A method of blind recognition of the coding parameters for binary Bose-Chaudhuri-Hocquenghem (BCH) codes is proposed in this paper. We consider an intelligent communication receiver which can blindly recognize the coding parameters of the received data stream. The only knowledge is that the stream is encoded using binary BCH codes, while the coding parameters are unknown. The problem can be addressed on the context of the non-cooperative communications or adaptive coding and modulations (ACM) for cognitive radio networks. The recognition processing includes two major procedures: code length estimation and generator polynomial reconstruction. A hard decision method has been proposed in a previous literature. In this paper we propose the recognition approach in soft decision situations with Binary-Phase-Shift-Key modulations and Additive-White-Gaussian-Noise (AWGN) channels. The code length is estimated by maximizing the root information dispersion entropy function. And then we search for the code roots to reconstruct the primitive and generator polynomials. By utilizing the soft output of the channel, the recognition performance is improved and the simulations show the efficiency of the proposed algorithm.

Blind identification of encoders has received increasing attention in recent years. In this paper, we focus on LDPC coded communication systems and study the problem of blind identification over a candidate set. We propose a blind identification method based on a two-stage maximum a posteriori probability estimation. The first stage measures the parity-check relationship between the received vectors and the rows of the parity-check matrices in the candidate set, and the second stage deals with the effect of row weights. Moreover, We theoretically explained the mechanism by which row weight affects identification results and proved that the proposed method can considerably suppress the preference of the existing methods for low row weights. Simulation results show that the proposed method always has a higher probability of correct identification for parity-check matrices with high row weights in the candidate set than the existing methods.

In digital communication systems, error correction codes are used to recover data that has been disturbed in a noisy environment. Convolutional code is a commonly used error correction code. In non-cooperative communication systems, it is very meaningful to blindly identify the synchronization position and coding parameters of convolutional codes. Existing reference had proposed a blind recognition algorithm for coding parameters of $k/n$ rate convolutional codes in a noisy environment, but it didn’t take into account the case where the received code words were not synchronized. In this paper, this problem is improved by using the quasi-correlation between the receive sequence and the check matrix to estimate the synchronization position, while obtaining the check matrix. It implements blind recognition of the synchronization position and coding parameters of $k/n$ rate convolutional codes in a noisy environment. Simulation experimental results show that the improved algorithm in this paper is effective.

As optical performance monitoring (OPM) requires accurate and robust solutions to tackle the increasing dynamic and complicated optical network architectures, we experimentally demonstrate an end-to-end optical signal-to-noise (OSNR) estimation method based on the convolutional neural network (CNN), named OptInception. The design principles of the proposed scheme are specified. The idea behind the combination of the Inception module and finite impulse response (FIR) filter is elaborated as well. We experimentally evaluate the mean absolute error (MAE) and root-mean-squared error (RMSE) of the OSNR monitored in PDM-QPSK and PDM-16QAM signals under various symbol rates. The results suggest that the MAE reaches as low as 0.125 dB and RMSE is 0.246 dB in general. OptInception is also proved to be insensitive to the symbol rate, modulation format, and chromatic dispersion. The investigation of kernels in CNN indicates that the proposed scheme helps convolutional layers learn much more than a lowpass filter or bandpass filter. Finally, a comparison in performance and complexity presents the advantages of OptInception.

This paper proposed a method of modulation format identification using Radial Basis Function Artificial Neural Network (RBF-ANN) trained with Asynchronous Amplitude Histograms (AAHs). Compare with the traditional RBF-ANN, the proposed method is improved by applying Expectation Maximization (EM), which takes advantage of the statistical feature of AAHs, to select center vector for radial basis function. Assuming distribution of each bin in AAH as Gaussian mixture model (GMM), the mean values of the model can be exploited as the center vector which obtained using EM. This approach ensures that the center vector is unbiased and optimal. The center vector is implemented to RBF-ANN to identify different modulation formats. Numerical simulation results demonstrated that identification accuracy was about 99% for three commonly-used modulation formats within the OSNR between 40~10dB. And the CD tolerance was 1000ps/nm. In comparison, former center vector selection approaches include K-means and random selection were applied. The result showed that the EM method improved the identification accuracy by 2% to 4% when OSNR=10dB and CD=100ps/nm. Owing to its excellent performance, this method can be employed in the next generation optical transport network for auto-adaption modulation format identification.

Modern communication systems tend to use different channel codes to find a balance between communication speed and quality. Accordingly, the blind identification techniques of encoders have drawn much research interest. In this letter, we propose a blind identification scheme for the Low-Density Parity Check (LDPC) coded communication systems based on a known candidate set. The proposed scheme can adaptively adjust the length of the received sequence to approach an arbitrary probability of missed detection and false alarm for codes in the candidate set, where the latter probability has received little attention by the existing methods. On this basis, the proposed scheme can determine whether there are unknown codes in the received vector, which is useful in practical applications. The performance of the proposed scheme is investigated using system simulations.