595 citations · 3.6k across the 131 of their papers we have counts for
83 papers · 1 filter
From Identification to Discovery: Holistic RF Fingerprinting via Learning-Aided Quantization
Omer Hazan, Gil Paryanti, Nir Shlezinger
Radio frequency fingerprint identification (RFFI) enables device identification and authentication by exploiting transmitter-specific hardware impairments embedded in received RF s…
Neural Kalman Filtering for Unknown Dynamics: Task-Aware Learning with a Koopman Backbone
Mintaek Oh, Jeonghun Park, Nir Shlezinger +2
Recent years have witnessed a growing interest in AI-aided Kalman filters. While emerging methodologies, such as KalmanNet, were shown to facilitate tracking in partially known sta…
AI-Aided ESPRIT for Joint DoA Estimation and Uncertainty Extraction
Raz Zohar, Nir Shlezinger
DoA estimation often requires not only accurate recovery of source directions, but also reliable characterization of the uncertainty in these estimates. While classical subspace me…
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,…
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…
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…
WiMamba: Linear-Scale Wireless Foundation Model
Tomer Raviv, Nir Shlezinger
Foundation models learn transferable representations, motivating growing interest in their application to wireless systems. Existing wireless foundation models are predominantly ba…
Knowledge Distillation for Collaborative Learning in Distributed Communications and Sensing
Nhan Thanh Nguyen, Mengyuan Ma, Nir Shlezinger +4
The rise of sixth generation (6G) wireless networks promises to deliver ultra-reliable, low-latency, and energy-efficient communications, sensing, and computing. However, tradition…
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…
AI-Aided Annealed Langevin Dynamics for Rapid Optimization of Programmable Channels
Tomer Shaked, Philipp del Hougne, George C. Alexandropoulos +1
Emerging technologies such as Reconfigurable Intelligent Surfaces (RIS) make it possible to optimize some parameters of wireless channels. Conventional approaches require relating…
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…
Attention-Enhanced Learning for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
Mengyuan Ma, Nhan Thanh Nguyen, Nir Shlezinger +2
Beam training and prediction in millimeter-wave communications are highly challenging due to fast time-varying channels and sensitivity to blockages and mobility. In this context,…
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…
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…
Extremely Large-Scale Dynamic Metasurface Antennas for 6G Near-Field Networks: Opportunities and Challenges
Haiyang Zhang, Nir Shlezinger, Giulia Torcolacci +6
6G networks will need to support higher data rates, high-precision localization, and imaging capabilities. Near-field technologies, enabled by extremely large-scale (XL)-arrays, ar…
Hybrid RISs for Simultaneous Tunable Reflections and Sensing
George C. Alexandropoulos, Nir Shlezinger, Ioannis Gavras +1
The concept of smart wireless environments envisions dynamic programmable propagation of information-bearing signals through the deployment of Reconfigurable Intelligent Surfaces (…
OLALa: Online Learned Adaptive Lattice Codes for Heterogeneous Federated Learning
Natalie Lang, Maya Simhi, Nir Shlezinger
Federated learning (FL) enables collaborative training across distributed clients without sharing raw data, often at the cost of substantial communication overhead induced by trans…
EOTNet: Deep Memory Aided Bayesian Filter for Extended Object Tracking
Zhixing Wang, Le Zheng, Shi Yan +3
Extended object tracking methods based on random matrices, founded on Bayesian filters, have been able to achieve efficient recursive processes while jointly estimating the kinemat…
Memory-Efficient Distributed Unlearning
Natalie Lang, Alon Helvitz, Nir Shlezinger
Machine unlearning considers the removal of the contribution of a set of data points from a trained model. In a distributed setting, where a server orchestrates training using data…
Near Field Localization via AI-Aided Subspace Methods
Arad Gast, Luc Le Magoarou, Nir Shlezinger
The increasing demands for high-throughput and energy-efficient wireless communications are driving the adoption of extremely large antennas operating at high-frequency bands. In t…
Remote Inference over Dynamic Links via Adaptive Rate Deep Task-Oriented Vector Quantization
Eyal Fishel, May Malka, Shai Ginzach +1
A broad range of technologies rely on remote inference, wherein data acquired is conveyed over a communication channel for inference in a remote server. Communication between the p…
Deep Unfolding-Empowered MmWave Massive MIMO Joint Communications and Sensing
Nhan Thanh Nguyen, Ly V. Nguyen, Nir Shlezinger +3
In this paper, we propose a low-complexity and fast hybrid beamforming design for joint communications and sensing (JCAS) based on deep unfolding. We first derive closed-form expre…
Model-Based Machine Learning for Max-Min Fairness Beamforming Design in JCAS Systems
Mengyuan Ma, Tianyu Fang, Nir Shlezinger +3
Joint communications and sensing (JCAS) is expected to be a crucial technology for future wireless systems. This paper investigates beamforming design for a multi-user multi-target…
Learning Flock: Enhancing Sets of Particles for Multi~Sub-State Particle Filtering with Neural Augmentation
Itai Nuri, Nir Shlezinger
A leading family of algorithms for state estimation in dynamic systems with multiple sub-states is based on particle filters (PFs). PFs often struggle when operating under complex…