MATEC Web Conf.
Volume 309, 20202019 International Conference on Computer Science Communication and Network Security (CSCNS2019)
|Number of page(s)||8|
|Published online||04 March 2020|
Rate-compatible LDPC convolutional codes over non-gaussian noise channel
School of Physics and Telecommunications Engineering, South China Normal University, Guangzhou, Guangdong, 510006, China
* Corresponding author: firstname.lastname@example.org
This paper is aimed to study the characteristics of the underwater acoustic channel with non-Gaussian noise channel. And Gaussian mixture model (GMM) is utilized to fit the background noise over the non-Gaussian noise channel. Furthermore, coding techniques which use a sequence of rate-compatible low-density parity-check (RC-LDPC) convolutional codes with separate rates are constructed based on graph extension method. The performance study of RC-LDPC convolutional codes over non-Gaussian noise channel and the additive white Gaussian noise (AWGN) channel is performed. Study implementation of simulation is that modulation with binary phase shift keying (BPSK), and iterative decoding based on pipeline log-likelihood rate belief propagation (LLRBP) algorithm. Finally, it is shown that RC-LDPC convolutional codes have good bit-rate-error (BER) performance and can effectively reduce the impact of noise.
Key words: Non-gaussian noise / Gaussian mixture model (GMM) / Rate-compatible low-density parity-check (RC-LDPC) convolutional codes
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.