Blind decoding in $\alpha$-Stable noise: An online learning approach

TL;DR

This paper introduces an online learning-based decoding method for error control in symmetric alpha-stable noise environments, capable of handling unknown alpha values and noise mixtures, with demonstrated improvements in turbo coded systems.

Contribution

It presents a novel online learning approach for decoding in alpha-stable noise without prior alpha knowledge, including handling noise mixtures, which is a new contribution.

Findings

Improved decoding performance in alpha-stable noise environments.

Effective handling of unknown alpha values and noise mixtures.

Demonstrated benefits in turbo coded systems.

Abstract

A novel method for performing error control coding in Symmetric $\alpha-$Stable noise environments without any prior knowledge about the value of $\alpha$ is introduced. We use an online learning framework which employs multiple distributions to decode the received block and then combines these results based on the past performance of each individual distributions. The proposed method is also able to handle a mixture of Symmetric $\alpha-$Stable distributed noises. Performance results in turbo coded system highlight the utility of the work.