An Unsupervised Learning Approach for Data Detection in the Presence of Channel Mismatch and Additive Noise
Signal Processing
2018-12-24 v1
Abstract
We investigate machine learning based on clustering techniques that are suitable for the detection of encoded strings of q-ary symbols transmitted over a noisy channel with partially unknown characteristics. We consider the detection of the q-ary data as a classification problem, where objects are recognized from a corrupted vector, which is obtained by an unknown corruption process. We first evaluate the error performance of k- means clustering technique without constrained coding. Secondly, we apply constrained codes that create an environment that improves the detection reliability and it allows a wider range of channel uncertainties.
Keywords
Cite
@article{arxiv.1812.09024,
title = {An Unsupervised Learning Approach for Data Detection in the Presence of Channel Mismatch and Additive Noise},
author = {Kees A. Schouhamer Immink and Kui Cai},
journal= {arXiv preprint arXiv:1812.09024},
year = {2018}
}