School of Electronic Countermeasures, National University of Defense Technology, Hefei 230000, China.
Sensors (Basel). 2021 Jun 4;21(11):3884. doi: 10.3390/s21113884.
Forward error correction coding is the most common way of channel coding and the key point of error correction coding. Therefore, the recognition of which coding type is an important issue in non-cooperative communication. At present, the recognition of FEC codes is mainly concentrated in the field of semi-blind identification with known types of codes. However, the receiver cannot know the types of channel coding previously in non-cooperative systems such as cognitive radio and remote sensing of communication. Therefore, it is important to recognize the error-correcting encoding type with no prior information. In the paper, we come up with a neoteric method to identify the types of FEC codes based on Recurrent Neural Network (RNN) under the condition of non-cooperative communication. The algorithm classifies the input data into Bose-Chaudhuri-Hocquenghem (BCH) codes, Low-density Parity-check (LDPC) codes, Turbo codes and convolutional codes. So as to train the RNN model with better performance, the weight initialization method is optimized and the network performance is improved. The experimental result indicates that the average recognition rate of this model is 99% when the signal-to-noise ratio (SNR) ranges from 0 dB to 10 dB, which is in line with the requirements of engineering practice under the condition of non-cooperative communication. Moreover, the comparison of different parameters and models show the effectiveness and practicability of the algorithm proposed.
前向纠错编码是信道编码中最常见的方式，也是纠错编码的关键。因此，识别哪种编码类型是非合作通信中的一个重要问题。目前，前向纠错码的识别主要集中在已知码型的半盲识别领域。然而，在认知无线电和通信遥感等非合作系统中，接收器无法事先知道信道编码的类型。因此，在没有先验信息的情况下识别纠错编码类型非常重要。在本文中，我们提出了一种基于递归神经网络（RNN）的非合作通信条件下识别前向纠错码类型的新方法。该算法将输入数据分为 Bose-Chaudhuri-Hocquenghem（BCH）码、低密度奇偶校验（LDPC）码、Turbo 码和卷积码。为了使 RNN 模型具有更好的性能，优化了权重初始化方法，提高了网络性能。实验结果表明，在信噪比（SNR）为 0dB 到 10dB 的范围内，该模型的平均识别率为 99%，符合非合作通信条件下工程实践的要求。此外，不同参数和模型的比较表明了所提出算法的有效性和实用性。
