University of Oulu

N. Rajapaksha, N. Rajatheva and M. Latva-aho, "Low Complexity Autoencoder based End-to-End Learning of Coded Communications Systems," 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium, 2020, pp. 1-7, doi: 10.1109/VTC2020-Spring48590.2020.9128456

Low complexity autoencoder based end-to-end learning of coded communications systems

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Author: Rajapaksha, Nuwanthika1; Rajatheva, Nandana1; Latva-aho, Matti1
Organizations: 1Centre for Wireless Communications, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.4 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2021-02-18


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 b /N 0 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 -5. 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 b /N 0 range and a better BER performance than the soft decision decoding in 0—4 dB E b /N 0 range.

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Series: IEEE Vehicular Technology Conference
ISSN: 1090-3038
ISSN-L: 1090-3038
ISBN: 978-1-7281-5207-3
ISBN Print: 978-1-7281-4053-7
Article number: 9128456
DOI: 10.1109/VTC2020-Spring48590.2020.9128456
Host publication: 91st IEEE Vehicular Technology Conference, VTC Spring 2020
Conference: IEEE Vehicular Technology Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
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