595 citations · 3.6k across the 131 of their papers we have counts for
45 papers · 1 filter
In-Context Learning for Gradient-Free Receiver Adaptation: Principles, Applications, and Theory
Matteo Zecchin, Tomer Raviv, Dileep Kalathil +3
In recent years, deep learning has facilitated the creation of wireless receivers capable of functioning effectively in conditions that challenge traditional model-based designs. L…
Optimal ISAC Beamforming Structure and Efficient Algorithms for Sum Rate and CRLB Balancing
Tianyu Fang, Mengyuan Ma, Markku Juntti +3
Integrated sensing and communications (ISAC) has emerged as a promising paradigm to unify wireless communications and radar sensing, enabling efficient spectrum and hardware utiliz…
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…
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…
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…
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…
Joint Communications and Sensing Hybrid Beamforming Design via Deep Unfolding
Nhan Thanh Nguyen, Ly V. Nguyen, Nir Shlezinger +3
Joint communications and sensing (JCAS) is envisioned as a key feature in future wireless communications networks. In massive MIMO-JCAS systems, hybrid beamforming (HBF) is typical…
Joint Communications and Sensing Design for Multi-Carrier MIMO Systems
Nhan Thanh Nguyen, Nir Shlezinger, Khac-Hoang Ngo +2
In conventional joint communications and sensing (JCAS) designs for multi-carrier multiple-input multiple-output (MIMO) systems, the dual-functional waveforms are often optimized f…
Adaptive and Flexible Model-Based AI for Deep Receivers in Dynamic Channels
Tomer Raviv, Sangwoo Park, Osvaldo Simeone +2
Artificial intelligence (AI) is envisioned to play a key role in future wireless technologies, with deep neural networks (DNNs) enabling digital receivers to learn to operate in ch…
AI-Empowered Hybrid MIMO Beamforming
Nir Shlezinger, Mengyuan Ma, Ortal Lavi +3
Hybrid multiple-input multiple-output (MIMO) is an attractive technology for realizing extreme massive MIMO systems envisioned for future wireless communications in a scalable and…
Deep Unfolding Hybrid Beamforming Designs for THz Massive MIMO Systems
Nhan Thanh Nguyen, Mengyuan Ma, Nir Shlezinger +3
Hybrid beamforming (HBF) is a key enabler for wideband terahertz (THz) massive multiple-input multiple-output (mMIMO) communications systems. A core challenge with designing HBF sy…
CRC-Aided Learned Ensembles of Belief-Propagation Polar Decoders
Tomer Raviv, Alon Goldman, Ofek Vayner +2
Polar codes have promising error-correction capabilities. Yet, decoding polar codes is often challenging, particularly with large blocks, with recently proposed decoders based on l…
On the Tacit Linearity Assumption in Common Cascaded Models of RIS-Parametrized Wireless Channels
Antonin Rabault, Luc Le Magoarou, Jérôme Sol +4
We analytically derive from first physical principles the functional dependence of wireless channels on the RIS configuration for generic (i.e., potentially complex-scattering) RIS…
Uncertainty-Aware and Reliable Neural MIMO Receivers via Modular Bayesian Deep Learning
Tomer Raviv, Sangwoo Park, Osvaldo Simeone +1
Deep learning is envisioned to play a key role in the design of future wireless receivers. A popular approach to design learning-aided receivers combines deep neural networks (DNNs…
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…
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…
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…
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…
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…
PhysFad: Physics-Based End-to-End Channel Modeling of RIS-Parametrized Environments with Adjustable Fading
Rashid Faqiri, Chloé Saigre-Tardif, George C. Alexandropoulos +3
Programmable radio environments parametrized by reconfigurable intelligent surfaces (RISs) are emerging as a new wireless communications paradigm, but currently used channel models…
Anomaly Search over Composite Hypotheses in Hierarchical Statistical Models
Benjamin Wolff, Tomer Gafni, Guy Revach +2
Detection of anomalies among a large number of processes is a fundamental task that has been studied in multiple research areas, with diverse applications spanning from spectrum ac…