A mΘ analysis and a decoding α-Viterbi algorithm of the mΘ convolutional codes on

2021 ◽  
pp. 1-26
Author(s):  
Jean Armand Tsimi ◽  
Armand Kémadjou Kétchandjeu ◽  
Laurence Um
Keyword(s):  
Viterbi Algorithm ◽  
Convolutional Codes

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.

scholarly journals Data Transmission Using Convolutional Codes and Bi-directional Soft Output Viterbi Algorithm over the Two-User Binary Adder Channel

2017 ◽  
Vol 32 (1) ◽  
pp. 126-132
Author(s):  
Maria de Lourdes Melo Guedes Alcoforado ◽  
Lucas Bulhões ◽  
Abner Andrade ◽  
Francisco Madeiro ◽  
Valdemar Cardoso da Rocha Jr.
Keyword(s):  
Data Transmission ◽  
Viterbi Algorithm ◽  
Convolutional Codes

NEW CONCATENATION SCHEMES BASED ON THE MULTITHRESHOLD DECODERS OF CONVOLUTIONAL SELF-ORTHOGONAL CODES FOR GAUSSIAN CHANNELS

2021 ◽  
Vol 9 (4) ◽  
pp. 51-62
Author(s):  
Dina Satybaldina ◽  
◽  
Valery Zolotarev ◽  
Gennady Ovechkin ◽  
Zhuldyz Sailau kyzy ◽  
...  
Keyword(s):  
Viterbi Algorithm ◽  
Convolutional Codes ◽  
Performance Characteristics ◽  
Moderate Increase ◽  
Two Dimensional ◽  
Reed Solomon Codes ◽  
Gaussian Channels ◽  
Orthogonal Codes ◽  
Simulation Results ◽  
Decoding Delay

New serial concatenation schemes based on the multithreshold decoders and di- vergent principle for the convolutional self-orthogonal codes under Gaussian channels are proposed. Using both binary and symbolic decoders on the second decoding stage of the convolutional codes are considered. Simulation results are indicated the higher performance characteristics of the proposed cascade schemes on majority decoders in comparison with clas- sical schemes based on the Viterbi algorithm and Reed-Solomon codes. A moderate increase in decoding delay during concatenation is revealed. It is determined by the absence of the need to use traditional two-dimensional concatenated structures.

scholarly journals Iterative Decoding of Turbo Codes

2018 ◽  
Vol 3 ◽  
pp. 15
Author(s):  
Dhaneshwar Sah
Keyword(s):  
Turbo Codes ◽  
Coding Theory ◽  
Iterative Decoding ◽  
Communication Systems ◽  
Error Control ◽  
Viterbi Algorithm ◽  
Convolutional Codes ◽  
Soft Information ◽  
Space Communications ◽  
College Of Engineering

<p><strong> </strong>This paper presents a Thesis which consists of a study of turbo codes as an error-control Code and the software implementation of two different decoders, namely the Maximum a Posteriori (MAP), and soft- Output Viterbi Algorithm (SOVA) decoders. Turbo codes were introduced in 1993 by berrouet at [2] and are perhaps the most exciting and potentially important development in coding theory in recent years. They achieve near- Shannon-Limit error correction performance with relatively simple component codes and large interleavers. They can be constructed by concatenating at least two component codes in a parallel fashion, separated by an interleaver. The convolutional codes can achieve very good results. In order of a concatenated scheme such as a turbo codes to work properly, the decoding algorithm must affect an exchange of soft information between component decoders. The concept behind turbo decoding is to pass soft information from the output of one decoder to the input of the succeeding one, and to iterate this process several times to produce better decisions. Turbo codes are still in the process of standardization but future applications will include mobile communication systems, deep space communications, telemetry and multimedia. Finally, we will compare these two algorithms which have less complexity and which can produce better performance.</p><p><strong>Journal of Advanced College of Engineering and Management</strong>, Vol.3, 2017, Page: 15-30</p>

Simplified Circular Viterbi Algorithm for Tailbiting Convolutional Codes

2011 ◽  
Author(s):  
Jorge Ortin ◽  
Paloma Garcia ◽  
Fernando Gutierrez ◽  
Antonio Valdovinos
Keyword(s):  
Viterbi Algorithm ◽  
Convolutional Codes
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