Blind Recognition Algorithm of Convolutional Code via Convolutional Neural Network

• Published in: International journal of intelligent systems Vol. 2025; no. 1
• Main Authors: Pan Deng, Zhang, Tianqi, Wen, Lianghua, Ma, Baoze, Wei, Ying, Cui, Linhao
• Format: Journal Article
• Language: English
• Published: New York John Wiley & Sons, Inc 01.01.2025
• Subjects: Accuracy; Algorithms; Artificial neural networks; Block codes; Classification; Codes; Communication; Convolutional codes; Fault tolerance; Feature extraction; Identification methods; Low density parity check codes; Methods; Neural networks; Parameter identification; Parameters; Recognition; Signal to noise ratio; Unmanned aerial vehicles
• ISSN: 0884-8173; 1098-111X; 1098-111X
• DOI: 10.1155/int/3183819

Pointing at the vexed question of blind recognition in the convolutional code class, this paper proposes a convolutional code blind identification method via convolutional neural networks (CNNs). First, this algorithm uses the traditional method to generate different convolutional codes, and the feature extraction algorithm adopts the theorem of Euclid’s algorithm. Then, the input signal is loaded to the CNN; next, the feature is extracted by convolutional kernel. Finally, the Softmax activation function is applied to full‐connection layer network. After the input signals pass through the above layers, the system classifies the signals. The research results indicate that the presented algorithm has improved the recognition performance of code length and rate. For different convolutional codes with parameters of (5, 7), (15, 17), (23, 35), (53, 75), and (133, 171) and similar convolutional codes with parameters of (3, 1, 6), (3, 1, 7), (2, 1, 7), (2, 1, 6), and (2, 1, 5), the recognition rate of parameter classification can reach 100% at signal‐to‐noise ratio (SNR) of 3 dB.