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The authors of [9, 10] develop methods for blind recovery of convolutional encoder in turbo code configuration.
Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best minimal ECOC code configuration.
The typical turbo code configuration is the parallel concatenation of two systematic recursive constituent convolutional encoders of rate 1/2 connected via an interleaver, resulting in a code of rate 1/3.
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All code, configurations, computing environment, and their provenance were preserved along with a high level diagram illustrating all steps of the analysis.
Compared to traditional methods, coding configuration optimization inevitably increases complexity.
In this experiment, we analyze coding performance when using the optimized coding configuration determined through our method, and compare these results against the coding performance when using other coding configurations.
Coding configuration optimization is mainly reflected in QP e and n determination for a given users' bandwidth distribution.
This section outlines the data preparation, coding configuration and the performance parameters used in the investigations reported in this paper.
Therefore, an optimal LT coding configuration is expected to result from a trade-off between quantizer resolution and LT modeling accuracy.
Our approach accurately optimizes coding configuration depending on the transmission rate and user environment, and the different user bandwidth probability density distributions for the two different videos.
However, the focus of most of the existing studies is mainly on determining the optimal coding configuration without considering the total energy consumed for encoding and transmission.
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