research
∙
05/12/2023
Adaptive and Flexible Model-Based AI for Deep Receivers in Dynamic Channels
Artificial intelligence (AI) is envisioned to play a key role in future ...
share
research
∙
02/05/2023
Modular Model-Based Bayesian Learning for Uncertainty-Aware and Reliable Deep MIMO Receivers
In the design of wireless receivers, DNNs can be combined with tradition...
research
∙
01/15/2023
CRC-Aided Learned Ensembles of Belief-Propagation Polar Decoders
Polar codes have promising error-correction capabilities. Yet, decoding ...
research
∙
09/03/2022
Data Augmentation for Deep Receivers
Deep neural networks (DNNs) allow digital receivers to learn to operate ...
research
∙
03/27/2022
Online Meta-Learning For Hybrid Model-Based Deep Receivers
Recent years have witnessed growing interest in the application of deep ...
research
∙
03/24/2021
Meta-ViterbiNet: Online Meta-Learned Viterbi Equalization for Non-Stationary Channels
Deep neural networks (DNNs) based digital receivers can potentially oper...
research
∙
09/05/2020
Deep Ensemble of Weighted Viterbi Decoders for Tail-Biting Convolutional Codes
Tail-biting convolutional codes extend the classical zero-termination co...
research
∙
02/06/2020
perm2vec: Graph Permutation Selection for Decoding of Error Correction Codes using Self-Attention
Error correction codes are integral part of communication applications, ...
research
∙
01/17/2020
Data-Driven Ensembles for Deep and Hard-Decision Hybrid Decoding
Ensemble models are widely used to solve complex tasks by their decompos...
research
∙
06/06/2019
