinger, Haiyang Zhang +2
Graph signals arise in various applications, ranging from sensor networks to social media data. The high-dimensional nature of these signals implies that they often need to be comp…
Unsupervised Learned Kalman Filtering
Guy Revach, Nir Shlezinger, Timur Locher +3
In this paper we adapt KalmanNet, which is a recently pro-posed deep neural network (DNN)-aided system whose architecture follows the operation of the model-based Kalman filter (KF…
Near-Field Wireless Power Transfer with Dynamic Metasurface Antennas
Haiyang Zhang, Nir Shlezinger, Francesco Guidi +3
Radio frequency wireless power transfer (WPT) enables charging low-power mobile devices without relying on wired infrastructure. Current existing WPT systems are typically designed…
Uncertainty in Data-Driven Kalman Filtering for Partially Known State-Space Models
Itzik Klein, Guy Revach, Nir Shlezinger +3
Providing a metric of uncertainty alongside a state estimate is often crucial when tracking a dynamical system. Classic state estimators, such as the Kalman filter (KF), provide a…
RTSNet: Learning to Smooth in Partially Known State-Space Models (Preprint)
Guy Revach, Xiaoyong Ni, Nir Shlezinger +2
The smoothing task is core to many signal processing applications. A widely popular smoother is the Rauch-Tung-Striebel (RTS) algorithm, which achieves minimal mean-squared error r…
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…
Near-field Wireless Power Transfer for 6G Internet-of-Everything Mobile Networks: Opportunities and Challenges
Haiyang Zhang, Nir Shlezinger, Francesco Guidi +3
Radiating wireless power transfer (WPT) brings forth the possibility to cost-efficiently charge wireless devices without requiring a wiring infrastructure. As such, it is expected…
Efficient Epileptic Seizure Detection Using CNN-Aided Factor Graphs
Bahareh Salafian, Eyal Fishel Ben, Nir Shlezinger +2
We propose a computationally efficient algorithm for seizure detection. Instead of using a purely data-driven approach, we develop a hybrid model-based/data-driven method, combinin…
KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics
Guy Revach, Nir Shlezinger, Xiaoyong Ni +3
State estimation of dynamical systems in real-time is a fundamental task in signal processing. For systems that are well-represented by a fully known linear Gaussian state space (S…
FRaC: FMCW-Based Joint Radar-Communications System via Index Modulation
Dingyou Ma, Nir Shlezinger, Tianyao Huang +2
Dual function radar communications (DFRC) systems are attractive technologies for autonomous vehicles, which utilize electromagnetic waves to constantly sense the environment while…
Beam Focusing for Near-Field Multi-User MIMO Communications
Haiyang Zhang, Nir Shlezinger, Francesco Guidi +3
Large antenna arrays and high-frequency bands are two key features of future wireless communication systems. The combination of large-scale antennas with high transmission frequenc…
Federated Learning: A Signal Processing Perspective
Tomer Gafni, Nir Shlezinger, Kobi Cohen +2
The dramatic success of deep learning is largely due to the availability of data. Data samples are often acquired on edge devices, such as smart phones, vehicles and sensors, and i…
LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receivers
Shahin Khobahi, Nir Shlezinger, Mojtaba Soltanalian +1
The need to recover high-dimensional signals from their noisy low-resolution quantized measurements is widely encountered in communications and sensing. In this paper, we focus on…
Model-Based Machine Learning for Communications
Nir Shlezinger, Nariman Farsad, Yonina C. Eldar +1
We present an introduction to model-based machine learning for communication systems. We begin by reviewing existing strategies for combining model-based algorithms and machine lea…