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.

A Novel Decoding Scheme for Polar Code Using Convolutional Neural Network Polar code is the first capacityachieving channel coding scheme which has been selected for the next generation of wireless communication standard(5G). However, the traditional decoding schemes such as CASCL and BP algorithms cannot take into account both BER and latency performance. Inspired by the deep learning technology, we propose a scheme that combine convolutional neural network (CNN) with traditional Belief Propagation (BP) decoding. By exploiting noise correlation for channel decoding under colored noise environment, the proposed scheme can obtain a more accurate estimation of channel noise. We simulate the performance of the proposed scheme under BPSK and 4QAM modulation. For QAM modulation, a scheme that extend the conventional realvalued CNN technique to the complexvalued one is given. The results demonstrate that bit error rates (BER) performance can be improved after removing the estimated noise. Compare to the traditional Polar decoding schemes, the proposed scheme has better tradeoff between BER and latency performance under colored noise environment.