A method, device and equipment for classifying cell coverage scenes

Abstract

The present invention discloses a classification method, device and equipment for cell coverage scenarios, the method comprising: receiving a measurement report reported by a terminal; obtaining a measurement feature sequence of a sampling point according to the measurement report; obtaining weights of multiple measurement feature sequences of at least one cell; and classifying the cell coverage scenarios according to the weights of the multiple measurement feature sequences. Through the above-mentioned manner, the present invention realizes the identifiable cell coverage scenario information, thereby improving the accuracy, and at the same time realizes the convenience of maintaining and updating the cell coverage scenario information.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a classification method, device and equipment for cell coverage scenarios. Background Art

[0002] In wireless network optimization, there are many cell coverage scenarios, such as roads, residential areas, commercial buildings, high-speed railways, industrial areas, etc. Different scenarios have various coverage and capacity requirements, corresponding to different parameter matching solutions. Accurate coverage scenario information can effectively guide the development of wireless network optimization work and improve network performance and user perception. However, the definition of cell coverage scenarios has always been a major problem in wireless network optimization. A cell coverage often involves multiple scenarios, which cannot be generalized.

[0003] The classification definition of existing base station coverage scenarios generally includes electronic maps or on-site surveys, based on the actual topography of the problem area, building features, traffic flow and other scenario attributes, combined with manual judgment to conduct scenario review and judgment. The correctness of the review and judgment is strongly related to the optimization experience and technical level of the personnel. At the same time, it is not sensitive to network changes and lacks an automatic update mechanism, so it is insufficient in accuracy. It can also be based on the measurement report MR data, combined with the tracking area TA, reference signal received power RSRP and other information in the measurement report MR report, to comprehensively judge the user's scenario, such as weak coverage, insufficient deep coverage, and open area. However, the definition of cell coverage scenarios is often a single value, and the information is relatively rough, and it is impossible to quantitatively evaluate the user distribution and actual coverage type within the cell coverage area. Summary of the invention

[0004] In view of the above problems, embodiments of the present invention are proposed to provide a method, apparatus and device for classifying cell coverage scenarios that overcome the above problems or at least partially solve the above problems.

[0005] According to one aspect of an embodiment of the present invention, a method for classifying cell coverage scenarios is provided, which is applied to a base station, and the method includes:

[0006] Receiving measurement reports reported by terminals;

[0007] According to the measurement report, obtaining a measurement feature sequence of the sampling points;

[0008] Obtaining weights of a plurality of measurement feature sequences of at least one cell;

[0009] The cell coverage scenarios are classified according to the weights of the multiple measurement feature sequences.

[0010] According to another aspect of an embodiment of the present invention, a method for classifying a cell coverage scenario is provided, which is applied to a terminal, and the method includes:

[0011] Generate a measurement report; the measurement report is used to enable the base station to obtain a measurement feature sequence of a sampling point according to the measurement report; obtain weights of multiple measurement feature sequences of at least one cell; and classify the cell coverage scenario according to the weights of the multiple measurement feature sequences;

[0012] The measurement report data is sent to a base station.

[0013] According to one aspect of an embodiment of the present invention, a classification device for cell coverage scenarios is provided, which is applied to a base station, and the device includes:

[0014] A receiving module, used for receiving a measurement report reported by a terminal;

[0015] A processing module is used to obtain a measurement feature sequence of a sampling point according to the measurement report; obtain weights of multiple measurement feature sequences of at least one cell; and classify the cell coverage scenarios according to the weights of the multiple measurement feature sequences.

[0016] According to another aspect of an embodiment of the present invention, a classification device for a cell coverage scenario is provided, which is applied to a terminal, and the device includes:

[0017] A generating module, configured to generate a measurement report; the measurement report is used to enable the base station to obtain a measurement feature sequence of a sampling point according to the measurement report; obtain weights of multiple measurement feature sequences of at least one cell; and classify the cell coverage scenario according to the weights of the multiple measurement feature sequences;

[0018] The sending module is used to send the measurement report data to the base station.

[0019] According to another aspect of an embodiment of the present invention, there is provided a computing device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus;

[0020] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the above-mentioned classification method for cell coverage scenarios applied to base stations or to perform operations corresponding to the above-mentioned classification method for cell coverage scenarios applied to terminals.

[0021] According to another aspect of an embodiment of the present invention, a computer storage medium is provided, wherein at least one executable instruction is stored in the storage medium, and the executable instruction enables a processor to perform operations corresponding to the classification method for cell coverage scenarios applied to base stations as described above, or to perform operations corresponding to the classification method for cell coverage scenarios applied to terminals as described above.

[0022] According to the solution provided by the above-mentioned embodiment of the present invention, the classification method of cell coverage scenarios can receive a measurement report reported by a terminal; obtain a measurement feature sequence of a sampling point according to the measurement report; obtain weights of multiple measurement feature sequences of at least one cell; and classify the cell coverage scenarios according to the weights of the multiple measurement feature sequences, thereby solving the problem of low accuracy in manually reviewing and determining cell coverage scenarios and the problem of being unable to quantitatively evaluate user distribution and coverage types within the scope of cell coverage scenarios, obtaining identifiable cell coverage scenario information, thereby improving accuracy, and achieving the beneficial effect of facilitating maintenance and updating of cell coverage scenario information.

[0023] The above description is only an overview of the technical solution of the embodiment of the present invention. In order to more clearly understand the technical means of the embodiment of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiment of the present invention more obvious and easy to understand, the specific implementation method of the embodiment of the present invention is specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the embodiments of the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0025] Figure 1 A flow chart of a method for classifying cell coverage scenarios provided by an embodiment of the present invention is shown;

[0026] Figure 2 A schematic diagram of a process for classifying cell coverage scenarios in a specific embodiment provided by the present invention is shown;

[0027] Figure 3 A flow chart of a classification method for a cell coverage scenario applied to a terminal provided by another embodiment of the present invention is shown;

[0028] Figure 4 A flow chart showing the generation of a measurement report by a terminal provided in an embodiment of the present invention is shown;

[0029] Figure 5 A schematic structural diagram of a classification device applied to a cell coverage scenario of a base station provided by an embodiment of the present invention is shown;

[0030] Figure 6 A schematic structural diagram of a classification device for a cell coverage scenario applied to a terminal provided by an embodiment of the present invention is shown;

[0031] Figure 7A schematic diagram of the structure of a computing device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0032] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0033] Figure 1 FIG. 1 is a flow chart showing a method for classifying cell coverage scenarios provided by an embodiment of the present invention. Figure 1 As shown, the method comprises the following steps:

[0034] Step 11, receiving a measurement report reported by the terminal;

[0035] Step 12, obtaining a measurement feature sequence of the sampling points according to the measurement report;

[0036] Step 13, obtaining weights of multiple measurement feature sequences of at least one cell;

[0037] Step 14: classify the cell coverage scenarios according to the weights of the multiple measurement feature sequences.

[0038] In this embodiment, the measurement feature sequence of the sampling point is obtained according to the measurement report reported by the terminal, the weights of multiple measurement feature sequences of at least one cell are obtained, and the cell coverage scenarios are classified according to the weights; the classification information of the identifiable cell coverage scenarios is realized, thereby improving the accuracy, and at the same time facilitating the maintenance and updating of the cell coverage scenario information.

[0039] In an optional embodiment of the present invention, step 12 includes:

[0040] Step 121, preprocessing the data of the sampling points in the measurement report to obtain a data processing result;

[0041] Step 122, classifying the signal strength value of the synchronization signal block carried by at least one beam in the data processing result to obtain at least one strength category and the number of beams corresponding to each strength category;

[0042] Step 123: Obtain a measurement feature sequence of sampling points according to the number of beams of the at least one intensity category.

[0043] The data processing result includes at least one of the following:

[0044] Sampling point identification;

[0045] Cell ID;

[0046] The combined value of the cell signal strength values;

[0047] The signal strength value of the synchronization signal block carried by at least one beam.

[0048] In this embodiment, the data of the sampling points in the measurement report are preprocessed, including:

[0049] Selecting a data processing result in the measurement report, the data processing result including a sampling point identifier; a cell identifier; a combined value of a cell signal strength value; and at least one of a signal strength value of a synchronization signal block carried by at least one beam;

[0050] Preferably, preprocessing the data of the sampling points in the measurement report may also include: screening the data processing results by a preset data range, screening out the data processing results of the sampling points that are not within the preset data range, so as to ensure the accuracy of the classification of the cell coverage scene;

[0051] The signal strength value of the synchronization signal block carried by the beam in the data processing result obtained after preprocessing is classified, preferably into strong, medium, and weak, but the present application is not limited thereto, and can also be divided into strong, medium-strong, medium, medium-weak, weak, and first-level strength, second-level strength, third-level strength, fourth-level strength, etc. The strength categories can be divided according to needs. The more the strength categories are divided, the more the classification categories of the cell coverage scenarios are divided;

[0052] According to the classification of the signal strength values ​​of the synchronization signal blocks carried by the beams, they are sorted in a preset order to obtain the number of beams of at least one intensity category, and according to the number of beams of at least one intensity category of each sampling point, a measurement feature sequence corresponding to the sampling point is obtained. It can also include obtaining the sequence value of the sampling point according to the measurement feature sequence corresponding to the sampling point.

[0053] In a specific embodiment, as shown in the following table;

[0054]

[0055] Table 1

[0056] As shown in Table 1, the measurement report MR reported by the terminal is obtained, and the measurement report MR is preprocessed to obtain a data processing report, which includes a sampling point ID (Identity document), an ECI (E-UTRAN Cell Identifier), a cell-level RSRP (Reference Signal Receiving Power) combined value, and a SSB (Synchronization Signal and PBCH) with beam numbers from 1 to 8. block, synchronization signal and PBCH block) beam measurement RSRP value; wherein, the attribution ECI value is a cell identifier used to number the cell; in order to ensure the accuracy of the classification of the cell coverage scenario, the preset data range is (-100dBm, +∞), and after screening, the combined value of the cell-level RSRP of the sampling point IDs 1 and 2 is within the preset data range, so the data of the sampling point ID 1 in Table 1 and the corresponding cell-level RSRP combined value of -86dBm are retained, and the sampling point ID is 2 and the corresponding cell-level RSRP combined value is -88dBm. The data is retained, and each sampling point in the data processing result is screened in turn to obtain the data processing result after screening;

[0057] In the above step 122, when specifically implemented, the strength categories may be set to strong, medium, and weak to classify the RSRP values ​​measured by the SSB beam; the range of the strength category is:

[0058] Strong: SSB beam measured RSRP value > combined value of cell-level RSRP + 5dBm;

[0059] Medium: The combined value of cell-level RSRP +5dBm ≥ SSB beam measurement RSRP value ≥ the combined value of cell-level RSRP -5dBm;

[0060] Weak: The RSRP value measured by the SSB beam is less than the combined value of the cell-level RSRP -5dBm;

[0061] When the SSB beam measured RSRP value is greater than the combined value of the cell-level RSRP + 5dBm, the SSB beam measured RSRP value is classified as strong; when the SSB beam measured RSRP value is less than the combined value of the cell-level RSRP - 5dBm, the SSB beam measured RSRP value is classified as weak; when the SSB beam measured RSRP value is less than the combined value of the cell-level RSRP + 5dBm, and the SSB beam measured RSRP value is greater than the combined value of the cell-level RSRP - 5dBm, the SSB beam measured RSRP value is classified as medium;

[0062] The division results are shown in the following table:

[0063]

[0064] Table 2

[0065] As shown in Table 2, the number of SSB beams corresponding to each sampling point in each intensity category is taken as an element of the measurement characteristic sequence of the sampling point, and the number of SSB beams corresponding to the intensity category is arranged in a preset order to obtain a measurement characteristic sequence; the measurement characteristic sequence is represented as a sequence value in decimal form; it should be noted here that the sequence value is a form of expression of the measurement characteristic sequence, and the measurement characteristic sequence can be represented by a sequence value or not;

[0066] In Table 2, the sampling point with sampling point ID 1 has a corresponding ECI value of A, the number of SSB beams with a strong intensity category is 1, the number of SSB beams with a medium intensity category is 1, and the number of SSB beams with a weak intensity category is 5. The values ​​of the number of beams are sorted in the order of strong, medium, and weak, and the measurement feature sequence ID is "1-1-5". In order to display the measurement feature sequence more intuitively, the sequence value is 115 in decimal form;

[0067] The sampling point with sampling point ID 2 has a corresponding ECI value of A, the number of SSB beams with a strong intensity category is 0, the number of SSB beams with a medium intensity category is 2, and the number of SSB beams with a weak intensity category is 6;

[0068] The quantity values ​​are sorted in the order of strong, medium, and weak, and the measurement feature sequence ID is obtained as "0-2-6". In order to display the measurement feature sequence more intuitively, the sequence value is obtained in decimal form as 026.

[0069] In an optional embodiment of the present invention, step 13 includes:

[0070] Step 131, obtaining the total number of sampling points of at least one cell and the number of sampling points corresponding to each measurement feature sequence;

[0071] Step 132: Obtain weights of multiple measurement feature sequences of at least one cell according to the ratio of the number of sampling points corresponding to each measurement feature sequence to the total number of sampling points.

[0072] In this embodiment, based on the cell identifier and measurement feature sequence corresponding to the sampling points, the total number of sampling points of at least one cell and the number of sampling points corresponding to each measurement feature sequence are obtained, wherein the total number of sampling points of the cell is the total number of sampling points corresponding to one cell identifier; the number of sampling points corresponding to the measurement feature sequence is the total number of sampling points corresponding to one measurement feature sequence; based on the number of sampling points corresponding to each measurement feature sequence and the total number of sampling points, the weights of multiple measurement feature sequences of at least one cell are obtained by ratio calculation.

[0073] In a specific embodiment, statistics are performed based on the belonging ECI and the measurement feature sequence of the sampling points to obtain the number of sampling points of each measurement feature sequence under the corresponding belonging ECI, as shown in the following table:

[0074] ECI Measuring feature sequence Sequence value Number of sampling points A 1-1-5 115 10 A 0-2-6 26 10 A 3-2-3 323 30 A 5-2-1 521 50 …

[0075] Table 3

[0076] As shown in Table 3, the corresponding measurement feature sequences under the cell with the attribute ECI value of A include "1-1-5", "0-2-6", "3-2-3" and "5-2-1", and their corresponding sequence values ​​are 115, 26, 323 and 521 respectively. The number of sampling points corresponding to the measurement feature sequence "1-1-5" is 10, the number of sampling points corresponding to the measurement feature sequence "0-2-6" is 10, the number of sampling points corresponding to the measurement feature sequence "3-2-3" is 30, and the number of sampling points corresponding to the measurement feature sequence "5-2-1" is 50;

[0077] According to the number of sampling points of the measurement feature sequence corresponding to each cell belonging to the ECI value and the total number of sampling points of each cell belonging to the ECI value, the weights of multiple measurement feature sequences of each cell belonging to the ECI value can be obtained by ratio calculation, as shown in the following table:

[0078]

[0079] Table 4

[0080] As shown in Table 4, the total number of sampling points of the cell with ECI value A is 100, and its corresponding first measurement feature sequence is "5-2-1". The number of corresponding sampling points in Table 3 is 50. Through ratio calculation, the weight 1 of the first measurement feature sequence of the cell with ECI value A is 50%; the second measurement feature sequence of the cell with ECI value A is "1-1-5". The number of corresponding sampling points in Table 3 is 30. Through ratio calculation, the weight 2 of the second measurement feature sequence of the cell with ECI value A is 30%; similarly, it can be obtained that the weight 1 of the first measurement feature sequence of the cell with ECI value B is 40%, and the weight 2 of the second measurement feature sequence is 30%; the weight 1 of the first measurement feature sequence of the cell with ECI value C is 60%, and the weight 2 of the second measurement feature sequence is 10%.

[0081] In an optional embodiment of the present invention, step 14 includes:

[0082] Step 141: classify the cell coverage scenario according to weights of multiple measurement feature sequences and at least one preset threshold.

[0083] Optionally, step 141 includes:

[0084] Step 1411: if the weight of at least one Nth measurement feature sequence of a cell is greater than an Nth preset threshold, determine that the sampling point corresponding to at least one Nth measurement feature sequence is an Nth type of cell coverage scenario;

[0085] Step 1412: If the weight of at least one N+1th measurement feature sequence of a cell is greater than the N+1th preset threshold and less than the Nth preset threshold, determine that the sampling point corresponding to at least one N+1th measurement feature sequence is the N+1th type of cell coverage scenario; wherein the N+1th measurement feature sequence and the Nth measurement feature sequence are different measurement feature sequences, the N+1th preset threshold is less than the Nth preset threshold, and N is a positive integer.

[0086] In this embodiment, at least one preset threshold is set for the weights of the multiple measurement feature sequences corresponding to the cell, and the preset threshold can preferably be determined according to the classification requirements;

[0087] When the weight of at least one Nth measurement feature sequence corresponding to a cell is greater than the Nth preset threshold, the cell corresponding to the at least one Nth measurement feature sequence is an Nth type of cell coverage scenario; when the weight of at least one N+1th measurement feature sequence of a cell is greater than the N+1th preset threshold and less than the Nth preset threshold, the cell corresponding to the at least one N+1th measurement feature sequence is an N+1th type of cell coverage scenario; wherein, when the weight of at least one first measurement feature sequence corresponding to a cell is greater than the first preset threshold, the measurement feature sequence with the largest weight in at least one first measurement feature sequence is preferably selected as the first type of cell coverage scenario;

[0088] It should be noted that the N+1th measurement feature sequence and the Nth measurement feature sequence are different measurement feature sequences, and the N+1th preset threshold is smaller than the Nth preset threshold.

[0089] In a specific embodiment, the weights of the corresponding multiple measurement feature sequences belonging to the ECI cell in Table 4 are classified, wherein the classification categories are primary scene, secondary scene 1 and secondary scene 2; the first preset threshold is 30%, and the second preset threshold is 15%, as shown in the following table:

[0090] ECI Main scene Weight Secondary scene 1 Weight 1 Secondary scene 2 Weight 2 A 5-2-1 50％ 1-1-5 30％ B 0-2-6 40％ 1-1-5 30％ C 3-2-3 60％ 2-2-4 20％ …

[0091] Table 5

[0092] As shown in Table 5, the cell belonging to the ECI value A can be seen from Table 4 that the weight 1 of the first measurement feature sequence is 50%, which is greater than the first preset threshold of 30%. Therefore, the sampling point corresponding to the first measurement feature sequence of the cell belonging to the ECI value A is classified as the main scene, and the weight 1 of the second measurement feature sequence is 30%, which is not greater than the first preset threshold of 30% and greater than the second preset threshold of 15%. Therefore, the sampling point corresponding to the second measurement feature sequence of the cell belonging to the ECI value A is classified as the secondary scene 1;

[0093] Similarly, the weight 1 of the first measurement feature sequence of the cell with an ECI value of B can be 40%, which is greater than the first preset threshold of 30%. Therefore, the sampling points corresponding to the first measurement feature sequence of the cell with an ECI value of B are classified as the main scene, and the weight 1 of the second measurement feature sequence is 30%, which is not greater than the first preset threshold of 30% and is greater than the second preset threshold of 15%. Therefore, the sampling points corresponding to the second measurement feature sequence of the cell with an ECI value of B are classified as secondary scene 1; the weight 1 of the first measurement feature sequence of the cell with an ECI value of C is 60%, which is greater than the first preset threshold of 30%. Therefore, the sampling points corresponding to the first measurement feature sequence of the cell with an ECI value of C are classified as the main scene, and the weight 1 of the second measurement feature sequence is 20%, which is not greater than the first preset threshold of 30% and is greater than the second preset threshold of 15%. Therefore, the sampling points corresponding to the second measurement feature sequence of the cell with an ECI value of C are classified as secondary scene 1.

[0094] Figure 2 FIG. 2 shows a schematic diagram of a process for classifying cell coverage scenarios in a specific embodiment provided by the present invention. Figure 2 As shown, in a specific embodiment, the base station receives the measurement report reported by the terminal, and classifies the cell coverage scenario according to the measurement report, specifically including:

[0095] Step 1, obtaining a data processing result by preprocessing the data in the reported measurement report;

[0096] Step 2, obtaining a measurement feature sequence corresponding to the cell by classifying the signal strength values ​​of the synchronization signal blocks carried by at least one beam of the data processing result;

[0097] Step 3, by proportionally calculating the weight of the measurement feature sequence corresponding to the cell, a weight value corresponding to the measurement feature sequence corresponding to the cell is obtained;

[0098] Step 4: Classify the cell coverage scenarios according to the weight values ​​corresponding to the measurement feature sequences corresponding to the cells.

[0099] The solution of the present invention is as follows: a measurement report reported by a receiving terminal is obtained; a measurement feature sequence of a sampling point is obtained according to the measurement report; weights of multiple measurement feature sequences of at least one cell are obtained; and the cell coverage scenarios are classified according to the weights of the multiple measurement feature sequences. The user groups with similar downlink coverage reception performance in the cell can be efficiently output, and the cell coverage scenarios are classified and determined in combination with the weight values ​​of the measurement feature sequences corresponding to the cell, thereby achieving coverage guidance and parameter optimization.

[0100] Figure 3 FIG. 2 is a flow chart showing a classification method for a cell coverage scenario applied to a terminal provided by another embodiment of the present invention. Figure 3 As shown, the method comprises the following steps:

[0101] Step 31, generating a measurement report; the measurement report is used to enable the base station to obtain a measurement feature sequence of a sampling point according to the measurement report; obtain weights of multiple measurement feature sequences of at least one cell; and classify the cell coverage scenario according to the weights of the multiple measurement feature sequences;

[0102] Step 32: Send the measurement report to a base station.

[0103] Figure 4 FIG. 4 shows a flow chart of a terminal generating a measurement report according to an embodiment of the present invention. Figure 4 As shown, in this embodiment, the terminal preferably performs measurement by scanning synchronization signal blocks carried by multiple beams, obtains signal strength values ​​measured by synchronization signal blocks carried by multiple beams, and sends a measurement report to the base station, wherein the measurement report sent to the base station includes report data of the synchronization signal block measurement carried by the beam and report data after merging the values ​​of the synchronization signal block measurement carried by the beam;

[0104] It should be noted that the combined value of the cell signal strength value can be obtained by merging the signal strength values ​​measured for the synchronization signal blocks carried by the beams; due to different scenarios and the influence of various emission, refraction and other factors affecting the signal, the terminal detects different signal strength values ​​of the synchronization signal blocks carried by each beam. By sending the measurement report to the base station for processing, the user coverage feature model is evaluated, the cell coverage scenarios are classified, and then the user groups with similar characteristics in the cell are identified.

[0105] Optionally, the combined value of the cell signal strength value includes:

[0106] When the signal strength values ​​of the synchronization signal blocks carried by all beams in the neighboring area measured by the terminal are lower than the combined value threshold of the cell signal strength value, the terminal preferably selects the maximum value of the signal strength values ​​of the synchronization signal blocks carried by all beams as the combined value of the cell signal strength value;

[0107] Otherwise, the terminal preferably uses the average value of all signal strength values ​​of the synchronization signal blocks carried by all beams in the neighboring cells measured by the terminal that are higher than the combined value threshold of the cell signal strength values ​​as the combined value of the cell signal strength values.

[0108] It should be noted that the classification method for cell coverage scenarios applied to terminals is a device corresponding to the above-mentioned classification method for cell coverage scenarios applied to base stations, and all implementation methods in the above-mentioned classification method embodiments for cell coverage scenarios applied to base stations are applicable to the embodiments of the method and can achieve the same technical effect.

[0109] Figure 5 FIG. 2 shows a schematic diagram of a structure of a classification device for a cell coverage scenario of a base station provided by an embodiment of the present invention. Figure 5 As shown, the device 50 comprises:

[0110] A receiving module 51, configured to receive a measurement report reported by a terminal;

[0111] The processing module 52 is used to obtain a measurement feature sequence of a sampling point according to the measurement report; obtain weights of multiple measurement feature sequences of at least one cell; and classify the cell coverage scenarios according to the weights of the multiple measurement feature sequences.

[0112] Optionally, obtaining a measurement feature sequence of a sampling point according to the measurement report includes:

[0113] Preprocessing the data of the sampling points in the measurement report to obtain a data processing result;

[0114] Classify the signal strength value of the synchronization signal block carried by at least one beam in the data processing result to obtain at least one strength category and the number of beams corresponding to each strength category;

[0115] A measurement feature sequence of sampling points is obtained according to the number of beams of the at least one intensity category.

[0116] Optionally, the data processing result includes at least one of the following:

[0117] Sampling point identification;

[0118] Cell ID;

[0119] The combined value of the cell signal strength values;

[0120] The signal strength value of the synchronization signal block carried by at least one beam.

[0121] Optionally, obtaining weights of multiple measurement feature sequences of at least one cell includes:

[0122] Obtaining the total number of sampling points of at least one cell and the number of sampling points corresponding to each measurement feature sequence;

[0123] The weights of multiple measurement feature sequences of at least one cell are obtained according to the ratio of the number of sampling points corresponding to each measurement feature sequence and the total number of sampling points.

[0124] Optionally, classifying the cell coverage scenarios according to weights of the multiple measurement feature sequences includes:

[0125] The cell coverage scenario is classified according to weights of multiple measurement feature sequences and at least one preset threshold.

[0126] Optionally, classifying the cell coverage scenario according to weights of multiple measurement feature sequences and at least one preset threshold includes:

[0127] If the weight of at least one Nth measurement feature sequence of a cell is greater than an Nth preset threshold, determining that the sampling point corresponding to the at least one Nth measurement feature sequence is an Nth type of cell coverage scenario;

[0128] If the weight of at least one N+1th measurement feature sequence of a cell is greater than the N+1th preset threshold and less than the Nth preset threshold, determine that the sampling point corresponding to at least one N+1th measurement feature sequence is the N+1th type of cell coverage scenario; wherein the N+1th measurement feature sequence and the Nth measurement feature sequence are different measurement feature sequences, the N+1th preset threshold is less than the Nth preset threshold, and N is a positive integer.

[0129] It should be noted that the device 50 is a device corresponding to the above method, and all implementation methods in the above-mentioned classification method embodiment of the cell coverage scenario are applicable to the embodiment of the device 50 and can achieve the same technical effect.

[0130] Figure 6 FIG. 2 shows a schematic diagram of a structure of a classification device for a cell coverage scenario of a terminal provided by an embodiment of the present invention. Figure 6 As shown, the device 60 includes:

[0131] A generating module 61 is used to generate a measurement report; the measurement report is used to enable the base station to obtain a measurement feature sequence of a sampling point according to the measurement report; obtain weights of multiple measurement feature sequences of at least one cell; and classify the cell coverage scenario according to the weights of the multiple measurement feature sequences;

[0132] The sending module 62 is configured to send the measurement report to a base station.

[0133] It should be noted that the device 60 is a device corresponding to the above method, and all implementation methods in the above-mentioned classification method embodiment of the cell coverage scenario are applicable to the embodiment of the device 60 and can achieve the same technical effect.

[0134] An embodiment of the present invention provides a non-volatile computer storage medium, wherein the computer storage medium stores at least one executable instruction, and the computer executable instruction can execute the classification method of the cell coverage scenario in any of the above method embodiments.

[0135] Figure 7A schematic diagram of the structure of a computing device provided by an embodiment of the present invention is shown, and the specific embodiment of the present invention does not limit the specific implementation of the computing device.

[0136] like Figure 7 As shown, the computing device may include: a processor, a communications interface, a memory, and a communication bus.

[0137] Wherein: the processor, the communication interface, and the memory communicate with each other through the communication bus. The communication interface is used to communicate with other devices such as a client or other server network elements. The processor is used to execute the program, and specifically can execute the relevant steps in the above-mentioned classification method embodiment for the cell coverage scenario of the computing device.

[0138] Specifically, the program may include program codes including computer operation instructions.

[0139] The processor may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. The one or more processors included in the computing device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0140] The memory is used to store programs. The memory may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0141] The program can be specifically used to enable the processor to execute the classification method of the cell coverage scenario in any of the above-mentioned method embodiments. The specific implementation of each step in the program can refer to the corresponding descriptions in the corresponding steps and units in the above-mentioned cell coverage scenario classification method embodiments, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described devices and modules can refer to the corresponding process description in the aforementioned method embodiments, which will not be repeated here.

[0142] The algorithm or display provided herein is not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing this type of system. In addition, the embodiment of the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to realize the content of the embodiment of the present invention described herein, and the description of the above specific language is to disclose the best mode of the embodiment of the present invention.

[0143] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0144] Similarly, it should be understood that in order to streamline the embodiments of the present invention and to aid in understanding one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present invention, the various features of the embodiments of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be interpreted as reflecting the following intention: that the claimed embodiments of the present invention require more features than the features explicitly recited in each claim. More specifically, as reflected in the claims below, the inventive aspects lie in less than all the features of the single embodiment disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, wherein each claim itself serves as a separate embodiment of the present invention.

[0145] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition they may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0146] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present invention and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0147] The various component embodiments of the present invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The embodiments of the present invention may also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing an embodiment of the present invention may be stored on a computer-readable medium, or may have the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0148] It should be noted that the above embodiments illustrate rather than limit the present invention, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim listing a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

Claims

1. A method for classifying cell coverage scenarios, characterized in that: Applied to a base station, the method comprises: Receiving measurement reports reported by terminals; Preprocessing the data of the sampling point in the measurement report to obtain a data processing result, classifying the signal strength value of the synchronization signal block carried by at least one beam in the data processing result to obtain at least one strength category and the number of beams corresponding to each strength category, arranging the number of beams of the strength category in a preset order, and obtaining a measurement feature sequence of the sampling point; Obtaining the total number of sampling points of at least one cell and the number of sampling points corresponding to each measurement feature sequence, and obtaining weights of multiple measurement feature sequences of at least one cell according to the ratio of the number of sampling points corresponding to each measurement feature sequence to the total number of sampling points; The cell coverage scenario is classified according to weights of multiple measurement feature sequences and at least one preset threshold.

2. The method for classifying cell coverage scenarios according to claim 1, characterized in that: Classifying the cell coverage scenario according to weights of multiple measurement feature sequences and at least one preset threshold, including: If the weight of at least one Nth measurement feature sequence of a cell is greater than an Nth preset threshold, determining that the sampling point corresponding to the at least one Nth measurement feature sequence is an Nth type of cell coverage scenario; If the weight of at least one N+1th measurement feature sequence of a cell is greater than the N+1th preset threshold and less than the Nth preset threshold, determine that the sampling point corresponding to at least one N+1th measurement feature sequence is the N+1th type of cell coverage scenario; wherein the N+1th measurement feature sequence and the Nth measurement feature sequence are different measurement feature sequences, the N+1th preset threshold is less than the Nth preset threshold, and N is a positive integer.

3. A method for classifying cell coverage scenarios, characterized in that: Applied to a terminal, the method comprises: Generate a measurement report; the measurement report is used to enable the base station to preprocess the data of the sampling points in the measurement report to obtain a data processing result, classify the signal strength value of the synchronization signal block carried by at least one beam in the data processing result, obtain at least one strength category and the number of beams corresponding to each strength category, arrange the number of beams of the strength category in a preset order, and obtain a measurement feature sequence of the sampling points; obtain the total number of sampling points of at least one cell and the number of sampling points corresponding to each measurement feature sequence, and obtain the weights of multiple measurement feature sequences of at least one cell and at least one preset threshold value according to the ratio of the number of sampling points corresponding to each measurement feature sequence and the total number of sampling points; classify the cell coverage scenario according to the weights of the multiple measurement feature sequences and at least one preset threshold value; The measurement report is sent to a base station.

4. A classification device for cell coverage scenes, characterized in that: Applied to a base station, the device comprises: A receiving module, used for receiving a measurement report reported by a terminal; A processing module is used to preprocess the data of the sampling points in the measurement report to obtain data processing results, classify the signal strength values ​​of the synchronization signal blocks carried by at least one beam in the data processing results, obtain at least one strength category and the number of beams corresponding to each strength category, arrange the number of beams of the strength category in a preset order, and obtain a measurement feature sequence of the sampling points; obtain the total number of sampling points of at least one cell and the number of sampling points corresponding to each measurement feature sequence, and obtain the weights of multiple measurement feature sequences of at least one cell according to the ratio of the number of sampling points corresponding to each measurement feature sequence and the total number of sampling points; and classify the cell coverage scenarios according to the weights of the multiple measurement feature sequences and at least one preset threshold.

5. A classification device for cell coverage scenes, characterized in that: Applied to a terminal, the device comprises: A generation module, used to generate a measurement report; the measurement report is used to enable the base station to preprocess the data of the sampling points in the measurement report to obtain a data processing result, classify the signal strength value of the synchronization signal block carried by at least one beam in the data processing result, obtain at least one strength category and the number of beams corresponding to each strength category, arrange the number of beams of the strength category in a preset order, and obtain a measurement feature sequence of the sampling points; obtain the total number of sampling points of at least one cell and the number of sampling points corresponding to each measurement feature sequence, and obtain the weights of multiple measurement feature sequences of at least one cell according to the ratio of the number of sampling points corresponding to each measurement feature sequence and the total number of sampling points; classify the cell coverage scenario according to the weights of the multiple measurement feature sequences and at least one preset threshold; The sending module is used to send the measurement report data to the base station.

6. A computing device comprising: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the classification method of cell coverage scenarios as described in claim 1 or 2 or to perform operations corresponding to the classification method of cell coverage scenarios as described in claim 3.

7. A computer storage medium, wherein at least one executable instruction is stored in the storage medium, wherein the executable instruction enables a processor to execute operations corresponding to the classification method for cell coverage scenarios as described in claim 1 or 2 or to execute operations corresponding to the classification method for cell coverage scenarios as described in claim 3.

Images