A method to improve iterative decoding convergence in SCCC

作者：Zhao Dan Feng^*; Xue Rui; Miao Yan Hui

来源：Journal of Harbin Engineering University, 2009, 30(10): 1170-1174.

DOI：10.3969/j.issn.1006-7043.2009.10.016

• Analysis and simulation
• Average numbers
• Bit error rate performance
• Data blocks
• Decoding algorithm
• Extrinsic information
• Iterative detection
• Key factors
• Likelihood ratios
• Real time performance
• Serial concatenated convolutional codes
• Serially concatenated convolutional codes
• Speed of convergence
• Turbo principle

Iterative decoding based on the turbo principle is a novel approach for improving the performance of serial concatenated convolutional code (SCCC) systems. The speed of convergence of iterative detection is one of the key factors determining system performance. To reduce the high levels of feedback required during iterative decoding of short data blocks, a solution that improves the exchange of extrinsic information was proposed. In this method, inter decoder feedback is reduced by weighting the probability of extrinsic information between the inner and outer decoders. Theoretical analysis and simulation results show that the proposed method decreases the need for feedback and improves bit error rate performance. As a result, the average number of iterations was reduced and real time performance improved. In addition, the proposed method does not require conversion from probability to likelihood ratios, nor its inversion in the process of transforming extrinsic information. This significantly decreases the complexity of the decoding algorithm.

• 出版日期2009
• 单位哈尔滨工程大学