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.

Utilizing multi-view infrared images to collaboratively identify the types of surface ship targets is a feasible approach in practice. This paper proposes a fine-grained object recognition algorithm based on self-supervised representation learning of multi- view infrared images. A contrastive learning framework for target recognition based on multi-view infrared images is introduced, employing a training method of pre-training with unlabeled data and fine-tuning with a small amount of labeled data, which enhances the generalization ability of the algorithm. By optimizing the instance construction method, the similarity and differences between instances are better extracted, learning invariant features of the same target from different views; by combining the cross-correlation matrix, redundancy in feature representation is reduced; a target recognition application method with adaptive input of multi-view images is proposed, improving the accuracy of downstream target recognition tasks; a multi-view infrared image dataset of ship targets is constructed and algorithm validation is performed. Experimental results show the Accuracy, Precision, Recall, and F1 score of proposed method are 95.1%, 95.2%, 95.3%, and 95.2%, respectively, which are higher than the results of the SimCLR algorithm by 7.1%, 6.7%, 6.8%, and 6.7%, and higher than the results of the BYOL algorithm by 7.7%, 7.6%, 7.3%, and 7.4%.

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.

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.

Distributed beamforming has emerged as a pivotal technology in modern communication systems, radar applications, and collaborative electronic warfare. Through distributed collaboration, these jamming nodes composed of unmanned aerial vehicles (UAVs) can execute flexible electronic jamming toward wireless communication systems despite energy limitations. However, they encounter challenges such as time and positioning inaccuracies, which can diminish the synthesis power of jamming beams and reduce jamming effectiveness. This paper examines the impact of angle and distance errors on jamming effectiveness, deriving upper error bounds under various suppression coefficients. The findings offer practical guidelines for enhancing the precision and effectiveness of distributed beamforming systems in electronic warfare applications.

Blind identification of channel codes is a key technology in non-cooperative communication as well as intelligent communication. In this paper, we are interested in recovering the parameters of low-density parity-check (LDPC) codes without any a priori information. We propose to find dual codewords from noisy intercepted sequences and prove that the problem is NP-complete. Afterward, we propose an improved collision-based algorithm that can avoid most of the invalid searches and thus allowing a significant reduction in complexity. The algorithm produces almost no errors when the noise is small. While in the case of non-negligible noise, we propose to use soft information to detect the outputs of the algorithm, and errors in which can therefore be extensively eliminated. After a sufficient number of dual codewords have been found, one can easily recover the code lengths and code rates of LDPC codes, as well as the sizes of sub-matrices for Quasi-Cyclic LDPC codes. Experimental results show that the proposed method has an improved noise tolerance and can be more efficient than existing methods.