Short-wave Communication Research Articles

Short-wave communication link prediction algorithm based on deep learning

In view of the shortcomings that the accuracy of the existing ionospheric models is not enough to reflect the local real-time changes, this paper uses the time series prediction tool of deep learning to obtain the model of a specific communication link, and combines it with the ionospheric theoretical calculation model to improve the accuracy of communication link prediction. The main work is to analyze and compare the international broadcasting data and the model calculation data, summarize and sort out the characteristics of the link data, and then on this basis, look at the change of the difference between the two, and carry out link prediction, which achieves the goal of improving the calculation effect of the link model, which is of great significance to the short-wave frequency management.

Occurrence and Characteristics of Medium‐Scale Traveling Ionospheric Disturbances Observed by BeiDou GEO Satellites Over Hong Kong

AbstractMedium‐scale traveling ionospheric disturbances (MSTIDs) can significantly alter a region's ionosphere features, severely impacting the performance and stability of services such as shortwave communication and navigation positioning. By utilizing the total electron content (TEC) data from BeiDou geostationary satellites for 2022–2023, this study investigated the characteristics of MSTIDs over Hong Kong concerning local time and seasons. A total of 622 MSTID events were observed, classified into three types: daytime (10:00–17:00 LT), twilight (17:00–22:00 LT), and nighttime (22:00–02:00 LT). The occurrence rates and excitation mechanisms of the three types of MSTIDs were analyzed. Daytime and twilight MSTIDs had higher occurrence rates during winter, while nighttime MSTIDs had higher occurrence rates in summer and were even absent during winter. Overall, daytime MSTIDs were the most common, followed by twilight MSTIDs, while nighttime MSTIDs were less frequent. The propagation directions of MSTIDs exhibited anisotropy but showed some clustering patterns. Daytime MSTIDs exhibited high directional diversity during summer, but more concentrated in winter. Nighttime MSTIDs, on the other hand, were more focused during summer. It is worth noting that twilight MSTIDs exhibit similar climatological characteristics to daytime MSTIDs, which have not been observed in previous studies. It is suggested that daytime MSTIDs in the Hong Kong region are likely primarily generated by atmospheric gravity waves (AGWs) from low‐latitude regions, while nighttime MSTIDs are likely caused by Perkins instability. Twilight MSTIDs may originate from AGWs at the solar terminator, as well as daytime MSTIDs propagated from mid‐latitude areas.

Design and Verification of Assessment Tool of Shortwave Communication Interference Impact Area

Design and Verification of Assessment Tool of Shortwave Communication Interference Impact Area

YFDM: YOLO for detecting Morse code

With the increasing complexity of the shortwave communication environment, the efficiency and accuracy of the manual detection of Morse code no longer meet actual needs. Therefore, this paper proposes a Morse code detection algorithm called YFDM. For the time–frequency image of the received signal, a combination module of deformable convolution and C3 is used to enhance the backbone network’s attention to the abstract semantics and location information of Morse code. GSConv and VOV-GSCSP modules are used to build a lightweight neck network. Finally, the confidence propagation cluster (CP-Cluster) algorithm is used to filter the detection frame. In an ablation experiment, the parameters and giga floating-point operations per second (GFLOPs) of YFDM were 5.961 M and 9.74 G, respectively, 15.11% and 38.9% less than those of YOLOv5. Moreover, when WIoUv1 was used as the loss function of the bounding box, the AP0.5:0.95 and frames per second (FPS) values of the algorithm reached the highest values, 0.68 and 72.4. The experimental results indicate that the algorithm can effectively reduce the weight of the model while ensuring the detection accuracy and inference speed.

Low-Complexity Harmonic Cancellation Scheme Based on Digital Multitone Mixing for Short-Wave Power Amplifier

In short-wave (SW) communication system, the harmonics of the power amplifier (PA), which may fall within the transmission range, are the principal consideration of harmonic interference. In the process of harmonic cancellation (HC), the number of sampling points always needs to be large enough when capturing multiple harmonics. It directly leads to the increase of modeling complexity. In order to solve this problem, this letter proposes a low-complexity HC scheme based on digital multitone mixing (DMTM), which uses multitone signals to mix harmonic signals into a narrow range for processing. The experimental results show that the multiple harmonics can be canceled to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$-$</tex-math> </inline-formula> 69 dBc in the proposed HC scheme. Also, the complexity is only 3.3% and 1.7% of the full-band HC scheme when the carrier frequencies are 5 and 8 MHz.

A Novel 3D Non-Stationary Massive MIMO Channel Model for Shortwave Communication Systems

In this paper, a novel three-dimensional (3D) non-stationary massive multiple-input multiple-output (MIMO) channel model for shortwave communication systems is proposed. Three transmission modes, i.e., groundwave, near vertical incident skywave (NVIS), and long-distance skywave are considered to eliminate the blind area and realize the full-coverage for shortwave communication. The ionospheric absorption loss and surface reflection loss during multi-hop transmissions are explored in the proposed channel model. In addition, new massive MIMO channel characteristics including the near-field spherical wavefront effect and spatial non-stationarity are considered. Temporal and frequency non-stationarities are also modeled due to the receiver (Rx) mobility and large relative bandwidth, respectively. The analytical and simulated space cross-correlation function (SCCF), time autocorrelation function (TACF), and frequency correlation function (FCF) of the proposed model are compared. The simulated path loss and singular value spread (SVS) are compared with those of the corresponding channel measurements, illustrating good fittings. In addition, the delay power spectral density (PSD) and Doppler PSD, and channel capacity are also simulated and analyzed. The proposed model can be used as a basis for the design and construction of shortwave communication systems.

Identification of Shortwave Radio Communication Behavior Based on Autocorrelation Spectrogram Features

Cognitive communication behavior is becoming a research hotspot in the field of communication confrontation. In theory, the behavioral intention of noncooperating parties can be obtained by analyzing communication signals. Considering the complexity of the actual electromagnetic environment, even when the signal-to-noise ratio (SNR) is low, a certain accuracy still needs to be guaranteed. In this paper, according to five types of physical burst waveforms defined by the shortwave radio interoperability standard, a signal feature extraction method based on autocorrelation spectrogram features is proposed, and a two-input convolutional neural network (CNN) for classification is designed to improve the identification ability of shortwave communication behavior. The experimental results illustrate that the five kinds of shortwave radio communication behaviors can be accurately identified even when the noise is large. The research in this paper can directly analyze the communication behavior through physical layer signal without demodulation, which has the ability to grasp the communication behavior of the shortwave radio station in real time.

Influence of helio-geophysical activity on reliability of short-wave communication in transport

The results of studies of the influence of coronal mass ejections and high-velocity solar wind streams on the characteristics of the ionosphere are presented. The analysis uses ionospheric data from the CADI vertical sounding station (Vasilsursk, Nizhny Novgorod region) and data from chirp stations for oblique ionospheric sounding on different paths of the Eurasian region of Russia: three subauroral (Lovozero-Vasilsursk, Sodankyulya-Vasilsursk and Salekhard-Vasilsursk) and one mid-latitude (Gorky, Leningrad region-Vasilsursk). A set of space weather parameters (type and speed of coronal mass ejections, high-speed solar wind speed) is considered, which made it possible to reveal the dominant physical relationships between the ionospheric dynamics and these two solar phenomena, which determine the reliability of short-wave radio communication between ships and the coast, especially in the area of the northern sea way.

Study on the Application of Shortwave Communication Technology in Civil Air Defense Command Communication System

Study on the Application of Shortwave Communication Technology in Civil Air Defense Command Communication System

Accurate Solar Wind Speed Prediction with Multimodality Information

When the solar wind passes over the Earth, it will cause geomagnetic storms, affect short-wave communications, and threaten the safety of pipelines such as electricity and oil. Accurate prediction of the solar wind speed will allow people to make adequate preparations to avoid wasting resources and affecting people’s life. Most existing methods only use single-modality data as input and do not consider the information complementarity between different modalities. This paper proposes a multimodality prediction (MMP) method that jointly learns vision and sequence information in a unified end-to-end framework for solar wind speed prediction. MMP includes three modules: Vmodule, Tmodule, and Fusion module. Vmodule, which uses pretrained GoogLeNet, is proposed to learn visual representations from the extreme ultraviolet (EUV) images. Tmodule combining one-dimensional CNN with bidirectional long short-term memory (BiLSTM) is applied for learning sequence representation from multivariate time series. Finally, a multimodality fusion method is applied to improve the overall performance. We adopt the EUV images observed by the solar dynamics observatory (SDO) satellite and the OMNIWEB dataset measured at Lagrangian point 1 (L1) to experiment. Comparative experiments have shown that the proposed MMP achieves best performance in many metrics. The ablation experiments also verify the validity of each module and the rationality of the hyperparameter setting.

The Gold Nanoparticles Enhanced ZnO/GaN UV Detector

Ultraviolet detectors can be used in ultraviolet disinfection, missile guidance and short-wave communication fields. Here we have grown GaN (002) film by chemical vapor deposition (CVD), through depositing 100 nm ZnO film as buffer layers on sapphire substrates by magnetron sputtering. Using Ni as the electrode, the metal-semiconductor-metal (MSM) ultraviolet (UV) detector was prepared. The device dark current, photocurrent and UV on/off current ratio were 5.19×10-9A, 2.52×10-7A and 54, under 2V bias. Then, using Au nanoparticles as plasmons, the photocurrent and photoresponsivity of the device were increased by 5.32 times and 5.25 times, respectively, and the UV on/off current ratio reached 176. The photoresponsivity of the device reaches the maximum value 0.42A/W at 370nm, the response time and relaxation time reach 0.1s and 0.12s, respectively.

Development of a module for broadband detection of digital transmissions for a radio broadcast monitoring system using parallel computing means

Short-wave communication is a means of trunk, zone, mobile and industrial communication for general and departmental use. Taking into account its technical potential, economic efficiency, strategic role as a necessary reserve, the task of monitoring the use of radio frequency resources in the short-wave range remains relevant. The main problem is the search for digital modes of transmission in the entire shortwave band. The purpose of the study is to develop a module for broadband signal detection operating in real time and integrate it into an operating radio monitoring system. The methodology of the work includes an analysis of the literature of foreign and domestic authors on solving monitoring problems, methods for detecting signals in the short-wave range; method of systematization and generalization of facts about the problems of choosing software and hardware for broadband signal detection. The features of short-wave communication have been studied, and the choice of comparison of signals has been made in this paper. The choice was made taking into account the computational complexity of the algorithms. Modules of broadband and narrowband signal detectors have been developed, the places of their integration into the existing radio monitoring system, as well as interfaces for their interaction, have been determined. During the pilot industrial operation the developed modules effectively coped with the solution of the problems of radio monitoring of the short-wave range in real time, so appropriate conclusions are formulated.

Intelligent Channel Parameter Estimation System Based on Neural Network Regression Model

How to estimate channel parameters more effectively, intelligently and accurately is the key problem to realize the requirements of intelligent and adaptive short-wave communication system. Based on the detailed analysis of chirp signal and fractional Fourier transform, an intelligent channel parameter estimation system is constructed. By building and training the regression model of multilayer fully connected neural network, the estimation error of Doppler shift is reduced. The simulation results show that the hierarchical channel estimation algorithm improves the precision of channel parameter estimation and the anti-noise performance of the system.

The Comparison of Predicting Storm-Time Ionospheric TEC by Three Methods: ARIMA, LSTM, and Seq2Seq

Ionospheric structure usually changes dramatically during a strong geomagnetic storm period, which will significantly affect the short-wave communication and satellite navigation systems. It is critically important to make accurate ionospheric predictions under the extreme space weather conditions. However, ionospheric prediction is always a challenge, and pure physical methods often fail to get a satisfactory result since the ionospheric behavior varies greatly with different geomagnetic storms. In this paper, in order to find an effective prediction method, one traditional mathematical method (autoregressive integrated moving average—ARIMA) and two deep learning algorithms (long short-term memory—LSTM and sequence-to-sequence—Seq2Seq) are investigated for the short-term predictions of ionospheric TEC (Total Electron Content) under different geomagnetic storm conditions based on the MIT (Massachusetts Institute of Technology) madrigal observation from 2001 to 2016. Under the extreme condition, the performance limitation of these methods can be found. When the storm is stronger, the effective prediction horizon of the methods will be shorter. The statistical analysis shows that the LSTM can achieve the best prediction accuracy and is robust for the accurate trend prediction of the strong geomagnetic storms. In contrast, ARIMA and Seq2Seq have relatively poor performance for the prediction of the strong geomagnetic storms. This study brings new insights to the deep learning applications in the space weather forecast.

Low Complexity MMSE-SQRD Signal Detection Based on Iteration

Aiming at the problem of high computational complexity of Vertical-BLAST (V-BLAST) algorithm in Multiple-Input Multiple-Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system signal detection, this paper first uses Sorted QR Decomposition (SQRD) iterative operation instead of matrix inversion to reduce the computational complexity of the algorithm, and then considering that the algorithm is greatly affected by noise, Minimum Mean Square Error (MMSE) criterion is used to weaken the noise effect. At the same time, in order to reduce the noise and computational complexity, MMSE and SQRD are combined, which can not only reduce the noise and computational complexity, but also obtain the sub-optimal detection order, thus improving the detection performance of the MIMO-OFDM system. Finally, the numerical simulation of the MMSE-SQRD detection algorithm is carried out. The results show that the Eb/No of MMSE-SQRD algorithm is 2 dB greater than that of the MMSE algorithm and the computational complexity is O(N T 3 ) under the conditions that NT = NR = 2 and the BER is 10−2. The detection algorithm satisfies the demand of short wave and wideband wireless communication.

Modelling of radio monitoring system of short-wave radio communication adaptive systems

The structure of model which allows to spend an estimation of system functioning efficiency of radio monitoring system of short-wave radio communication adaptive systems in adverse conditions of influence of external jams is described.

Development of IEEE 802.11 Compliant Antenna with WLAN Mobile Multimedia Messaging Application for Emergency Communication Purposes

In emergency or disaster situations, when communications were interrupted due to damaged local network infrastructures, coordination are affected. Moreover, the deployment of specialized technologies such as satellite and shortwave communication network were found impractical. The objective of the study was first, to construct an improvised antenna that will be tested on an area with no cellular network and test the router’s operating range using it, and second, to design and develop a mobile multimedia messaging application that utilizes WLAN to send data. Results of the testing shows that improvised antenna achieved operating range of 192 meters, the range achieved 215 meters in area with no cellular network, and the application was capable of sending multimedia messages over WLAN.

Application of Modern Communication Technology in Water Conservancy Work

The role of communication is very important in the process of flood prevention work. Modern communication technology can transmit information effectively and implement the communication target of dispatching instructions. Today, the communication technology covers various types such as mobile communication, satellite communication, short-wave communication, optical fiber communication and digital microwave communication. These modern communication technologies are widely used in transmission of information during the process of domestic flood prevention and water conditions. This article focuses on the research of using modern communication technology as the main objective and its specific application in water conservancy work.

ANALYSIS OF PROVIDING OF INFORMATION EXCHANGE ON SHORTWAVE AND ULTRASHORT WAVE CHANNELS

The analysis of a possibility of use of two main transport protocols is carried out: Transmission Control Protocol (TCP) and the User Datagram Protocol (UDP) for the organization of information exchange for short-wave (SW) and ultrashort-wave (USW) communication channels. Research of dependence of time of delivery of UDP packets of the different size on data transmission rate and value of losses in SW and USW communication systems is conducted. On the basis of data retrieved correlation of time of finishing the guaranteed message and flow capacity of noisy low‑speed communication channels is calculated and constructed, and also influence of quantity of the lost packets for the period of delivery of one message is investigated. During the analysis of transfer of the message with the guaranteed delivery several methods of the organization of information exchange with use of the UDP protocol were considered.

Design of Multi-sensor Human Environment Monitoring Equipment

In order to meet the patient's daily monitoring needs, in view of the problems existing in monitoring equipment at home and abroad, such as the single sensing parameter, the loss of data association and 433Mhz, ZigBee and other short wave communication modes are limited by the transmission distance, high power consumption, etc. It uses IEEE 802.11 WIFI transmission protocol, directly connected with the Internet. It integrates a number of high-precision sensors, such as light intensity, temperature, humidity, magnetic field, acceleration, vibration, etc., which can be used to monitor the activities of the environment data in a multi-dimensional way. The experiments show that the real-time, event correlation and reliability of the data are verified. Families and doctors can fully monitor the patient through mobile phones and PC side data, call the police in case of emergency, and the minimum power consumption can reach 59uA.