The adoption of 5G for Railways(5G-R)is expanding,particularly in high-speed trains,due to the benefits offered by 5G technology.High-speed trains must provide seamless connectivity and Quality of Service(QoS)to ensur...The adoption of 5G for Railways(5G-R)is expanding,particularly in high-speed trains,due to the benefits offered by 5G technology.High-speed trains must provide seamless connectivity and Quality of Service(QoS)to ensure passengers have a satisfactory experience throughout their journey.Installing base stations along urban environments can improve coverage but can dramatically reduce the experience of users due to interference.In particular,when a user with a mobile phone is a passenger in a high speed train traversing between urban centres,the coverage and the 5G resources in general need to be adequate not to diminish her experience of the service.The utilization of macro,pico,and femto cells may optimize the utilization of 5G resources.In this paper,a Genetic Algorithm(GA)-based approach to address the challenges of 5G network planning for 5G-R services is presented.The network is divided into three cell types,macro,pico,and femto cells—and the optimization process is designed to achieve a balance between key objectives:providing comprehensive coverage,minimizing interference,and maximizing energy efficiency.The study focuses on environments with high user density,such as high-speed trains,where reliable and high-quality connectivity is critical.Through simulations,the effectiveness of the GA-driven framework in optimizing coverage and performance in such scenarios is demonstrated.The algorithm is compared with the Particle Swarm Optimisation(PSO)and the Simulated Annealing(SA)methods and interesting insights emerged.The GA offers a strong balance between coverage and efficiency,achieving significantly higher coverage than PSO while maintaining competitive energy efficiency and interference levels.Its steady fitness improvement and adaptability make it well-suited for scenarios where wide coverage is a priority alongside acceptable performance trade-offs.展开更多
This paper is to explore the problems of intelligent connected vehicles(ICVs)autonomous driving decision-making under a 5G-V2X structured road environment.Through literature review and interviews with autonomous drivi...This paper is to explore the problems of intelligent connected vehicles(ICVs)autonomous driving decision-making under a 5G-V2X structured road environment.Through literature review and interviews with autonomous driving practitioners,this paper firstly puts forward a logical framework for designing a cerebrum-like autonomous driving system.Secondly,situated on this framework,it builds a hierarchical finite state machine(HFSM)model as well as a TOPSIS-GRA algorithm for making ICV autonomous driving decisions by employing a data fusion approach between the entropy weight method(EWM)and analytic hierarchy process method(AHP)and by employing a model fusion approach between the technique for order preference by similarity to an ideal solution(TOPSIS)and grey relational analysis(GRA).The HFSM model is composed of two layers:the global FSM model and the local FSM model.The decision of the former acts as partial input information of the latter and the result of the latter is sent forward to the local pathplanning module,meanwhile pulsating feedback to the former as real-time refresh data.To identify different traffic scenarios in a cerebrum-like way,the global FSM model is designed as 7 driving behavior states and 17 driving characteristic events,and the local FSM model is designed as 16 states and 8 characteristic events.In respect to designing a cerebrum-like algorithm for state transition,this paper firstly fuses AHP weight and EWM weight at their output layer to generate a synthetic weight coefficient for each characteristic event;then,it further fuses TOPSIS method and GRA method at the model building layer to obtain the implementable order of state transition.To verify the feasibility,reliability,and safety of theHFSMmodel aswell as its TOPSISGRA state transition algorithm,this paper elaborates on a series of simulative experiments conducted on the PreScan8.50 platform.The results display that the accuracy of obstacle detection gets 98%,lane line prediction is beyond 70 m,the speed of collision avoidance is higher than 45 km/h,the distance of collision avoidance is less than 5 m,path planning time for obstacle avoidance is averagely less than 50 ms,and brake deceleration is controlled under 6 m/s2.These technical indexes support that the driving states set and characteristic events set for the HFSM model as well as its TOPSIS-GRA algorithm may bring about cerebrum-like decision-making effectiveness for ICV autonomous driving under 5G-V2X intelligent road infrastructure.展开更多
Based on the data of daily precipitation in Lianyungang area from 1951 to 2012 and various climate signal data from the National Climate Center website and the NOAA website,a model for predicting whether the number of...Based on the data of daily precipitation in Lianyungang area from 1951 to 2012 and various climate signal data from the National Climate Center website and the NOAA website,a model for predicting whether the number of rainstorm days in summer in Lianyungang area is large was established by the classical C5. 0 decision tree algorithm. The data samples in 48 years( accounting for about 80% of total number of samples)was as the training set of a model,and the training accuracy rate of the model was 95. 83%. The data samples in the remaining 14 years( accounting for about 20% of total number of samples) were used as the test set of the model to test the model,and the test accuracy of the model was 85. 71%. The results showed that the prediction model of number of rainstorm days in summer constructed by C5. 0 algorithm had high accuracy and was easy to explain. Moreover,it is convenient for meteorological staff to use directly. At the same time,this study provides a new idea for short-term climate prediction of number of rainstorm days in summer.展开更多
Visible light cameras are excellent at capturing subtle features of moving targets in well⁃wit and stable scenes.However,such cameras may not be able to accurately detect targets when encountering occlusion,fluctuatin...Visible light cameras are excellent at capturing subtle features of moving targets in well⁃wit and stable scenes.However,such cameras may not be able to accurately detect targets when encountering occlusion,fluctuating light intensity,or shadow effects,leading to the occurrence of missed or false alarms.Aiming at the problem of poor anti⁃interference ability of visible light images in complex scenes,a target detection method based on the combination of visible light and infrared images is proposed.The Canny algorithm is used to preprocess the unmanned aerial vehicle(UAV)infrared image,the seed points are obtained through the Sobel operator,and the image segmentation is performed using the maximum inter⁃class variance value of the image as the growth criterion in order to locate the UAV region in the infrared image.Then the corresponding region of the visible image is cropped,and UAV target detection is performed in this region.The joint detection method narrows the scope of detection and effectively reduces the interference of factors such as illumination changes and interfering objects on the detection results.Experimental results show that the proposed method achieves 87.6%precision and 75.9%recall,with mAP0.5 and mAP0.5:0.95 values of 83.9%and 52.9%,respectively.展开更多
The initial cell search plays an important role during the process of downlink synchronization establishment between the User Equipment(UE) and the base station. In particular, the uncertainty of the synchronization s...The initial cell search plays an important role during the process of downlink synchronization establishment between the User Equipment(UE) and the base station. In particular, the uncertainty of the synchronization signals on the frequency domain and the flexibility of frame structure configuration have brought great challenges to the initial cell search for the fifth-generation(5G) new radio(NR). To solve this problem, firstly, we analyze the physical layer frame structure of 5G NR systems. Then, by focusing on the knowledge of synchronization signals, the 5G NR cell search process is designed, and the primary synchronization signal(PSS) timing synchronization algorithm is proposed, including a 5G-based coarse synchronization algorithm and conjugate symmetry-based fine synchronization algorithm. Finally, the performance of the proposed cell search algorithm in 5G NR systems is verified through the combination of Digital Signal Processing(DSP) and personal computer(PC). And the MATLAB simulation proves that the proposed algorithm has better performance than the conventional cross-correlation algorithm when a certain frequency offset exists.展开更多
Non-orthogonal multiple access(NOMA)is a strong contender multicarrier waveform technique for the fth generation(5G)communication system.The high peak-to-average power ratio(PAPR)is a serious concern in designing the ...Non-orthogonal multiple access(NOMA)is a strong contender multicarrier waveform technique for the fth generation(5G)communication system.The high peak-to-average power ratio(PAPR)is a serious concern in designing the NOMA waveform.However,the arrangement of NOMA is different from the orthogonal frequency division multiplexing.Thus,traditional reduction methods cannot be applied to NOMA.A partial transmission sequence(PTS)is commonly utilized to minimize the PAPR of the transmitting NOMA symbol.The choice phase aspect in the PTS is the only non-linear optimization obstacle that creates a huge computational complication due to the respective non-carrying sub-blocks in the unitary NOMA symbol.In this study,an efcient phase factor is proposed by presenting a novel bacterial foraging optimization algorithm(BFOA)for PTS(BFOA-PTS).The PAPR minimization is accomplished in a two-stage process.In the initial stage,PTS is applied to the NOMA signal,resulting in the partition of the NOMA signal into an act of sub-blocks.In the second stage,the best phase factor is generated using BFOA.The performance of the proposed BFOA-PTS is thoroughly investigated and compared to the traditional PTS.The simulation outcomes reveal that the BFOA-PTS efciently optimizes the PAPR performance with inconsequential complexity.The proposed method can signicantly offer a gain of 4.1 dB and low complexity compared with the traditional OFDM.展开更多
摘要The adoption of 5G for Railways(5G-R)is expanding,particularly in high-speed trains,due to the benefits offered by 5G technology.High-speed trains must provide seamless connectivity and Quality of Service(QoS)to ensure passengers have a satisfactory experience throughout their journey.Installing base stations along urban environments can improve coverage but can dramatically reduce the experience of users due to interference.In particular,when a user with a mobile phone is a passenger in a high speed train traversing between urban centres,the coverage and the 5G resources in general need to be adequate not to diminish her experience of the service.The utilization of macro,pico,and femto cells may optimize the utilization of 5G resources.In this paper,a Genetic Algorithm(GA)-based approach to address the challenges of 5G network planning for 5G-R services is presented.The network is divided into three cell types,macro,pico,and femto cells—and the optimization process is designed to achieve a balance between key objectives:providing comprehensive coverage,minimizing interference,and maximizing energy efficiency.The study focuses on environments with high user density,such as high-speed trains,where reliable and high-quality connectivity is critical.Through simulations,the effectiveness of the GA-driven framework in optimizing coverage and performance in such scenarios is demonstrated.The algorithm is compared with the Particle Swarm Optimisation(PSO)and the Simulated Annealing(SA)methods and interesting insights emerged.The GA offers a strong balance between coverage and efficiency,achieving significantly higher coverage than PSO while maintaining competitive energy efficiency and interference levels.Its steady fitness improvement and adaptability make it well-suited for scenarios where wide coverage is a priority alongside acceptable performance trade-offs.
基金funded by Chongqing Science and Technology Bureau (No.cstc2021jsyj-yzysbAX0008)Chongqing University of Arts and Sciences (No.P2021JG13)2021 Humanities and Social Sciences Program of Chongqing Education Commission (No.21SKGH227).
摘要This paper is to explore the problems of intelligent connected vehicles(ICVs)autonomous driving decision-making under a 5G-V2X structured road environment.Through literature review and interviews with autonomous driving practitioners,this paper firstly puts forward a logical framework for designing a cerebrum-like autonomous driving system.Secondly,situated on this framework,it builds a hierarchical finite state machine(HFSM)model as well as a TOPSIS-GRA algorithm for making ICV autonomous driving decisions by employing a data fusion approach between the entropy weight method(EWM)and analytic hierarchy process method(AHP)and by employing a model fusion approach between the technique for order preference by similarity to an ideal solution(TOPSIS)and grey relational analysis(GRA).The HFSM model is composed of two layers:the global FSM model and the local FSM model.The decision of the former acts as partial input information of the latter and the result of the latter is sent forward to the local pathplanning module,meanwhile pulsating feedback to the former as real-time refresh data.To identify different traffic scenarios in a cerebrum-like way,the global FSM model is designed as 7 driving behavior states and 17 driving characteristic events,and the local FSM model is designed as 16 states and 8 characteristic events.In respect to designing a cerebrum-like algorithm for state transition,this paper firstly fuses AHP weight and EWM weight at their output layer to generate a synthetic weight coefficient for each characteristic event;then,it further fuses TOPSIS method and GRA method at the model building layer to obtain the implementable order of state transition.To verify the feasibility,reliability,and safety of theHFSMmodel aswell as its TOPSISGRA state transition algorithm,this paper elaborates on a series of simulative experiments conducted on the PreScan8.50 platform.The results display that the accuracy of obstacle detection gets 98%,lane line prediction is beyond 70 m,the speed of collision avoidance is higher than 45 km/h,the distance of collision avoidance is less than 5 m,path planning time for obstacle avoidance is averagely less than 50 ms,and brake deceleration is controlled under 6 m/s2.These technical indexes support that the driving states set and characteristic events set for the HFSM model as well as its TOPSIS-GRA algorithm may bring about cerebrum-like decision-making effectiveness for ICV autonomous driving under 5G-V2X intelligent road infrastructure.
基金Support by Meteorological Open Research Foundation for the Huaihe River Basin(HRM201602)Foundation for Young Scholars of Jiangsu Meteorological Bureau(Q201708,KQ201802)+2 种基金Science and Technology Innovation Team Foundation for Marine Meteorological Forecast Technology of Lianyungang Meteorological BureauKey Technology R&D Program Project of Lianyungang City(SH1634)Special Project for Forecasters of Jiangsu Meteorological Bureau(JSYBY201811,JSYBY201812,JSYBY201810)
摘要Based on the data of daily precipitation in Lianyungang area from 1951 to 2012 and various climate signal data from the National Climate Center website and the NOAA website,a model for predicting whether the number of rainstorm days in summer in Lianyungang area is large was established by the classical C5. 0 decision tree algorithm. The data samples in 48 years( accounting for about 80% of total number of samples)was as the training set of a model,and the training accuracy rate of the model was 95. 83%. The data samples in the remaining 14 years( accounting for about 20% of total number of samples) were used as the test set of the model to test the model,and the test accuracy of the model was 85. 71%. The results showed that the prediction model of number of rainstorm days in summer constructed by C5. 0 algorithm had high accuracy and was easy to explain. Moreover,it is convenient for meteorological staff to use directly. At the same time,this study provides a new idea for short-term climate prediction of number of rainstorm days in summer.
基金supported by the Open Fund of State key Laboratory of Extreme Environment Optoelectronic Dynamic Testing Technology and Instrument,North University of China(No.2023⁃SYSJJ⁃04)the Aeronautical Science Foundation of China(No.20240055054001)+3 种基金the Open Fund of Key Laboratory of Spatio⁃temporal Sensing and Intelligent Processing,Ministry of Natural Resources of the People’s Republic of China(No.232203)the Open Fund of Key Laboratory of Technology and Equipment of Tianjin Urban Air Transportation System(No.TJKL-UAM-202305)the Open Fund of Key Laboratory of Civil Aviation Flights Wide Area Surveillance and Safety Control technology of Civil Aviation University of China(No.202105)the Open Fund of Key Laboratory of Flight Techniques and Flight Safety CAAC(No.FZ2021KF15).
摘要Visible light cameras are excellent at capturing subtle features of moving targets in well⁃wit and stable scenes.However,such cameras may not be able to accurately detect targets when encountering occlusion,fluctuating light intensity,or shadow effects,leading to the occurrence of missed or false alarms.Aiming at the problem of poor anti⁃interference ability of visible light images in complex scenes,a target detection method based on the combination of visible light and infrared images is proposed.The Canny algorithm is used to preprocess the unmanned aerial vehicle(UAV)infrared image,the seed points are obtained through the Sobel operator,and the image segmentation is performed using the maximum inter⁃class variance value of the image as the growth criterion in order to locate the UAV region in the infrared image.Then the corresponding region of the visible image is cropped,and UAV target detection is performed in this region.The joint detection method narrows the scope of detection and effectively reduces the interference of factors such as illumination changes and interfering objects on the detection results.Experimental results show that the proposed method achieves 87.6%precision and 75.9%recall,with mAP0.5 and mAP0.5:0.95 values of 83.9%and 52.9%,respectively.
基金partially the Chongqing Municipality’s Major Theme Project “R & D and Application of 5G terminal simulation equipment” (Grant No. Cstc2017zdcy-zdzx0030)
摘要The initial cell search plays an important role during the process of downlink synchronization establishment between the User Equipment(UE) and the base station. In particular, the uncertainty of the synchronization signals on the frequency domain and the flexibility of frame structure configuration have brought great challenges to the initial cell search for the fifth-generation(5G) new radio(NR). To solve this problem, firstly, we analyze the physical layer frame structure of 5G NR systems. Then, by focusing on the knowledge of synchronization signals, the 5G NR cell search process is designed, and the primary synchronization signal(PSS) timing synchronization algorithm is proposed, including a 5G-based coarse synchronization algorithm and conjugate symmetry-based fine synchronization algorithm. Finally, the performance of the proposed cell search algorithm in 5G NR systems is verified through the combination of Digital Signal Processing(DSP) and personal computer(PC). And the MATLAB simulation proves that the proposed algorithm has better performance than the conventional cross-correlation algorithm when a certain frequency offset exists.
摘要Non-orthogonal multiple access(NOMA)is a strong contender multicarrier waveform technique for the fth generation(5G)communication system.The high peak-to-average power ratio(PAPR)is a serious concern in designing the NOMA waveform.However,the arrangement of NOMA is different from the orthogonal frequency division multiplexing.Thus,traditional reduction methods cannot be applied to NOMA.A partial transmission sequence(PTS)is commonly utilized to minimize the PAPR of the transmitting NOMA symbol.The choice phase aspect in the PTS is the only non-linear optimization obstacle that creates a huge computational complication due to the respective non-carrying sub-blocks in the unitary NOMA symbol.In this study,an efcient phase factor is proposed by presenting a novel bacterial foraging optimization algorithm(BFOA)for PTS(BFOA-PTS).The PAPR minimization is accomplished in a two-stage process.In the initial stage,PTS is applied to the NOMA signal,resulting in the partition of the NOMA signal into an act of sub-blocks.In the second stage,the best phase factor is generated using BFOA.The performance of the proposed BFOA-PTS is thoroughly investigated and compared to the traditional PTS.The simulation outcomes reveal that the BFOA-PTS efciently optimizes the PAPR performance with inconsequential complexity.The proposed method can signicantly offer a gain of 4.1 dB and low complexity compared with the traditional OFDM.