The Internet of Vehicles(IoV) is a research hotspot of the fifth generation(5G) mobile communication network. Cellular Vehicle To Everything(C-V2X) is an Internet of Vehicles solution based on cellular network technology and is an important part of ultra-Reliable and Low Latency Communication(uRLLC) in 5G network. The realization of the Internet of Vehicles technology is of great significance to modern transportation. This paper offers a systematic survey of existing research achievements of the domestic and foreign researchers in recent years. Firstly, a brief description of the definition of the Internet of Vehicles is given, and the standard research progress of C-V2X is summarized. Next, the centralized and distributed resource scheduling methods under LTE-V2X and NR-V2X are described respectively, and the existing research methods are classified. Finally, a perspective of the future work in this research area is discussed.

With the development of urban communication technology and the increase of frequency equipment, the electromagnetic environment becomes more and more complex. Fully understanding the characteristics of spectrum resource utilization in the past is the key to improve the efficiency of spectrum management. A complete process about detailed data quality analysis for big data in complex and diverse electromagnetic environment is proposed, in order to explore the characteristics of spectrum utilization more comprehensively. The spectrum correlation for different channels in the same service, and for different channels in different services, is performed. Attribute construction is carried out for big data of electromagnetic environment, including the attributes of frequency dimension occupancy and time dimension occupancy. The multi-dimensional Gaussian mixture model in the field of image processing is introduced to remove the background noise of the electromagnetic signal and extract the electromagnetic signal, which can lay the foundation for the subsequent information mining and association analysis.

With the proliferation of frequency-using devices and the advent of the era of big data, spectrum management and control are faced with challenges of effectiveness and accuracy. Modulation classification technology is the foundation and key part of spectrum management and control. Therefore, the effectiveness of modulation classification technology in big data scenario is very important. This paper considers not only the validity of the classification model under the background of big data, but also the dynamics of noise in the complex electromagnetic environment. A big dataset containing different signals under different Mixed Signal-to-Noise Ratios(MSNR) is constructed, and the big data is utilized to drive the Deep Learning model, and the classification results are finally obtained. The proposed method can realize modulation classification by training just one model, which avoids the redundancy of model training in previous algorithms. The simulation results demonstrate the effectiveness and reliability of the proposed method.

Based on tensor splitting technique, a decentralized computing method of neural network model for electromagnetic object detection is introduced. In this method, different tensor splitting techniques are selected according to different hidden layers, and the weights are distributed to multiple distributed nodes losslessly. The simulation results on Raspberry PI show that this method can decompose and deploy the centralized detection model losslessly, and ensure the same accuracy as the original model. And when the original model is too heavy to be loaded into memory for calculation, this method can still complete the calculation properly.

In recent years, many high-quality datasets have supported the rapid development of deep learning in the field of computer vision, speech and natural language processing. Nevertheless, there is still a lack of high-quality datasets in the field of electromagnetic signal recognition. In order to promote in-depth learning in the application of electromagnetic signal recognition, a large-scale real electromagnetic signal dataset is established based on Automatic Dependent Surveillance-Broadcast(ADS-B). An automatic data collection and labeling system is designed to automatically capture ADS-B electromagnetic signals in open and real scenes. A high quality ADS-B signal dataset is established by data cleaning and sorting of ADS-B signals. The performance of in-depth learning models using datasets is studied, and the models are evaluated comprehensively under different signal-to-noise ratios, sampling rates and number of samples. The data set provides a valuable benchmark for relevant researchers.

The electromagnetic space of large-scale Low Earth Orbit(LEO) satellite constellation system is complex and difficult to observe. The link characteristics of LEO satellite constellation are studied. Firstly, taking Starlink and OneWeb constellations as the research objects, according to the constellation parameters of LEO satellites, Equivalent Isotropically Radiated Power(EIRP) values are obtained and visualized. Then the data of LEO electromagnetic satellite are analyzed to obtain the numerical relationship between attenuation characteristics and time, frequency, etc. The inter-satellite link interference and its temporal distribution characteristics are calculated. The characteristic values of relative interference time are obtained, as well as the attenuation and time-frequency multi-dimensional characteristics of inter-satellite data. The time characteristics of interference in different scenarios are analyzed. The inter-satellite link interference of large-scale LEO satellite constellation system is studied from multiple dimensions and verified by simulation. Finally, it is proved that there is interference in the inter-satellite links among large-scale LEO satellite constellation systems, and the higher the frequency is, the more obvious the interference is.

Frequency-hopping signal shows good performance in anti-interference. Accurately identifying the modulation methods of frequency-hopping signals can provide strong support for military information warfare such as judging the attributes of enemy and enemy targets and interfering with enemy signals. Nevertheless, there is still a big gap in the modulation recognition of frequency hopping signals at home and abroad. A frequency-hopping signal modulation recognition method based on time-frequency features is proposed. Through Smoothed Pseudo Wigner-Ville Distribution(SPWVD) time-frequency transformation, time-frequency images of frequency-hopping signals of different modulation types are obtained, and the time-frequency images are sent to a Convolutional Neural Network(CNN) for feature extraction and classification recognition. Simulation experiments prove that the proposed CNN model has achieved better recognition results under low Signal-to-Noise Ratios(SNRs).

Base station location optimization is a research hotspot in mobile communication. A good base station location scheme can not only save resources, but also improve users' communication experience. However, the base station layout is often faced with a complex problem of multi-parameter, multi-constraint and nonlinearity, which is difficult to be solved by traditional optimization methods. In this paper, an intelligent base station layout method based on big data is proposed. Firstly, the radio wave propagation model based on deep learning is built according to the measured big data of electromagnetic environment, which makes the propagation model more accurate. Then, the spatial adaptive learning method is utilized to construct the base station location optimization model on the basis of the propagation model. By selecting the base station placement points having poor performance with a small probability in each iteration process, the algorithm can avoid falling into local optimality. The experimental simulation results show that the proposed base station layout method has fast convergence speed, wide coverage rate and good user communication experience.

The spectrum situation prediction of non-cooperative wireless network in the complex electromagnetic environment is investigated. Based on machine learning theory, the three-dimensional characteristics of time, space, and frequency of collected spectrum situation data are extracted; the inherent correlations in the three-dimensional characteristics are fully data mined; and the spectrum prediction frameworks are built to predict frequency adjustment behavior of non-cooperative communication nodes. The results show that the single-step or multi-step prediction for the frequency can be performed on the frequency adjustment for future moments by exploiting the spectrum prediction frameworks as long as sufficient spectrum situation data can be intercepted when the frequency adjustment exists in the communication process of non-cooperative wireless networks. Therefore, the possible frequency used in the future for the target system can be accurately locked in. This work can provide key technical support for the subsequent communication tracking and interference tasks.

Vacuum electronic devices, with the natural advantage of high power in millimeter wave and terahertz bands, can be applied to construct high-efficiency and high-power radiation sources. This is of great significance to the development of high-power microwave technology and terahertz technology. The output window is one of the key components of vacuum electronic devices. The breakdown of the output window mainly accounts for the failure of the devices. The multipactor is considered as the main reason for the breakdown of the output window. This paper summarizes current advances of output window in the decimeter and centimeter-wave bands. On this basis, the tendencies of this research fields are predicted in order to provide reference for the future development of vacuum electronic devices in higher power and higher frequency levels.

Backward Wave Oscillator(BWO) is one of the most attractive terahertz sources belong to the vacuum electron devices, which has superior performances in power capacity, high frequency, and bandwidth. For the purpose of improving the interaction between the circular electron beam and the grating slow-wave structure in BWO, a novel slow-wave structure with double electron beams embedded in the rectangular grating is proposed. The dispersion characteristics of such structure are verified by numerical calculation and simulation part, and the results show that it can achieve higher operating frequency and coupling impedance compared with a common rectangular single grating with the same structural parameters. Moreover, PIC simulations are carried out to optimize its structure, and a stable output 10.6 W with 501 GHz frequency is obtained. This research would provide guidance for the design of 0.5 THz BWO.

In recent years, the research for transmitter and receiver of terahertz is increasing with the development of terahertz devices. The working frequency and the bandwidth increase gradually, and the error caused by device non-ideality becomes an important factor affecting the performance of terahertz system. A Frequency Modulated Continuous Wave(FMCW) terahertz system with a two-stage mixing design is studied. This system has different sources of transmitter and receiver. The clock synchronization error, the frequency modulation nonlinear error, and the error from IQ unbalance are analyzed. The modeling of these three kinds of errors provides a theoretical basis for error elimination and compensation in the future.

A quadrifilar helical antenna equipped with chokes is designed in order to solve the problem of multipath interference in the complex satellite surface electromagnetic environment, and to meet the requirements of satellite high-speed data transmission for telemetry, track and command system. This antenna has two arms at equal amplitude and the feed at 90° phase difference. It works in the S-band, with half-space beam coverage, high gain, wide-angle circular polarization, small size, and light weight. The simulation and measurement results show that the circular polarization axis ratio of the antenna is less than 3 dB in the range of ±110° and the simulation results are in good agreement with measurement. A solution to the problem of multipath interference is also proposed in this paper. The gain and axis ratio of specific direction are adjusted to meet different requirements.

Aiming at the problems of insufficient coverage and poor timeliness of existing Electrocardiograph(ECG) and Phonocardiogram(PCG) monitors, a wireless transmission-based ECG and heart sound monitoring system design is proposed. The system consists of 64 monitoring nodes. Each node is composed of a sensor module, a conditioning acquisition module, a storage module, a main control management module, a communication module, and a power management module. Each node can respond to 12-lead ECG signals and HKY-06B PCG signal that are collected and transmitted synchronously, and the information is collected to the monitoring center and fed back in time to realize remote real-time monitoring and provide parameter basis for early diagnosis of cardiovascular diseases. This system is suitable for hospitals, communities, disaster rescue sites such as shelter hospitals, which greatly improves the work efficiency of medical staff while ensuring high reliability.

The systematic thermodynamic optimization on the cooling performance of the hybrid cryocooler composed of multi-stage pulse tube and Joule-Thomson(JT) working at 1-2 K for cooling the Superconducting Nanowire Single Photon Detectors(SNSPD) is carried out. The structure design and working mechanism are described. An enthalpy flow model is proposed based on the thermodynamic cycle analysis, and a real fluid mass flow model for the temperature region below 3K is established. The two models are combined to analyze the hybrid cryocooler performance. The variations of the gross cooling capacity with the last stage precooling temperature and upstream pressure under ideal conditions are discussed and the proposed model is utilized to optimize the two variables by discrete parameter fitting method. For He-4 and He-3 working fluids, the optimal last stage precooling temperature of the multi-stage pulse tube is 11 K and 8 K, respectively. The results show that the hybrid cryocooler with He-4 as the working fluid can provide more than 85 mW cooling capacity at 2.2 K, and that with He-3 as the working fluid can provide 18.5 mW cooling capacity at 1.0 K. The performance of the hybrid cryocooler can meet the requirement of the practical applications of SNSPD.