595 citations · 3.6k across the 131 of their papers we have counts for
15 papers · 1 filter
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…
AI-Aided Kalman Filters
Nir Shlezinger, Guy Revach, Anubhab Ghosh +7
The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. These methods are used for state estimation of dynamic systems by relying on…
Learning Task-Based Trainable Neuromorphic ADCs via Power-Aware Distillation
Tal Vol, Loai Danial, Nir Shlezinger
The ability to process signals in digital form depends on analog-to-digital converters (ADCs). Traditionally, ADCs are designed to ensure that the digital representation closely ma…
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…
Modular Hypernetworks for Scalable and Adaptive Deep MIMO Receivers
Tomer Raviv, Nir Shlezinger
Deep neural networks (DNNs) were shown to facilitate the operation of uplink multiple-input multiple-output (MIMO) receivers, with emerging architectures augmenting modules of clas…
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…
Optimization of Iterative Blind Detection based on Expectation Maximization and Belief Propagation
Luca Schmid, Tomer Raviv, Nir Shlezinger +1
We study iterative blind symbol detection for block-fading linear inter-symbol interference channels. Based on the factor graph framework, we design a joint channel estimation and…
Rapid and Power-Aware Learned Optimization for Modular Receive Beamforming
Ohad Levy, Nir Shlezinger
Multiple-input multiple-output (MIMO) systems play a key role in wireless communication technologies. A widely considered approach to realize scalable MIMO systems involves archite…
Beam Focusing for Near-Field Multi-User Localization
Qianyu Yang, Anna Guerra, Francesco Guidi +5
Extremely large-scale antenna arrays are poised to play a pivotal role in sixth-generation (6G) networks. Utilizing such arrays often results in a near-field spherical wave transmi…
Asynchronous Online Adaptation via Modular Drift Detection for Deep Receivers
Nicole Uzlaner, Tomer Raviv, Nir Shlezinger +1
Deep learning is envisioned to facilitate the operation of wireless receivers, with emerging architectures integrating deep neural networks (DNNs) with traditional modular receiver…
Information Compression in the AI Era: Recent Advances and Future Challenges
Jun Chen, Yong Fang, Ashish Khisti +3
This survey articles focuses on emerging connections between the fields of machine learning and data compression. While fundamental limits of classical (lossy) data compression are…
Rapid Optimization of Superposition Codes for Multi-Hop NOMA MANETs via Deep Unfolding
Tomer Alter, Nir Shlezinger
Various communication technologies are expected to utilize mobile ad hoc networks (MANETs). By combining MANETs with non-orthogonal multiple access (NOMA) communications, one can s…
A Deep-NN Beamforming Approach for Dual Function Radar-Communication THz UAV
Gianluca Fontanesi, Anna Guerra, Francesco Guidi +7
In this paper, we consider a scenario with one UAV equipped with a ULA, which sends combined information and sensing signals to communicate with multiple GBS and, at the same time,…
Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates
Natalie Lang, Alejandro Cohen, Nir Shlezinger
Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. It typically involves a set of heterogeneous devices locally training neural network (NN)…
Blind Channel Estimation and Joint Symbol Detection with Data-Driven Factor Graphs
Luca Schmid, Tomer Raviv, Nir Shlezinger +1
We investigate the application of the factor graph framework for blind joint channel estimation and symbol detection on time-variant linear inter-symbol interference channels. In p…