5G high and low frequency collaborative networking optimization method and device and medium

Abstract

The invention discloses a 5G high and low frequency collaborative networking optimization method and device and a medium. The method comprises the following steps: firstly, acquiring communication data of all sampling points in a preset area in a preset period; then determining a theoretical distance between the sampling point and the target base station and dividing the theoretical distance into a plurality of distance intervals; evaluating a distance interval with insufficient coverage; clustering the distance intervals with insufficient coverage into weak coverage areas according to the designed coverage distance of the target base station; judging whether the signal coverage of the target base station reaches the expectation or not according to the number of the weak coverage areas: if the number of the weak coverage areas exceeds a preset threshold value, judging that the signal coverage of the target base station cannot reach the expectation; and finally, in the target base station of which the signal coverage cannot reach the expectation, implementing TDD-NR and FDD-NR frequency spectrum aggregation so as to realize 5G high and low frequency collaborative networking optimization. The method can reduce the dependence on manual testing, and can comprehensively and dynamically optimize the high and low frequency resource configuration, thereby improving the resource utilization efficiency, and reducing the manpower and material resource costs.

Description

Technical Field

[0001] The present invention belongs to the field of network communication technology, and in particular, relates to a 5G high- and low-frequency collaborative networking optimization method, device and medium. Background Art

[0002] As technology develops, 5G networks will connect more users and carry more traffic. However, one of the major challenges currently facing 5G is limited uplink coverage. As the frequency increases, path loss and penetration loss also increase, which shortens the coverage distance of high-frequency bands. Currently, the frequency bands commonly used by 5G networks (such as 3.5GHz and 4.9GHz) are more significant in signal attenuation than the frequency bands used by 4G, especially when penetrating obstacles (such as glass walls). The penetration loss of 3.5GHz is about 3dB higher than that of 2.1GHz. Although the transmit power of 5G terminals has been doubled, as users move to the edge of the base station, the uplink multi-antenna and large bandwidth advantages of the terminal gradually disappear, making the coverage problem more prominent.

[0003] To meet these challenges, operators urgently need to enhance the capabilities of 5G networks to improve user experience. With the continuous expansion of spectrum for 5G commercial deployment, frequency band re-farming and spectrum sharing have become effective strategies for high- and low-frequency networking. When high-frequency uplink is insufficient, operators can "borrow" resources from medium and low frequency bands as uplink channels. The penetration loss of medium and low frequency bands is small and the propagation distance is longer, which can effectively improve the coverage of 5G. Therefore, the implementation plan is to use high-frequency bands for downlink and medium and low frequency bands for uplink.

[0004] However, in the prior art, whether to perform high-frequency and low-frequency networking mainly depends on the on-site RSRP test information of the tester. When the test finds that the RSRP of the base station coverage area is low, high-frequency and low-frequency networking will be considered.

[0005] However, this method has several disadvantages: first, on-site manual testing is usually carried out in a specific time period and cannot reflect the dynamic changes of the network in real time, so some important problems may be missed; second, since each scene needs to be tested to determine whether it is suitable for high- and low-frequency networking, this not only consumes a lot of manpower and material resources, but also leads to inefficient resource utilization. Summary of the invention

[0006] The technical problem to be solved by the present invention is to propose a 5G high and low frequency collaborative networking optimization method, device and medium in response to the above-mentioned deficiencies in the prior art. This method can reduce the reliance on manual testing, and at the same time can comprehensively and dynamically optimize the allocation of high and low frequency resources, thereby improving resource utilization efficiency, reducing manpower and material costs, and further improving the comprehensiveness and efficiency of decision-making.

[0007] In a first aspect, the present invention provides a 5G high-low frequency collaborative networking optimization method, the method comprising the following steps:

[0008] Obtain communication data of all sampling points within a preset period and within a preset area; and obtain the longitude and latitude of the sampling points;

[0009] The communication data of the sampling points include the designed coverage distance of the target base station,

[0010] Location data of the target base station, and AGPS MR data; the AGPS MR data is data reported to the target base station by a terminal connected to the target base station;

[0011] Determine a theoretical distance between the sampling point and the target base station according to the latitude and longitude of the sampling point and the location data of the target base station; and divide the theoretical distance into a plurality of distance intervals;

[0012] assessing insufficiently covered distance intervals based on the AGPS MR data;

[0013] According to the designed coverage distance of the target base station, clustering the insufficiently covered distance intervals into weak coverage areas;

[0014] According to the number of weak coverage areas, it is determined whether the signal coverage of the target base station is reached.

[0015] expect:

[0016] If the number of weak coverage areas exceeds a preset threshold, it is determined that the signal coverage of the target base station cannot meet expectations;

[0017] In target base stations where signal coverage cannot reach the expected level, spectrum aggregation of TDD-NR and FDD-NR is implemented to achieve 5G high and low frequency collaborative networking optimization.

[0018] Furthermore, the theoretical distance between the sampling point and the target base station is calculated according to the Euclidean distance calculation formula, and the calculation formula is as follows:

[0019]

[0020] Where d represents the theoretical distance between the sampling point and the target base station;

[0021] lat 基站 Indicates the latitude of the base station;

[0022] lat 采样点 Indicates the latitude of the sampling point;

[0023] lon 基站 Indicates the longitude of the base station;

[0024] lon采样点 Indicates the longitude of the base station.

[0025] Further, the AGPS MR data includes RSRP data and CQI data;

[0026] The step of evaluating the insufficiently covered distance interval according to the AGPS MR data specifically comprises the following steps:

[0027] Get RSRP data and CQI data;

[0028] Filtering out the collection points whose RSRP values ​​are lower than the preset RSRP threshold from the RSRP data to obtain weak RSRP collection points; and filtering out the collection points whose CQI values ​​are lower than the preset CQI threshold from the CQI data to obtain weak CQI collection points;

[0029] Performing intersection processing on the weak RSRP collection point and the weak CQI collection point to determine insufficient coverage collection points;

[0030] Compare the insufficient coverage points in each distance interval with the preset coverage threshold:

[0031] If the number of insufficiently covered collection points within a certain distance interval is higher than a preset coverage threshold, the distance interval is determined to be an insufficiently covered distance interval.

[0032] Furthermore, clustering the insufficiently covered distance areas into weakly covered areas is specifically as follows:

[0033] The DBSCAN algorithm is used to cluster the insufficiently covered distance intervals into weakly covered areas;

[0034] The DBSCAN algorithm is used to cluster insufficiently covered distance intervals into weakly covered areas, specifically comprising the following steps:

[0035] Step A1: Set the domain radius ∈ and the minimum number of samples MinPts of the DBSCAN algorithm;

[0036] Wherein, the area radius ∈ is set according to the insufficient coverage distance interval and the designed coverage distance of the target base station;

[0037] Step A2: Based on the domain radius ∈ and the minimum sample number MinPts, the insufficiently covered distance interval data is input into the DBSCAN algorithm for calculation to obtain the clustering density;

[0038] Step A3: Identify weak coverage areas according to the clustering density.

[0039] Furthermore, the implementation of spectrum aggregation of TDD-NR and FDD-NR specifically includes the following steps:

[0040] Determine the scope of RSRP increase:

[0041] If the RSRP improvement range is within the first threshold range, a 2.1 GHz FDD-NR and 3.4 GHz TDD-NR aggregation method is implemented, or a 2.3 GHz FDD-NR and 4.9 GHz TDD-NR aggregation method is implemented;

[0042] If the RSRP improvement range is within the second threshold range, a 1.8 GHz FDD-NR and 3.4 GHz TDD-NR aggregation method is implemented, or a 1.8 GHz FDD-NR and 4.9 GHz TDD-NR aggregation method is implemented;

[0043] If the RSRP improvement range is within the third threshold range, the 800MHz FDD-NR and 3.4GHz TDD-NR aggregation method is implemented.

[0044] In a second aspect, the present invention provides a 5G high-low frequency collaborative networking optimization device, the device comprising:

[0045] An acquisition unit, used to acquire communication data of all sampling points within a preset period and a preset area; and to acquire the longitude and latitude of the sampling points;

[0046] The sampling point communication data includes the designed coverage distance of the target base station, the location data of the target base station, and AGPS MR data; the AGPS MR data is the data reported to the target base station by the terminal connected to the target base station;

[0047] a processing unit connected to the acquisition unit, configured to determine a theoretical distance between the sampling point and the target base station according to the latitude and longitude of the sampling point and the location data of the target base station; and to divide the theoretical distance into a plurality of distance intervals;

[0048] an evaluation unit connected to the processing unit, and configured to evaluate the insufficiently covered distance interval according to the AGPS MR data;

[0049] A clustering unit, connected to the evaluation unit, configured to cluster insufficiently covered distance intervals into weak coverage areas according to the designed coverage distance of the target base station;

[0050] A determination unit, connected to the clustering unit, configured to determine whether the signal coverage of the target base station meets expectations according to the number of weak coverage areas;

[0051] An aggregation unit is connected to the determination unit and is used to implement spectrum aggregation of TDD-NR and FDD-NR when the determination unit determines that the signal coverage cannot reach the expected target base station, so as to achieve 5G high and low frequency collaborative networking optimization.

[0052] Furthermore, the evaluation unit comprises:

[0053] An acquisition module, used to acquire RSRP data and CQI data;

[0054] A screening module, connected to the acquisition module, for screening out the collection points whose RSRP values ​​are lower than the preset RSRP threshold from the RSRP data to obtain weak RSRP collection points; and for screening out the collection points whose CQI values ​​are lower than the preset CQI threshold from the CQI data to obtain weak CQI collection points;

[0055] A processing module, connected to the screening module, for performing intersection processing on the weak RSRP collection points and the weak CQI collection points to determine insufficient coverage collection points;

[0056] A comparison module, connected to the processing module, for comparing insufficient coverage collection points within each distance interval with a preset coverage threshold;

[0057] The first determination module is connected to the comparison module and is used to determine that a distance interval is an insufficiently covered distance interval when the comparison module obtains that the insufficiently covered collection points in a certain distance interval are higher than a preset coverage threshold.

[0058] Furthermore, the clustering unit comprises:

[0059] The setting module is used to set the domain radius ∈ and the minimum number of samples MinPts of the DBSCAN algorithm;

[0060] Wherein, the area radius ∈ is set according to the insufficient coverage distance interval and the designed coverage distance of the target base station;

[0061] An input module, connected to the setting module, is used to input insufficiently covered distance interval data into the DBSCAN algorithm for calculation based on the domain radius ∈ and the minimum sample number MinPts to obtain cluster density;

[0062] The identification module is connected to the input module and is used to identify the weak coverage area according to the clustering density.

[0063] Furthermore, the polymerization unit comprises:

[0064] The second determination module is used to determine the range of RSRP improvement;

[0065] A first aggregation module is connected to the second determination module, and is configured to implement a 2.1 GHz FDD-NR and 3.4 GHz TDD-NR aggregation mode, or implement a 2.3 GHz FDD-NR and 4.9 GHz TDD-NR aggregation mode when the second determination module determines that the RSRP improvement range is within the first threshold range;

[0066] A second aggregation module, connected to the second determination module, configured to implement a 1.8 GHz FDD-NR and 3.4 GHz TDD-NR aggregation mode, or implement a 1.8 GHz FDD-NR and 4.9 GHz TDD-NR aggregation mode when the second determination module determines that the RSRP improvement range is within a second threshold range;

[0067] The third aggregation module is connected to the second determination module, and is used to implement an 800MHz FDD-NR and 3.4GHz TDD-NR aggregation method when the second determination module determines that the RSRP improvement range is within a third threshold range.

[0068] In a third aspect, the present invention provides a computer storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the 5G high and low frequency collaborative networking optimization method described in the first aspect is implemented.

[0069] The present invention is based on large-scale terminal measurement data and cluster analysis. The present invention can reduce the reliance on manual testing and can comprehensively and dynamically optimize the allocation of high- and low-frequency resources, thereby improving resource utilization efficiency, reducing manpower and material costs, and further improving the comprehensiveness and efficiency of decision-making. The specific effective effects are as follows:

[0070] 1. Data-driven decision making:

[0071] The present invention achieves in-depth understanding of network conditions through large-scale terminal measurement data, ensures that optimization decisions are based on real user experience and network performance, and avoids uncertainty caused by subjective inference.

[0072] 2. Accurate coverage assessment:

[0073] The present invention adopts cluster analysis, which can effectively identify areas with weak coverage and insufficient coverage, thereby accurately locating signal coverage problems, facilitating targeted network optimization and improving coverage quality.

[0074] 3. Dynamic optimization capability:

[0075] The present invention utilizes real-time measurement data to dynamically adjust the configuration of high- and low-frequency resources to adapt to changing user needs and network conditions, thereby improving the flexibility and response speed of the network.

[0076] 4. Improve user experience:

[0077] The present invention can significantly increase the user's uplink and downlink rates, reduce signal attenuation, improve the overall user experience, and enhance user satisfaction by optimizing the coordinated use of high-frequency and low-frequency spectrums.

[0078] 5. Reduce operating costs:

[0079] The present invention can efficiently identify and solve coverage problems, reduce the need for manual on-site testing, thereby reducing the cost of manpower and material resources in the network optimization process and improving operational efficiency.

[0080] 6. Enhance network robustness:

[0081] The present invention can help operators identify potential risks, enhance the stability and reliability of the network in different environments, and ensure the continuity of services.

[0082] 7. Forward-looking network planning:

[0083] The present invention can support long-term network expansion and upgrade planning by analyzing historical and real-time data, ensuring that the network has good adaptability in the face of future user growth and technological progress.

[0084] 8. Comprehensive spectrum utilization:

[0085] The present invention adopts high and low frequency collaborative networking, which can achieve the optimal combination of spectrum resources and improve resource utilization efficiency. Especially when the high frequency band is insufficient, the medium and low frequency bands can be flexibly scheduled to effectively meet the diverse needs of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 Schematic diagram of a 5G high- and low-frequency collaborative networking optimization method in an embodiment of the present invention;

[0087] Figure 2 Schematic diagram of the relationship between the distance interval and the RSRP signal in an embodiment of the present invention;

[0088] Figure 3 A schematic diagram of base station coverage distance in an embodiment of the present invention;

[0089] Figure 4 Schematic diagram of a 5G high- and low-frequency collaborative networking optimization device in an embodiment of the present invention;

[0090] Figure 5 Schematic diagram of longitude and latitude data in an embodiment of the present invention;

[0091] Among them, the figure numerals are: 10, acquisition unit, 20, processing unit, 30, evaluation unit, 40, clustering unit, 50, determination unit, 60, aggregation unit. DETAILED DESCRIPTION

[0092] In order to enable those skilled in the art to better understand the technical solution of the present invention, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0093] It should be understood that the specific embodiments and drawings described herein are only used to explain the present invention rather than to limit the present invention.

[0094] It can be understood that, in the absence of conflict, the various embodiments of the present invention and the various features in the embodiments can be combined with each other.

[0095] It can be understood that, for the convenience of description, the drawings of the present invention only show the parts related to the present invention, while the parts irrelevant to the present invention are not shown in the drawings.

[0096] It can be understood that each unit and module involved in the embodiments of the present invention may correspond to only one physical structure, or may be composed of multiple physical structures, or multiple units and modules may be integrated into one physical structure.

[0097] It can be understood that, without conflict, the functions and steps marked in the flowcharts and block diagrams of the present invention may occur in an order different from that marked in the drawings.

[0098] It is understood that the flowcharts and block diagrams of the present invention illustrate the possible architectures, functions, and operations of the systems, devices, equipment, and methods according to the various embodiments of the present invention. Each box in the flowchart or block diagram may represent a unit, module, program segment, or code, which contains executable instructions for implementing the specified functions. Moreover, each box or combination of boxes in the block diagram and flowchart may be implemented by a hardware-based system that implements the specified functions, or may be implemented by a combination of hardware and computer instructions.

[0099] It can be understood that the units and modules involved in the embodiments of the present invention can be implemented by software or hardware. For example, the units and modules can be located in a processor.

[0100] Embodiment 1:

[0101] like Figure 1 As shown, this embodiment provides a 5G high-low frequency collaborative networking optimization method, the method comprising the following steps:

[0102] Step S1: Obtain communication data of all sampling points within a preset period and a preset area; and obtain the longitude and latitude of the sampling points;

[0103] The sampling point communication data includes the designed coverage distance of the target base station, the location data of the target base station, and AGPS MR data; the AGPS MR data is the data reported to the target base station by the terminal connected to the target base station.

[0104] AGPS MR data refers to the mobile measurement (Measurement Reporting, MR) data of the Assisted Global Positioning System (AGPS), which may also include information such as longitude, latitude, altitude, etc. It may also specifically refer to communication data, which may not include longitude and latitude information.

[0105] As a specific implementation manner, the theoretical distance between the sampling point and the target base station is calculated according to the Euclidean distance calculation formula, and the calculation formula is as follows:

[0106]

[0107] Where d represents the theoretical distance between the sampling point and the target base station;

[0108] lat 基站 Indicates the latitude of the base station;

[0109] lat 采样点 Indicates the latitude of the sampling point;

[0110] lon 基站 Indicates the longitude of the base station;

[0111] lon 采样点 Indicates the longitude of the base station.

[0112] In specific implementation, the calculation formula may also use the Hafsin formula, but the Hafsin formula is generally applicable to long distances and large ranges, and the Euclidean distance is selected in this embodiment for greater convenience.

[0113] Step S2: determining a theoretical distance between the sampling point and the target base station according to the latitude and longitude of the sampling point and the location data of the target base station; and dividing the theoretical distance into a plurality of distance intervals.

[0114] Step S3: evaluating the insufficiently covered distance interval according to the AGPS MR data;

[0115] As a specific implementation manner, the AGPS MR data includes RSRP data and CQI data; and the step of evaluating the insufficient coverage distance interval according to the AGPS MR data specifically includes the following steps:

[0116] Get RSRP data and CQI data;

[0117] RSRP (Reference Signal Received Power) data is an important indicator for measuring wireless signal strength, usually expressed in dBm, with a value range of -140dBm to -44dBm. It is used to determine network coverage, evaluate signal overlap between base stations, and help determine the location of the terminal and the quality of the network connection.

[0118] CQI (channel quality indicator) data is used to provide feedback on the quality of the current wireless channel, with a value between 1 and 15. A larger value indicates better channel quality. CQI helps base stations optimize modulation methods and coding rates to improve data transmission efficiency and dynamically adjust network resources, while also reflecting interference conditions and channel status in the user's mobile environment.

[0119] Filtering out the collection points whose RSRP values ​​are lower than the preset RSRP threshold from the RSRP data to obtain weak RSRP collection points; and filtering out the collection points whose CQI values ​​are lower than the preset CQI threshold from the CQI data to obtain weak CQI collection points;

[0120] Performing intersection processing on the weak RSRP collection point and the weak CQI collection point to determine insufficient coverage collection points;

[0121] Compare the insufficient coverage points in each distance interval with the preset coverage threshold:

[0122] If the number of insufficiently covered collection points within a certain distance interval is higher than a preset coverage threshold, the distance interval is determined to be an insufficiently covered distance interval.

[0123] Step S4: according to the designed coverage distance of the target base station, cluster the insufficiently covered distance intervals into weak coverage areas.

[0124] As a specific implementation manner, clustering insufficiently covered distance areas into weak coverage areas is specifically as follows:

[0125] The DBSCAN algorithm is used to cluster the insufficiently covered distance intervals into weakly covered areas;

[0126] DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm that identifies clusters and noise in a data set by specifying two key parameters: the neighborhood radius (Epsilon, ε) and the minimum number of points (MinPts). The algorithm first checks the neighborhood of each data point. If the number of points in the neighborhood is greater than or equal to MinPts, the point is marked as a core point, and its adjacent density-reachable points are classified into the same cluster; if the point does not meet the core point condition but is in the neighborhood of the core point, it is marked as a boundary point; if the point is neither a core point nor in the neighborhood of any core point, it is considered noise. In this way, DBSCAN can effectively identify clusters of arbitrary shapes and automatically process noise data with uneven attributes. It is widely used in fields such as geographic information analysis, anomaly detection, and image processing.

[0127] The DBSCAN algorithm is used to cluster insufficiently covered distance intervals into weakly covered areas, specifically comprising the following steps:

[0128] Step A1: Set the domain radius ∈ and the minimum number of samples MinPts of the DBSCAN algorithm;

[0129] Wherein, the area radius ∈ is set according to the insufficient coverage distance interval and the designed coverage distance of the target base station;

[0130] Step A2: Based on the domain radius ∈ and the minimum sample number MinPts, the insufficiently covered distance interval data is input into the DBSCAN algorithm for calculation to obtain the clustering density;

[0131] Step A3: Identify weak coverage areas according to the clustering density.

[0132] Step S5: judging whether the signal coverage of the target base station meets expectations according to the number of weak coverage areas:

[0133] If the number of weak coverage areas exceeds a preset threshold, it is determined that the signal coverage of the target base station cannot meet expectations.

[0134] Step S6: In the target base station where the signal coverage cannot reach the expected level, spectrum aggregation of TDD-NR and FDD-NR is implemented to achieve 5G high and low frequency collaborative networking optimization.

[0135] As a specific implementation manner, the implementation of spectrum aggregation of TDD-NR and FDD-NR specifically includes the following steps:

[0136] Determine the scope of RSRP increase:

[0137] If the RSRP improvement range is within the first threshold range, a 2.1 GHz FDD-NR and 3.4 GHz TDD-NR aggregation method is implemented, or a 2.3 GHz FDD-NR and 4.9 GHz TDD-NR aggregation method is implemented;

[0138] If the RSRP improvement range is within the second threshold range, a 1.8 GHz FDD-NR and 3.4 GHz TDD-NR aggregation method is implemented, or a 1.8 GHz FDD-NR and 4.9 GHz TDD-NR aggregation method is implemented;

[0139] If the RSRP improvement range is within the third threshold range, the 800MHz FDD-NR and 3.4GHz TDD-NR aggregation method is implemented.

[0140] TDD-NR (Time Division Duplex New Radio) is a wireless communication technology in which uplink and downlink data transmission share the same frequency band and are managed by time division, that is, the same frequency is used alternately in different time windows to achieve two-way communication. This design enables the network to dynamically adjust the uplink and downlink time ratio according to real-time traffic demand to optimize resource utilization and adapt to different application scenarios, such as video streaming and online games that require asymmetric traffic. TDD-NR not only improves the efficiency of spectrum use, but also reduces interference and supports communication needs with high data rates and low latency.

[0141] FDD-NR (Frequency Division Duplex New Radio) is a wireless communication technology in which uplink and downlink data transmission occupy different frequency bands, and the two frequency bands operate simultaneously. This configuration enables base stations and user devices to exchange data simultaneously on different frequencies, thereby achieving low latency and efficient two-way communication. The key advantage of FDD-NR is that it provides stable bandwidth and continuous connection performance, which is particularly suitable for application scenarios with high latency and bandwidth requirements such as voice calls, video conferencing and high-definition video streaming. By allocating fixed frequency resources for uplink and downlink communications, FDD-NR can ensure good service quality even during peak traffic hours.

[0142] This embodiment can take advantage of the high-frequency and low-frequency bands to improve the network architecture and optimization strategy for network coverage, capacity and user experience.

[0143] This embodiment proposes a 5G high-low frequency collaborative networking optimization method based on large-scale terminal measurement data and cluster analysis, which is applicable to the field of communication technology. To more clearly explain the specific process of the embodiment, the following are the specific implementation steps of the method:

[0144] S101. Obtain all base stations and related data in the area within a certain period of time. These data include the engineering information of the base station and the AGPS MR data reported by the terminal connected to the base station. MR (measurement report) is the measurement data that the base station requires the mobile terminal to report. Therefore, the mobile terminal will generate a large number of sampling points. Each sampling point contains complete MR data, including time offset (TA), reference signal received power (RSRP, a measure of signal strength) of the serving base station, and channel quality indication (CQI, a measure of signal quality). AGPS MR data also contains additional latitude and longitude information of the sampling point. As shown in the figure below:

[0145]

[0146] Engineering data of base stations: usually the physical information of base stations, including latitude, longitude, altitude, etc.

[0147] S102. Determine the relationship between the distance and RSRP and CQI according to the RSRP, CQI, longitude and latitude corresponding to the sampling point in the MR data of the base station:

[0148] According to the RSRP value and longitude and latitude corresponding to the sampling point in the base station measurement report (MR) data, the distance from each sampling point to the base station can be calculated. Then, based on different distance intervals, the average RSRP value and the proportion of insufficient coverage can be calculated. Since the longitude and latitude of the base station are known and can be obtained through the engineering parameter table, and the longitude and latitude of the sampling point are also known, these two longitudes and latitudes can be used to determine the distance from the sampling point to the base station. The calculation formula is as follows:

[0149]

[0150] Then calculate the corresponding distance and the average RSRP value and CQI value of the sampling points within this distance range, as shown in the following table:

[0151]

[0152] For each distance interval (e.g., [d1-d2], (d2-d3]), the average RSRP and CQI of all sampling points in the interval are counted. To ensure the accuracy of the method, the distance interval should not be too large, for example, 5 meters is better, that is, the interval is 0-5 meters, 5-10 meters, and so on.

[0153] In the table, the distance interval increases gradually from small to large. This is because according to the propagation characteristics of electromagnetic waves, the longer the distance or the obstacles on the propagation path, the signal will be attenuated. Therefore, in theory, the closer to the service base station, the stronger the RSRP value, and the farther away from the base station, the lower the RSRP value. Figure 2As shown, it can be considered that the RSRP in the red area is excellent, the signal in the orange area is good, the signal in the yellow area is moderate, the signal in the dark green area is poor, and the light green area belongs to edge coverage.

[0154] S103: Determine the proportion of insufficient coverage in each distance interval.

[0155] For each distance interval, determine the insufficient coverage judgment condition.

[0156] For example, in the interval (d7-d8], RSRP <-110dBm, CQI <7. In each distance interval, the proportion of insufficiently covered sampling points to the total number of sampling points is counted. The insufficient coverage ratio P = the number of insufficiently covered sampling points / the total number of sampling points.

[0157] Note that different insufficient coverage judgment conditions are set for each distance interval, because the RSRP and CQI are better at places close to the base station, while they are worse at places far away.

[0158]

[0159]

[0160] S104: Determine the distance D that the base station needs to cover.

[0161] The GPS information of the coverage area consists of the GPS coordinates of the boundary positions of the area. Since the coverage area is usually irregular in shape, the GPS information of a specific area often contains multiple coordinate points. GPS information includes longitude and latitude. The GPS coordinates of the base station can be directly obtained from the engineering parameter information of the base station, so they are known.

[0162] The distance between the base station and the coverage area can be calculated using the GPS information of the base station and the GPS information of the coverage area. The largest distance is considered as the appropriate coverage distance of the base station, which is recorded as D.

[0163] Based on the GPS information of the base station and the edge of a certain area, the distance is calculated, where D is the appropriate coverage distance, assuming it is 300 meters. Figure 3 As shown, for the sake of simplicity, only the location information of the four endpoints is marked, but in fact the entire edge position needs to be marked.

[0164] S105 . Substitute the coverage distance D of the base station into the corresponding distance interval in the relationship table in S103 to obtain the insufficient coverage ratio P of the corresponding interval.

[0165] S106: If the undercoverage ratio P is greater than a certain threshold, proceed to S107.

[0166] S107, if the number of clusters with insufficient coverage and a ratio of sampling points exceeding a certain threshold in the clusters clustered in the corresponding distance interval exceeds x, it is considered that the coverage within the base station range does not meet expectations, and an FDD-NR carrier can be enabled on the same site for high and low frequency networking to improve uplink coverage.

[0167] For example, if the coverage shortage ratio P in the corresponding interval is greater than a preset threshold (for example, 20%), and the number of clusters in which the number and ratio of insufficient coverage sampling points in the clustered clusters exceed a certain threshold exceeds 2, high- and low-frequency networking needs to be considered.

[0168] Among them, clustering uses the clustering algorithm DBSCAN to perform cluster analysis on the insufficiently covered sampling points and identify the insufficiently covered areas.

[0169] The specific steps are as follows:

[0170] (1) Collect data from all insufficiently covered sampling points in the corresponding interval.

[0171] (2) The spatial clustering algorithm DBSCAN is used to identify high-density areas of insufficient coverage points, that is, specific areas where insufficient coverage points are densely distributed.

[0172] (3) Use the DBSCAN algorithm for cluster analysis. The advantage of the DBSCAN algorithm is that it does not require the number of clusters to be specified in advance and can identify clusters of any shape. DBSCAN algorithm parameter settings:

[0173] Neighborhood radius∈: Set an appropriate radius range to determine whether the distance between points is close enough.

[0174] Minimum number of samples MinPts: Sets the minimum number of points required within the radius ∈ to form a cluster.

[0175] Select appropriate parameters ∈ (neighborhood radius) and minimum number of samples MinPts (minimum number of samples), which can be determined through experiments and data characteristics.

[0176] The longitude and latitude data of insufficiently covered sampling points were input into the DBSCAN algorithm for cluster analysis.

[0177] (4) The DBSCAN algorithm will assign a cluster label to each high-density area, identifying the regions with significantly high density.

[0178] (5) If the number of clusters with insufficient coverage and a ratio of sampling points exceeding a certain threshold exceeds x, it is considered that the coverage within the base station range does not meet expectations, and an FDD-NR carrier can be enabled on the same site for high- and low-frequency networking to improve uplink coverage. Otherwise, it is not necessary.

[0179] For the above specific implementation steps, specific examples are as follows:

[0180] Obtain all base stations and base station-related data within a certain period from AGPS MR data, including AGPS MR data reported by terminals connected to base stations and engineering data of base stations. Calculate the distance from each sampling point to the base station based on the RSRP and longitude and latitude corresponding to the sampling point in the base station MR data, and then count the average RSRP value and coverage shortage ratio in different distance intervals.

[0181] Suppose that the following latitude and longitude data of insufficiently covered sampling points are collected within a certain distance interval:

[0182] Apply DBSCAN for spatial clustering: Set DBSCAN parameters: ∈ = 0.002 degrees, MinPts = 500. Based on these parameters, apply the DBSCAN algorithm to cluster the insufficiently covered point data in a certain distance interval.

[0183] Assume that we have collected latitude and longitude data for several thousand points with insufficient coverage. Figure 5 The sampling points are shown.

[0184] Count the number and proportion of insufficient coverage points in each cluster.

[0185] Total number of insufficient coverage points: 3,000

[0186] Cluster 1: 1000 insufficient coverage points

[0187] Cluster 2: 800 insufficient coverage points

[0188] Cluster 3: 1600 insufficient coverage points

[0189] Cluster 4: 1400 insufficient coverage points

[0190] Cluster 5: 200 insufficient coverage points

[0191] Calculation of insufficient coverage ratio of clustering clusters:

[0192] Ratio = total number of insufficient coverage points in clusters / number of insufficient coverage points × 100%

[0193] For example, the undercoverage ratio for cluster 1 is:

[0194] Ratio = 1000 / 3000 × 100% = 33.33%. Assuming the number threshold is 600 and the coverage shortage ratio is 30%, there are 3 clusters, that is, x is equal to 3. Assuming the threshold is 2, then 5. If the coverage does not meet expectations, you can enable an FDD-NR carrier on the same site for high- and low-frequency networking to improve uplink coverage.

[0195] Select the corresponding high or low frequency method according to the range of insufficient coverage that needs to be improved.

[0196] If the coverage shortage ratio P is within a certain threshold, for example, it needs to be reduced by less than 10%, the 2.1GHz FDD-NR + 3.5GHz TDD-NR aggregation method can be used.

[0197] If the insufficient coverage ratio P needs to be reduced within 10%-20%, 900MHz FDD-NR+3.5GHzTDD-NR can be selected.

[0198] This threshold can be adjusted according to actual conditions and is not specifically limited here.

[0199] Note: Each operator uses a different frequency band.

[0200] For example, one operator's 4G is FDD, and the downlink frequency bands used are 800M, 1.8G, and 2.1G. These frequency bands originally used for 4G can be used for 5G, which is FDD-NR. The frequency band of 5G itself is 3.4GHz, which is TDD-NR.

[0201] 1. If the RSRP improvement range is within a certain threshold, such as 3-6dbm, the 2.1GHz FDD-NR + 3.4GHz TDD-NR aggregation method can be used.

[0202] 2. If the RSRP improvement range is within a certain threshold, such as 6-9dbm, the 1.8GHz FDD-NR + 3.4GHz TDD-NR aggregation method can be used.

[0203] 3. If the RSRP improvement range is within a certain threshold, such as 9-12dBm, 800MHz FDD-NR+3.4GHz TDD-NR can be selected.

[0204] Another operator's 4G is FDD, and the downlink frequency bands used are 900M, 1.8G, and 2.1G. These frequency bands used for 4G can be used for 5G, which is FDD-NR. The frequency band of 5G itself is 3.5GHz, which is TDD-NR.

[0205] 1. If the RSRP improvement range is within a certain threshold, such as 3-6dbm, the 2.1GHz FDD-NR + 3.5GHz TDD-NR aggregation method can be used.

[0206] 2. If the RSRP improvement range is within a certain threshold, such as 6-9dbm, the 1.8GHz FDD-NR + 3.5GHz TDD-NR aggregation method can be used.

[0207] 3. If the RSRP improvement range is within a certain threshold, such as 9-12dBm, 900MHz FDD-NR+3.5GHz TDD-NR can be selected.

[0208] The third operator: The downlink frequency band used for 1.8GHz is: 1805-1830MHz, currently deployed: 4G;

[0209] The downlink frequency band used by 2.3GHz is: 2320-2370MHz, currently deployed: 4G;

[0210] The downlink frequency band used by 4.9GHz is: 4800-4900MHz, currently deployed: 5G;

[0211] 1. If the RSRP improvement range is within a certain threshold, such as 3-6dbm, the 2.3GHz FDD-NR + 4.9GHz TDD-NR aggregation method can be used.

[0212] 2. If the RSRP improvement range is within a certain threshold, such as 6-9dbm, the 1.8GHz FDD-NR + 4.9GHz TDD-NR aggregation method can be used.

[0213] Secondly, both the first and second operators use 1.8Ghz and 2.1Ghz, which are actually just a general term for a frequency band. Specifically, for example, in 1.8Ghz, the second operator uses the 1830-1860Mhz band, and the first operator uses 1860-1880MHz, which are collectively referred to as 1.8Ghz. Similarly, the same is true for 2.1Ghz. The second operator's 2.1Ghz downlink: 2130-2155MHz, the first operator's downlink: 2110-2130MHz.

[0214] This embodiment is implemented based on large-scale terminal measurement data and cluster analysis, which can effectively reduce the dependence on manual testing, and can comprehensively and dynamically optimize the allocation of high- and low-frequency resources, thereby improving resource utilization efficiency, reducing manpower and material costs, and further improving the comprehensiveness and efficiency of decision-making.

[0215] Embodiment 2:

[0216] like Figure 4 As shown, this embodiment provides a 5G high-low frequency collaborative networking optimization device, the device comprising:

[0217] An acquisition unit 10 is used to acquire communication data of all sampling points within a preset area during a preset period; and to acquire the longitude and latitude of the sampling points;

[0218] The sampling point communication data includes the designed coverage distance of the target base station, the location data of the target base station, and AGPS MR data; the AGPS MR data is the data reported to the target base station by the terminal connected to the target base station;

[0219] The processing unit 20 is connected to the acquisition unit 10, and is used to determine the theoretical distance between the sampling point and the target base station according to the latitude and longitude of the sampling point and the location data of the target base station; and divide the theoretical distance into a plurality of distance intervals;

[0220] An evaluation unit 30, connected to the processing unit 20, for evaluating the insufficiently covered distance interval according to the AGPS MR data;

[0221] The clustering unit 40 is connected to the evaluation unit 30 and is used to cluster insufficiently covered distance intervals into weak coverage areas according to the designed coverage distance of the target base station;

[0222] The determination unit 50 is connected to the clustering unit 40 and is used to determine whether the signal coverage of the target base station meets expectations according to the number of weak coverage areas;

[0223] The aggregation unit 60 is connected to the determination unit 50, and is used to implement spectrum aggregation of TDD-NR and FDD-NR when the determination unit determines that the signal coverage cannot reach the expected target base station, so as to achieve 5G high and low frequency collaborative networking optimization.

[0224] As a specific implementation, the evaluation unit 30 includes:

[0225] An acquisition module, used to acquire RSRP data and CQI data;

[0226] A screening module, connected to the acquisition module, for screening out the collection points whose RSRP values ​​are lower than the preset RSRP threshold from the RSRP data to obtain weak RSRP collection points; and for screening out the collection points whose CQI values ​​are lower than the preset CQI threshold from the CQI data to obtain weak CQI collection points;

[0227] A processing module, connected to the screening module, for performing intersection processing on the weak RSRP collection points and the weak CQI collection points to determine insufficient coverage collection points;

[0228] A comparison module, connected to the processing module, for comparing insufficient coverage collection points within each distance interval with a preset coverage threshold;

[0229] The first determination module is connected to the comparison module and is used to determine that a distance interval is an insufficiently covered distance interval when the comparison module obtains that the insufficiently covered collection points in a certain distance interval are higher than a preset coverage threshold.

[0230] As a specific implementation, the clustering unit 40 includes:

[0231] The setting module is used to set the domain radius ∈ and the minimum number of samples MinPts of the DBSCAN algorithm;

[0232] Wherein, the area radius ∈ is set according to the insufficient coverage distance interval and the designed coverage distance of the target base station;

[0233] An input module, connected to the setting module, is used to input insufficiently covered distance interval data into the DBSCAN algorithm for calculation based on the domain radius ∈ and the minimum sample number MinPts to obtain cluster density;

[0234] The identification module is connected to the input module and is used to identify the weak coverage area according to the clustering density.

[0235] As a specific implementation, the aggregation unit 60 includes:

[0236] The second determination module is used to determine the range of RSRP improvement;

[0237] A first aggregation module is connected to the second determination module, and is configured to implement a 2.1 GHz FDD-NR and 3.4 GHz TDD-NR aggregation mode, or implement a 2.3 GHz FDD-NR and 4.9 GHz TDD-NR aggregation mode when the second determination module determines that the RSRP improvement range is within the first threshold range;

[0238] A second aggregation module, connected to the second determination module, configured to implement a 1.8 GHz FDD-NR and 3.4 GHz TDD-NR aggregation mode, or implement a 1.8 GHz FDD-NR and 4.9 GHz TDD-NR aggregation mode when the second determination module determines that the RSRP improvement range is within a second threshold range;

[0239] The third aggregation module is connected to the second determination module, and is used to implement an 800MHz FDD-NR and 3.4GHz TDD-NR aggregation method when the second determination module determines that the RSRP improvement range is within a third threshold range.

[0240] The device in this embodiment can execute the method in embodiment 1.

[0241] Embodiment 3:

[0242] This embodiment provides a computer storage medium, on which a computer program is stored. When the computer program is executed by a processor, the 5G high and low frequency collaborative networking optimization method described in Example 1 is implemented.

[0243] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present invention, but the present invention is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A 5G high-low frequency collaborative networking optimization method, characterized in that: The method comprises the following steps: Obtain communication data of all sampling points within a preset period and within a preset area; and obtain the longitude and latitude of the sampling points; The sampling point communication data includes the designed coverage distance of the target base station, the location data of the target base station, and AGPS MR data; the AGPS MR data is the data reported to the target base station by the terminal connected to the target base station; Determine a theoretical distance between the sampling point and the target base station according to the latitude and longitude of the sampling point and the location data of the target base station; and divide the theoretical distance into a plurality of distance intervals; assessing insufficiently covered distance intervals based on the AGPS MR data; According to the designed coverage distance of the target base station, clustering the insufficiently covered distance intervals into weak coverage areas; According to the number of weak coverage areas, determine whether the signal coverage of the target base station meets expectations: If the number of weak coverage areas exceeds a preset threshold, it is determined that the signal coverage of the target base station cannot meet expectations; In target base stations where signal coverage cannot reach the expected level, spectrum aggregation of TDD-NR and FDD-NR is implemented to achieve 5G high and low frequency collaborative networking optimization.

2. The 5G high-low frequency collaborative networking optimization method according to claim 1 is characterized in that: The theoretical distance between the sampling point and the target base station is calculated according to the Euclidean distance calculation formula, and the calculation formula is as follows: Where d represents the theoretical distance between the sampling point and the target base station; lat 基站 Indicates the latitude of the base station; lat 采样点 Indicates the latitude of the sampling point; lon 基站 Indicates the longitude of the base station; lon 采样点 Indicates the longitude of the base station.

3. The 5G high-low frequency collaborative networking optimization method according to claim 1 is characterized in that: The AGPS MR data includes RSRP data and CQI data; The step of evaluating the insufficiently covered distance interval according to the AGPS MR data specifically comprises the following steps: Get RSRP data and CQI data; Filtering out the collection points whose RSRP values ​​are lower than the preset RSRP threshold from the RSRP data to obtain weak RSRP collection points; and filtering out the collection points whose CQI values ​​are lower than the preset CQI threshold from the CQI data to obtain weak CQI collection points; Performing intersection processing on the weak RSRP collection point and the weak CQI collection point to determine insufficient coverage collection points; Compare the insufficient coverage points in each distance interval with the preset coverage threshold: If the number of insufficiently covered collection points within a certain distance interval is higher than a preset coverage threshold, the distance interval is determined to be an insufficiently covered distance interval.

4. The 5G high-low frequency collaborative networking optimization method according to claim 1 is characterized in that: The clustering of insufficiently covered distance areas into weak coverage areas is specifically as follows: The DBSCAN algorithm is used to cluster the insufficiently covered distance intervals into weakly covered areas; The DBSCAN algorithm is used to cluster insufficiently covered distance intervals into weakly covered areas, specifically comprising the following steps: Step A1: Set the domain radius ∈ and the minimum number of samples MinPts of the DBSCAN algorithm; Wherein, the area radius ∈ is set according to the insufficient coverage distance interval and the designed coverage distance of the target base station; Step A2: Based on the domain radius ∈ and the minimum sample number MinPts, the insufficiently covered distance interval data is input into the DBSCAN algorithm for calculation to obtain the clustering density; Step A3: Identify weak coverage areas according to the clustering density.

5. The 5G high-low frequency collaborative networking optimization method according to any one of claims 1 to 4, characterized in that: The implementation of spectrum aggregation of TDD-NR and FDD-NR specifically includes the following steps: Determine the scope of RSRP increase: If the RSRP improvement range is within the first threshold range, a 2.1 GHz FDD-NR and 3.4 GHz TDD-NR aggregation method is implemented, or a 2.3 GHz FDD-NR and 4.9 GHz TDD-NR aggregation method is implemented; If the RSRP improvement range is within the second threshold range, a 1.8 GHz FDD-NR and 3.4 GHz TDD-NR aggregation method is implemented, or a 1.8 GHz FDD-NR and 4.9 GHz TDD-NR aggregation method is implemented; If the RSRP improvement range is within the third threshold range, the 800MHz FDD-NR and 3.4GHz TDD-NR aggregation method is implemented.

6. A 5G high-low frequency collaborative networking optimization device, characterized in that: The device comprises: An acquisition unit, used to acquire communication data of all sampling points within a preset period and a preset area; and to acquire the longitude and latitude of the sampling points; The sampling point communication data includes the designed coverage distance of the target base station, the location data of the target base station, and AGPS MR data; the AGPS MR data is the data reported to the target base station by the terminal connected to the target base station; a processing unit connected to the acquisition unit, configured to determine a theoretical distance between the sampling point and the target base station according to the latitude and longitude of the sampling point and the location data of the target base station; and to divide the theoretical distance into a plurality of distance intervals; an evaluation unit connected to the processing unit, and configured to evaluate the insufficiently covered distance interval according to the AGPS MR data; A clustering unit, connected to the evaluation unit, configured to cluster insufficiently covered distance intervals into weak coverage areas according to the designed coverage distance of the target base station; A determination unit, connected to the clustering unit, configured to determine whether the signal coverage of the target base station meets expectations according to the number of weak coverage areas; An aggregation unit is connected to the determination unit and is used to implement spectrum aggregation of TDD-NR and FDD-NR when the determination unit determines that the signal coverage cannot reach the expected target base station, so as to achieve 5G high and low frequency collaborative networking optimization.

7. The 5G high-low frequency collaborative networking optimization device according to claim 6 is characterized in that: The evaluation unit comprises: An acquisition module, used to acquire RSRP data and CQI data; A screening module, connected to the acquisition module, for screening out the collection points whose RSRP values ​​are lower than the preset RSRP threshold from the RSRP data to obtain weak RSRP collection points; and for screening out the collection points whose CQI values ​​are lower than the preset CQI threshold from the CQI data to obtain weak CQI collection points; A processing module, connected to the screening module, for performing intersection processing on the weak RSRP collection points and the weak CQI collection points to determine insufficient coverage collection points; A comparison module, connected to the processing module, for comparing insufficient coverage collection points within each distance interval with a preset coverage threshold; The first determination module is connected to the comparison module and is used to determine that a distance interval is an insufficiently covered distance interval when the comparison module obtains that the insufficiently covered collection points in a certain distance interval are higher than a preset coverage threshold.

8. The 5G high-low frequency collaborative networking optimization device according to claim 6, characterized in that: The clustering unit comprises: The setting module is used to set the domain radius ∈ and the minimum number of samples MinPts of the DBSCAN algorithm; Wherein, the area radius ∈ is set according to the insufficient coverage distance interval and the designed coverage distance of the target base station; An input module, connected to the setting module, is used to input insufficiently covered distance interval data into the DBSCAN algorithm for calculation based on the domain radius ∈ and the minimum sample number MinPts to obtain cluster density; The identification module is connected to the input module and is used to identify the weak coverage area according to the clustering density.

9. The 5G high-low frequency collaborative networking optimization device according to any one of claims 6 to 8, characterized in that: The polymeric unit comprises: The second determination module is used to determine the range of RSRP improvement; A first aggregation module is connected to the second determination module, and is configured to implement a 2.1 GHz FDD-NR and 3.4 GHz TDD-NR aggregation mode, or implement a 2.3 GHz FDD-NR and 4.9 GHz TDD-NR aggregation mode when the second determination module determines that the RSRP improvement range is within the first threshold range; A second aggregation module, connected to the second determination module, configured to implement a 1.8 GHz FDD-NR and 3.4 GHz TDD-NR aggregation mode, or implement a 1.8 GHz FDD-NR and 4.9 GHz TDD-NR aggregation mode when the second determination module determines that the RSRP improvement range is within a second threshold range; The third aggregation module is connected to the second determination module, and is used to implement an 800MHz FDD-NR and 3.4GHz TDD-NR aggregation method when the second determination module determines that the RSRP improvement range is within a third threshold range.

10. A computer storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the 5G high-low frequency collaborative networking optimization method described in any one of claims 1 to 5 is implemented.

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