Method for railway 5g private network mcx cluster communication service roaming

Abstract

The present application provides a kind of railway 5G private network MCX cluster communication service roaming method, it is related to communication service roaming technical field, the present application is according to the environmental data of train travel area, the quality influence coefficient of environmental base station signal is calculated, while the signal evaluation quality of each base station is judged whether mobile terminal is car group terminal, with base station, communication terminal as node, with the communication relationship between base station and communication terminal as edge, constructs base station switching selection model and selects target base station, target MCX system is registered according to the fingerprint information of mobile terminal, and generates verification code, key, after mobile terminal is verified and identified by target core network, according to the priority of mobile terminal, roaming processing is carried out, according to the communication data of current base station and the communication data of target base station, check is carried out, judge the integrity of communication information in base station overlapping area, after checking, cancel current MCX system.

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:

[0008] A method for roaming of railway 5G private network MCX cluster communication service, the specific steps include:

[0009] 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;

[0010] 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 and the mobile terminal parameters as node features, and the communication connection parameters and the quality influence coefficient as edge features, and construct a base station switching selection model through a graph neural network;

[0011] Step 3: Taking the base station parameters, the mobile terminal parameters, the communication connection parameters and the quality influence coefficient as input data set, obtaining the signal evaluation value of the base station at the train position through the input of the trained base station switching selection model, and selecting the target base station;

[0012] 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 performed according to the priority of the mobile terminal;

[0013] 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 verification is completed.

[0014] Further, the propagation parameters include base station transmission frequency, base station transmission power and base station antenna gain;

[0015] The environmental data includes temperature, humidity and particulate matter concentration;

[0016] The historical communication data includes received signal strength, signal-to-noise ratio, transmission rate and packet loss rate.

[0017] Further, the calculation method for calculating the quality influence coefficient of the environmental base station signal is:

[0018] 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:

[0019]

[0020] 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;

[0021] The calculation method for the space loss correction term is as follows:

[0022]

[0023] in, The distance between the base station and the mobile terminal. For base station transmission frequency;

[0024] The calculation method for the environmental degradation term is as follows:

[0025]

[0026]

[0027] in, This is an empirical coefficient. For humidity, For temperature, The particle scattering coefficient, This represents the particulate matter concentration.

[0028] Furthermore, the specific method for calculating the signal evaluation quality of each base station based on its historical communication data is as follows:

[0029] 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.

[0030] The formula for calculating signal quality assessment is:

[0031]

[0032] in, 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, .

[0033] Further, the node features include base station node features and terminal node features, the base station node features including base station position coordinates, coverage radius, load state, failure rate;

[0034] The terminal node features include mobile terminal position coordinates, speed, session state;

[0035] The communication connection parameters include edge signal receiving power, transmission delay, distance, historical conversion success rate.

[0036] Further, the model is based on a graph neural network, including a neighborhood aggregation layer, a node update layer and an output layer:

[0037] Neighborhood aggregation layer:

[0038] For each terminal node , aggregate the information of its connectable base station neighbors

[0039]

[0040] wherein, is the neighborhood aggregation feature of the terminal node , is the weight matrix of the layer, is the aggregation function, is the base station node feature of the layer, is the connection edge of the base station node and the terminal node , is the th base station node, is the number of neural network structure layers; Node update layer:

[0041]

[0042]

[0043] wherein, is the terminal node feature of the layer, is the node update weight, is the activation function, is the terminal node feature of the layer;

[0044] Output layer:

[0045] ​​​

[0046] wherein, is the output base station score, is the output layer weight matrix transpose, is the output layer bias term, is the output layer base station node feature.

[0047] Further, the specific process of step 4 is:

[0048] The base station selected by the base station handover selection model is the target base station, the core network connected by the target base station and the MCX system are the target core network and the target MCX system, the current MCX system sends the roaming request data and the 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 with 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.

[0049] 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 the average phase, the phase fingerprint feature is calculated according to the real-time phase and the average phase, and the calculation formula is:

[0050]

[0051] wherein, is the phase fingerprint feature, is the observation window, is the the signal received by the base station at time t, is the the theoretical signal transmitted by the terminal at time t, is the carrier frequency, is the imaginary unit, is the the signal received by the base station at time t, is the the theoretical signal transmitted by the terminal at time t.

[0052] Further, the roaming processing according to the mobile terminal priority is that the judgment method of the mobile terminal priority is:

[0053] ​The mobile terminal includes a driver terminal, a dispatcher terminal, a maintenance personnel terminal and a service personnel terminal, and the priority of the mobile terminal is assigned according to the type of the terminal, and the priority of the mobile terminal of the same type is determined according to the communication state and the communication frequency.

[0054] Further, the method for checking the communication data is as follows:

[0055] The time for roaming with the target base station is The time for logging out of the current base station is The time for The data packets between the communication terminal and the target base station and the current base station within the time are checked for consistency in real time, each data packet includes a sequence number and a time stamp, and the consistency is checked by comparing the contents of the data packets with the same sequence number from the current base station and the target base station and the time stamp, and the data packets that are inconsistent or missing are completed using the data of the other base station.

[0056] Compared with the prior art, the beneficial effects of the present application are as follows:

[0057] According to the environmental data of the train running area, the influence coefficient of the environment on the quality of the base station signal is calculated, the signal quality of each base station is evaluated, whether the mobile terminal is a train terminal is judged, the base station and the communication terminal are taken as nodes, the communication relationship between the base station and the communication terminal is taken as an edge, a base station switching selection model is constructed to select a target base station, 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 is verified and identified through the target core network, the mobile terminal is processed according to the priority of the mobile terminal, the communication data of the current base station and the communication data of the target base station are checked, the integrity of the communication information in the base station overlapping area is judged, and the current MCX system is logged out after the checking is completed;

[0058] Through real-time evaluation of the quality of the base station signal and optimization of the switching between the mobile terminal and the base station, the present application ensures that high-quality and low-latency communication experience can be maintained during roaming, especially when the train crosses the switching area of different base stations. Through the graph neural network model, the system can accurately determine the optimal target base station, realize fast and accurate switching, and avoid link fluctuations and switching failures in traditional network switching. The parameterization analysis of the base station and the communication terminal in the scheme can efficiently cope with the network connection challenges brought by the mobile terminal during cross-area roaming, thereby ensuring that the communication is not affected.

[0059] This invention also ensures effective identity verification and data integrity verification during roaming through fingerprint information verification and base station communication data verification mechanisms. Communication data verification ensures that in the event of anchor point migration failure during handover, the system's automatic identification mechanism can promptly terminate the abnormal session state, avoiding the phenomenon of "false call holding". This not only improves the reliability and security of the system, but also enhances the transparency of communication status for dispatchers or operators, reducing the risks caused by mistransmission of information or communication interruption. This is especially crucial in key scenarios such as train operation control and emergency response in railways. Attached Figure Description

[0060] Figure 1 This is a schematic diagram of the overall method flow of the present invention. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0062] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0063] Example:

[0064] Please see Figure 1 The present invention provides a technical solution:

[0065] A method for roaming railway 5G private network MCX trunking communication services includes the following steps:

[0066] 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 quality assessment 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.

[0067] In one embodiment, the propagation parameters include the base station transmit frequency, the base station transmit power, and the base station antenna gain;

[0068] The environmental data includes temperature, humidity, and particulate matter concentration;

[0069] The historical communication data includes received signal strength, signal-to-noise ratio, transmission rate, and packet loss rate;

[0070] The key to calculating the impact coefficient of the environment on base station signal quality lies in accurately assessing the influence of environmental factors on signal propagation, thereby optimizing the coverage and performance of the communication network. In railway 5G private network MCX trunking communication services, environmental conditions such as temperature, humidity, and particulate matter concentration significantly affect the propagation of base station signals during high-speed train operation. For example, high humidity may lead to signal attenuation, and high particulate matter concentration may cause signal scattering or absorption. These factors directly affect 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, thus helping to select the optimal base station for communication handover and avoiding communication interruptions or delays caused by environmental changes.

[0071] This method enhances the adaptability and robustness of communication systems, especially in high-speed mobile scenarios. When a train crosses different base station coverage areas, variations in environmental conditions can cause significant fluctuations in signal quality. By calculating and adjusting base station signal quality assessments in real time, the system can dynamically optimize base station handover strategies based on actual environmental changes. This ensures that the train can always access the base station with the best signal quality during its journey, guaranteeing the continuity and real-time nature of communication services. This effectively reduces handover failures or communication interruptions caused by environmental factors and provides more accurate decision support for the system, further enhancing the stability and reliability of the railway 5G private network in complex environments.

[0072] The purpose of assessing the signal quality of each base station is to accurately understand its communication performance under different times and conditions, including signal strength, stability, and transmission rate. This assessment helps the system determine the operating status of base stations, identifying which base stations perform better or worse in specific environments or time periods, thereby avoiding the use of base stations with poor signal quality and reducing communication interruptions, delays, and packet loss. In railway 5G private network MCX trunking communication services, due to the high speed of trains, the handover requirements between base stations are extremely high. Signal quality assessment can provide the system with more precise handover strategies, ensuring that trains maintain connections with the best base station during operation. When base station signal quality is assessed, 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. Simultaneously, signal quality assessment can also predict future signal quality changes through historical data analysis, further optimizing resource allocation and handover timing, thereby improving the efficiency and reliability of the entire communication network and ensuring smooth communication for trains in complex environments. Through this step, the overall solution's signal management, roaming control, and communication stability are significantly improved, enabling high-quality, seamless communication services in complex, high-speed environments.

[0073] The primary purpose of determining whether a mobile terminal is a train-dedicated terminal based on its relative speed to the train is to identify whether it is a dedicated terminal for the train in high-speed environments. Train-dedicated terminals have different needs and behavioral characteristics than ordinary user terminals, especially in communication. Train-dedicated terminals need to support high-speed, high-stability network connections and frequently switch between different base stations. Therefore, determining whether a terminal is a train-dedicated terminal helps the network system allocate resources more accurately and optimize communication quality. When the system identifies a terminal in a high-speed state, it can prioritize its special communication needs and adjust network strategies accordingly, such as optimizing handover mechanisms, adjusting bandwidth allocation, or prioritizing access to higher-quality base stations to ensure that the train can maintain a stable, high-quality communication connection while in motion. Without this step, the system may not be able to accurately distinguish between train-dedicated terminals and ordinary mobile terminals, leading to the neglect of the communication needs of train-dedicated 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, thereby implementing more precise resource scheduling and network management. When determining based on relative speed, the relative speed between the train and the terminal (via GPS or base station signals) can be measured to determine whether the terminal is within the train's movement range. If the relative speed exceeds a preset threshold, the system can identify the terminal as a train set terminal and make corresponding network adjustments based on this information. This step significantly improves the overall effectiveness of the solution, ensuring the stability and high-quality experience of train communication in complex and high-speed operating environments.

[0074] In one embodiment, the method for calculating the quality impact coefficient of the base station signal in the computing environment is as follows:

[0075] 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:

[0076]

[0077] 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;

[0078] The calculation method for the space loss correction term is as follows:

[0079]

[0080] in, The distance between the base station and the mobile terminal. For base station transmission frequency;

[0081] The calculation method for the environmental degradation term is as follows:

[0082]

[0083]

[0084] in, This is an empirical coefficient. For humidity, For temperature, The particle scattering coefficient, This represents the particulate matter concentration.

[0085] In one embodiment, the specific method for calculating the signal evaluation quality of each base station based on the historical communication data of each base station is as follows:

[0086] 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.

[0087] The formula for calculating signal quality assessment is:

[0088]

[0089] in, 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. .

[0090] Step 2: Using the base station and the communication terminal determined to be the train terminal as nodes, and the communication relationship between the base station and the communication terminal as edges, a base station handover selection model is constructed by obtaining base station parameters and mobile terminal parameters as node features, and communication connection parameters and quality influence coefficient as edge features through a graph neural network.

[0091] By constructing a base station handover selection model using Graph Neural Networks (GNNs), the structured nature of graphs can be leveraged to effectively handle the complex relationships between base stations and communication terminals. Base stations and communication terminals are represented as nodes in the graph, while their communication relationships are represented by edges. This approach flexibly handles various features of nodes and edges, enabling intelligent learning and prediction based on these features. Specifically, by using base station parameters and mobile terminal parameters as node features, and communication connection parameters and quality impact coefficients as edge features, GNNs can effectively capture the dynamic relationships between base stations and terminals, including factors such as signal quality changes, communication latency, and bandwidth requirements. Through this modeling, GNNs can accurately predict the handover timing and selection strategies for different base stations, thereby dynamically selecting the optimal base station during high-speed train movement, reducing signal handover latency, and improving communication stability and quality, especially in high-speed scenarios in complex environments. The advantage of this model is that it comprehensively considers multiple factors and performs global optimization, rather than making decisions based on a single factor, making base station handover decisions more intelligent and efficient. By adopting this step, the overall solution can achieve more efficient network resource scheduling, improve network reliability and communication quality, especially in high-speed moving environments, better meet the needs of train terminal units, and improve the overall performance of the system. As for how to determine relative speed, the system can compare the vehicle's motion status (e.g., train speed obtained via GPS) with the connection status between the base station and the terminal. If the terminal's relative speed is higher than a certain set threshold, the system can identify the terminal as a train terminal and thus provide it with higher priority services and handover strategies.

[0092] In one embodiment, the node features include base station node features and terminal node features, wherein the base station node features include base station location coordinates, coverage radius, load status, and failure rate;

[0093] Terminal node characteristics include mobile terminal location coordinates, speed, and session state;

[0094] The communication connection parameters include side signal receiving power, transmission delay, distance, and historical conversion power.

[0095] In one embodiment, the model is based on a graph neural network and includes a neighborhood aggregation layer, a node update layer, and an output layer:

[0096] Neighborhood aggregation layer:

[0097] For each terminal node It can aggregate its connected base station neighbors Information

[0098]

[0099] 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;

[0100] Node update layer:

[0101]

[0102] in, For the first Layer terminal node characteristics, Update the weights for the nodes. For activation function, For the first Layer terminal node characteristics;

[0103] Output layer:

[0104]

[0105] 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.

[0106] 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;

[0107] 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.

[0108] The mobile terminal establishes a connection with the base station via wireless signals. 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 connects to the core network via a backhaul link. The core network is the core part of the network, responsible for handling communication, control, routing, and other tasks between the base station and the terminal. The mobile terminal connects to the base station via wireless access, and the base station is responsible for transmitting the terminal's data to the core network. The core network then forwards the data to the MCX system for the management and processing of mission-critical communications.

[0109] In one embodiment, the specific process of step 4 is as follows:

[0110] 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.

[0111] This step provides an efficient, secure, and intelligent management mechanism for mobile terminal roaming. By selecting a target base station through a base station handover selection model and completing the terminal device's roaming registration through cooperation between the target base station and the core network, the system can accurately provide resources to the terminal during base station handover and maintain communication continuity and quality during roaming. Compared to the traditional method of registration by the base station, this invention predicts the base station and pre-registers with the target MCX system through the MCX system, significantly improving the efficiency and flexibility of roaming processing. By completing the roaming request and fingerprint information registration with the target MCX system in advance, verification and resource allocation can be quickly completed after the mobile terminal arrives at the target base station, avoiding the delays caused by registration and resource allocation only after the traditional method arrives at the base station. This not only reduces the time consumed by registration and authentication during roaming but also enables precise resource allocation based on the resource availability of the target base station, improving system resource utilization efficiency. Through pre-registration, the system can perform more intelligent roaming processing based on the mobile terminal's priority, ensuring that high-priority users enjoy priority network resources and service quality, while also ensuring load balancing of the target base station and avoiding resource conflicts and overload.

[0112] Meanwhile, by combining the mobile terminal's fingerprint information, verification code, and key for verification, not only is system security improved, preventing identity forgery and malicious attacks, but device authentication can also be performed quickly and accurately. Dynamic resource allocation and optimization, taking into account the target base station's resource usage, ensures load balancing at the target base station, avoiding resource waste or congestion and contributing to improved overall network efficiency. Furthermore, this scheme ensures a smooth transition when the mobile terminal switches to the target base station, minimizing service interruption time caused by base station switching, thereby optimizing the end-user experience. Especially for real-time communication needs in high-mobility scenarios, it effectively improves system response speed and stability, promotes overall solution performance improvement, and meets the personalized needs of different users in different scenarios.

[0113] Priority-based roaming optimizes network resource allocation and improves system efficiency and service quality. By assigning different priority weights to different types of mobile terminals, it ensures that critical personnel or equipment receive stable connections and services in situations of network congestion or limited resources. For example, prioritizing terminals for drivers, dispatchers, maintenance personnel, and service personnel ensures that terminals in critical positions receive higher priority when connecting to or switching base stations, preventing important communication interruptions or network congestion due to improper priority settings. For terminals of the same type, priorities can be further refined based on communication status and frequency to ensure that frequently used terminals or those with poor communication quality receive priority access to quality service in high-demand environments. This priority-based roaming approach not only improves network resource utilization efficiency but also ensures network service stability in various business scenarios, preventing low-priority terminals from consuming excessive network resources or causing service bottlenecks. This optimizes overall system performance and enhances the end-user experience, especially in high-density or high-traffic environments, ensuring the system operates efficiently on demand.

[0114] In one embodiment, 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:

[0115]

[0116] 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.

[0117] Using phase fingerprint features as fingerprint information can significantly improve the accuracy and anti-interference capability of fingerprint recognition. Phase fingerprint features calculate the difference between the average phase and the real-time phase of the received signal, allowing for detailed observation of phase changes in the signal. This method is more accurate and stable than traditional fingerprint recognition methods based on signal strength or frequency. First, using phase fingerprint features can effectively avoid interference from multipath propagation and environmental changes, because phase changes are more stable than signal strength, providing more reliable fingerprint recognition in complex radio environments. Second, by dividing the received signal into observation windows and calculating the average phase within each window, the capture of signal features can be refined, improving the accuracy of mobile terminal positioning and authentication. Furthermore, the calculation of phase fingerprint features is relatively simple and requires less computational resources, maintaining good response speed in scenarios with high real-time requirements and reducing the computational burden on the system. This approach significantly improves the accuracy and efficiency of roaming processing, avoiding misidentification or registration failures caused by signal interference or environmental changes in traditional methods. It enhances the stability and intelligence of the overall solution, especially in complex network environments and scenarios with high-speed terminal switching. It provides more reliable and faster authentication, improves user experience, and optimizes resource allocation and system performance.

[0118] In one embodiment, 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:

[0119] 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.

[0120] 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.

[0121] In one embodiment, the method for verifying the communication data is as follows:

[0122] 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.

[0123] Verifying real-time communication data in overlapping base station areas effectively ensures the integrity and consistency of communication data during roaming. Overlapping base station areas are common in mobile communication systems, especially during base station handovers, where data packets may be lost or corrupted due to signal coverage differences, transmission delays, or network issues between different base stations. By verifying the communication data between the current and target base stations in real time, inconsistencies or missing data packets can be promptly detected and corrected, ensuring uninterrupted data transmission. This method addresses several common issues in the technical context, such as communication interruptions during base station handovers, data packet loss, synchronization problems between base stations, and transmission delays between base stations. By comparing the sequence number and timestamp of each data packet, data continuity and consistency can be verified, ensuring that even if inconsistencies or data loss occur during base station handovers, data from another base station can be quickly used to complete the data, thus avoiding communication quality degradation or service interruption. This verification process improves system reliability and enhances fault tolerance, especially during rapid base station handovers by mobile devices, ensuring uninterrupted user experience and improving network stability and transmission quality. Therefore, adopting this step can provide more stable and seamless services during roaming, ensure the complete transmission of data in complex environments, optimize the overall system performance, and further enhance the system's ability to respond to emergencies, especially in high-traffic and high-density network environments.

[0124] All the above formulas use dimensionless numerical values ​​for calculation, and the numerical values ​​substituted into the formulas are all in the International System of Units (SI). The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0125] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0126] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0127] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this 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. The fingerprint information is a phase fingerprint feature. The received signal from the mobile terminal is divided into observation windows 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 base station selected by the base station handover selection model is the target base station. The core network and MCX system connected to the target base station are the target core network and the target MCX system. The mobile terminal connects to the target base station according to the target base station information and sends a verification code to the target base station. After the target base station performs fingerprint recognition on the mobile terminal, it sends the mobile terminal's fingerprint information and verification code to the target core network for verification and then establishes a roaming connection. 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, deregister the current MCX system. 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.

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: ； in, 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 It can aggregate its connected 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 function, For the first Layer terminal node characteristics; 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 current MCX system sends roaming request data and mobile terminal fingerprint information to the target MCX system. The target MCX system allocates resources based on the resource usage of the target base station, registers the terminal device for roaming, generates a mobile terminal roaming verification code and key, and sends the verification code and target base station information to the mobile terminal through the current base station of the current MCX system.

8. The method for roaming railway 5G private network MCX trunking communication services according to claim 1, characterized in that: The phase fingerprint feature is calculated based on the real-time phase and the average phase, and 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.

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