Unsupervised Linear and Nonlinear Channel Equalization and Decoding using Variational Autoencoders

05/21/2019
by   Avi Caciularu, et al.
0

A new approach for blind channel equalization and decoding, using variational autoencoders (VAEs), is introduced. 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. We derive an approximated maximum likelihood estimate to the channel parameters and reconstruct the transmitted data. We demonstrate significant and consistent improvements in the error rate of the reconstructed symbols, compared to existing blind equalization methods such as constant modulus, thus enabling faster channel acquisition. The VAE equalizer uses a fully convolutional neural network with a small number of free parameters. These results are extended to blind equalization over a noisy nonlinear ISI channel with unknown parameters. We then consider coded communication using low-density parity-check (LDPC) codes transmitted over a noisy linear or nonlinear ISI channel. The goal is to reconstruct the transmitted message from the channel observations corresponding to a transmitted codeword, without using pilot symbols. We demonstrate substantial improvements compared to expectation maximization (EM) using turbo equalization. Furthermore, in our simulations we demonstrate a relatively small gap between the performance of the new unsupervised equalization method and that of the fully channel informed (non-blind) turbo equalizer.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/05/2018

Blind Channel Equalization using Variational Autoencoders

A new maximum likelihood estimation approach for blind channel equalizat...
research
06/26/2018

Blind Decoding-Metric Estimation for Probabilistic Shaping via Expectation Maximization

An unsupervised learning approach based on expectation maximization is p...
research
05/20/2023

Matched-Filter Design to Improve Self-Interference Cancellation in Full-Duplex Communication Systems

A new method for capacity and spectral efficiency increases is a full-du...
research
09/15/2022

Blind and Channel-agnostic Equalization Using Adversarial Networks

Due to the rapid development of autonomous driving, the Internet of Thin...
research
09/21/2023

Semi-Supervised Variational Inference over Nonlinear Channels

Deep learning methods for communications over unknown nonlinear channels...
research
04/02/2016

Channel Equalization Using Multilayer Perceptron Networks

In most digital communication systems, bandwidth limited channel along w...
research
08/17/2022

Learn to Detect and Detect to Learn: Structure Learning and Decision Feedback for MIMO-OFDM Receive Processing

One of the major open challenges in MIMO-OFDM receive processing is how ...

Please sign up or login with your details

Forgot password? Click here to reset