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Parallel concatenated cyclic low density parity check codes based on convolutional interleaver

Adhikari, Kritika (2017). Parallel concatenated cyclic low density parity check codes based on convolutional interleaver. Master of Engineering Thesis, Charles Darwin University.

Document type: Thesis
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Author Adhikari, Kritika
Title Parallel concatenated cyclic low density parity check codes based on convolutional interleaver
Institution Charles Darwin University
Publication Date 2017-05
Thesis Type Master of Engineering
Supervisor Vafi, Sina
Chan, Erwin H. W.
0906 - Electrical and Electronic Engineering
Abstract During the transmission of data in digital communication systems, errors occur due to the unpredictable behavior of the channel. This means, a reliable system must be developed to receive an actual data with the minimum error. For this purpose, channel coding, which adds some redundant data to the original one is being implemented. This technique is also named forward error correcting (FEC) codes as there is no feedback from the receiver to transmitter for correction of errors. Block code and convolutional code are represented as two main types of FEC.

Two or more than two simpler codes are combined to form powerful error correcting codes with high performance and moderate complexity. Codes formed by this system are called concatenated codes. Concatenation is an idea that overcomes the complexity of encoding and decoding of the long length codes. Product code is represented as one of the high performance concatenated codes. It is formed by the serial concatenation of two block codes linked by an interleaver. Parallel concatenated codes (PCC) are alternative concatenated codes that complement product codes. The performance of PCC is based on the structure of interleaver. The major disadvantage of PCC over product code is a loss in minimum free distance compared to product codes.

This thesis aims to represent high performance parallel concatenated codes constituted by cyclic low density parity check codes and a convolutional interleaver. It expresses how the convolutional interleaver is formed as block interleaver to improve the minimum weight of code, which consequently improves its error correcting capability.

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