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Compared to traditional methods, coding configuration optimization inevitably increases complexity.
This section outlines the data preparation, coding configuration and the performance parameters used in the investigations reported in this paper.
Coding configuration optimization is mainly reflected in QP e and n determination for a given users' bandwidth distribution.
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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The performance of the proposed video parameter estimation methods are validated by experimental results in Section 4. The proposed methods were implemented in a prototype system as a proof-of-concept and several experiments were performed with regard to the estimation accuracy of bit rate, frame rate, and PLR using a variety of bitstreams with different coding configurations.
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.
This study analyzes and calculates dual unipolar and bipolar coded configurations of spectral-amplitude-coding optical code division multiple access (SAC-OCDMA) systems by using simulation methods.
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