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Article List
2024, 22(3):227-239.
DOI: 10.11805/TKYDA2023316
Abstract:
Satellite communication, as an important means of communication, plays a key role in modern society. However, with the development of communication technology, satellite communication system is also facing the increasingly serious interference problem and the challenge of interference recognition technology. In view of this situation, this paper first introduces the common patterns of satellite interference signals, and summarizes the development trends of satellite interference signals. Secondly, the research status of satellite interference signal classification and recognition technology is introduced from three aspects: feature extraction based interference recognition method, traditional machine learning method and deep learning method, and their respective advantages and disadvantages are analyzed. Finally, the future classification and recognition technology of satellite interference signal is prospected.
Satellite communication, as an important means of communication, plays a key role in modern society. However, with the development of communication technology, satellite communication system is also facing the increasingly serious interference problem and the challenge of interference recognition technology. In view of this situation, this paper first introduces the common patterns of satellite interference signals, and summarizes the development trends of satellite interference signals. Secondly, the research status of satellite interference signal classification and recognition technology is introduced from three aspects: feature extraction based interference recognition method, traditional machine learning method and deep learning method, and their respective advantages and disadvantages are analyzed. Finally, the future classification and recognition technology of satellite interference signal is prospected.
2024, 22(3):240-248.
DOI: 10.11805/TKYDA2023419
Abstract:
Satellite communication can provide over-the-horizon, large-capacity communication services. Nevertheless, there will be serious line-of-sight fading and significant multipath effect in urban areas, canyons, forests and other areas where the line of sight is seriously obscured, which will result in the degradation of network communication performance. Recently, an emerging technology―the Reconfigurable Intelligence Surface(RIS) is capable of dynamically altering wireless channels by flexibly controlling the electromagnetic properties of passive reflecting elements, providing a novel route for improving the quality of Low Earth Orbit(LEO) satellite communication. Based on the basic principles and advantages of RIS, the key technologies such as channel modeling, channel estimation, and beamforming in the RIS aided LEO satellite communication are discussed. Finally, the main challenges faced of RIS in practical applications are analyzed. This work can provide a reference for research in the field of RIS aided LEO satellite communication.
Satellite communication can provide over-the-horizon, large-capacity communication services. Nevertheless, there will be serious line-of-sight fading and significant multipath effect in urban areas, canyons, forests and other areas where the line of sight is seriously obscured, which will result in the degradation of network communication performance. Recently, an emerging technology―the Reconfigurable Intelligence Surface(RIS) is capable of dynamically altering wireless channels by flexibly controlling the electromagnetic properties of passive reflecting elements, providing a novel route for improving the quality of Low Earth Orbit(LEO) satellite communication. Based on the basic principles and advantages of RIS, the key technologies such as channel modeling, channel estimation, and beamforming in the RIS aided LEO satellite communication are discussed. Finally, the main challenges faced of RIS in practical applications are analyzed. This work can provide a reference for research in the field of RIS aided LEO satellite communication.
2024, 22(3):249-260.
DOI: 10.11805/TKYDA2023393
Abstract:
For addressing the issue of unauthorized actions bypassing security mechanisms to attack systems in the integrated network of heaven and earth in the open electromagnetic environments, an improved Genetic Algorithm(GA) is proposed. It uses the Decision Tree(DT) algorithm as the fitness function, and significantly improves the interception rate of network attacks by deleting redundant features in the dataset.Anomaly classification is performed through machine learning, and the feature selection function of the genetic algorithm is employed to enhance the classification efficiency of machine learning. To verify the effectiveness of the proposed algorithm, the UNSW_NB15 and UGRansome1819 datasets are selected for training and testing. Four machine learning classifiers, namely Random Forest(RF), Artificial Neural Network(ANN), K-Nearest Neighbor(KNN), and Support Vector Machine(SVM), are used for evaluation. The performance of the algorithm is evaluated through indicators such as accuracy, F1 score, recall rate, and confusion matrix. The experiment results prove that the genetic algorithm as a feature selection tool can significantly improve the classification accuracy and achieve significant improvement in algorithm performance. Meanwhile, to tackle with the instability of weak classifiers, this paper further proposes an ensemble learning optimization technique, which integrates weak classifiers and strong classifiers for optimization. The experiment confirms the excellent performance of this optimization algorithm in improving the stability of weak classifiers.
For addressing the issue of unauthorized actions bypassing security mechanisms to attack systems in the integrated network of heaven and earth in the open electromagnetic environments, an improved Genetic Algorithm(GA) is proposed. It uses the Decision Tree(DT) algorithm as the fitness function, and significantly improves the interception rate of network attacks by deleting redundant features in the dataset.Anomaly classification is performed through machine learning, and the feature selection function of the genetic algorithm is employed to enhance the classification efficiency of machine learning. To verify the effectiveness of the proposed algorithm, the UNSW_NB15 and UGRansome1819 datasets are selected for training and testing. Four machine learning classifiers, namely Random Forest(RF), Artificial Neural Network(ANN), K-Nearest Neighbor(KNN), and Support Vector Machine(SVM), are used for evaluation. The performance of the algorithm is evaluated through indicators such as accuracy, F1 score, recall rate, and confusion matrix. The experiment results prove that the genetic algorithm as a feature selection tool can significantly improve the classification accuracy and achieve significant improvement in algorithm performance. Meanwhile, to tackle with the instability of weak classifiers, this paper further proposes an ensemble learning optimization technique, which integrates weak classifiers and strong classifiers for optimization. The experiment confirms the excellent performance of this optimization algorithm in improving the stability of weak classifiers.
2024, 22(3):261-268.
DOI: 10.11805/TKYDA2023439
Abstract:
In response to the problems of resource waste and long propagation time delay in traditional satellite power control methods, this paper proposes a satellite adaptive power control method based on Online Gate Recurrent Unit(Online-GRU) channel prediction, which solves the cumulative error of offline prediction algorithms by updating network parameters through online training. The simulation results show that the proposed online training algorithm improves the prediction accuracy by 38.30% compared to offline algorithms, saves 63.21% of training time compared to Online Long Short Term Memory(Online-LSTM), and saves 55.74% of transmission power compared to the fixed transmission power method. At the same time, the proposed adaptive power control method has better robustness compared to the adaptive power control method based on ground timing feedback channel state.
In response to the problems of resource waste and long propagation time delay in traditional satellite power control methods, this paper proposes a satellite adaptive power control method based on Online Gate Recurrent Unit(Online-GRU) channel prediction, which solves the cumulative error of offline prediction algorithms by updating network parameters through online training. The simulation results show that the proposed online training algorithm improves the prediction accuracy by 38.30% compared to offline algorithms, saves 63.21% of training time compared to Online Long Short Term Memory(Online-LSTM), and saves 55.74% of transmission power compared to the fixed transmission power method. At the same time, the proposed adaptive power control method has better robustness compared to the adaptive power control method based on ground timing feedback channel state.
2024, 22(3):269-277.
DOI: 10.11805/TKYDA2023440
Abstract:
In the ultra-high dynamic and low Signal-to-Noise Ratio(SNR) communication scenarios like deep space communication, traditional signal acquisition methods suffer from inadequate dynamic range support, insufficient accuracy, and the use of a narrowband Doppler model leading to poor approximation accuracy in the received signal. A wideband Doppler model is employed to model the received signal and a chirp signal is used for Doppler estimation and timing estimation. A sampling rate adjustment method based on binary-search is proposed for the receiver to overcome the mismatch between the Doppler-stretched signal and the local signal sampling rate. The simulation results of the synchronization performance show that the proposed method using Up-Down Linear Frequency Modulation(UD-LFM) signal is capable of achieving a Doppler estimation error below 400 Hz and a timing estimation error below 60 ns under a SNR of -49 dB and a maximum Doppler frequency of 2 MHz. Compared to direct acquiring, the proposed method bears a higher acquisition accuracy and a lower SNR threshold.
In the ultra-high dynamic and low Signal-to-Noise Ratio(SNR) communication scenarios like deep space communication, traditional signal acquisition methods suffer from inadequate dynamic range support, insufficient accuracy, and the use of a narrowband Doppler model leading to poor approximation accuracy in the received signal. A wideband Doppler model is employed to model the received signal and a chirp signal is used for Doppler estimation and timing estimation. A sampling rate adjustment method based on binary-search is proposed for the receiver to overcome the mismatch between the Doppler-stretched signal and the local signal sampling rate. The simulation results of the synchronization performance show that the proposed method using Up-Down Linear Frequency Modulation(UD-LFM) signal is capable of achieving a Doppler estimation error below 400 Hz and a timing estimation error below 60 ns under a SNR of -49 dB and a maximum Doppler frequency of 2 MHz. Compared to direct acquiring, the proposed method bears a higher acquisition accuracy and a lower SNR threshold.
2024, 22(3):278-284.
DOI: 10.11805/TKYDA2024003
Abstract:
As an important supplement to the terrestrial network, the Low Earth Orbit(LEO) satellite network is an important part of the future earth-earth integrated network. However, due to the high moving speed of LEO satellites and the long propagation distance of satellite-ground communication, large propagation delay is caused, therefore a new calculation strategy for the Timing Advance(TA) of the uplink in the satellite-ground communication background of LEO satellites is needed. Based on the satellite-earth communication scenario of LEO satellite, TA and its provisions in the protocol are introduced firstly. Then, according to the characteristics of LEO satellite, a timing advance calculation method of LEO satellite communication is proposed. The effectiveness of the proposed scheme is verified by simulation analysis, which provides a reference for the design of satellite-earth communication system of LEO satellite.
As an important supplement to the terrestrial network, the Low Earth Orbit(LEO) satellite network is an important part of the future earth-earth integrated network. However, due to the high moving speed of LEO satellites and the long propagation distance of satellite-ground communication, large propagation delay is caused, therefore a new calculation strategy for the Timing Advance(TA) of the uplink in the satellite-ground communication background of LEO satellites is needed. Based on the satellite-earth communication scenario of LEO satellite, TA and its provisions in the protocol are introduced firstly. Then, according to the characteristics of LEO satellite, a timing advance calculation method of LEO satellite communication is proposed. The effectiveness of the proposed scheme is verified by simulation analysis, which provides a reference for the design of satellite-earth communication system of LEO satellite.
2024, 22(3):285-289.
DOI: 10.11805/TKYDA2023249
Abstract:
Adaptive antenna array technology is the most effective method for anti-interference of Global Navigation Satellite Systems(GNSS). However, this anti-interference processing generates carrier phase measurement bias related to the direction of signal incidence, which limits the effective application in high-precision measurement. In order to reduce the bias of anti-interference processing, an analysis method of introducing measurement bias is proposed, a corresponding model is established, and a software receiver is built for simulation experiments. The results show that without adding additional constraints, adopting the space frequency minimum variance distortionless response algorithm to achieve a space frequency anti-interference processor would not introduce carrier phase measurement bias, making it very suitable for applications in fields with strict requirements on anti-interference performance and measurement accuracy.
Adaptive antenna array technology is the most effective method for anti-interference of Global Navigation Satellite Systems(GNSS). However, this anti-interference processing generates carrier phase measurement bias related to the direction of signal incidence, which limits the effective application in high-precision measurement. In order to reduce the bias of anti-interference processing, an analysis method of introducing measurement bias is proposed, a corresponding model is established, and a software receiver is built for simulation experiments. The results show that without adding additional constraints, adopting the space frequency minimum variance distortionless response algorithm to achieve a space frequency anti-interference processor would not introduce carrier phase measurement bias, making it very suitable for applications in fields with strict requirements on anti-interference performance and measurement accuracy.
2024, 22(5):467-475.
DOI: 10.11805/TKYDA2024002
Abstract:
Due to uneven ground network coverage and unstable signal quality, it is impossible to achieve full, fast, and real-time monitoring of the railway transportation network, which hinders the rapid development of China's railway transportation network. At the same time, the integration of Low Earth Orbit(LEO) satellite communication and ground 5G has become a hot topic, and the fusion of the two can make satellite networks an effective supplement to ground networks. The current application of satellite ground fusion in 5G-Railway(5G-R) technology scenarios is relatively limited. This article first reviews the current research status and the development trends of railway satellite communication and satellite ground fusion networks, points out development trends, and then analyzes the communication needs of various businesses in railway communication systems. Based on this, a network architecture that integrates low orbit satellites with 5G-R networks is proposed, which not only fills the blind spots in railway communication network coverage, but also provides high-capacity information feedback. It can effectively ensure the safe operation of railway transportation and achieve information and intelligent management of railway transportation.
Due to uneven ground network coverage and unstable signal quality, it is impossible to achieve full, fast, and real-time monitoring of the railway transportation network, which hinders the rapid development of China's railway transportation network. At the same time, the integration of Low Earth Orbit(LEO) satellite communication and ground 5G has become a hot topic, and the fusion of the two can make satellite networks an effective supplement to ground networks. The current application of satellite ground fusion in 5G-Railway(5G-R) technology scenarios is relatively limited. This article first reviews the current research status and the development trends of railway satellite communication and satellite ground fusion networks, points out development trends, and then analyzes the communication needs of various businesses in railway communication systems. Based on this, a network architecture that integrates low orbit satellites with 5G-R networks is proposed, which not only fills the blind spots in railway communication network coverage, but also provides high-capacity information feedback. It can effectively ensure the safe operation of railway transportation and achieve information and intelligent management of railway transportation.
2024, 22(5):476-484.
DOI: 10.11805/TKYDA2024006
Abstract:
Traction spoofing is a highly covert type of spoofing interference that can induce the receiver to track spoofing signals without causing the tracking loop to lose lock. For scalar receivers, the tracking loops are independent of each other, so the traction spoofing for a single signal will not be affected by the signals of other channels. All tracking loops of vector receiver are coupled through receiver state and have mutual influence, which means that traction spoofing has different influence on vector and scalar tracking loops. Based on the assumption that there is no noise and the carrier frequency and carrier phase of the spoofing signal and the corresponding authentic signal are equal, this paper analyzes the influence of traction spoofing on the vector tracking loop, derives the success condition of spoofing, and verifies the analysis results by using the signal source simulator and software receiver. The results show that the conditions for successfully implementing traction spoofing on vector tracking loop are more stringent than those of scalar tracking loop, which reflects the inherent anti-spoofing capability of vector tracking loop.
Traction spoofing is a highly covert type of spoofing interference that can induce the receiver to track spoofing signals without causing the tracking loop to lose lock. For scalar receivers, the tracking loops are independent of each other, so the traction spoofing for a single signal will not be affected by the signals of other channels. All tracking loops of vector receiver are coupled through receiver state and have mutual influence, which means that traction spoofing has different influence on vector and scalar tracking loops. Based on the assumption that there is no noise and the carrier frequency and carrier phase of the spoofing signal and the corresponding authentic signal are equal, this paper analyzes the influence of traction spoofing on the vector tracking loop, derives the success condition of spoofing, and verifies the analysis results by using the signal source simulator and software receiver. The results show that the conditions for successfully implementing traction spoofing on vector tracking loop are more stringent than those of scalar tracking loop, which reflects the inherent anti-spoofing capability of vector tracking loop.
2024, 22(5):485-494.
DOI: 10.11805/TKYDA2023438
Abstract:
In the Low Earth Orbit(LEO) scenario, beam hopping technology can flexibly allocate system resources and is suitable for the scenario of uneven service distribution. Time Division Duplex (TDD) mode has low requirements on the terminal and is conducive to the development of upstream and downstream asymmetric services. Therefore, a resource allocation algorithm is proposed for low-orbit satellite beam hopping based on TDD. Firstly, the resource allocation model of LEO reverse link supporting beam hopping and Multifrequency-Time Division Multiple Access(MF-TDMA) is established to minimize the time domain resource consumption in condition of satisfying the service requirements. Secondly, considering the dynamic time-delay compensation of satellite and earth, a multi-level beam hopping time slot architecture design is adopted to maximize the available time slot. The upstream and downstream time slot switching model is established, and a beam hopping time slot scheduling optimization method is proposed based on TDD. Simulation results show that compared with traditional MF-TDMA resource allocation method or fixed multi-beam splitting algorithm, the proposed algorithm can effectively improve the time slot utilization and throughput of the system.
In the Low Earth Orbit(LEO) scenario, beam hopping technology can flexibly allocate system resources and is suitable for the scenario of uneven service distribution. Time Division Duplex (TDD) mode has low requirements on the terminal and is conducive to the development of upstream and downstream asymmetric services. Therefore, a resource allocation algorithm is proposed for low-orbit satellite beam hopping based on TDD. Firstly, the resource allocation model of LEO reverse link supporting beam hopping and Multifrequency-Time Division Multiple Access(MF-TDMA) is established to minimize the time domain resource consumption in condition of satisfying the service requirements. Secondly, considering the dynamic time-delay compensation of satellite and earth, a multi-level beam hopping time slot architecture design is adopted to maximize the available time slot. The upstream and downstream time slot switching model is established, and a beam hopping time slot scheduling optimization method is proposed based on TDD. Simulation results show that compared with traditional MF-TDMA resource allocation method or fixed multi-beam splitting algorithm, the proposed algorithm can effectively improve the time slot utilization and throughput of the system.
2024, 22(5):495-502.
DOI: 10.11805/TKYDA2024020
Abstract:
Low Eatht Orbit mega-constellations can provide global seamless coverage of satellite communication services, but also lead to users being covered by multiple satellites. How to choose the optimal satellite for access has become a key issue in the access technology of low-orbit mega-constellations. In response to the above problems, this paper proposes a multi-objective satellite access optimization algorithm based on particle swarm, the objective function comprehensively considers the distance between the satellite and the earth, the remaining service time of the satellite, and the remaining load of the satellite. According to the Quality of Service(QoS) requirements of different traffic types, corresponding weights are assigned to the parameters. Finally, considering the characteristics of a large number of visible satellites and dynamic changes, a low-complexity solution is obtained through the particle swarm algorithm. Simulations show that compared with the comparison algorithm, the proposed algorithm optimizes the trade-off between the distance from the satellite to the earth and the remaining service time of the satellite, guarantees the service time and communication quality, improves the success rate of access, and can flexibly meet the needs of users' different traffic types, adapt to the scenario of users' traffic distribution at different time and spaces, and the scenario of traffic discretization.
Low Eatht Orbit mega-constellations can provide global seamless coverage of satellite communication services, but also lead to users being covered by multiple satellites. How to choose the optimal satellite for access has become a key issue in the access technology of low-orbit mega-constellations. In response to the above problems, this paper proposes a multi-objective satellite access optimization algorithm based on particle swarm, the objective function comprehensively considers the distance between the satellite and the earth, the remaining service time of the satellite, and the remaining load of the satellite. According to the Quality of Service(QoS) requirements of different traffic types, corresponding weights are assigned to the parameters. Finally, considering the characteristics of a large number of visible satellites and dynamic changes, a low-complexity solution is obtained through the particle swarm algorithm. Simulations show that compared with the comparison algorithm, the proposed algorithm optimizes the trade-off between the distance from the satellite to the earth and the remaining service time of the satellite, guarantees the service time and communication quality, improves the success rate of access, and can flexibly meet the needs of users' different traffic types, adapt to the scenario of users' traffic distribution at different time and spaces, and the scenario of traffic discretization.
2024, 22(5):503-515.
DOI: 10.11805/TKYDA2023436
Abstract:
Entity recognition technology, as an important step in constructing knowledge graphs, has been extensively applied in natural language processing applications such as semantic network, machine translation, and question answering systems. It plays a crucial role in promoting the practical application of natural language processing technology. According to the development process of entity recognition technology, the existing entity recognition methods are investigated in this paper. These methods can be classified as: early rule and dictionary based entity recognition methods, machine learning based entity recognition methods, and deep learning-based entity recognition methods. The core ideas, advantages and disadvantages, and representative models of each entity recognition method are summarized, especially the latest entity recognition methods based on Bi-directional Long Short-term Memory(BiLSTM) and Transformer. Additionally, the current mainstream datasets and evaluation criteria are introduced. Finally, facing the semantic requirements of future machine communication, we have summarized the challenges faced by entity recognition technology, and its future advancement in Internet of Things(IoT) business data is anticipated.
Entity recognition technology, as an important step in constructing knowledge graphs, has been extensively applied in natural language processing applications such as semantic network, machine translation, and question answering systems. It plays a crucial role in promoting the practical application of natural language processing technology. According to the development process of entity recognition technology, the existing entity recognition methods are investigated in this paper. These methods can be classified as: early rule and dictionary based entity recognition methods, machine learning based entity recognition methods, and deep learning-based entity recognition methods. The core ideas, advantages and disadvantages, and representative models of each entity recognition method are summarized, especially the latest entity recognition methods based on Bi-directional Long Short-term Memory(BiLSTM) and Transformer. Additionally, the current mainstream datasets and evaluation criteria are introduced. Finally, facing the semantic requirements of future machine communication, we have summarized the challenges faced by entity recognition technology, and its future advancement in Internet of Things(IoT) business data is anticipated.
