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The existing multi-carrier composite modulation recognition methods have failed to effectively integrate inner and outer modulation characteristics, thereby limiting the potential for improving recognition performance under low signal-to-noise ratio (SNR) conditions. To address this issue, this paper proposes a multi-carrier composite signal modulation recognition algorithm based on a multi-dimensional time-frequency superimposed spectrum (MD-TFSS) with integrated inner and outer features, which can recognize composite modulation signals in the set BPSK-PM, QPSK-PM, BPSK-QPSK-PM, BPSK-BPSK-PM, QPSK-QPSK-PM. The proposed method constructs a dual spectrum through multiplying an inner modulation spectrum and a squared spectrum, then combines the inner modulation dual spectrum with the outer modulation time-frequency diagram in dual-channel mode to form MD-TFSS features. Based on the MD-TFSS, a blind recognition algorithm is implemented using the dual-channel input ECA-ResNet18 (DECA-ResNet18) incorporating the ECA attention mechanism. The proposed algorithm first converts the complex features of multi-carrier composite modulation signals into visually interpretable image features (including the quantity and concentration of bright spots and lines) through the MD-TFSS, achieving intuitive representation of multiple modulation characteristics. Meanwhile, the dual-channel input mechanism enables collaborative expression of outer modulation time-frequency diagram and inner modulation dual spectrum features, ensuring tight integration of inner and outer characteristics while avoiding feature isolation issues in traditional multi-diagram concatenation methods. Secondly, the DECA-ResNet18 network dynamically allocates weights through an adaptive regulation mechanism based on input feature differences, autonomously adjusting channel attention levels to effectively capture complementary characteristics from both inner and outer modulation features, thereby enhancing recognition accuracy and generalization capability for multi-carrier composite modulation signals. Theoretical analysis and simulation results demonstrate that, compared with the existing methods that use isolated outer and inner features or conventional multi-feature diagram construction approaches, the proposed algorithm achieves superior recognition performance under low SNR conditions. Additionally, DECA-ResNet18 demonstrates enhanced recognition performance for multi-carrier composite modulated signals compared to the traditional ResNet18.

In this paper, a deep-learning-based frame synchronization blind recognition algorithm is proposed to improve the detection performance in non-cooperative communication systems. Current methods face challenges in accurately detecting frames under high bit error rates (BER). Our approach begins with flat-top interpolation of binary data and converting it into a series of grayscale images, enabling the application of image processing techniques. By incorporating a scaling factor, we generate RGB images. Based on the matching radius, frame length, and frame synchronization code, RGB images with distinct stripe features are classified as positive samples for each category, while the remaining images are classified as negative samples. Finally, the neural network is trained on these sets to classify test data effectively. Simulation results demonstrate that the proposed algorithm achieves a 100% probability in frame recognition when BER is below 0.2. Even with a BER of 0.25, the recognition probability remains above 90%, which exhibits a performance improvement of over 60% compared with traditional algorithms. This work addresses the shortcomings of existing methods under high error conditions, and the idea of converting sequences into RGB images also provides a reliable solution for frame synchronization in challenging communication environments.

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.

To solve the problem of blind identification of channel coding in signal interception and intelligent communication systems, a blind recognition algorithm of channel coding based on a convolutional neural network (CNN) is proposed. To deal better with the input characteristics of one-dimensional soft decision sequences under the low SNR regime, we design and construct a low-complexity one-dimensional CNN classifier. Firstly, the dataset of five types of channel codes is generated, including linear block codes, Turbo codes, convolutional codes, LDPC codes and Polar codes. Secondly, the one-dimensional CNN classifier is initialized and imported into the database. Thirdly, the training dataset.mat file generated in matlab is imported into the convolutional neural network, and the dataset of five types of channel codes is labeled. Finally, a one-dimensional convolutional neural network model is established, and the dataset in the model is compiled and trained, the training data tags in the model are predicted and the confusion matrix is saved. The simulation results show that the proposed algorithm expands the recognition range of existing types of channel codes, and for the first time completes the blind recognition of five types of channel codes. Compared with the traditional algorithm LSTM (Long Short Term Memory), the algorithm proposed in this paper has lower complexity, the recognition rate can be kept 92% when the SNR is 0 dB, and the performance is improved by 24%.

This paper studies the blind recognition method of the sparse parity‐check matrices of low‐density parity‐check codes in noncooperative communication, which is critical to the reverse analysis of communication protocols using LDPC codes. In this paper, two improvements are made to the algorithm of Liu Qian et al. (2021) for this problem. Firstly, a Gaussian elimination method based on random column exchange and soft information is proposed to enhance the fault tolerance of the elimination process. Secondly, according to the sparse property of the parity‐check matrices of LDPC codes, a random extraction method is proposed to further improve the fault tolerance of the algorithm, and it is verified theoretically. Finally, simulations verify the superior performance of the algorithm proposed in this paper. This paper studies the blind recognition method of the sparse parity‐check matrices of low‐density parity‐check (LDPC) codes in noncooperative communication, which is critical to the reverse analysis of communication protocols using LDPC codes.

To tackle the issue of unknown spoofing attacks in the Internet of Medical Things (IoMT), we put forward an iterative deep clustering model for blind RF fingerprint recognition. This model seamlessly combines a representation learning module and a clustering module, facilitating end—to—end training and optimization. Its parameters are updated according to an innovative loss function. Moreover, this model incorporates a noise—canceling self—encoder module to reduce noise and extract the noise—independent intrinsic fingerprints of devices. In comparison with other algorithms, the proposed model remarkably improves the blind recognition performance for the identification of unknown devices in the IoMT.

In the blind recognition of air frequency block code signals, a blind recognition algorithm of MIMO-SFBC based on symbolic eigenvalue was proposed to solve the problems of poor recognition performance and large number of samples under the condition of low SNR. According to the symbol correlation characteristics of different air frequency block codes in frequency domain, the eigenvector sequences of different air frequency block codes are derived, symbolic eigenvalue are estimated by binary hypothesis testing, and different coding types are distinguished by decision tree classification recognition algorithm. Simulation results show that the algorithm does not need prior information such as modulation prediction, and has good recognition performance under low SNR and small sample conditions, and can be applied to engineering fields such as cognitive radio.

In cognitive radio context, the coding parameters are unknown at the receiver. The design of an intelligent receiver is essentially to identify these parameters from the received data blindly. In this paper, we are interested in the blind identification of binary linear block codes from received noisy data. In order to recognize the code length, the concept of the normalized column weight vector is defined and cosine similarity is used to measure the difference between linear block codes and random codes. Then, the correct code length could be obtained by finding the local minimum of cosine similarity. The proposed code length recognition method needs no prior knowledge about the codes, which results in completely blind identification. To reconstruct the parity check matrix, the concept of association rules mining is introduced to the problem of blind identification of channel codes for the first time. Furthermore, five criteria are proposed to reduce the redundant rules mined by the association rules mining algorithm and to recognize the parity check vectors effectively. Simulations show that the proposed two methods have excellent performance even in a high error rate transport environment. The performance comparisons with existing methods validate the advantages of our two proposed methods.

Coding blind recognition plays a vital role in non-cooperative communication. Most of the algorithm for coding blind recognition of Low Density Parity Check (LDPC) codes is difficult to apply and the problem of high time complexity and high space complexity cannot be solved. Inspired by deep learning, we propose an architecture for coding blind recognition of LDPC codes. This architecture concatenates a Transformer-based network with a convolution neural network (CNN). The CNN is used to suppress the noise in real time, followed by a Transformer-based neural network aimed to identify the rate and length of the LDPC codes. In order to train denoise networks and recognition networks with high performance, we build our own datasets and define loss functions for the denoise networks. Simulation results show that this architecture is able to achieve better performance than the traditional method at a lower signal-noise ratio (SNR). Compared with the existing methods, this approach is more flexible and can therefore be quickly deployed.