Zadeh, Seyed Ali Gorji (2005) An adaptive M-algorithm based convolutional decoder. Masters thesis, Concordia University.
MR04371.pdf - Accepted Version
The Viterbi algorithm is one of the most popular convolutional decoders. This algorithm suffers from the high complexity in the decoding of the long constraint length codes. The M-algorithm is a simplified Viterbi algorithm and it is practical for the decoding of the long constraint length codes but it suffers from catastrophic error caused by the correct path loss in the algorithm. In this thesis we propose two different ways of the correct path recovery based on M-algorithm convolutional decoder. The first method is called Ancestor Based Survivor Decision in M-algorithm Convolutional Decoder. We propose a survivor decision not only based on the path metric but also based on the path ancestor metric. This algorithm has been designed for the systems with an abrupt noise. Simulation results for the Additive White Gaussian Noise (AWGN) channel will show slightly improved error performance in some cases since the AWGN does not act as abrupt noise. For the AWGN channels we propose another method which is called Adaptive M-algorithm Based Convolutional Decoder. In this method, we suggest using small number of survivors for most of the decoding attempts and we use higher number of survivors only in case of error decoding. The Cyclic Redundancy Check (CRC) error detection code is used to detect if the frame is an erroneous frame. Monte-Carlo simulation for the AWGN channel shows that in most of the cases the error performance of the proposed algorithm outperforms the Viterbi algorithm or the conventional M-algorithm error performance.
|Divisions:||Concordia University > Faculty of Engineering and Computer Science > Electrical and Computer Engineering|
|Item Type:||Thesis (Masters)|
|Authors:||Zadeh, Seyed Ali Gorji|
|Pagination:||xiv, 94 leaves : ill. ; 29 cm.|
|Degree Name:||M.A. Sc.|
|Program:||Electrical and Computer Engineering|
|Thesis Supervisor(s):||Soleymani, M. Reza|
|Deposited By:||Concordia University Libraries|
|Deposited On:||18 Aug 2011 18:26|
|Last Modified:||05 Nov 2016 00:24|
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