595 citations · 3.6k across the 131 of their papers we have counts for
30 papers · 1 filter
Robust Task-Specific Beamforming with Low-Resolution ADCs for Power-Efficient Hybrid MIMO Receivers
Eyyup Tasci, Timur Zirtiloglu, Alperen Yasar +3
Multiple-input multiple-output (MIMO) systems exploit spatial diversity to facilitate multi-user communications with high spectral efficiency by beamforming. As MIMO systems utiliz…
Integrated Sensing and Communications with Reconfigurable Intelligent Surfaces
Sundeep Prabhakar Chepuri, Nir Shlezinger, Fan Liu +3
Integrated sensing and communications (ISAC) are envisioned to be an integral part of future wireless networks, especially when operating at the millimeter-wave (mmWave) and terahe…
Near-field Localization with Dynamic Metasurface Antennas
Qianyu Yang, Anna Guerra, Francesco Guidi +5
Sixth generation (6G) cellular communications are expected to support enhanced wireless localization capabilities. The widespread deployment of large arrays and high-frequency band…
LQGNet: Hybrid Model-Based and Data-Driven Linear Quadratic Stochastic Control
Solomon Goldgraber Casspi, Oliver Husser, Guy Revach +1
Stochastic control deals with finding an optimal control signal for a dynamical system in a setting with uncertainty, playing a key role in numerous applications. The linear quadra…
Joint Microstrip Selection and Beamforming Design for MmWave Systems with Dynamic Metasurface Antennas
Wei Huang, Haiyang Zhang, Nir Shlezinger +1
Dynamic metasurface antennas (DMAs) provide a new paradigm to realize large-scale antenna arrays for future wireless systems. In this paper, we study the downlink millimeter wave (…
Split-KalmanNet: A Robust Model-Based Deep Learning Approach for SLAM
Geon Choi, Jeonghun Park, Nir Shlezinger +2
Simultaneous localization and mapping (SLAM) is a method that constructs a map of an unknown environment and localizes the position of a moving agent on the map simultaneously. Ext…
Outlier-Insensitive Kalman Filtering Using NUV Priors
Shunit Truzman, Guy Revach, Nir Shlezinger +1
The Kalman filter (KF) is a widely-used algorithm for tracking the latent state of a dynamical system from noisy observations. For systems that are well-described by linear Gaussia…
Neural Augmented Kalman Filtering with Bollinger Bands for Pairs Trading
Amit Milstein, Haoran Deng, Guy Revach +2
Pairs trading is a family of trading techniques that determine their policies based on monitoring the relationships between pairs of assets. A common pairs trading approach relies…
HKF: Hierarchical Kalman Filtering with Online Learned Evolution Priors for Adaptive ECG Denoising
Guy Revach, Timur Locher, Nir Shlezinger +2
Electrocardiography (ECG) signals play a pivotal role in many healthcare applications, especially in at-home monitoring of vital signs. Wearable technologies, which these applicati…
Data Augmentation for Deep Receivers
Tomer Raviv, Nir Shlezinger
Deep neural networks (DNNs) allow digital receivers to learn to operate in complex environments. To do so, DNNs should preferably be trained using large labeled data sets with a si…
Joint Privacy Enhancement and Quantization in Federated Learning
Natalie Lang, Elad Sofer, Tomer Shaked +1
Federated learning (FL) is an emerging paradigm for training machine learning models using possibly private data available at edge devices. The distributed operation of FL gives ri…
Deep Learning Based Successive Interference Cancellation for the Non-Orthogonal Downlink
Thien Van Luong, Nir Shlezinger, Chao Xu +3
Non-orthogonal communications are expected to play a key role in future wireless systems. In downlink transmissions, the data symbols are broadcast from a base station to different…
Collaborative Inference for AI-Empowered IoT Devices
Nir Shlezinger, Ivan V. Bajic
Artificial intelligence (AI) technologies, and particularly deep learning systems, are traditionally the domain of large-scale cloud servers, which have access to high computationa…
Deep-Learning-Aided Distributed Clock Synchronization for Wireless Networks
Emeka Abakasanga, Nir Shlezinger, Ron Dabora
The proliferation of wireless communications networks over the past decades, combined with the scarcity of the wireless spectrum, have motivated a significant effort towards increa…
Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization
Nir Shlezinger, Yonina C. Eldar, Stephen P. Boyd
Decision making algorithms are used in a multitude of different applications. Conventional approaches for designing decision algorithms employ principled and simplified modelling,…
Decentralized Low-Latency Collaborative Inference via Ensembles on the Edge
May Malka, Erez Farhan, Hai Morgenstern +1
The success of deep neural networks (DNNs) is heavily dependent on computational resources. While DNNs are often employed on cloud servers, there is a growing need to operate DNNs…
MICAL: Mutual Information-Based CNN-Aided Learned Factor Graphs for Seizure Detection from EEG Signals
Bahareh Salafian, Eyal Fishel Ben-Knaan, Nir Shlezinger +2
We develop a hybrid model-based data-driven seizure detection algorithm called Mutual Information-based CNNAided Learned factor graphs (MICAL) for detection of eclectic seizures fr…
Discriminative and Generative Learning for Linear Estimation of Random Signals [Lecture Notes]
Nir Shlezinger, Tirza Routtenberg
Inference tasks in signal processing are often characterized by the availability of reliable statistical modeling with some missing instance-specific parameters. One conventional a…
Channel Estimation with Hybrid Reconfigurable Intelligent Metasurfaces
Haiyang Zhang, Nir Shlezinger, George C Alexandropoulos +3
Reconfigurable Intelligent Surfaces (RISs) are envisioned to play a key role in future wireless communications, enabling programmable radio propagation environments. They are usual…
Wideband Multi-User MIMO Communications with Frequency Selective RISs: Element Response Modeling and Sum-Rate Maximization
Konstantinos D. Katsanos, Nir Shlezinger, Mohammadreza F. Imani +1
Reconfigurable Intelligent Surfaces (RISs) are an emerging technology for future wireless communication systems, enabling improved coverage in an energy efficient manner. RISs are…
CNN-Aided Factor Graphs with Estimated Mutual Information Features for Seizure Detection
Bahareh Salafian, Eyal Fishel Ben-Knaan, Nir Shlezinger +2
We propose a convolutional neural network (CNN) aided factor graphs assisted by mutual information features estimated by a neural network for seizure detection. Specifically, we us…
Online Meta-Learning For Hybrid Model-Based Deep Receivers
Tomer Raviv, Sangwoo Park, Osvaldo Simeone +2
Recent years have witnessed growing interest in the application of deep neural networks (DNNs) for receiver design, which can potentially be applied in complex environments without…
6G Wireless Communications: From Far-field Beam Steering to Near-field Beam Focusing
Haiyang Zhang, Nir Shlezinger, Francesco Guidi +2
6G networks will be required to support higher data rates, improved energy efficiency, lower latency, and more diverse users compared with 5G systems. To meet these requirements, e…
Deep-Learning-Assisted Configuration of Reconfigurable Intelligent Surfaces in Dynamic rich-scattering Environments
Kyriakos Stylianopoulos, Nir Shlezinger, Philipp del Hougne +1
The integration of Reconfigurable Intelligent Surfaces (RISs) into wireless environments endows channels with programmability, and is expected to play a key role in future communic…