Simulation of Low Density Parity Check Codes & Applications
Project description
version 0.7.1
Bug-fix release
In Brief:
Generates coding and decoding matrices.
Probabilistic decoding: Belief Propagation algorithm.
Images transmission simulation (channel model: AGWN).
Sound transmission simulation (channel model :AGWN).
Image coding-decoding example:
Sound coding-decoding example:
Installation
From pip:
$ pip install --upgrade pyldpc
Tutorials:
Jupyter notebooks:
Many changes in tutorials in v.0.7.0
Users’ Guide:
1- LDPC Coding-Decoding Simulation
2- Images Coding-DecodingTutorial
3- Sound Coding-DecodingTutorial
4- LDPC Matrices Construction Tutorial
For LDPC construction details:
1- pyLDPC Construction(French)
version 0.7.1
Contains:
- Coding and decoding matrices Generators:
Regular parity-check matrix using Callager’s method.
Coding Matrix G both non-systematic and systematic.
Coding function adding Additive White Gaussian Noise.
- Decoding functions using Probabilistic Decoding (Belief propagation algorithm):
Default BP algorithm.
Full-log BP algorithm.
- Images transmission sub-module:
Coding and Decoding Grayscale and RGB Images.
Pixel by pixel coding & decoding (small matrices)
Row by row coding & decoding (large sparse matrices)
BER: Bit Error Rate function.
- Sound transmission sub-module:
Coding and Decoding audio files.
BER_audio: Bit Error Rate function.
What’s new:
5 to 10 times faster decoding.
Compatibility of scipy.sparse.csr objects (CSR format) and numpy arrays.
Row by row image decoding (More efficient than pixel coding) using large matrices.
4 times faster coding.
In the upcoming versions:
Use of large matrices (csr) in sound transmission sub-module.
Library of ready-to-use large matrices (csr).
Text Transmission functions.
Contact:
Please contact hicham.janati@ensae.fr for any bug encountered / any further information.
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