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Accurate meteorological information is crucial for the safety of civil aviation flights. Complete wind field information is particularly helpful for planning flight routes. To address the challenge of accurately reconstructing wind fields, this paper introduces a deep learning neural network method based on the Vision Mamba Decoder. The goal of the method is to reconstruct the original complete wind field from incomplete wind data distributed along air routes. This paper proposes improvements to the Vision Mamba model to fit our mission, showing that the developed model can accurately reconstruct the complete wind field. The experimental results demonstrate a mean absolute error (MAE) of wind speed of approximately 1.83 m/s, a mean relative error (MRE) of around 7.87%, an R-square value of about 0.92, and an MAE of wind direction of 5.78 degrees. [ABSTRACT FROM AUTHOR]

Radio Determination Satellite Service (RDSS) is a characteristic service of BeiDou, which can provide users with short message communication services. Since the working frequency of an RDSS system is close to that of a 5G system, the RDSS system is very susceptible to interference from 5G out-of-band radiation. This paper analyzes the compatibility of 5G interference with an RDSS system from the perspective of the signal and the system. Firstly, the compatibility assessment is carried out from the perspective of the signal, the impact of interference on the capture and tracking performance of BeiDou is illustrated, and the safe coexistence distance of the two systems from the perspective of capture probability is obtained from the perspective of the signal. Subsequently, based on the link budget criterion, the interference of 5G base stations and 5G terminals to RDSS receivers under different frequency isolation and the required distance isolation for safe coexistence are analyzed from the system perspective. Finally, from the perspective of civil aviation safety, the aggregate interference is used as an evaluation index to evaluate the interference suffered by the aircraft during takeoff and landing and to obtain the interference suffered by the ground-based 5G base station during the takeoff and landing of the aircraft on different routes and in different 5G propagation environments. The simulation results show that when the airplane is closer to the ground, the ground 5G base stations will cause harmful interference to the RDSS receiver. In this study, the real flight data are combined with the simulation model to obtain the exact influence range of 5G interference on the RDSS system under different viewpoints. [ABSTRACT FROM AUTHOR]

The Beidou Radio Determination Satellite Service (RDSS) is an advantageous service of the Beidou system. However, due to the weak landing power of Beidou RDSS signals and an operating frequency close to the 5G frequency, the system is vulnerable to interference from ground signals. In this paper, from the perspective of civil aviation safety, different evaluation indicators are used for the takeoff and cruise phases, respectively, to study the impact caused by adjacent frequency interference on airborne Beidou RDSS equipment. In the takeoff phase, accurate aircraft position information is obtained by processing real trajectory files. Deterministic analysis methods are used to determine the safety distance for the coexistence of the two systems. During the cruise phase, ground-based 5G base stations have less influence on the airborne RDSS receiver due to the high flight altitude, so the main consideration is electromagnetic compatibility between the airborne Beidou RDSS system and the 5G ATG system. By establishing a Boeing 737–800 simulation model, the antenna isolation degree is used as the evaluation index, and a reasonable antenna layout is given according to the evaluation results. In this study, the theoretical simulation and real flight data are combined to summarise the exact range of adjacent frequency influence during the takeoff phase and a reasonable antenna layout during the cruise phase. [ABSTRACT FROM AUTHOR]

In this paper, we propose a spectrum sensing algorithm based on the Jones vector covariance matrix (JCM) and AlexNet model, i.e., the JCM-AlexNet algorithm, by taking advantage of the different state characteristics of the signal and noise in the polarization domain. We use the AlexNet model, which is good at extracting matrix features, as the classification model and use the Jones vector, which characterizes the polarization state, to calculate its covariance matrix and convert it into an image and then use it as the input to the AlexNet model. Then, we calculate the likelihood ratio test statistic (AlexNet-LRT) based on the output of the model to achieve the classification of the signal and noise. The simulation analysis shows that the JCM-AlexNet algorithm performs better than the conventional polarization detection (PSD) algorithm and the other three (LeNet5, long short-term memory (LSTM), multilayer perceptron (MLP)) excellent deep-learning-based spectrum sensing algorithms for different signal-to-noise ratios and different false alarm probabilities. [ABSTRACT FROM AUTHOR]

The vast majority of existing sub-Nyquist sampling wideband spectrum sensing (WSS) methods default to a sparse spectrum. However, research data suggests that in the near future, the wideband spectrum will no longer be sparse. This article proposes a sub-Nyquist sampling WSS algorithm that can adapt well to non-sparse spectrum scenarios. The algorithm continues to implement the idea of our previously proposed "no reconstruction (NoR) of spectrum" algorithm, thus having low computational complexity. The new one is actually an advanced version of the NoR algorithm, so it is called AdNoR. The key to its advancement lies in the establishment of a folded time-frequency (TF) spectrum model with the same special structure as in the fold spectrum model of the NoR algorithm. For this purpose, we have designed a comprehensive sampling technique which consists of multicoset sampling, digital fractional delay, and TF transform. It is verified by simulation that the AdNoR algorithm maintains a good sensing performance with low computational complexity in the non-sparse scenario. [ABSTRACT FROM AUTHOR]

This paper presents spectrum sensing as a classification problem, and uses a spectrum-sensing algorithm based on a signal covariance matrix and long short-term memory network (CM-LSTM). We jointly exploited the spatial cross-correlation of multiple signals received by the antenna array and the temporal autocorrelation of single signals; we used the long short-term memory network (LSTM), which is good at extracting temporal correlation features, as the classification model; we then input the covariance matrix of the signals received by the array into the LSTM classification model to achieve the fusion learning of spatial correlation features and temporal correlation features of the signals, thus significantly improving the performance of spectrum sensing. Simulation analysis shows that the CM-LSTM-based spectrum-sensing algorithm shows better performance compared with support vector machine (SVM), gradient boosting machine (GBM), random forest (RF), and energy detection (ED) algorithm-based spectrum-sensing algorithms for different signal-to-noise ratios (SNRs) and different numbers of secondary users (SUs). Among them, SVM is a classical machine-learning algorithm, GBM and RF are two integrated learning methods with better generalization capability, and ED is a classical, traditional, and spectrum-sensing algorithm. [ABSTRACT FROM AUTHOR]