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Article List
2024, 22(11):1181-1192.
DOI: 10.11805/TKYDA2023400
Abstract:
To promote the application of fifth-generation mobile communication technology(5G) in the construction of high-speed rail dedicated networks, this paper takes the Xiamenbei Railway Station in the 2.1 GHz frequency band as the research scenario, studying the significant multipath spatial characteristics brought about by objects such as columns, tracks, and platforms in the scenario. Through Ray Tracing(RT) simulation, multidimensional multipath data from the transmitter to the receiver in the high-speed railway station scenario is obtained; based on the K-means clustering algorithm, the multipath signals are clustered, and relevant cluster parameters are extracted and analyzed to complete the construction of the Cluster Delay Line(CDL) model for the high-speed railway station scenario. This provides spatial domain information of multipath for different polarization combinations of transmitters and receivers in the high-speed railway station scenario, complementing the channel model of high-speed railway station scenario for 5G-Railway(5G-R), and serving the construction of dedicated mobile communication systems for railways using 5G.
To promote the application of fifth-generation mobile communication technology(5G) in the construction of high-speed rail dedicated networks, this paper takes the Xiamenbei Railway Station in the 2.1 GHz frequency band as the research scenario, studying the significant multipath spatial characteristics brought about by objects such as columns, tracks, and platforms in the scenario. Through Ray Tracing(RT) simulation, multidimensional multipath data from the transmitter to the receiver in the high-speed railway station scenario is obtained; based on the K-means clustering algorithm, the multipath signals are clustered, and relevant cluster parameters are extracted and analyzed to complete the construction of the Cluster Delay Line(CDL) model for the high-speed railway station scenario. This provides spatial domain information of multipath for different polarization combinations of transmitters and receivers in the high-speed railway station scenario, complementing the channel model of high-speed railway station scenario for 5G-Railway(5G-R), and serving the construction of dedicated mobile communication systems for railways using 5G.
2024, 22(11):1193-1198.
DOI: 10.11805/TKYDA2023044
Abstract:
Urban rail transit is one of the primary systems for urban transportation capacity. With the increasing demand for the development of smart cities in recent years, the design, research, and optimization of smart rail transit systems have become the research direction and focus for many scholars. Smart rail transit systems require trains to have intelligent computing power to meet a variety of intelligent service needs. Due to the numerous limitations of on-board equipment in trains, it is not practical to deploy high-performance computing devices on them, hence the need to introduce other devices to provide computational support. This study, aimed at the special scenarios of smart rail transit systems, designs a cloud-edge collaborative computing architecture for intelligent tasks based on 5G and edge intelligence. The resource allocation process in this architecture is mathematically modeled and transformed into an optimization problem that minimizes task latency. To solve this optimization problem, this paper employs a discrete stochastic approximation algorithm to minimize the total processing delay of tasks in the smart rail transit system. Simulation results indicate that the algorithm can effectively reduce the processing delay of intelligent tasks in smart rail transit systems.
Urban rail transit is one of the primary systems for urban transportation capacity. With the increasing demand for the development of smart cities in recent years, the design, research, and optimization of smart rail transit systems have become the research direction and focus for many scholars. Smart rail transit systems require trains to have intelligent computing power to meet a variety of intelligent service needs. Due to the numerous limitations of on-board equipment in trains, it is not practical to deploy high-performance computing devices on them, hence the need to introduce other devices to provide computational support. This study, aimed at the special scenarios of smart rail transit systems, designs a cloud-edge collaborative computing architecture for intelligent tasks based on 5G and edge intelligence. The resource allocation process in this architecture is mathematically modeled and transformed into an optimization problem that minimizes task latency. To solve this optimization problem, this paper employs a discrete stochastic approximation algorithm to minimize the total processing delay of tasks in the smart rail transit system. Simulation results indicate that the algorithm can effectively reduce the processing delay of intelligent tasks in smart rail transit systems.
2024, 22(11):1199-1208.
DOI: 10.11805/TKYDA2023049
Abstract:
Urban rail transit plays a significant role in alleviating urban traffic congestion, and the coordinated control of multiple urban rail vehicles has been a research hotspot in recent years. The multi-vehicle coordinated computing task is limited by communication, leading to issues such as poor resource allocation balance, slow system response to environmental changes, and limited cooperative operation capabilities. The integration of 5G communication and Mobile Edge Computing(MEC) can effectively improve the real-time and accuracy of task processing, enhancing the overall system performance. This paper designs an autonomous coordinated computing architecture for urban rail vehicle operation control systems based on 5G and MEC. According to the characteristics of multi-vehicle coordinated control tasks, the problem of edge server selection in multi-vehicle coordinated computing offloading is modeled as a Multi-Armed Bandit(MAB) learning model, and a solution based on the Upper Confidence Bound(UCB) algorithm is proposed to minimize the overall energy consumption and latency of the urban rail vehicle multi-vehicle coordinated control system. Simulation results show that the proposed algorithm model has significant performance advantages in terms of average reward, best selection probability, average execution latency, and weighted total cost.
Urban rail transit plays a significant role in alleviating urban traffic congestion, and the coordinated control of multiple urban rail vehicles has been a research hotspot in recent years. The multi-vehicle coordinated computing task is limited by communication, leading to issues such as poor resource allocation balance, slow system response to environmental changes, and limited cooperative operation capabilities. The integration of 5G communication and Mobile Edge Computing(MEC) can effectively improve the real-time and accuracy of task processing, enhancing the overall system performance. This paper designs an autonomous coordinated computing architecture for urban rail vehicle operation control systems based on 5G and MEC. According to the characteristics of multi-vehicle coordinated control tasks, the problem of edge server selection in multi-vehicle coordinated computing offloading is modeled as a Multi-Armed Bandit(MAB) learning model, and a solution based on the Upper Confidence Bound(UCB) algorithm is proposed to minimize the overall energy consumption and latency of the urban rail vehicle multi-vehicle coordinated control system. Simulation results show that the proposed algorithm model has significant performance advantages in terms of average reward, best selection probability, average execution latency, and weighted total cost.
2024, 22(11):1209-1220.
DOI: 10.11805/TKYDA2024520
Abstract:
To assist in determining the base station spacing for the new generation of mobile communication systems for 5G-Railway(5G-R) in complex railway environments, a link-level transmission rate simulation technology is proposed. This technology relies on various empirical propagation models to obtain the path loss of electromagnetic waves in different scenarios along the railway line, thereby assessing the Reference Signal Received Power(RSRP) of the wireless link. Based on the obtained RSRP data, link-level transmission rates are calculated by using the Monte Carlo simulation method. Simulation results indicate that in open-field scenarios, base station spacing can be set between 2 000 meters to 3 000 meters, while in urban and complex terrain environments, the spacing should be reduced to maintain performance. This technique accurately reflects the signal transmission characteristics of 5G-R systems across various scenarios, including the changes in transmission rate and RSRP with different base station spacings. And it provides a scientific basis for future base station deployment planning in 5G-R systems.
To assist in determining the base station spacing for the new generation of mobile communication systems for 5G-Railway(5G-R) in complex railway environments, a link-level transmission rate simulation technology is proposed. This technology relies on various empirical propagation models to obtain the path loss of electromagnetic waves in different scenarios along the railway line, thereby assessing the Reference Signal Received Power(RSRP) of the wireless link. Based on the obtained RSRP data, link-level transmission rates are calculated by using the Monte Carlo simulation method. Simulation results indicate that in open-field scenarios, base station spacing can be set between 2 000 meters to 3 000 meters, while in urban and complex terrain environments, the spacing should be reduced to maintain performance. This technique accurately reflects the signal transmission characteristics of 5G-R systems across various scenarios, including the changes in transmission rate and RSRP with different base station spacings. And it provides a scientific basis for future base station deployment planning in 5G-R systems.
2024, 22(11):1221-1227.
DOI: 10.11805/TKYDA2023253
Abstract:
In response to the difficulty in detecting defects in high-speed rail clip springs caused by complex lighting environments, an improved Faster Region Convolutional Neural Networks(R-CNN)-based defect detection method for clip springs is proposed. By extracting defect feature maps through multi-layer convolutional neural networks, the network's attention to defect features is enhanced, and the impact of interference from complex lighting environments is reduced. A region proposal network is designed to generate candidate regions, and based on these regions, pooling is performed to extract the corresponding specific defect locations in the feature maps. The fully connected layers of the region proposal network are employed to calculate the specific categories and precise locations of defects, yielding the final detection results. The proposed algorithm can fully suppress the interference of lighting environments, significantly enhance the representation ability of defect features, simplify the image pre-processing stage, and reduce the requirements for the quality of the original image. Experimental results show that the proposed algorithm can effectively detect defects in high-speed rail clip springs, and compared to existing algorithms, it has a higher accuracy, stronger robustness, and significantly improved computational efficiency.
In response to the difficulty in detecting defects in high-speed rail clip springs caused by complex lighting environments, an improved Faster Region Convolutional Neural Networks(R-CNN)-based defect detection method for clip springs is proposed. By extracting defect feature maps through multi-layer convolutional neural networks, the network's attention to defect features is enhanced, and the impact of interference from complex lighting environments is reduced. A region proposal network is designed to generate candidate regions, and based on these regions, pooling is performed to extract the corresponding specific defect locations in the feature maps. The fully connected layers of the region proposal network are employed to calculate the specific categories and precise locations of defects, yielding the final detection results. The proposed algorithm can fully suppress the interference of lighting environments, significantly enhance the representation ability of defect features, simplify the image pre-processing stage, and reduce the requirements for the quality of the original image. Experimental results show that the proposed algorithm can effectively detect defects in high-speed rail clip springs, and compared to existing algorithms, it has a higher accuracy, stronger robustness, and significantly improved computational efficiency.
