9 citations · 18 across the 13 of their papers we have counts for
15 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…
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…
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…
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…
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…
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…
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…
Meta-ViterbiNet: Online Meta-Learned Viterbi Equalization for Non-Stationary Channels
Tomer Raviv, Sangwoo Park, Nir Shlezinger +3
Deep neural networks (DNNs) based digital receivers can potentially operate in complex environments. However, the dynamic nature of communication channels implies that in some scen…
Deep Ensemble of Weighted Viterbi Decoders for Tail-Biting Convolutional Codes
Tomer Raviv, Asaf Schwartz, Yair Be'ery
Tail-biting convolutional codes extend the classical zero-termination convolutional codes: Both encoding schemes force the equality of start and end states, but under the tail-biti…
perm2vec: Graph Permutation Selection for Decoding of Error Correction Codes using Self-Attention
Nir Raviv, Avi Caciularu, Tomer Raviv +2
Error correction codes are an integral part of communication applications, boosting the reliability of transmission. The optimal decoding of transmitted codewords is the maximum li…
Data-Driven Ensembles for Deep and Hard-Decision Hybrid Decoding
Tomer Raviv, Nir Raviv, Yair Be'ery
Ensemble models are widely used to solve complex tasks by their decomposition into multiple simpler tasks, each one solved locally by a single member of the ensemble. Decoding of e…
Active Deep Decoding of Linear Codes
Ishay Be'ery, Nir Raviv, Tomer Raviv +1
High quality data is essential in deep learning to train a robust model. While in other fields data is sparse and costly to collect, in error decoding it is free to query and label…