Performance evaluation of suboptimal decoding schemes for tail biting convolutional codes in EDGE headers

2003 ◽  
Vol 39 (17) ◽  
pp. 1280
Author(s):  
M. Mzyece ◽  
J. Dunlop
Keyword(s):  
Performance Evaluation ◽  
Convolutional Codes ◽  
Tail Biting

Trap Detection Based Decoding Algorithm for Tail-biting Convolutional Codes

2011 ◽  
Vol 33 (10) ◽  
pp. 2300-2305
Author(s):  
Xiao-tao Wang ◽  
Hua Qian ◽  
Jing Xu ◽  
Yang Yang
Keyword(s):  
Convolutional Codes ◽  
Decoding Algorithm ◽  
Tail Biting

scholarly journals Deep Ensemble of Weighted Viterbi Decoders for Tail-Biting Convolutional Codes

2020 ◽  
Author(s):  
Tomer Raviv ◽  
Asaf Schwartz ◽  
Yair Be'ery
Keyword(s):  
Machine Learning ◽  
Viterbi Algorithm ◽  
State Of The Art ◽  
Convolutional Codes ◽  
Specific Region ◽  
Short Length ◽  
Learning Approach ◽  
Tail Biting ◽  
Machine Learning Approach ◽  
Viterbi Decoders

Tail-biting convolutional codes extend the classical zero-termination convolutional codes: Both encoding schemes force the equality of start and end states, but under the tail-biting each state is a valid termination. This paper proposes a machine-learning approach to improve the state-of-the-art decoding of tail-biting codes, focusing on the widely employed short length regime as in the LTE standard. This standard also includes a CRC code. First, we parameterize the circular Viterbi algorithm, a baseline decoder that exploits the circular nature of the underlying trellis. An ensemble combines multiple such weighted decoders, each decoder specializes in decoding words from a specific region of the channel words' distribution. A region corresponds to a subset of termination states; the ensemble covers the entire states space. A non-learnable gating satisfies two goals: it filters easily decoded words and mitigates the overhead of executing multiple weighted decoders. The CRC criterion is employed to choose only a subset of experts for decoding purpose. Our method achieves FER improvement of up to 0.75dB over the CVA in the waterfall region for multiple code lengths, adding negligible computational complexity compared to the circular Viterbi algorithm in high SNRs.

A Low-Complexity Maximum-Likelihood Decoder for Tail-Biting Convolutional Codes

2018 ◽  
Vol 66 (5) ◽  
pp. 1859-1870 ◽  
Author(s):  
Yunghsiang S. Han ◽  
Ting-Yi Wu ◽  
Po-Ning Chen ◽  
Pramod K. Varshney
Keyword(s):  
Maximum Likelihood ◽  
Convolutional Codes ◽  
Low Complexity ◽  
Tail Biting
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