Unmanned aerial vehicles (UAVs) can serve as air relays to improve communication coverage by optimizing positions. UAV position optimisation is a typical nonlinear, nonconvex problem, and obtaining an analytical solution is difficult. Although swarm intelligence algorithms can effectively deal with such complex problems, they often face the challenges of complex parameter adjustment and a difficult balance between exploration and exploitation. Therefore, this paper proposes a biogeography-based optimization algorithm based on hybrid migration and mutation (BBOHMM). The algorithm dynamically combines migration rates with fitness and iteration times to enhance exploration of high-quality solutions. Meanwhile, the function of Gaussian and uniform mutation is coordinated to accelerate the solution space search in the early stage, while in the later stage, the fine exploitation is carried out around the potential optimal solutions, thereby balancing convergence speed with solution accuracy. Compared with the existing algorithms, the experimental results show that the BBOHMM can deploy UAVs more effectively and achieve a wider coverage. The test in a complex environment further verifies that the algorithm has good stability and real-time performance.

Abstract This paper investigates blind identification methods for linear scramblers under non‐cooperative conditions, which are essential for the inverse analysis of communication protocols using scramblers. In this paper, a blind identification scheme for feedback polynomials of synchronous scramblers is proposed. A variable is first proposed that measures the correctness of the test polynomial by using the soft information of the received sequence, then the mean and variance of the variable in different cases are obtained, and finally the optimal threshold value to determine whether the test primitive polynomial is correct or not is obtained. That is, the blind identification problem is transformed into a hypothesis testing problem. The simulations verify that the proposed scheme requires a much smaller scrambled sequence length than existing blind identification schemes. Furthermore, the proposed scheme is more fault tolerant than existing schemes and has a signal‐to‐noise ratio (SNR) gain of at least 3 dB when the intercepted scrambled sequences are of the same length and high identification accuracy is achieved.

Abstract 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.

Abstract The inverse analysis of the intercepted signals to reconstruct the communication scheme used by the transmitter is a key problem in the non‐cooperative context. In this paper, the problem of blind reconstruction of cyclic codes and self‐synchronous scramblers is considered. There is no existing solution to this problem. To solve this problem, a joint blind reconstruction method for cyclic codes and self‐synchronous scramblers is proposed in this paper. A lemma is proposed and proved theoretically that the maximum common factor of the inverse polynomial of the polynomial form of the dual vectors of the self‐synchronous scrambled bit matrix is the product of the parity polynomial of the cyclic code and the feedback polynomial of the self‐synchronous scrambler. Using this lemma, the joint blind reconstruction of the cyclic code and the self‐synchronous scrambler is completed. Finally, a Gaussian elimination based on random column exchange and soft information (GERCESI) method is proposed, which can apply the proposed method to a noisy environment. Simulations prove that the fault‐tolerance performance of the GERCESI method proposed in this paper is at least 0.5 dB higher than that of the GJETP method.

Reverse analysis of the intercepted signal to reconstruct the communication scheme used by the transmitter is a key problem in the non-cooperative context. In this paper, we propose a new algorithm for the joint blind reconstruction of the cyclic codes and self-synchronous scramblers directly using soft-decision sequences. First, we derive the conclusion that if the parity polynomial of the factor of the generator polynomial of the cyclic code and the feedback polynomial of the self-synchronous scrambler are multiplied, then the vector corresponding to the inverse polynomial of this product forms a parity-check relation with the row vector of the self-synchronously scrambled cyclic code bit matrix, which is constructed by the sequence of self-synchronously scrambled cyclic codes. To detect this parity-check relation, the concept of average parity-check probability (APCP) is defined, which uses the intercepted soft-decision sequence to measure the reliability of the parity-check relation. An optimal threshold based on the minimum error criterion is then derived. When the APCP is greater than this optimal threshold, the parity-check relation is considered to be detected, i.e., the corresponding irreducible polynomial is a factor of the generator polynomial of the cyclic code and the corresponding candidate feedback polynomial is the feedback polynomial of the self-synchronous scrambler. Therefore, the blind reconstruction problem in this paper is exactly equivalent to the hypothesis-testing problem. Simulation results show that the algorithm is more fault-tolerant than existing algorithms in noisy environments.

As an important tool for exploring and defending the ocean, autonomous underwater vehicles (AUVs) play an irreplaceable role.With the help of simulation models, the R&D test cycle of AUV equipment can be accelerated, but the simulation credibility assessment of AUVs faces many challenges: uncertainty, emergence and nonlinearity.This paper starts from the credibility evaluation of the simulation model of AUVs.Based on small-sample judgment criterion, Bayesian Sequential Mess Test (SMT) that makes full use of prior knowledge is proposed for the credibility evaluation of static parameters.For the reliability evaluation of the dynamic simulation model, the NARX steady-state response algorithm and the prior-based identification are used to evaluate the reliability of the dynamic simulation model.The application performance of the data analysis method in the credibility evaluation of AUVs is analyzed.

Abstract As one of the key technologies of modern communication, a linear scrambler is a technique to randomize the data to be transmitted at the bit layer to improve the timing recovery and confidentiality of the transmitted data. Therefore, the reconstruction of scramblers under non‐cooperative communication conditions has attracted extensive research interest. Existing efficient reconstruction methods are implemented by traversing the primitive polynomial of all orders, leading to extremely high computational complexity. Here, a fast reconstruction method for feedback polynomials of synchronous scramblers is proposed. The order of the feedback polynomial is first estimated by a hypothesis testing method, and then the primitive polynomial of the corresponding order is traversed to reduce the traversal range of the primitive polynomial, which greatly reduces the computational complexity of the reconstruction method. The simulation results verify the performance of the scheme.

Abstract The recent concept of contextual information in error correcting code (ECC) can significantly improve the capacity of the blind recognition of codes with deep learning (DL) approaches. However, the fundamental challenges of existing DL‐based methods are inflexible structure and limited kernel size which bring great difficulties to exploit the characteristics of contextual information in ECC. To handle this problem, in this paper, a state‐of‐the‐art framework for natural language processing (NLP), bidirectional encoder representation from transformers (BERT), is utilized in ECC classification scenarios. To strengthen the effectiveness of contextual information, the BERT model is improved by weighted relative positional encoding and error bit embedding. The proposed approach achieves higher classification accuracy than the methods based on Gauss‐Jordan elimination and traditional deep learning schemes. Further simulation results show that the classification accuracy is affected by block length and the employment of weighted relative positional encoding and error bit embedding to a large extent.

Blind identification of encoders has received increasing attention in recent years. In this letter, 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.

The curse of dimensionality is a fundamental difficulty in anomaly detection for high dimensional data. To deal with this problem, the autoencoder based approach is an elegant solution. However, existing works require a clean training dataset that is not always guaranteed in real scenarios. In this paper, we propose a novel anomaly detection method named RVAE-ABFA (robust variational autoencoder with attention based feature adaptation for high dimensional data anomaly detection), which significantly improves the anomaly detection performance when training data is contaminated. Rather than only utilize reconstruction error, we take the learned low dimensional embeddings generated by variational autoencoder into consideration. In RVAE-ABFA, the learned low dimensional embeddings are helpful to detect anomalies in contaminated data because of the ability of variational inference. We also propose an ABFA (attention based feature adaptation) mechanism to adjust the weights of low dimensional embeddings and reconstruction error. Furthermore, we adopt the adversarial training criterion to perform variational inference by the adversarial network named RAAE-ABFA (robust adversarial autoencoder with attention based feature adaptation for high dimensional data anomaly detection) in which we can generate extra samples when training data is not enough. Experimental results on several benchmark datasets show that the proposed method significantly outperforms state-of-the-art unsupervised anomaly detection methods and is more robust when training data is contaminated.

With the recent increase in IP traffic owing to fiber communication, previous schemes have become inadequate for link-layer processing of IP over SONET/SDH(POS). In this study, a proposal based on [Formula: see text] processors to provide mapping or demapping of IP datagrams from or into SONET/SDH is presented, and the value of [Formula: see text] is decided based on the link layer rate of POS. Further, the mathematic model of proposed architecture are presented in detail. Then the realization procedures are implemented in a Field-Programmable Gate Array (FPGA). Both theoretical analysis and experimental test prove that the proposed scheme is efficient, portable, cost-efficient and has a lower hardware resources consumption.