GSM-R network optimization method and device

Abstract

The invention provides a GSM-R (Global System for Mobile Communications-Railway) network optimization method and device. The method comprises the following steps: acquiring radio signals, positioning information and machine account information of a railway GSM-R frequency band; decoding the radio signal to obtain decoded data; calculating a Manhattan distance between every two frequency points, and constructing a GSM-R network frequency optimization scheme comprising a numerical model and optimization parameters in combination with a preset frequency point interference protection distance; and solving the model to judge whether an optimizable frequency point exists or not, if so, optimizing the network according to the scheme, and if not, evaluating the coverage rationality based on the decoded data, and adjusting the antenna parameters of the base station. According to the GSM-R network optimization method and system, network optimization is carried out by combining the radio signal data with the standing book and the positioning information, systematicness and scientificity of GSM-R network optimization are improved, interference risks are reduced, and efficient and reliable GSM-R network optimization is achieved.

Description

Technical Field

[0001] This invention belongs to the field of GSM-R wireless communication network optimization technology, and particularly relates to a GSM-R network optimization method and apparatus. Background Technology

[0002] With the continuous expansion of the railway network, the coverage mileage of the GSM-R network is constantly increasing, and new lines often achieve continuous coverage by adding base stations. However, due to the limited number of available frequency points in the GSM-R network, the frequency reuse distance is greatly compressed in areas such as hubs and intersections, leading to increasingly prominent problems of co-channel and adjacent-channel interference within the network, which seriously affects communication quality.

[0003] During the GSM-R network design phase, the selection of base station sites and frequencies mainly relies on theoretical propagation model simulations and construction experience. It is difficult to fully take into account the impact of complex terrain on wireless signal propagation, resulting in a significant deviation between the actual network coverage and the design scheme. At the same time, the terrain along railway lines changes naturally over time, and the continuous introduction of new lines also changes the surrounding wireless propagation environment, causing the GSM-R network status to fluctuate dynamically, further exacerbating the uncertainty of coverage and interference.

[0004] Currently, GSM-R network optimization relies heavily on dynamically detected problems and network management fault alarms, lacking systematic optimization methods and tools. This passive and localized adjustment model not only struggles to adapt to the operational needs of complex scenarios but also easily triggers secondary risks such as interference from adjacent lines, affecting the stability and reliability of railway communications. Summary of the Invention

[0005] This invention provides a GSM-R network optimization method that combines radio signal data with ledgers and location information to improve the systematic and scientific nature of GSM-R network optimization, while reducing interference risks and achieving efficient and reliable GSM-R network optimization. The GSM-R network optimization method includes: Acquire railway GSM-R band radio signals, positioning information, and ledger information; the ledger information includes GSM-R: base station information, frequency configuration information, and service range information; The railway GSM-R band radio signal is decoded to obtain decoded data; the decoded data includes cell codes and signal measurement parameters for multiple frequency points in GSM-R. Based on the decoded data, ledger information, and location information, calculate the Manhattan distance between every two frequency points in GSM-R; Based on the Manhattan distance between every two frequency points in GSM-R, and combined with the preset interference protection distance between frequency points, a GSM-R network frequency optimization scheme is determined; the GSM-R network frequency optimization scheme includes a GSM-R network frequency optimization numerical model and GSM-R network frequency optimization parameters; Solve the numerical model for frequency optimization in the GSM-R network to determine whether there are optimizable frequency points, where: If no optimizable frequency points exist, determine the GSM-R coverage data based on the decoded data; adjust the antenna parameters of the GSM-R base station according to the GSM-R coverage data. If there are optimizable frequency points, then optimize the GSM-R network based on the GSM-R network frequency optimization scheme.

[0006] This invention provides a GSM-R network optimization device that combines radio signal data with ledgers and location information to perform network optimization, thereby improving the systematic and scientific nature of GSM-R network optimization, reducing interference risks, and achieving efficient and reliable GSM-R network optimization. The GSM-R network optimization device includes: The data acquisition module is used to acquire railway GSM-R band radio signals, positioning information, and ledger information; the ledger information includes GSM-R: base station information, frequency configuration information, and service range information; The decoding module is used to decode the railway GSM-R band radio signals to obtain decoded data; the decoded data includes cell codes and signal measurement parameters for multiple frequency points in GSM-R. The distance calculation module is used to calculate the Manhattan distance between every two frequency points in GSM-R based on decoded data, ledger information, and location information; The optimization scheme determination module is used to determine the GSM-R network frequency optimization scheme based on the Manhattan distance between every two frequency points in GSM-R and the preset frequency point interference protection distance; the GSM-R network frequency optimization scheme includes a GSM-R network frequency optimization numerical model and GSM-R network frequency optimization parameters; Solve the numerical model for frequency optimization in the GSM-R network to determine whether there are optimizable frequency points, where: The antenna parameter adjustment module is used to determine the GSM-R coverage data based on the decoded data if no optimizable frequency point exists; and to adjust the antenna parameters of the GSM-R base station according to the GSM-R coverage data. The network optimization module is used to optimize the GSM-R network based on the GSM-R network frequency optimization scheme if there are optimizable frequency points.

[0007] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described GSM-R network optimization method.

[0008] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described GSM-R network optimization method.

[0009] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described GSM-R network optimization method.

[0010] In this embodiment of the invention, railway GSM-R band radio signals, location information, and ledger information are acquired. The ledger information includes GSM-R base station information, frequency configuration information, and service range information. The railway GSM-R band radio signals are decoded to obtain decoded data. The decoded data includes cell codes and signal measurement parameters for multiple frequency points in GSM-R. Based on the decoded data, ledger information, and location information, the Manhattan distance between every two frequency points in GSM-R is calculated. Based on the Manhattan distance between every two frequency points in GSM-R, combined with a preset interference protection distance between frequency points, a GSM-R network frequency optimization scheme is determined. The scheme includes a numerical model for GSM-R network frequency optimization and GSM-R network frequency optimization parameters; solving the numerical model for GSM-R network frequency optimization determines whether there are optimizable frequency points, wherein: if there are no optimizable frequency points, GSM-R coverage data is determined based on decoded data; antenna parameters of GSM-R base stations are adjusted according to GSM-R coverage data; if there are optimizable frequency points, the GSM-R network is optimized based on the GSM-R network frequency optimization scheme; the embodiments of the present invention improve the systematicness and scientificity of GSM-R network optimization by combining radio signal data with ledgers and location information, while reducing interference risks and achieving efficient and reliable GSM-R network optimization. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 This is a flowchart of the GSM-R network optimization method in an embodiment of the present invention; Figure 2 This is a specific example diagram illustrating the decoding of railway GSM-R band radio signals in an embodiment of the present invention; Figure 3 This is a specific example diagram illustrating the calculation of the Manhattan distance between every two frequency points in GSM-R according to an embodiment of the present invention; Figure 4 This is a specific example diagram of obtaining the decoded dataset in an embodiment of the present invention; Figure 5 This is a specific example diagram illustrating the adjustment of antenna parameters of a GSM-R base station based on GSM-R coverage data in an embodiment of the present invention. Figure 6 This is a structural example diagram of the GSM-R network optimization device in an embodiment of the present invention; Figure 7 This is a specific example diagram of the structure of the GSM-R network optimization device in an embodiment of the present invention; Figure 8 This is a structural diagram of a computer device in an embodiment of the present invention. Detailed Implementation

[0012] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0013] As mentioned earlier, in the existing technology, the selection of base station sites and frequencies during the GSM-R network design phase mainly relies on theoretical propagation model simulation and construction experience, which cannot fully consider the impact of on-site terrain on wireless signal propagation, resulting in a large deviation between the actual network coverage and the design scheme. Moreover, the terrain along the railway line may change over time, altering the wireless signal propagation environment. Current network optimization mainly relies on dynamic problem detection and network management fault alarms, lacking systematic methods and tools. Co-channel and adjacent channel interference problems are prominent in areas with dense frequency reuse, such as hubs and crossover areas, and local adjustments can easily trigger secondary risks.

[0014] To address this issue, the inventors proposed a GSM-R network optimization method and device. By collecting and decoding GSM-R signals along the railway line, and combining location information with ledger data for in-depth fusion analysis, the device autonomously senses the network status, first generates a frequency optimization scheme, and then generates a coverage optimization scheme when no solution is found. At the same time, it assesses the interference risk of adjacent lines, thereby achieving systematic and low-risk GSM-R network optimization.

[0015] Figure 1 This is a flowchart of the GSM-R network optimization method in an embodiment of the present invention, as shown below. Figure 1As shown, the GSM-R network optimization method includes: Step 101: Obtain railway GSM-R band radio signals, positioning information, and ledger information; the ledger information includes GSM-R: base station information, frequency configuration information, and service range information; Step 102: Decode the railway GSM-R band radio signal to obtain decoded data; the decoded data includes cell codes and signal measurement parameters for multiple frequency points in GSM-R. Step 103: Calculate the Manhattan distance between every two frequency points in GSM-R based on the decoded data, ledger information, and location information; Step 104: Based on the Manhattan distance between every two frequency points in GSM-R, and combined with the preset interference protection distance between frequency points, determine the GSM-R network frequency optimization scheme; the GSM-R network frequency optimization scheme includes a GSM-R network frequency optimization numerical model and GSM-R network frequency optimization parameters; Step 105: Solve the numerical model for frequency optimization of the GSM-R network to determine whether there are optimizable frequency points, where: Step 106: If no optimizable frequency points exist, determine the GSM-R coverage data based on the decoded data; adjust the antenna parameters of the GSM-R base station according to the GSM-R coverage data. Step 107: If there are optimizable frequency points, optimize the GSM-R network based on the GSM-R network frequency optimization scheme.

[0016] Depend on Figure 1 As shown in the flowchart, in this embodiment of the invention, a GSM-R network optimization method includes: acquiring railway GSM-R band radio signals, location information, and ledger information; the ledger information includes GSM-R's: base station information, frequency point configuration information, and service range information; decoding the railway GSM-R band radio signals to obtain decoded data; the decoded data includes multiple frequency points in GSM-R: cell codes and signal measurement parameters; calculating the Manhattan distance between every two frequency points in GSM-R based on the decoded data, ledger information, and location information; and calculating the Manhattan distance between every two frequency points in GSM-R based on the Manhattan distance between each frequency point in GSM-R. The Manhattan distance, combined with a preset inter-frequency interference protection distance, determines the GSM-R network frequency optimization scheme. This scheme includes a GSM-R network frequency optimization numerical model and GSM-R network frequency optimization parameters. The GSM-R network frequency optimization numerical model is solved to determine if any optimizable frequencies exist. If no optimizable frequencies exist, GSM-R coverage data is determined based on decoded data. The antenna parameters of the GSM-R base station are adjusted according to the GSM-R coverage data. If optimizable frequencies exist, the GSM-R network is optimized based on the GSM-R network frequency optimization scheme.

[0017] Compared with existing GSM-R network optimization technologies that rely on theoretical simulation, construction experience, and problem identification, this new technology collects and decodes radio signals, integrates location information and ledger data to calculate the Manhattan distance of frequency points, combines interference protection distance to generate frequency optimization schemes, adjusts base station antenna parameters when no solution is found, and assesses the risk of interference from adjacent lines. This enables autonomous perception of GSM-R network status and automatic generation of optimization schemes, thereby solving the problem of network optimization in complex areas and improving network adjustment efficiency.

[0018] In step 101, the railway GSM-R band radio signals, positioning information, and ledger information are obtained; the ledger information includes GSM-R base station information, frequency configuration information, and service range information.

[0019] In a specific embodiment, acquiring railway GSM-R band radio signals, location information, and ledger information includes: Obtaining GSM-R band radio signals for railways: The system uses a dedicated GSM-R antenna mounted on the roof of the dynamic inspection vehicle to receive signals. This antenna operates in the 870MHz-960MHz frequency range, adapting to the train's operating environment and accurately capturing GSM-R band radio signals along the railway line. The received signals are transmitted to the interior of the dynamic inspection vehicle via a feeder, providing the original signal source for subsequent decoding processing. The dynamic inspection vehicle is a dedicated railway inspection vehicle, including a high-speed comprehensive inspection train for high-speed railways and a signaling inspection train for conventional railways, specifically used for monitoring the condition of communication infrastructure along the line.

[0020] Obtain location information: The location information comes from the spatiotemporal positioning system onboard the dynamic testing vehicle and is transmitted to the real-time processing unit via Ethernet. The information includes the current railway line, class, mileage, and latitude and longitude of the dynamic testing vehicle, providing a location benchmark for subsequent data fusion and analysis, and ensuring that the optimization plan accurately corresponds to the actual line location.

[0021] Obtain ledger information: The ledger is an electronic spreadsheet containing information on GSM-R base stations along railway lines. The real-time processing unit uses the ledger to retrieve the required information, specifically including: Base station information: includes base station name and location, such as mileage, latitude and longitude, and jurisdiction boundary; Frequency configuration information: including configuration data such as the base station's BCCH frequency and TCH frequency; Service range information: Clarify the start and end service range mileage of each GSM-R cell to provide a basis for dataset segmentation.

[0022] In step 102, the railway GSM-R band radio signal is decoded to obtain decoded data; the decoded data includes cell codes and signal measurement parameters for multiple frequency points in GSM-R.

[0023] Figure 2 This is a specific example diagram illustrating the decoding of railway GSM-R band radio signals in an embodiment of the present invention, as shown below. Figure 2 As shown, decoding the railway GSM-R band radio signal to obtain decoded data may include: Step 201: Perform frequency sweep analysis on the railway GSM-R band radio signals; Step 202: Decode the railway GSM-R band radio signal based on the frequency sweep analysis results to obtain decoded data.

[0024] In a specific embodiment, decoding the railway GSM-R band radio signal to obtain decoded data includes: Frequency sweep analysis of railway GSM-R band radio signals: The railway GSM-R band radio signals received by the dedicated GSM-R antenna and transmitted via the feeder are input to a high-performance GSM-R sweeper. This sweeper has the capability to decode GSM-R cell broadcast information and is configured with a GSM-R network scanning frequency range of 999-1019, totaling 21 frequency points. Through frequency sweep analysis, it comprehensively captures the radio signals corresponding to each frequency point, providing a signal foundation covering all frequency points for subsequent decoding.

[0025] Decoding is performed based on the frequency sweep analysis results to obtain the decoded data: The high-performance GSM-R frequency sweeper decodes the GSM-R network cell broadcast information from the downlink frequency band radio signals after frequency sweep analysis. The single-shot decoded data output contains relevant information for the aforementioned 21 frequency points, specifically divided into two categories: If the cell information is successfully decoded, the decoded data contains a valid and complete mobile communication code and configuration parameters. The mobile communication code includes the Mobile Country Code (MCC), Mobile Network Code (MNC), Location Area Code (LAC), and Cell Number (CI). The configuration parameter is the received signal level (RxLev). If decoding fails, including cases where no cell has been assigned to the current frequency point, only the received level (RxLev) is a valid configuration parameter in the decoded data; the Mobile Country Code (MCC), Mobile Network Code (MNC), Location Area Code (LAC), and Cell Number (CI) are all invalid data.

[0026] Figure 3This is a specific example diagram illustrating the calculation of the Manhattan distance between every two frequency points in GSM-R according to an embodiment of the present invention, as shown below. Figure 3 As shown, calculating the Manhattan distance between every two frequency points in GSM-R based on decoded data, ledger information, and location information can include: Step 301: Based on the ledger information and location information, resample the decoded data to obtain the decoded dataset; Step 302: Based on the decoded dataset, calculate the Manhattan distance between every two frequency points in GSM-R.

[0027] Figure 4 This is a specific example diagram of obtaining the decoded dataset in an embodiment of the present invention, such as... Figure 4 As shown, based on the ledger information and location information, the decoded data is resampled to obtain a decoded dataset, which may include: Step 401: Combine the mileage markers corresponding to the positioning information to perform equidistant resampling of the decoded data; Step 402: Based on the frequency configuration information in the ledger information, adjust the signal reception level of different frequency points in the decoded data after equidistant resampling; Step 403: Segment the adjusted decoded data according to the service scope information in the ledger information to obtain the decoded dataset.

[0028] In a specific embodiment, the Manhattan distance between every two frequency points in GSM-R is calculated according to steps 301-302, wherein step 301 obtains the decoding dataset through steps 401-403, specifically including: Obtain the decoded dataset Decoding data by equidistant resampling: The positioning information includes the mileage marker of the current location of the dynamic detection vehicle. Combined with this mileage marker, the decoded data output by the frequency sweeper is resampled at equal intervals to make the mileage distribution of the decoded data uniform. This ensures that the frequency signal data calculated subsequently has spatial consistency and lays the foundation for accurately calculating the distance between frequency points.

[0029] Adjust the signal reception level at different frequencies: Based on the frequency configuration information in the ledger, including the TCH frequency configuration of the GSM-R cell, the decoded data after equidistant resampling is traversed. At the same kilometer marker location, the received level (RxLev) of the TCH frequency point of the cell is compared with the received level (RxLev) of the BCCH frequency point. If the RxLev of the TCH frequency point is lower than the RxLev of the BCCH frequency point, the RxLev of the TCH frequency point is replaced with the RxLev of the BCCH frequency point. If the RxLev of the TCH frequency point is not lower than the RxLev of the BCCH frequency point, no processing is performed on the RxLev of the TCH frequency point to ensure the accuracy of the frequency point received level data.

[0030] Segmented and adjusted decoded data: The service range information of each GSM-R cell is obtained from the ledger information to determine the start and end service range mileage of the cell. Based on the mileage information, the decoded data adjusted in step 402 is segmented so that each segmented dataset corresponds to the service range of a GSM-R cell, and finally a structured decoded dataset is obtained.

[0031] Calculate the Manhattan distance between every two frequency points

[0032] Based on the decoded dataset obtained in step 301, the received signal level of each frequency point (21 frequency points in total, 999-1019) at the corresponding kilometer marker location is extracted from each dataset. Based on this, the distance between frequency points is calculated, and an adaptive weighting strategy is used to construct a weighted Manhattan distance matrix. Specifically: According to the definition of Manhattan distance (MD), the distance between two frequency signals is defined as the weighted Manhattan distance WMD. The Manhattan distance MD is the sum of the difference RxLev received by the first frequency signal and the second frequency signal at the same location and the frequency interval. The weighting strategy is as follows: sort the Manhattan distances (MD) of all frequency pairs in descending order to obtain the sorted distance set (MD). and take 1 / MD As adaptive weights W, sort W in descending order to obtain Wi This assigns greater weight to frequency pairs that are further apart and less weight to frequency pairs that are closer together, thus achieving weighting for signal difference-sensitive regions. With MD Multiplying them yields the WMD matrix. This matching rule, which sorts both the Manhattan distance MD and the weights W in descending order, ensures that the weighted logic aligns with the goal of maximizing signal distance, thus penalizing close-range frequency pairs.

[0033] in, Let i be the Manhattan distance between frequency i and frequency j at kilometer marker k.

[0034] This adaptive weighting strategy can dynamically adjust the weight allocation according to the actual signal differences between frequency points, avoiding the distance assessment bias caused by fixed weights, making the distance calculation between frequency points more targeted and accurate, and providing reliable data support for the generation of subsequent network frequency optimization schemes.

[0035] In step 104, based on the Manhattan distance between every two frequency points in GSM-R and combined with the preset interference protection distance between frequency points, a GSM-R network frequency optimization scheme is determined; the GSM-R network frequency optimization scheme includes a GSM-R network frequency optimization numerical model and GSM-R network frequency optimization parameters.

[0036] In a specific embodiment, determining the GSM-R network frequency optimization scheme includes: Set the interference protection distance parameters between frequency points: preset the interference protection ratio parameters corresponding to the interference protection distance between two types of frequency points. The default value of the interference protection ratio for 200kHz is -6, and the default value of the interference protection ratio for 400kHz is -38. The two types of parameters can be flexibly adjusted according to the railway line grade and used as constraints for frequency optimization.

[0037] Constructing a numerical model for frequency optimization in GSM-R networks: With maximizing signal distance as the core optimization objective, and considering interference protection distance constraints, the total weighted Manhattan distance (TWMD) of the cell frequency point and other GSM-R frequency points is calculated sequentially within the cell's service area.

[0038] in, Let be the total weighted Manhattan distance between frequency point i and frequency point j.

[0039] Then, the optimization equations for the current cell frequency point i are solved sequentially using the optimization method. The model equations are as follows:

[0040] in, Interference protection ratio at 200kHz Interference protection ratio at 400kHz.

[0041] Traverse the interval from the current cell coverage to the adjacent line. Based on the distance assessment results, fill the RxLev of frequency point j within this interval with the RxLev of frequency point i, and assess the interference risk in conjunction with the interference protection ratio. If there is no risk of co-channel or adjacent-channel interference, then frequency point j is the optimized adjustment scheme for frequency point i; if co-channel or adjacent-channel interference exists, then iteratively solve the following optimization equation:

[0042] in, This refers to the set of frequency points in the solution that are at risk of co-channel or adjacent-channel interference.

[0043] Define the frequency optimization parameters for the GSM-R network: The optimization parameters include core calculation parameters and constraint parameters, specifically: Manhattan distance (MD), weighted Manhattan distance (WMD), and total weighted Manhattan distance (TWMD) for each frequency pair; interference protection ratio at 200kHz and 400kHz; set of adjacent line interference risk frequencies; target frequency identifiers after optimization, frequency adjustment range, etc., to provide clear parameter support for model solving and network optimization adjustments.

[0044] A comprehensive GSM-R network frequency optimization scheme is formed by integrating the numerical model and optimization parameters constructed above. This scheme clarifies the objective function, constraints, core calculation parameters, and target frequency configuration for frequency optimization, providing systematic guidance for solving the optimization model and determining optimizable frequencies.

[0045] In step 105, the numerical model for frequency optimization of the GSM-R network is solved to determine whether there are optimizable frequency points, wherein: In step 106, if there are no optimizable frequency points, the GSM-R coverage data is determined based on the decoded data; the antenna parameters of the GSM-R base station are adjusted according to the GSM-R coverage data.

[0046] Figure 5 This is a specific example diagram illustrating the adjustment of GSM-R base station antenna parameters based on GSM-R coverage data in an embodiment of the present invention. Figure 5 As shown, based on the decoded data, GSM-R coverage data is determined; adjusting the antenna parameters of the GSM-R base station according to the GSM-R coverage data may include: Step 501: Based on the decoded data and ledger information, and combined with the GSM-R network uplink / downlink handover location, determine the overlapping coverage area of ​​the GSM-R network; Step 502: Identify coverage anomaly information of the GSM-R network based on the overlapping coverage area of ​​the GSM-R network; the coverage anomaly information includes over-coverage information and weak coverage information. Step 503: Adjust the antenna parameters of the GSM-R base station based on the coverage anomaly information of the GSM-R network.

[0047] In a specific embodiment, the numerical model for frequency optimization of the GSM-R network is solved to determine whether there are optimizable frequency points: The GSM-R network frequency optimization numerical model constructed in step 104 is solved and judged according to the following logic: First, within the current cell's service area, solve for the objective function max(TWMD), where TWMD is the total weighted Manhattan distance between the cell's frequency and all other frequencies, with the following constraints: and ( Interference protection ratio at 200kHz, default -6; Interference protection ratio at 400kHz (default -38). Traverse the interval from the current cell to the adjacent line, fill the received level (RxLev) of frequency point j in the interval with the RxLev of frequency point i, and evaluate the interference risk of the adjacent line in combination with the interference protection ratio; If there exists a frequency point j that satisfies the above constraints and has no risk of co-channel or adjacent-channel interference, or if an effective frequency point j is obtained by iteratively solving the equation, then it is determined that there is an optimizable frequency point. If, after the above solution process, no frequency point satisfies the constraints, or if the interference risk cannot be avoided after iteration, then it is determined that there is no frequency point that can be optimized.

[0048] If no optimizable frequency point exists, adjust the antenna parameters of the GSM-R base station: The first step involves coverage analysis based on coverage data. Specifically, coverage analysis refers to evaluating the coverage of the GSM-R network based on received signal level (RxLev) data to identify unreasonable coverage issues such as over-coverage and weak coverage. The steps are as follows: (1) Based on the frequency sweeper decoding data and combined with the ledger data, extract the RxLev data of the serving cell and neighboring cells, and extract the overlapping coverage area according to the uplink and downlink handover location on the GSM-R network. Select the midpoint of the overlapping coverage area ; (2) Extract the RxLev data of interfering cells and neighboring cells, and extract the overlapping coverage area based on the uplink / downlink handover location. Select the midpoint of the overlapping coverage area ; (3) If This reduces the elevation angle of the antenna in the direction of the interfering cell of the serving cell base station; if and This increases the elevation angle of the antenna in the direction of the serving cell of the interfering cell base station; among which, , These are the locations of the base stations serving the cell and those in neighboring cells, respectively. , These are the locations of the interfering cell and its neighboring base stations.

[0049] In step 107, if there are optimizable frequency points, the GSM-R network is optimized based on the GSM-R network frequency optimization scheme.

[0050] In a specific embodiment, optimizing the GSM-R network based on the GSM-R network frequency optimization scheme includes: Define the target frequency point for optimization: Based on the solution results of step 105, lock the optimizable frequency point j that meets the constraints and has no risk of co-channel or adjacent channel interference after adjacent line interference assessment, or the optimal frequency point determined by iteratively solving the equation, and use it as the replacement or adjustment target frequency point of the current cell frequency point i.

[0051] Perform frequency point optimization configuration: Based on the frequency point configuration parameters in the GSM-R network frequency optimization scheme, replace or adjust the original frequency points of the current cell, and configure the target frequency point j as the working frequency point of the cell, including the BCCH frequency point or TCH frequency point, to ensure that the optimized frequency point meets the network frequency reuse requirements, maximizes the signal distance between frequency points, and reduces co-channel and adjacent channel interference.

[0052] In this embodiment, after optimizing the GSM-R network based on the GSM-R network frequency optimization scheme, the following may also be included: Obtain the optimized railway GSM-R band radio signal and evaluate the GSM-R network optimization effect based on the optimized railway GSM-R band radio signal.

[0053] In a specific embodiment, the optimization effect is verified as follows: After the frequency point configuration is adjusted, the dynamic detection vehicle continuously collects the radio signals of the adjusted GSM-R network. The frequency sweeper decodes and outputs new decoding data in real time, including the received level RxLev of the target frequency point j, mobile communication code, etc. Based on the new decoding data, positioning information and ledger information, the Manhattan distance, weighted Manhattan distance and total weighted Manhattan distance TWMD of the target frequency point j and other frequency points are recalculated to verify whether the optimized frequency point still meets the interference protection ratio constraint.

[0054] Optimization scheme update: If the new fusion analysis results show that the optimization effect meets the standard, that is, TWMD remains within the constraint range and there is no new interference risk, then the current frequency configuration is maintained; if new decoding data is detected that reflects changes in network status, such as changes in terrain and topography leading to changes in the signal propagation environment, the optimization model is resolved, the GSM-R network frequency optimization scheme is updated, and the frequency configuration is adjusted synchronously to ensure that the network continues to be in the optimal operating state.

[0055] The GSM-R network optimization method of this invention has been verified to have the following beneficial effects: 1. Based on measured radio signals along the railway line, and combined with in-depth analysis of positioning information and ledger data, it eliminates the reliance on theoretical propagation model simulation and construction experience, and can fully consider the impact of on-site terrain on wireless signal propagation, effectively solving the problem of difficulty in covering special scenarios during the network design stage.

[0056] 2. By constructing a signal distance matrix, solving optimization equations, and assessing the risk of interference from adjacent lines, a systematic frequency optimization scheme is formed. For complex areas with dense frequency reuse and severe interference, such as hubs and cross-line intersections, the optimization direction can be accurately located, significantly improving the efficiency of on-site network adjustments and minimizing the impact of network adjustments on railway operations.

[0057] 3. The optimization process fully incorporates the adjacent line interference assessment logic, and eliminates the risk of co-channel and adjacent-channel interference by iteratively solving the optimization equation. This avoids the secondary problems that are easily caused by traditional local adjustments, ensuring that the adjustment of the local network will not have a negative impact on the operation of the adjacent network, and achieving a balance between safety and efficiency in network optimization.

[0058] This invention also provides a GSM-R network optimization device, as described in the following embodiments. Since the principle by which this device solves the problem is similar to that of the GSM-R network optimization method, the implementation of this device can refer to the implementation of the GSM-R network optimization method; repeated details will not be elaborated further.

[0059] Figure 6 This is a structural example diagram of the GSM-R network optimization device in an embodiment of the present invention, as shown below. Figure 6 As shown, the GSM-R network optimization device includes: The data acquisition module 601 is used to acquire railway GSM-R band radio signals, positioning information, and ledger information; the ledger information includes GSM-R: base station information, frequency configuration information, and service range information; The decoding module 602 is used to decode the railway GSM-R band radio signals to obtain decoded data; the decoded data includes cell codes and signal measurement parameters for multiple frequency points in GSM-R. The distance calculation module 603 is used to calculate the Manhattan distance between every two frequency points in GSM-R based on the decoded data, ledger information and positioning information; The optimization scheme determination module 604 is used to determine the GSM-R network frequency optimization scheme based on the Manhattan distance between every two frequency points in GSM-R and the preset frequency point interference protection distance; the GSM-R network frequency optimization scheme includes a GSM-R network frequency optimization numerical model and GSM-R network frequency optimization parameters; Solve the numerical model for frequency optimization in the GSM-R network to determine whether there are optimizable frequency points, where: The antenna parameter adjustment module 605 is used to determine the GSM-R coverage data based on the decoded data if no optimizable frequency point exists; and to adjust the antenna parameters of the GSM-R base station according to the GSM-R coverage data. The network optimization module 606 is used to optimize the GSM-R network based on the GSM-R network frequency optimization scheme if there are optimizable frequency points.

[0060] In one embodiment, the decoding module 602 is specifically used for: Frequency sweep analysis of railway GSM-R band radio signals; Based on the frequency sweep analysis results, the railway GSM-R band radio signals were decoded to obtain decoded data.

[0061] In one embodiment, the distance calculation module 603 is specifically used for: Based on the ledger information and location information, the decoded data is resampled to obtain the decoded dataset; Based on the decoded dataset, the Manhattan distance between every two frequency points in GSM-R is calculated.

[0062] In one embodiment, the distance calculation module 603 is further used for: By combining the mileage markers corresponding to the location information, the decoded data is resampled at equal intervals; Based on the frequency configuration information in the ledger information, the signal reception level of different frequency points in the decoded data after equidistant resampling is adjusted; The adjusted decoded data is segmented according to the service scope information in the ledger information to obtain the decoded dataset.

[0063] In one embodiment, the antenna parameter adjustment module 605 is specifically used for: Based on the decoded data and ledger information, combined with the GSM-R network uplink / downlink handover location, the overlapping coverage area of ​​the GSM-R network is determined; Based on the overlapping coverage area of ​​the GSM-R network, identify coverage anomaly information of the GSM-R network; the coverage anomaly information includes over-coverage information and weak coverage information; Adjust the antenna parameters of the GSM-R base station based on the coverage anomaly information of the GSM-R network.

[0064] Figure 7 This is a specific example diagram of the structure of the GSM-R network optimization device in an embodiment of the present invention, as shown below. Figure 7 As shown in one embodiment, Figure 6 The GSM-R network optimization device shown in the embodiment of the present invention may further include: an optimization effect evaluation module 701.

[0065] In one embodiment, the optimization effect evaluation module 701 is specifically used for: After optimizing the GSM-R network based on the GSM-R network frequency optimization scheme, the optimized railway GSM-R band radio signal is obtained, and the optimization effect of the GSM-R network is evaluated based on the optimized railway GSM-R band radio signal.

[0066] Based on the aforementioned inventive concept, such as Figure 8 As shown, the present invention also proposes a computer device 800, including a memory 810, a processor 820, and a computer program 830 stored in the memory 810 and executable on the processor 820. When the processor 820 executes the computer program 830, it implements the aforementioned GSM-R network optimization method.

[0067] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described GSM-R network optimization method.

[0068] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the above-described GSM-R network optimization method.

[0069] In this embodiment of the invention, railway GSM-R band radio signals, location information, and ledger information are acquired. The ledger information includes GSM-R base station information, frequency configuration information, and service range information. The railway GSM-R band radio signals are decoded to obtain decoded data. The decoded data includes cell codes and signal measurement parameters for multiple frequency points in GSM-R. Based on the decoded data, ledger information, and location information, the Manhattan distance between every two frequency points in GSM-R is calculated. Based on the Manhattan distance between every two frequency points in GSM-R, combined with a preset interference protection distance between frequency points, a GSM-R network frequency optimization scheme is determined. The scheme includes a numerical model for GSM-R network frequency optimization and GSM-R network frequency optimization parameters; solving the numerical model for GSM-R network frequency optimization determines whether there are optimizable frequency points, wherein: if there are no optimizable frequency points, GSM-R coverage data is determined based on decoded data; antenna parameters of GSM-R base stations are adjusted according to GSM-R coverage data; if there are optimizable frequency points, the GSM-R network is optimized based on the GSM-R network frequency optimization scheme; the embodiments of the present invention improve the systematicness and adaptability of GSM-R network optimization by combining radio signal data with ledgers and location information, while reducing interference risks and achieving efficient and reliable GSM-R network optimization.

[0070] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0071] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0072] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0073] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0074] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A GSM-R network optimization method, characterized in that, include: Acquire railway GSM-R band radio signals, positioning information, and ledger information; the ledger information includes GSM-R: base station information, frequency configuration information, and service range information; The railway GSM-R band radio signal is decoded to obtain decoded data; the decoded data includes cell codes and signal measurement parameters for multiple frequency points in GSM-R. Based on the decoded data, ledger information, and location information, calculate the Manhattan distance between every two frequency points in GSM-R; Based on the Manhattan distance between every two frequency points in GSM-R, and combined with the preset interference protection distance between frequency points, a GSM-R network frequency optimization scheme is determined; the GSM-R network frequency optimization scheme includes a GSM-R network frequency optimization numerical model and GSM-R network frequency optimization parameters; Solve the numerical model for frequency optimization in the GSM-R network to determine whether there are optimizable frequency points, where: If no optimizable frequency points exist, determine the GSM-R coverage data based on the decoded data; adjust the antenna parameters of the GSM-R base station according to the GSM-R coverage data. If there are optimizable frequency points, then optimize the GSM-R network based on the GSM-R network frequency optimization scheme.

2. The method as described in claim 1, characterized in that, Decoding the railway GSM-R band radio signals yields decoded data, including: Frequency sweep analysis of railway GSM-R band radio signals; Based on the frequency sweep analysis results, the railway GSM-R band radio signals were decoded to obtain decoded data.

3. The method as described in claim 1, characterized in that, Based on decoded data, ledger information, and location information, calculate the Manhattan distance between every two frequency points in GSM-R, including: Based on the ledger information and location information, the decoded data is resampled to obtain the decoded dataset; Based on the decoded dataset, the Manhattan distance between every two frequency points in GSM-R is calculated.

4. The method as described in claim 3, characterized in that, Based on the ledger information and location information, the decoded data is resampled to obtain the decoded dataset, which includes: By combining the mileage markers corresponding to the location information, the decoded data is resampled at equal intervals; Based on the frequency configuration information in the ledger information, the signal reception level of different frequency points in the decoded data after equidistant resampling is adjusted; The adjusted decoded data is segmented according to the service scope information in the ledger information to obtain the decoded dataset.

5. The method as described in claim 1, characterized in that, Based on the decoded data, determine the GSM-R coverage data; adjust the antenna parameters of the GSM-R base station according to the GSM-R coverage data, including: Based on the decoded data and ledger information, combined with the GSM-R network uplink / downlink handover location, the overlapping coverage area of ​​the GSM-R network is determined; Based on the overlapping coverage area of ​​the GSM-R network, identify coverage anomaly information of the GSM-R network; the coverage anomaly information includes over-coverage information and weak coverage information; Adjust the antenna parameters of the GSM-R base station based on the coverage anomaly information of the GSM-R network.

6. The method as described in claim 1, characterized in that, After optimizing the GSM-R network based on the GSM-R network frequency optimization scheme, the following is also included: Obtain the optimized railway GSM-R band radio signal and evaluate the GSM-R network optimization effect based on the optimized railway GSM-R band radio signal.

7. A GSM-R network optimization device, characterized in that, include: The data acquisition module is used to acquire railway GSM-R band radio signals, positioning information, and ledger information; The ledger information includes GSM-R's: base station information, frequency configuration information, and service range information; The decoding module is used to decode the railway GSM-R band radio signals to obtain decoded data; the decoded data includes cell codes and signal measurement parameters for multiple frequency points in GSM-R. The distance calculation module is used to calculate the Manhattan distance between every two frequency points in GSM-R based on decoded data, ledger information, and location information; The optimization scheme determination module is used to determine the GSM-R network frequency optimization scheme based on the Manhattan distance between every two frequency points in GSM-R and the preset frequency point interference protection distance; the GSM-R network frequency optimization scheme includes a GSM-R network frequency optimization numerical model and GSM-R network frequency optimization parameters; Solve the numerical model for frequency optimization in the GSM-R network to determine whether there are optimizable frequency points, where: The antenna parameter adjustment module is used to determine the GSM-R coverage data based on the decoded data if no optimizable frequency point exists; and to adjust the antenna parameters of the GSM-R base station according to the GSM-R coverage data. The network optimization module is used to optimize the GSM-R network based on the GSM-R network frequency optimization scheme if there are optimizable frequency points.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1-6.

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