An Unsupervised Learning Approach for Data Detection in the Presence of Channel Mismatch and Additive Noise

Kees A. Schouhamer Immink; Kui Cai

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arXiv · 1812.09024

An Unsupervised Learning Approach for Data Detection in the Presence of Channel Mismatch and Additive Noise

Kees A. Schouhamer Immink·Kui Cai

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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.

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BibTeX RIS

Kees A. Schouhamer Immink, Kui Cai. 2018-12-21. An Unsupervised Learning Approach for Data Detection in the Presence of Channel Mismatch and Additive Noise. https://arxiv.org/abs/1812.09024

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