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  5. Low Complexity Autoencoder based End-to-End Learning of Coded Communications Systems

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Article
English
2020

Low Complexity Autoencoder based End-to-End Learning of Coded Communications Systems

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0 Files

English
2020
DOI: 10.1109/vtc2020-spring48590.2020.9128456

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Matti Latva-aho
Matti Latva-aho

University Of Oulu

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Nuwanthika Rajapaksha
Nandana Rajatheva
Matti Latva-aho

Abstract

End-to-end learning of a communications system using the deep learning-based autoencoder concept has drawn interest in recent research due to its simplicity, flexibility and its potential of adapting to complex channel models and practical system imperfections. In this paper, we have compared the bit error rate (BER) performance of autoencoder based systems and conventional channel coded systems with convolutional coding (CC), in order to understand the potential of deep learning-based systems as alternatives to conventional systems. From the simulations, autoencoder implementation was observed to have a better BER in 0-5 dB E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> /N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> range than its equivalent half-rate convolutional coded BPSK with hard decision decoding, and to have only less than 1 dB gap at a BER of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-5</sup> . Furthermore, we have also proposed a novel low complexity autoencoder architecture to implement end-to-end learning of coded systems in which we have shown better BER performance than the baseline implementation. The newly proposed low complexity autoencoder was capable of achieving a better BER performance than half-rate 16-QAM with hard decision decoding over the full 0-10 dB E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> /N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> range and a better BER performance than the soft decision decoding in 0-4 dB E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> /N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> range.

How to cite this publication

Nuwanthika Rajapaksha, Nandana Rajatheva, Matti Latva-aho (2020). Low Complexity Autoencoder based End-to-End Learning of Coded Communications Systems. , pp. 1-7, DOI: 10.1109/vtc2020-spring48590.2020.9128456.

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Publication Details

Type

Article

Year

2020

Authors

3

Datasets

0

Total Files

0

Language

English

DOI

10.1109/vtc2020-spring48590.2020.9128456

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