Iterative Decoding of Non-binary Turbo Codes Using Symbol Based SOVA Algorithm

2006 ◽  
Author(s):  
Jianjun Liu ◽  
Guofang Tu
Keyword(s):  
Turbo Codes ◽  
Iterative Decoding ◽  
Sova Algorithm

Efficient stopping criterion for iterative decoding of turbo codes

2003 ◽  
Vol 39 (1) ◽  
pp. 73 ◽  
Author(s):  
Nam Yul Yu ◽  
Min Goo Kim ◽  
Yong Serk Kim ◽  
Sang Uoon Chung
Keyword(s):  
Turbo Codes ◽  
Iterative Decoding ◽  
Stopping Criterion

Application of Extrinsic Information Transfer Charts to Anticipate Turbo Code Behavior

2013 ◽  
pp. 97-104
Author(s):  
Izabella Lokshina
Keyword(s):  
Turbo Codes ◽  
Iterative Decoding ◽  
Information Transfer ◽  
Turbo Code ◽  
A Priori ◽  
Substantial Reduction ◽  
International Standards ◽  
Extrinsic Information ◽  
Exit Chart ◽  
Analysis Technique

This paper examines turbo codes that are currently introduced in many international standards, including the UMTS standard for third generation personal communications and the ETSI DVB-T standard for Terrestrial Digital Video Broadcasting. The convergence properties of the iterative decoding process associated with a given turbo-coding scheme are estimated using the analysis technique based on so-called extrinsic information transfer (EXIT) chart. This approach provides a possibility to anticipate the bit error rate (BER) of a turbo code system using only the EXIT chart. It is shown that EXIT charts are powerful tools to analyze and optimize the convergence behavior of iterative systems utilizing the turbo principle. The idea is to consider the associated SISO stages as information processors that map input a priori LLR’s onto output extrinsic LLR’s, the information content being obviously assumed to increase from input to output, and introduce them to the design of turbo systems without the reliance on extensive simulation. Compared with the other methods for generating EXIT functions, the suggested approach provides insight into the iterative behavior of linear turbo systems with substantial reduction in numerical complexity.

An Advanced Self-Adaptive Iterative Decoding Scheme Based on Stopping Criterion

2011 ◽  
Vol 179-180 ◽  
pp. 167-173 ◽  
Author(s):  
Shu Yang Liu ◽  
Jian Ping Li ◽  
Chao Shi Cai
Keyword(s):  
Turbo Codes ◽  
Iterative Decoding ◽  
Stopping Criterion ◽  
Lower Complexity ◽  
Simulation Results ◽  
To Receive ◽  
Self Adaptive

This paper proposes an advanced iterative decoding scheme based on stopping criterion for BICM-ID embedded turbo codes. A fixed iterative number scheme has been used in the receiver and lower complexity than the conventional schedule can be achieved. To acquire a smaller total iterative number and fewer calculations of CE, the maximum and minimum iterative numbers (Imax/Imin) are introduced in this proposed scheme. However, iterative numbers which correspond to different SNRs are varied. In order to receive better flexibility, both Imax and Imin, whose values are not unique, are determined by the statistics of the average iterative numbers and the CG criterion. Simulation results confirm that, compared with the fixed iterative number scheme, similar BER performances and much lower complexity can be achieved. Further improvement of the adaptability results from this self-adaptive option of Imax/Imin.

scholarly journals Error Detection in Turbo Decoding using Neural Network

2019 ◽  
Vol 9 (1S) ◽  
pp. 218-221
Keyword(s):  
Neural Network ◽  
Bit Error Rate ◽  
Error Rate ◽  
Error Detection ◽  
Turbo Codes ◽  
Iterative Decoding ◽  
Turbo Decoding ◽  
Turbo Decoder ◽  
The Neural Network ◽  
Turbo Encoder

In this paper reduction of errors in turbo decoding is done using neural network. Turbo codes was one of the first thriving attempt for obtaining error correcting performance in the vicinity of the theoretical Shannon bound of –1.6 db. Parallel concatenated encoding and iterative decoding are the two techniques available for constructing turbo codes. Decrease in Eb/No necessary to get a desired bit-error rate (BER) is achieved for every iteration in turbo decoding. But the improvement in Eb/No decreases for each iteration. From the turbo encoder, the output is taken and this is added with noise, when transmitting through the channel. The noisy data is fed as an input to the neural network. The neural network is trained for getting the desired target. The desired target is the encoded data. The turbo decoder decodes the output of neural network. The neural network help to reduce the number of errors. Bit error rate of turbo decoder trained using neural network is less than the bit error rate of turbo decoder without training.

Application of Extrinsic Information Transfer Charts to Anticipate Turbo Code Behavior

2011 ◽  
Vol 3 (2) ◽  
pp. 31-37 ◽  
Author(s):  
Izabella Lokshina
Keyword(s):  
Turbo Codes ◽  
Iterative Decoding ◽  
Information Transfer ◽  
Turbo Code ◽  
A Priori ◽  
Substantial Reduction ◽  
International Standards ◽  
Extrinsic Information ◽  
Suggested Approach ◽  
Exit Chart

This paper examines turbo codes that are currently introduced in many international standards, including the UMTS standard for third generation personal communications and the ETSI DVB-T standard for Terrestrial Digital Video Broadcasting. The convergence properties of the iterative decoding process associated with a given turbo-coding scheme are estimated using the analysis technique based on so-called extrinsic information transfer (EXIT) chart. This approach provides a possibility to anticipate the bit error rate (BER) of a turbo code system using only the EXIT chart. It is shown that EXIT charts are powerful tools to analyze and optimize the convergence behavior of iterative systems utilizing the turbo principle. The idea is to consider the associated SISO stages as information processors that map input a priori LLR’s onto output extrinsic LLR’s, the information content being obviously assumed to increase from input to output, and introduce them to the design of turbo systems without the reliance on extensive simulation. Compared with the other methods for generating EXIT functions, the suggested approach provides insight into the iterative behavior of linear turbo systems with substantial reduction in numerical complexity.

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