Comparing Decoding Performance of LDPC Codes and Convolutional Codes for Short Packet Transmission In the field of industrial automation research, wireless control in key application scenarios has become a research hotspot. Nevertheless, transmission over wireless channels in industrial environments is prone to interference, resulting in frequent erroneous packet deliveries. Forward Error Correction (FEC) code as an approach is able to effectively improve reliability and reduce the number of retransmissions. Therefore, channel coding needs further analysis to achieve better industrial wireless application. To this aim, this paper compares the decoding performance of convolutional codes and lowdensity paritycheck (LDPC) codes on the condition of short packet transmission. The metrics employed for evaluation are bit error rate (BER) and packet error rate (PER). The logarithmic belief propagation (LogBP) algorithm and the Viterbi decoding algorithm are adopted to LDPC codes and convolutional codes respectively. The results show that the decoding algorithm of LDPC codes is prominent in short packet transmission.

Finegrained recognition of error correcting codes based on 1D convolutional neural network Abstract   Forward error correction (FEC) codes are the most commonly used method in channel coding, and it is capable of automatically detecting and correcting errors at the receiving end during transmission. At present, the channel coding semiblind recognition given known FEC codes type has been extensively studied. However, in cognitive radio and noncooperative systems, the types of channel coding are unknown at the receiving end. Therefore, the ability to identify the type of error correcting codes without any a priori knowledge is essential. In this paper, we proposed a novel method based on convolutional neural network that can achieve finegrained type recognition for error correcting codes in noncooperative systems. The proposed algorithm classifies the incoming data symbols among hamming codes, BoseChaudhuriHocquenghem (BCH) codes, ReedSolomon (RS) codes, Lowdensity Paritycheck (LDPC) codes, turbo codes, polar codes, and convolutional codes. Further, to train a wellbehaved CNN model, we constructed a deep fusion model based on 1D convolutional layer and modified 1D inception architecture that can achieve endtoend extraction of features. Experimental results show that the proposed model earns an average recognition accuracy of roughly 99% under the condition of signaltonoise ratio (SNR) ranging from 6 dB to 20 dB. In addition, we conduct a comprehensively and thoroughly investigation on the performance of convolutional neural network based code recognition for digital communications.