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Design and Implementation of Improved Decoding Algorithms for LDPC Convolutional Codes

Velumani, Sakthivel (2019)
Design and Implementation of Improved Decoding Algorithms for LDPC Convolutional Codes.
Technische Universität
Master Thesis, Primary publication

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Item Type: Master Thesis
Type of entry: Primary publication
Title: Design and Implementation of Improved Decoding Algorithms for LDPC Convolutional Codes
Language: English
Referees: Alt, Mr Bastian
Date: 14 January 2019
Place of Publication: Darmstadt
Date of oral examination: 14 January 2019
Abstract:

A windowed decoder in its basic form converges rather slowly and has a large performance gap to a full-block decoder. In this work, we propose two techniques to improve the performance of windowed decoders for Low-Density Parity-Check Convolutional Codes (LDPC-CCs). The first technique: the LRL decoder, focuses on the movement direction of the window in which the window moves forward and backward across the Parity-Check Matrix (PCM). The second technique: the IPSC, focuses on the convergence criterion for the windows where the criterion is dependent on window size. We chose the LDPC-CCs specified in the standard IEEE 1901 Broadband Power Line (BPL) to evaluate our techniques. We found that a proper end-termination for the BPL’s LDPC-CCs is infeasible. We show that although the termination procedure mentioned in the standard fails to reduces the Check Node (CN) degree, the known termination bits at the decoder effectively reduce the CN degree. Simulation results show that compared to the sliding-window decoder, the LRL decoder has a decoding performance gain of about 1.6 dB while simultaneously reducing the decoding complexity by up to 40% . On the other hand, the IPSC technique proves to reduce the decoding complexity by up to 34% depending on the window sizes.

URN: urn:nbn:de:tuda-tuprints-82349
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
Date Deposited: 23 Apr 2019 08:59
Last Modified: 09 Jul 2020 02:25
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/8234
PPN: 447879308
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