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Found 8 papers, 2 papers with code
Semi-Supervised Variational Inference over Nonlinear Channels
Deep learning methods for communications over unknown nonlinear channels have attracted considerable interest recently.
Unsupervised Linear and Nonlinear Channel Equalization and Decoding using Variational Autoencoders
We first consider the reconstruction of uncoded data symbols transmitted over a noisy linear intersymbol interference (ISI) channel, with an unknown impulse response, without using pilot symbols.
Blind Channel Equalization using Variational Autoencoders
A new maximum likelihood estimation approach for blind channel equalization, using variational autoencoders (VAEs), is introduced.
Near Maximum Likelihood Decoding with Deep Learning
The proposed decoder is based on the neural Belief Propagation algorithm and the Automorphism Group.
Deep Learning Methods for Improved Decoding of Linear Codes
Furthermore, we demonstrate that the neural belief propagation decoder can be used to improve the performance, or alternatively reduce the computational complexity, of a close to optimal decoder of short BCH codes.
Simplified End-to-End MMI Training and Voting for ASR
A simplified speech recognition system that uses the maximum mutual information (MMI) criterion is considered.
RNN Decoding of Linear Block Codes
We also demonstrate improved performance over belief propagation on sparser Tanner graph representations of the codes.
Learning to Decode Linear Codes Using Deep Learning
A novel deep learning method for improving the belief propagation algorithm is proposed.
