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A Rank-Iteration Based Blind Identification Method for Convolutional Codes
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DOI：10.1109/LCOMM.2023.3257870.png)
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Abstract
En 中文
A blind identification technology of channel coding has received increasing attentions in recent years. In this letter, we focus on blind identification of convolutional codes without a candidate set. To improve the blind identification accuracy, a novel rank-iteration based blind identification algorithm for convolutional codes is proposed in this letter. Specifically, a weight-dependent threshold for the Binary Symmetric Channel (BSC) channel is firstly proposed to calculate the rank of a received data matrix more accurately. Then, we propose a new iterative operation that repeats the Gauss-Jordan Elimination Through Pivoting (GJETP) algorithm for several times, and select the smallest rank as the rank of the data matrix. Employing this strategy, the rank of a rank-deficient matrix in noisy environments can be close to its noiseless rank. Further, a rank-deficient threshold is introduced to amend the rank of the full-rank matrix after the end of the whole iterative process, which can reduce the rank deviation of the full-rank matrix. Finally, the effectiveness of the proposed method is verified by simulations and comparisons.
Keywords:
Convolutional codes
Codes
Symbols
Matrix converters
Optimized production technology
Symmetric matrices
Noise measurement
Blind identification
convolutional codes
encoder parameters
rank-iteration
GJETP algorithm
Journal
IF:
4.4
Papers:
1.3W
Citations:
2.2W
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