Railway 5G private network MCX trunking communication service roaming method

Abstract

The invention provides a railway 5G private network MCX trunking communication service roaming method, and relates to the technical field of communication service roaming. According to environment data of a train driving area, the quality influence coefficient of the environment on base station signals is calculated, meanwhile, the quality of the signals of each base station is evaluated, and whether a mobile terminal is a train set terminal or not is judged; a base station and a communication terminal are used as nodes, a communication relation between the base station and the communication terminal is used as an edge, a base station switching selection model is constructed to select a target base station, a target MCX system carries out registration according to fingerprint information of a mobile terminal and generates a verification code and a secret key, and after the mobile terminal carries out verification identification through a target core network, the verification code and the secret key are sent to the mobile terminal. Roaming processing is carried out according to the priority of the mobile terminal, verification is carried out according to the communication data of the current base station and the communication data of the target base station, the integrity of communication information in the base station overlapping area is judged, and the current MCX system is logged out after verification is completed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of communication service roaming, in particular to a method for railway 5G private network MCX (Mission Critical Services) group communication service roaming. BACKGROUND

[0002] Railway 5G private network MCX (Mission Critical Services) group communication service roaming refers to the process of maintaining the continuity and real-time performance of MCX voice, video, short message and other key communication services, automatically completing user identity authentication, session switching and permission inheritance when the train or mobile operator crosses different communication areas along the railway (such as from one base station coverage area to another). This roaming not only covers the "location switching" in traditional cellular networks, but also must ensure that the command and dispatching authority, group call state and priority policy are seamlessly synchronized in the new area, ensuring uninterrupted and delay-free group communication between drivers, dispatchers and maintenance personnel in high-speed mobile scenarios, meeting the stringent requirements of the railway industry for high reliability and low latency communication.

[0003] In the existing MCX (Mission Critical Service) group call communication process, especially in the high-speed mobile scenario of railway 5G private network, the calling terminal needs to complete the dynamic switching operation of the session anchor (base station) during cross-area roaming to maintain the continuity of voice communication. However, in actual operation, due to link fluctuations in network switching moments, core network context synchronization delays or abnormal execution of switching strategies, the session anchor migration may fail. More seriously, in the case of failure to establish a new anchor connection, the system incorrectly retains the original session state or misjudges the current call session as a hold state, resulting in the actual interruption of the link between the caller and the callee, but the MCX dispatching system and user terminal still display as "on call" state.

[0004] Such "pseudo-hold call" problem is hidden and misleading, and the dispatcher or operator may not be able to detect the communication interruption at the first time, which may easily lead to failure to convey critical task instructions or miscommunication of information, especially in critical scenarios such as train operation control, emergency repair and dispatching instruction, which will directly threaten the safety of railway operation and the efficiency of on-site operation response. Therefore, how to ensure that the MCX group call service can timely identify and terminate abnormal session state after anchor switching failure and avoid pseudo-hold call state is one of the technical problems that need to be solved in the current railway 5G key communication system.

[0005] The above information disclosed in the background section is only used to enhance the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0006] The purpose of the present application is to provide a method for roaming of railway 5G private network MCX cluster communication service, to solve the problems raised in the background art.

[0007] To achieve the above purpose, the present application provides the following technical solutions: A method for roaming of railway 5G private network MCX cluster communication service, the specific steps include: Step 1: Obtain the propagation parameters of the 5G signal of the base station to be roamed and the environmental data of the train running area, calculate the quality influence coefficient of the environmental base station signal through the environmental data, calculate the signal evaluation quality of each base station according to the historical communication data and the quality influence coefficient of each base station, and judge whether it is a train set terminal according to the relative speed of the mobile terminal and the train; Step 2: Taking the base station and the communication terminal judged as the train set terminal as nodes, and the communication relationship between the base station and the communication terminal as edges, obtain the base station parameters, mobile terminal parameters as node features, communication connection parameters and quality influence coefficient as edge features, and construct a base station switching selection model through a graph neural network; Step 3: Taking the base station parameters, mobile terminal parameters, communication connection parameters and quality influence coefficient as input data set, obtaining the signal evaluation value of the base station at the train position through inputting the completed base station switching selection model, and selecting the target base station; Step 4: The target MCX system registers according to the fingerprint information of the mobile terminal, generates a verification code and a key, and after the mobile terminal connects the target base station, the fingerprint information and the verification code are verified and identified through the target core network, and the roaming processing is carried out according to the priority of the mobile terminal; Step 5: After roaming connection, the communication data of the current base station and the communication data of the target base station are checked to judge the integrity of the communication information in the base station overlapping area, and the current MCX system is unregistered after the checking is completed.

[0008] Further, the propagation parameters include base station transmission frequency, base station transmission power and base station antenna gain; The environmental data includes temperature, humidity and particulate matter concentration; The historical communication data includes received signal strength, signal-to-noise ratio, transmission rate and packet loss rate.

[0009] Further, the calculation method of the quality influence coefficient of the environmental base station signal is: The quality influence coefficient of the environment on the signal is calculated through a free space loss correction term and an environmental attenuation term, and the calculation formula is:

[0010] Wherein, is the quality influence coefficient, This is the attenuation term due to humidity and temperature. This is the particulate matter scattering attenuation term. This is a space loss correction term; The calculation method for the space loss correction term is as follows:

[0011] in, The distance between the base station and the mobile terminal. For base station transmission frequency; The calculation method for the environmental degradation term is as follows:

[0012]

[0013] in, This is an empirical coefficient. For humidity, For temperature, The particle scattering coefficient, This represents the particulate matter concentration.

[0014] Furthermore, the specific method for calculating the signal evaluation quality of each base station based on its historical communication data is as follows: Historical communication data of each base station over a period of nearly one week is obtained. The signal quality of the base station is judged by the signal-to-noise ratio, transmission rate, and packet loss rate. The connection time quality is judged by the connection duration between each mobile terminal and the base station within the time period. The signal quality is evaluated by combining the signal quality and the connection time quality. The formula for calculating signal quality assessment is:

[0015] piece, To assess signal quality, The weight assigned to signal quality. Weighting of time quality For the first Connection duration of each mobile terminal The number of mobile terminals connected to the base station. .

[0016] Furthermore, the node features include base station node features and terminal node features. The base station node features include base station location coordinates, coverage radius, load status, and failure rate. Terminal node characteristics include mobile terminal location coordinates, speed, and session state; The communication connection parameters include side signal receiving power, transmission delay, distance, and historical conversion power.

[0017] Furthermore, the model is based on a graph neural network and includes a neighborhood aggregation layer, a node update layer, and an output layer: Neighborhood aggregation layer: For each terminal node Aggregates its connectable base station neighbors Information

[0018] in, For terminal nodes Neighborhood aggregation characteristics For the first Layer weight matrix, For aggregate functions, For the first Layer base station node characteristics, For base station nodes With terminal nodes The connecting edge, For the first One base station node, The number of layers in the neural network structure; Node update layer:

[0019] in, For the first Layer terminal node characteristics, Update the weights for the nodes. For activation functions; Output layer:

[0020] in, Rate the output base station. This is the transpose of the output layer weight matrix. For output layer bias terms, For the first Layer base station node characteristics.

[0021] Furthermore, the specific process of step 4 is as follows: The base station selected by the base station handover selection model is the target base station, the core network connected to the target base station is the target core network, and the MCX system connected to the target base station is the target MCX system; the current MCX system sends a roaming request data and mobile terminal fingerprint information to the target MCX system; the target MCX system allocates resources according to the resource usage of the target base station, and performs roaming registration of the terminal device, generates a mobile terminal roaming verification code and a key, and sends the verification code and the target base station information to the mobile terminal through the current base station of the current MCX system; the mobile terminal connects to the target base station according to the target base station information, and sends the verification code to the target base station; after the target base station performs fingerprint identification on the mobile terminal, the mobile terminal fingerprint information and the verification code are sent to the target core network for verification, and then the target base station performs roaming connection.

[0022] Further, the fingerprint information is a phase fingerprint feature, the received signal of the mobile terminal is divided into an observation window T, the signal in the window time is calculated for an average phase, and the real-time phase and the average phase are calculated for a phase fingerprint feature, and the calculation formula is:

[0023] wherein, is the phase fingerprint feature, is the observation window, is is the signal received by the base station at the moment, is is the theoretical signal transmitted by the terminal at the moment, is the carrier frequency, is the imaginary unit, is is the signal received by the base station at the moment, is is the theoretical signal transmitted by the terminal at the moment.

[0024] Further, the roaming processing according to the mobile terminal priority is that the judgment method of the mobile terminal priority is that; The mobile terminal includes a driver terminal, a dispatcher terminal, a maintenance personnel terminal, and a service personnel terminal, and the mobile terminal priority is assigned a priority weight according to the terminal type; the same type of mobile terminal is judged according to the communication state and the communication frequency.

[0025] Further, the method for verifying the communication data is that: The time for roaming by connecting to the target base station is , the time for logging out of the current base station is , and In the time period, the communication terminal respectively with the communication data packet between target base station, current base station, real-time consistency is checked, each data packet includes serial number and time stamp, whether the same serial number data packet content and time stamp from current base station and target base station are consistent is checked by comparison, for the inconsistent or missing data packet, another base station data is used to complete.

[0026] Compared with the prior art, the beneficial effects of the present application are: The present application calculates the environmental influence coefficient of the base station signal quality according to the environmental data of the train running area, and evaluates the signal quality of each base station, judges whether the mobile terminal is a train terminal, takes the base station and the communication terminal as the node, and takes the communication relationship between the base station and the communication terminal as the edge, constructs the base station switching selection model to select the target base station, the target MCX system registers according to the fingerprint information of the mobile terminal, generates the verification code and the key, and after the mobile terminal is verified and identified through the target core network, the mobile terminal is processed according to the priority, the communication data of the current base station and the communication data of the target base station are checked to judge the integrity of the communication information in the base station overlapping area, and the current MCX system is unregistered after the checking is completed; The present application realizes the real-time evaluation of the base station signal quality and the switching optimization between the mobile terminal and the base station, ensures that the high-quality and low-latency communication experience can be maintained during the roaming process, especially when the train crosses the switching area of different base stations. Through the graph neural network model, the system can accurately judge the optimal target base station, realize fast and accurate switching, and avoid link fluctuation and switching failure in traditional network switching. The parameterized analysis of the base station and the communication terminal in the scheme can efficiently cope with the network connection challenge brought by the mobile terminal in the cross-regional roaming, so as to ensure that the communication is not affected.

[0027] The present application also realizes effective identity verification and data integrity checking during the roaming process through the fingerprint information verification mechanism and the base station communication data checking mechanism, ensures that in the case of anchor point migration failure during the switching process, the abnormal session state can be suspended in time through the automatic identification mechanism of the system, avoids the "pseudo-keeping call" phenomenon, not only improves the reliability and safety of the system, but also enhances the transparency of the communication state for the dispatcher or operator, reduces the risk caused by information miscommunication or communication interruption, especially in the key scenes such as train operation control and emergency response of railway. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 It is the whole method flow diagram of the present application. DETAILED DESCRIPTION

[0029] For the purposes of making the objectives, technical solutions, and advantages of the present application clearer, further detailed explanations of the present application are provided below in conjunction with specific embodiments.

[0030] It should be noted that, unless otherwise defined, technical terms or scientific terms used in the present application should be understood as their common meanings to those having ordinary skill in the art to which the present application pertains. The terms "first", "second", and similar terms used in the present application do not denote any order, quantity, or importance, but are only used to distinguish different components. The terms "include", "contain", and similar terms mean that the components or objects listed before the terms encompass the components or objects listed after the terms and their equivalents, and do not exclude other components or objects. The terms "connect" or "connected" and similar terms do not mean physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms "upper", "lower", "left", "right", and the like only represent relative positional relationships, and when the absolute positions of the described objects change, the relative positional relationships can also change accordingly.

[0031] Embodiment:

[0032] Please refer to Figure 1 The present application provides a technical solution: A method for railway 5G private network MCX cluster communication service roaming, the specific steps comprising: Step 1: Obtain the propagation parameters of the 5G signal of the base station to be roamed and the environmental data of the train running area, calculate the quality influence coefficient of the environmental base station signal through the environmental data, calculate the signal evaluation quality of each base station according to the historical communication data and the quality influence coefficient of each base station, and determine whether it is a train set terminal according to the relative speed of the mobile terminal and the train.

[0033] In one of the embodiments, the propagation parameters include base station transmission frequency, base station transmission power, and base station antenna gain. The environmental data includes temperature, humidity, and particulate matter concentration. The historical communication data includes received signal strength, signal-to-noise ratio, transmission rate, and packet loss rate. The key to calculating the environmental impact coefficient of base station signal quality is to accurately assess the impact of environmental factors on signal propagation, thereby optimizing the coverage and performance of the communication network. In the railway 5G private network MCX cluster communication service, during the high-speed running of the train, environmental conditions such as temperature, humidity, and particulate matter concentration can significantly affect the propagation of base station signals. For example, high humidity can cause signal attenuation, and high particulate matter concentration can cause signal scattering or absorption, which directly affects the stability and quality of communication. By calculating the impact of these environmental factors on signal quality, the signal strength and reliability of each base station under different environments can be dynamically evaluated, helping to select the optimal base station for communication switching and avoiding communication interruptions or delays caused by environmental changes.

[0034] This method can improve the adaptability and robustness of the communication system, especially in high-speed mobile scenarios. When the train crosses different base station coverage areas, the difference in environmental conditions can cause significant fluctuations in signal quality. By calculating and adjusting the quality assessment of base station signals in real time, the system can dynamically optimize the base station switching strategy according to actual environmental changes, ensuring that the train can always access the base station with the best signal quality during the journey, ensuring the continuity and real-time performance of communication services. In this way, it can effectively reduce switching failures or communication interruptions caused by environmental factors, and provide more accurate decision support for the system, further enhancing the stability and reliability of the railway 5G private network in complex environments.

[0035] The purpose of evaluating the signal quality of each base station is to accurately understand the communication performance of each base station under different time and conditions, including signal strength, stability, transmission rate, etc. This evaluation can help the system determine the working state of the base station and identify which base stations perform better or worse in a specific environment or time period, thereby avoiding using base stations with poor signal quality for communication and reducing communication interruptions, delays, and packet loss. In the railway 5G private network MCX cluster communication service, due to the high-speed running of the train, the switching requirement between base stations is extremely high, and the evaluation of signal quality can provide more accurate switching strategies for the system to ensure that the train maintains connection with the best base station during the journey. When the signal quality of the base station is evaluated, the system can select the best base station for roaming based on real-time communication quality information, avoiding unnecessary network interruptions or instability caused by signal fluctuations. At the same time, evaluating signal quality can also make predictions about future signal quality changes through historical data analysis, further optimizing resource allocation and switching timing, thereby improving the efficiency and reliability of the entire communication network and ensuring smooth communication of the train in complex environments. Through this step, the signal management, roaming control, and communication stability of the overall scheme are greatly improved, enabling high-quality and seamless communication services in complex environments with high-speed movement.

[0036] The main purpose of determining whether the mobile terminal is a train terminal according to the relative speed between the mobile terminal and the train is to identify whether it is a dedicated terminal for the train in a high-speed running environment. The train terminal and the ordinary user terminal have different needs and behavior characteristics, especially in terms of communication. The train terminal needs to support high-speed and high-stability network connection and needs to frequently switch between different base stations. Therefore, determining whether the terminal is a train terminal can help the network system to allocate resources more accurately and optimize communication quality. When the system identifies that a terminal is in a high-speed running state, it can prioritize its special communication needs and adjust the network strategy accordingly, such as optimizing the handover mechanism, adjusting the bandwidth allocation, or preferentially accessing the base station with higher quality, to ensure that the train can maintain stable and high-quality communication connection during movement. Without this step, the system may not be able to accurately distinguish between train terminals and ordinary mobile terminals, resulting in neglect of the communication needs of train terminals and affecting communication quality and network efficiency. By determining the relative speed of the terminal, the system can determine whether the terminal is associated with the train, and then implement more accurate resource scheduling and network management. When determining the relative speed, the system can measure the relative speed between the train and the terminal (through GPS, base station signal) to determine whether the terminal is within the moving range of the train. If the relative speed is higher than a certain preset threshold, the system can confirm that the terminal is a train terminal, and make corresponding network adjustments based on this information. This step can significantly improve the effectiveness of the overall solution and ensure the stability and quality of train communication in complex and high-speed running environments.

[0037] In one embodiment, the method for calculating the quality impact coefficient of the base station signal in the computing environment is as follows: The quality impact coefficient of the signal in the environment is calculated through the free space loss correction term and the environmental attenuation term, and the calculation formula is as follows:

[0038] wherein, is the quality impact coefficient, is the humidity and temperature attenuation term, is the particulate matter scattering attenuation term, is the space loss correction term; The calculation method of the space loss correction term is as follows:

[0039] wherein, is the distance between the base station and the mobile terminal, is the base station transmission frequency; The calculation method of the environmental attenuation term is as follows:

[0040]

[0041] wherein, is an empirical coefficient, is humidity, is temperature, is a particle scattering coefficient, is a particle concentration.

[0042] In one of the embodiments, the specific method for calculating the signal evaluation quality of each base station according to the historical communication data of each base station is: acquiring the historical communication data of each base station within a period of one week, judging the signal quality of the base station through the signal-to-noise ratio, transmission rate, and packet loss rate, judging the connection time quality through the connection time length of each mobile terminal with the base station within the period, and calculating the signal evaluation quality through the signal quality and the connection time quality together; The calculation formula of the signal evaluation quality is:

[0043] the chess piece, is the signal evaluation quality, is the weight of the signal quality, is the weight of the time quality, is the connection time length of the i-th mobile terminal, is the number of mobile terminals connected to the base station, .

[0044] Step 2: taking the base station and the communication terminal judged as the vehicle group terminal as nodes, taking the communication relationship between the base station and the communication terminal as edges, acquiring the base station parameters and the mobile terminal parameters as node features, and taking the communication connection parameters and the quality influence coefficient as edge features, and constructing the base station switching selection model through the graph neural network.

[0045] ​By constructing a base station handover selection model through a graph neural network (GNN), the structured characteristics of graphs can be utilized to effectively handle the complex relationships between base stations and communication terminals. Base stations and communication terminals serve as nodes of the graph, while their communication relationships are represented by edges. This approach can flexibly handle various features of nodes and edges and make intelligent learning and prediction based on these features. Specifically, by taking base station parameters, mobile terminal parameters, and other information as node features, and communication connection parameters and quality influence coefficients as edge features, the GNN can effectively capture the dynamic relationships between base stations and terminals, including signal quality changes, communication delays, bandwidth requirements, and other factors. Through such modeling, the graph neural network can accurately predict the handover timing and selection strategy of different base stations, dynamically selecting the best base station during high-speed train movement, reducing signal handover delay, and improving communication stability and quality, especially in complex environments at high speeds. The advantage of this model is that it can consider multiple factors and perform global optimization, rather than making decisions based on a single factor, making the base station handover decision more intelligent and efficient. By adopting this step, the overall scheme can achieve more efficient network resource scheduling, improve network reliability and communication quality, especially in high-speed moving environments, better meeting the needs of train terminal, and improving the overall performance of the system. As for how to determine the relative speed, the connection between the vehicle's motion state (such as the train's speed obtained through GPS) and the base station can be compared. If the terminal's relative speed is higher than a certain threshold, the system can identify it as a train terminal and provide it with higher priority service and handover strategy.

[0046] In one embodiment, the node features include base station node features and terminal node features, the base station node features include base station location coordinates, coverage radius, load status, failure rate; The terminal node features include mobile terminal location coordinates, speed, session status; The communication connection parameters include edge signal reception power, transmission delay, distance, historical conversion success rate.

[0047] In one embodiment, the model is based on a graph neural network, including a neighborhood aggregation layer, a node update layer, and an output layer: Neighborhood aggregation layer: For each terminal node , aggregate the information of its connectable base station neighbors

[0048] where is the neighborhood aggregation feature of the terminal node , and is the ​layer weight matrix, is an aggregation function, is a first layer base station node feature, is a base station node connection edge with a terminal node , is a first base station node, is a number of layers of the neural network structure; node update layer:

[0049] wherein, is a first layer terminal node feature, is a node update weight, is an activation function; output layer:

[0050] wherein, is an output base station score, is an output layer weight matrix transpose, is an output layer bias term, is a first layer base station node feature.

[0051] Step 3: Taking the base station parameters, mobile terminal parameters, communication connection parameters and quality influence coefficients as input data sets, the signal evaluation value of the base station at the train position is obtained by inputting the trained base station handover selection model, and the target base station is selected; Step 4: The target MCX system registers according to the fingerprint information of the mobile terminal, generates a verification code and a key, and after the mobile terminal connects the target base station, the fingerprint information and the verification code are verified and identified through the target core network, and the mobile terminal is processed according to the roaming priority.

[0052] The mobile terminal establishes a connection with the base station through a wireless signal. The base station provides a wireless access network and is responsible for data transmission between the mobile terminal and the base station. The base station is connected to the core network through a backhaul link. The core network is the core part of the network and is responsible for tasks such as communication, control, routing, etc. between the base station and the terminal. The mobile terminal is connected to the base station through wireless access, and the base station is responsible for transmitting the data of the terminal to the core network. The core network forwards the data to the MCX system for management and processing of critical task communication.

[0053] In one embodiment, the specific process of step 4 is as follows: The base station selected by the base station handover selection model is taken as a target base station, a core network connected to the target base station is taken as a target core network, a MCX system connected to the target base station is taken as a target MCX system, the current MCX system sends a roaming request data and mobile terminal fingerprint information to the target MCX system, the target MCX system allocates resources according to the resource usage of the target base station, and performs roaming registration of the terminal device, and generates a mobile terminal roaming verification code and a key, and sends the verification code and the target base station information to the mobile terminal through the current base station of the current MCX system, the mobile terminal connects to the target base station according to the target base station information, and sends the verification code to the target base station, the target base station performs fingerprint identification on the mobile terminal, and sends the mobile terminal fingerprint information and the verification code to the target core network for verification, and then performs roaming connection.

[0054] This step provides an efficient, secure and intelligent management mechanism for the roaming process of the mobile terminal. By selecting the target base station through the base station handover selection model, and through the cooperation of the target base station and the core network, the roaming registration of the terminal device is completed, which can ensure that the system can accurately provide resources for the terminal during the base station handover process, and maintain the continuity and quality of communication during the roaming process. Compared with the traditional registration method by the base station, the present application can significantly improve the efficiency and flexibility of roaming processing by predicting the base station and registering in advance through the MCX system and the target MCX system. By completing the roaming request and fingerprint information registration with the target MCX system in advance, the verification and resource allocation can be quickly completed after the mobile terminal reaches the target base station, avoiding the delay caused by the need to register and allocate resources after the base station arrives in the traditional way. In this way, not only the time consumption caused by registration and authentication in the roaming process is reduced, but also accurate resource allocation can be made according to the resource situation of the target base station, improving the utilization efficiency of system resources. Through early registration, the system can perform more intelligent roaming processing according to the priority of the mobile terminal, ensuring that high-priority users can enjoy preferential network resources and service quality, while ensuring load balancing of the target base station, avoiding resource conflicts and overload.

[0055] Meanwhile, by combining the fingerprint information of the mobile terminal, the verification code and the key for verification, not only the security of the system is improved to prevent identity forgery and malicious attacks, but also the device authentication can be quickly and accurately performed. By considering the resource usage of the target base station for dynamic resource allocation and optimization, the load balancing of the target base station is ensured, and the waste or congestion of resources is avoided, which helps to improve the overall network efficiency. In addition, this scheme can ensure smooth transition during the process of the mobile terminal switching to the target base station, and minimize the service interruption time caused by base station switching, thereby optimizing the user experience of the terminal user, especially for real-time communication requirements in high mobility scenarios, which can effectively improve the response speed and stability of the system, promote the performance improvement of the overall scheme, and meet the individual needs of different users and different scenarios.

[0056] The roaming processing mode according to the priority can optimize the allocation of network resources and improve the efficiency and quality of service of the system. By assigning different priority weights to different types of mobile terminals, it can ensure that key personnel or devices have priority to obtain stable connection and service in the case of network congestion or limited resources. For example, the priority settings of the driver end, the dispatcher end, the maintenance personnel end and the service personnel end enable the terminals of key positions to obtain higher priority protection when connecting or switching base stations, avoiding important communication interruption or network congestion caused by improper priority settings. For terminals of the same type, the priority can be further refined according to the communication state and communication frequency to ensure that frequently used or poor quality terminals are given priority to high-quality service in high-demand environments. This roaming processing mode based on priority not only improves the resource utilization efficiency of the network, but also ensures the stability of network services in various business scenarios, avoiding low-priority terminals from occupying too many network resources or causing service bottlenecks, thereby optimizing the operation efficiency of the overall system and improving the user experience of the terminal user. Especially in high-density or high-traffic environments, the system can operate efficiently as needed.

[0057] In one embodiment, the fingerprint information is a phase fingerprint feature, the received signal of the mobile terminal is divided into an observation window T, the average phase of the signal in the window time is calculated, and the phase fingerprint feature is calculated according to the real-time phase and the average phase. The calculation formula is:

[0058] wherein, is the phase fingerprint feature, is the observation window, is the signal received by the base station at time t, is the theoretical signal transmitted by the terminal at time t, is the carrier frequency, is a complex unit, is the base station receives the signal from the terminal, is the terminal theoretically transmits the signal.

[0059] By using phase fingerprint features as fingerprint information, the accuracy and anti-interference ability of fingerprint identification can be significantly improved. Phase fingerprint features are calculated by the difference between the average phase of the received signal and the real-time phase, which can observe the phase changes in the signal in detail. This method is more accurate and stable than the traditional fingerprint identification method based on signal strength or frequency. First, using phase fingerprint features can effectively avoid interference caused by factors such as multipath propagation and environmental changes, because phase changes are more stable than signal strength, which can provide more reliable fingerprint identification in complex radio environments. Second, by dividing the received signal into observation windows and calculating the average phase in each window, the capture of signal features can be refined, improving the accuracy of positioning and identity verification of mobile terminals. In addition, the calculation of phase fingerprint features is relatively simple and requires less computing resources, which can maintain good response speed in real-time scenarios, reducing the computational burden of the system. This method can significantly improve the accuracy and efficiency of roaming processing, avoiding misidentification or registration failure caused by signal interference or environmental changes in traditional methods, promoting the stability and intelligence level of the overall scheme, especially in complex network environments and terminal high-speed switching scenarios, which can provide more reliable and fast authentication, improve user experience, and optimize resource allocation and system performance.

[0060] In one embodiment, the roaming processing according to the priority of the mobile terminal is performed, and the priority of the mobile terminal is determined by: The mobile terminal includes a driver terminal, a dispatcher terminal, a maintenance personnel terminal, and a service personnel terminal. The priority of the mobile terminal is determined according to the type of the terminal, and the priority of the same type of mobile terminal is determined according to the communication state and the communication frequency.

[0061] Step 5: After roaming connection, the communication data of the current base station and the communication data of the target base station are verified to determine the integrity of the communication information in the base station overlap area. After verification, the current MCX system is logged out.

[0062] In one embodiment, the method for verifying the communication data is: The time for roaming connection to the target base station is The time for logging out of the current base station is ​In the time period, the communication terminal respectively with the target base station, the current base station between the communication data packet, real-time consistency check, each data packet includes a serial number and a timestamp, by comparing the same serial number data packet content and timestamp from the current base station and target base station whether consistent for checking, for the inconsistent or missing data packet, using another base station data to complete.

[0063] In the base station overlapping area, the real-time communication data is checked, which can effectively guarantee the integrity and consistency of the communication data during roaming. The base station overlapping area is a common area in the mobile communication system, especially when the device is switched between base stations, the data packet may be lost or disordered due to different base station signal coverage, transmission delay or network problems. By checking the communication data between the current base station and the target base station in real time, the inconsistency or loss of data packets can be found and repaired in time, ensuring uninterrupted data transmission. This method can solve several common problems in the technical background, such as communication interruption during base station switching, data packet loss, base station synchronization problem and transmission delay between base stations. By comparing the serial number and timestamp of each data packet, the continuity and consistency of the data can be verified, ensuring that even in the process of base station switching, inconsistency or loss can be quickly completed by using data from another base station, thereby avoiding communication quality degradation or service interruption. This checking process improves the reliability of the system, enhances the fault tolerance, especially when the mobile device quickly switches between base stations, it can ensure uninterrupted user experience, and improves the stability and transmission quality of the network. Therefore, the use of this step can provide more stable and seamless service during roaming, ensure the complete transmission of data in complex environments, optimize the overall system performance, especially in high-density network environment, further enhance the system's ability to respond to sudden conditions.

[0064] The above formulas are calculated using dimensionless values, and the values brought into the formula are all in the International System of Units. The formula is obtained by collecting a large amount of data to simulate the most recent real situation. The preset parameters in the formula are set by a person skilled in the art according to the actual situation.

[0065] The above embodiments can be realized wholly or partially by software, hardware, firmware or any other combination. When realized by software, the above embodiments can be realized wholly or partially in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solutions.

[0066] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, and may be located in one place, or distributed on multiple network units. Part or all of the units can be selected to achieve the purpose of the embodiment of the present application according to actual needs.

[0067] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A method for roaming in railway 5G private network MCX trunking communication services, characterized in that, The specific steps include: Step 1: Obtain the propagation parameters of the 5G signal of the base station to be roamed and the environmental data of the train's travel area. Calculate the environmental base station signal quality impact coefficient through the environmental data. Calculate the signal evaluation quality of each base station based on its historical communication data and quality impact coefficient. Determine whether the mobile terminal is a train set terminal based on the relative speed between the mobile terminal and the train. Step 2: Using base stations and communication terminals identified as vehicle group terminals as nodes, and the communication relationship between base stations and communication terminals as edges, a base station handover selection model is constructed by acquiring base station parameters and mobile terminal parameters as node features, and communication connection parameters and quality influence coefficients as edge features, through a graph neural network. Step 3: Using base station parameters, mobile terminal parameters, communication connection parameters, and quality impact coefficients as input datasets, obtain the signal evaluation values ​​of the base stations at the train's location by inputting the trained base station handover selection model, and select the target base station; Step 4: The target MCX system registers the mobile terminal based on its fingerprint information and generates a verification code and key. After the mobile terminal connects to the target base station, it is verified and identified by the target core network through the fingerprint information and verification code, and then roaming is performed according to the priority of the mobile terminal. Step 5: After roaming connection, verify the communication data of the current base station with the communication data of the target base station to determine the integrity of the communication information in the base station overlap area. After verification, log out of the current MCX system.

2. The method for roaming railway 5G private network MCX trunking communication services according to claim 1, characterized in that: The propagation parameters include the base station transmission frequency, base station transmission power, and base station antenna gain; The environmental data includes temperature, humidity, and particulate matter concentration; The historical communication data includes received signal strength, signal-to-noise ratio, transmission rate, and packet loss rate.

3. The method for roaming railway 5G private network MCX trunking communication services according to claim 2, characterized in that: The method for calculating the quality impact coefficient of the base station signal in the computing environment is as follows: The environmental impact coefficient on signal quality is calculated using the free space loss correction term and the environmental attenuation term. The calculation formula is as follows: ； in, This is the quality influence coefficient. This is the attenuation term due to humidity and temperature. This is the particulate matter scattering attenuation term. This is a space loss correction term; The calculation method for the space loss correction term is as follows: ； in, The distance between the base station and the mobile terminal. For base station transmission frequency; The calculation method for the environmental degradation term is as follows: ； ； in, This is an empirical coefficient. For humidity, For temperature, The particle scattering coefficient, This represents the particulate matter concentration.

4. The method for roaming railway 5G private network MCX trunking communication services according to claim 1, characterized in that: The specific method for calculating the signal quality assessment of each base station based on its historical communication data is as follows: Historical communication data of each base station over a period of nearly one week is obtained. The signal quality of the base station is judged by the signal-to-noise ratio, transmission rate, and packet loss rate. The connection time quality is judged by the connection duration between each mobile terminal and the base station within the time period. The signal quality is evaluated by combining the signal quality and the connection time quality. The formula for calculating signal quality assessment is: ； piece, To assess signal quality, This is the quality influence coefficient. For transmission rate, For signal-to-noise ratio, For packet loss rate, The weight assigned to signal quality. Weighting of time quality For the first Connection duration of each mobile terminal The number of mobile terminals connected to the base station. .

5. The method for roaming railway 5G private network MCX trunking communication services according to claim 1, characterized in that: The node features include base station node features and terminal node features. Base station node features include base station location coordinates, coverage radius, load status, and failure rate. Terminal node characteristics include mobile terminal location coordinates, speed, and session state; The communication connection parameters include side signal receiving power, transmission delay, distance, and historical conversion power.

6. The method for roaming railway 5G private network MCX trunking communication services according to claim 5, characterized in that: The model is based on a graph neural network and includes a neighborhood aggregation layer, a node update layer, and an output layer. Neighborhood aggregation layer: For each terminal node Aggregates its connectable base station neighbors Information ； in, For terminal nodes Neighborhood aggregation characteristics For the first Layer weight matrix, It is an aggregate function. For the first Layer base station node characteristics, For base station nodes With terminal nodes The connecting edge, For the first One base station node, The number of layers in the neural network structure; Node update layer: ； in, For the first Layer terminal node characteristics, Update the weights for the nodes. For activation functions; Output layer: ； in, Rate the output base station. This is the transpose of the output layer weight matrix. For output layer bias terms, For the first Layer base station node characteristics.

7. The method for roaming railway 5G private network MCX trunking communication services according to claim 1, characterized in that: The specific process of step 4 is as follows: The base station selected by the base station handover selection model is taken as the target base station. The core network and MCX system connected to the target base station are taken as the target core network and target MCX system, respectively. The current MCX system sends roaming request data and mobile terminal fingerprint information to the target MCX system. The target MCX system allocates resources according to the resource usage of the target base station and performs roaming registration for the terminal device. At the same time, it generates a roaming verification code and key for the mobile terminal and sends the verification code and target base station information to the mobile terminal through the current base station of the current MCX system. The mobile terminal connects to the target base station according to the target base station information and sends the verification code to the target base station. After the target base station performs fingerprint recognition on the mobile terminal, it sends the mobile terminal fingerprint information and verification code to the target core network for verification and then establishes a roaming connection.

8. The method for roaming railway 5G private network MCX trunking communication services according to claim 1, characterized in that: The fingerprint information is a phase fingerprint feature. The received signal from the mobile terminal is divided into an observation window T. The average phase of the signal within the window time is calculated. The phase fingerprint feature is calculated based on the real-time phase and the average phase. The calculation formula is as follows: ； in, Phase fingerprint features For observation window, for The base station receives signals from the terminal at any time. for The terminal theoretically transmits signals at any given time. For carrier frequency, The imaginary unit, for The base station receives signals from the terminal at any time. for The terminal theoretically transmits signals at a given time.

9. The method for roaming railway 5G private network MCX trunking communication services according to claim 1, characterized in that: The roaming process is performed based on the priority of the mobile terminal, and the method for determining the priority of the mobile terminal is as follows: The mobile terminals include driver terminals, dispatcher terminals, maintenance personnel terminals, and service personnel terminals. The priority of mobile terminals is assigned according to the terminal type. For mobile terminals of the same type, priority is determined based on communication status and communication frequency.

10. The method for roaming railway 5G private network MCX trunking communication services according to claim 1, characterized in that: The method for verifying the communication data is as follows: The roaming time is based on the connection time to the target base station. The time for canceling the current base station is ,Will Within a given time period, the communication terminal performs real-time consistency checks on the data packets between itself and the target base station and the current base station. Each data packet includes a sequence number and a timestamp. The consistency is verified by comparing the content of data packets with the same sequence number from the current base station and the target base station with the timestamp. For inconsistent or missing data packets, data from another base station is used to complete the data packet.

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