Method for determining relative position of terminal, base station and computer readable medium

Abstract

The invention provides a method for determining the relative position of a terminal, and the method comprises the steps: obtaining a time sequence feature vector set which comprises a time sequence feature vector of at least one terminal, and the time sequence feature vector is composed of feature values, corresponding to a plurality of moments, of at least one key performance indicator of the terminal; determining at least one terminal group according to the time sequence feature vector set; and determining the relative position between the terminal groups according to the relationship between the time sequence feature vectors corresponding to the terminal groups. The invention further provides a base station and a computer readable storage medium.

Description

Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular to a method for determining a relative position of a terminal, a base station, and a computer-readable medium. Background Art

[0002] The operating length of high-speed rail lines is 28,000 kilometers, and the length under construction is 12,000 kilometers. With the development of communication technology and mobile terminal equipment, in recent years, users have increasingly higher requirements for networks in specific scenarios (such as high-speed rail). Ensuring the user experience of high-speed rail users has become one of the main goals of mobile operators.

[0003] In order to ensure the user experience of high-speed rail users, network operators and equipment suppliers have two main measures: on the one hand, establish a dedicated network service for high-speed rail users and optimize the technical support services for the dedicated network. For example, in the high-speed rail scenario, the high-speed rail users are identified through the base station, so as to control the mobile terminal devices on the high-speed rail to remain in the dedicated network coverage area. On the other hand, in the research on the identification of high-speed rail users by the base station algorithm, the location of high-speed rail users is identified to improve the user experience of high-speed rail dedicated network users.

[0004] However, it is currently impossible to accurately identify the relative positions of high-speed rail users. In other words, the current communication network is not able to perceive the user distribution in specific communication scenarios (such as high-speed rail) sufficiently, resulting in a lack of important input dimensions during network optimization. As a result, when adaptively configuring uplink and downlink control channel resources and service channel resources, inaccurate uplink and downlink configurations occur, which has a significant impact on high-speed rail trajectory identification. Summary of the invention

[0005] The embodiments of the present disclosure provide a method for determining a relative position of a terminal, a base station, and a computer-readable medium.

[0006] In a first aspect, an embodiment of the present disclosure provides a method for determining a relative position of a terminal, including:

[0007] Acquire a set of time series feature vectors, wherein the set of time series feature vectors includes at least one time series feature vector of the terminal, and the time series feature vector is composed of feature values ​​of at least one key performance indicator of the terminal corresponding to multiple moments;

[0008] Determining at least one terminal group according to the set of timing feature vectors;

[0009] The relative positions between the terminal groups are determined according to the relationship between the time sequence feature vectors corresponding to the terminal groups.

[0010] In a second aspect, an embodiment of the present disclosure provides a base station, including:

[0011] one or more processors;

[0012] A memory having one or more programs stored thereon, when the one or more programs are executed by the one or more processors, the one or more processors implement the relative position determination method of the terminal described in the first aspect of the embodiment of the present disclosure.

[0013] In a third aspect, an embodiment of the present disclosure provides a computer-readable medium having a computer program stored thereon, and when the program is executed by a processor, the method for determining the relative position of a terminal described in the first aspect of the embodiment of the present disclosure is implemented.

[0014] In the method for determining the relative position of a terminal provided in an embodiment of the present disclosure, the relative position of the terminal is determined by a time series feature vector composed of multiple feature values ​​corresponding to at least one selected key performance indicator. The relative position between terminals can be accurately judged from multiple dimensions, thereby improving the recognition of the terminal. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flow chart of a method for determining a relative position of a terminal in an embodiment of the present disclosure;

[0016] Figure 2 is a flowchart of some steps in another method for determining the relative position of a terminal in an embodiment of the present disclosure;

[0017] Figure 3 is a flow chart of determining terminal grouping by a preset clustering algorithm in an embodiment of the present disclosure;

[0018] Figure 4 is a flowchart of some steps in another method for determining the relative position of a terminal in an embodiment of the present disclosure;

[0019] Figure 5 is a flowchart of some steps in another method for determining the relative position of a terminal in an embodiment of the present disclosure;

[0020] Figure 6 is a flow chart for determining the relative positions of each terminal in an embodiment of the present disclosure;

[0021] Figure 7 is a flowchart of some steps in another method for determining the relative position of a terminal in an embodiment of the present disclosure;

[0022] Figure 8 is a structural diagram of a base station in an embodiment of the present disclosure;

[0023] Fig. 9 is a result schematic diagram of a computer-readable medium in an embodiment of the present disclosure;

[0024] Fig.10 It is a flowchart of a part of the steps in an embodiment of a method for determining a relative position of a terminal in an embodiment of the present disclosure;

[0025] Fig.11 It is a flowchart of another part of steps in an embodiment of a method for determining a relative position of a terminal in an embodiment of the present disclosure;

[0026] Fig.12 is a flowchart of some steps in an embodiment of another method for determining the relative position of a terminal in an embodiment of the present disclosure;

[0027] Fig.13 It is a flowchart of some steps in an embodiment of another method for determining the relative position of a terminal in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the technical solution of the present disclosure, the relative position determination method of the terminal, the base station and the computer-readable medium provided by the present disclosure are described in detail below with reference to the accompanying drawings.

[0029] Example embodiments will be described more fully below with reference to the accompanying drawings, but the example embodiments may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. On the contrary, the purpose of providing these embodiments is to make the present disclosure thorough and complete and to enable those skilled in the art to fully understand the scope of the present disclosure.

[0030] In the absence of conflict, the various embodiments of the present disclosure and the various features therein may be combined with each other.

[0031] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0032] The terms used herein are only used to describe specific embodiments and are not intended to limit the present disclosure. As used herein, the singular forms "a", "an" and "the" are also intended to include the plural forms, unless the context clearly indicates otherwise. It will also be understood that when the terms "comprising" and / or "made of" are used in this specification, the presence of the features, wholes, steps, operations, elements and / or components is specified, but the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof is not excluded.

[0033] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted as having an idealized or overly formal meaning unless explicitly defined as such herein.

[0034] Figure 1 The present invention is a flowchart of a method for determining a relative position of a terminal according to an embodiment of the present invention.

[0035] First, refer to Figure 1 , an embodiment of the present disclosure provides a method for determining a relative position of a terminal, the method comprising:

[0036] S1. Acquire a set of time series feature vectors, wherein the set of time series feature vectors includes a time series feature vector of at least one terminal, and the time series feature vector is composed of feature values ​​of at least one key performance indicator of the terminal corresponding to multiple moments;

[0037] S2. Determine at least one terminal group according to the set of timing feature vectors;

[0038] S3. Determine the relative positions between the terminal groups according to the relationship between the timing feature vectors corresponding to the terminal groups.

[0039] In an embodiment of the present disclosure, a method for determining the relative position of a terminal is applied to a high-speed rail communication scenario, which has the characteristics of a large linear span and a fast terminal movement speed within the coverage of a base station, but the application scenario of the method is not limited thereto, and may be any other communication scenario with such characteristics. The application environment of the method is an adaptive optimization scenario of terminal relative position identification driven by data of NR (New Radio), LTE (Long Term Evolution), FDD (Frequency Division Duplexing), and TDD (Time Division Duplexing). Based on the characteristics of the scenario, multiple key performance indicators that affect the determination of the relative position are analyzed, which can effectively improve the accuracy and reduce the judgment error of the relative position.

[0040] Among them, the set of timing feature vectors describes the characteristic values ​​of at least one key performance indicator of at least one terminal at multiple moments, and the key performance indicator is an indicator related to the relative position of the terminal selected based on the scheduling data analysis within the scene; wherein the multiple moments of the timing feature vector refer to moments separated by a preset time interval, and the preset time interval can be determined according to actual conditions.

[0041] The embodiments of the present disclosure do not impose any special restrictions on the relationship between the timing feature vectors. It can be a statistical value of the timing feature vector corresponding to each terminal in the terminal group, such as the median, average, etc., or it can be the timing feature vector corresponding to the cluster center of the terminal group.

[0042] The relative positions between terminal groups regard the terminals in the group as a whole, take one terminal group as a reference, and describe the position of another terminal group relative to the reference. The present disclosure uses the relative positions between terminal groups as the relative positions between the terminals in the terminal group. The relative positions can be fixed or not, and will change with the change of the reference.

[0043] The disclosed embodiment determines the relative position of the terminal by a time series feature vector composed of multiple feature values ​​corresponding to at least one selected key performance indicator, and can accurately judge the relative position between terminals from multiple dimensions, thereby improving the recognition of the terminal. In addition, the method is deployed in a base station, and the relative position of the terminal is identified on the base station side, without the need to use external data and / or measurement report data for relative position identification, which is more efficient.

[0044] In some embodiments, the key performance indicator includes at least one of frequency deviation, partial cell, timing advance, transmit power and super cell identification. The key performance indicator includes necessary indicators and auxiliary indicators. In some examples, frequency deviation, CP (Cell Portion, partial cell) and TA (Timing Advance, timing advance) are necessary indicators, and transmit power and super cell ID (Identity document, identification) are auxiliary indicators. Among them, CP reflects the cell where the terminal is located, Ta reflects the distance between the terminal and the base station, and the timing advance at different times can be the same or different.

[0045] In the embodiments of the present disclosure, KPI (Key Performance Indicator) is an indicator related to the relative position of the terminal obtained through analysis. KPI describes the characteristic quantities associated with the relative position corresponding to multiple terminals moving at high speed within the coverage of the base station. The characteristic value of the key performance indicator can be used to quantitatively measure the positional relationship between the corresponding terminals. The characteristic values ​​of the key performance indicator corresponding to different moments constitute a time series characteristic vector.

[0046] By adding auxiliary indicators on the basis of necessary indicators, the relative position of the terminal can be determined from multiple dimensions with higher recognition.

[0047] The key performance indicators are obtained by analyzing and selecting features related to the relative position of the terminal. More specifically, the characteristic values ​​of the key performance indicators of the terminal at different times can be used to determine the positional relationship between the terminal and the base station. The characteristic values ​​of the key performance indicators of the terminal at different times can be used to define a timing characteristic vector. The timing characteristic vector includes the characteristic values ​​of necessary indicators (such as frequency deviation, CP, and Ta, etc.), and may also include the characteristic values ​​of auxiliary indicators (transmit power, super cell ID, etc.).

[0048] In some embodiments, reference Figure 2 , S1 includes:

[0049] S11, obtaining characteristic values ​​of at least one key performance indicator of each of the terminals corresponding to multiple moments, and determining a time series characteristic vector of each of the terminals;

[0050] S12: Construct a set of timing feature vectors according to the timing feature vector of at least one terminal.

[0051] In this embodiment, analysis is performed based on various scheduling data collected by the base station to obtain key performance indicators related to the relative positions between terminals, and a timing feature vector is constructed using the characteristic values ​​of the key performance indicators at different times; the scheduling data refers to the scheduling resource data allocated to the terminal by the network side and the resource data reported by the service when the terminal communicates within the coverage range of the base station.

[0052] The time series feature vector of each terminal includes the feature values ​​of at least one key performance indicator corresponding to multiple moments. For example, the time series feature vector of the terminal UE1 is [f 1t1 ,f 1t2 ,f 1t3 ,......,f 1t9 ,f 1t10 ], where f 1t1 Indicates the frequency deviation of UE1 at time t1.

[0053] In one example, the timing feature vector of the terminal in the high-speed rail scenario is determined, and the base station collects the high-speed rail wireless scheduling data, which mainly describes the scheduling resource data allocated to the terminal by the network side when the user uses the terminal on the high-speed rail and the resource data reported by the terminal when doing business. The key performance indicators related to the relative position of the terminal in the high-speed rail wireless scheduling data are analyzed and selected, and the timing feature vectors constructed using the characteristic values ​​of the key performance indicators at multiple moments are determined.

[0054] In some embodiments, determining at least one terminal group according to the set of timing feature vectors includes:

[0055] According to the set of time series feature vectors, clustering processing is performed on the terminals by using a preset clustering algorithm to obtain at least one terminal group.

[0056] The embodiments of the present disclosure do not limit the preset clustering algorithm, which may be k-means (k-means clustering algorithm), GMM (Gaussian Mixture Model), etc.

[0057] The basis for clustering is the similarity or difference between the time series feature vector of each terminal in the time series feature vector set and the cluster center. In some examples, the similarity or difference between the time series feature vector of each terminal and the cluster center can be quantified by calculating the distance or similarity between the terminal and the cluster center. The terminal groups obtained by clustering are different from each other, and the terminals contained in each terminal group are similar.

[0058] That is, the clustering rule followed by the clustering result (i.e., terminal grouping) is: the feature vectors of each terminal in the class are similar, and the feature vectors of each terminal between classes are different. Among them, one class is a terminal grouping. The similarity within a class means that the time series feature vectors of the terminals in the terminal group of the same class are centered on the cluster center time series feature vector of the terminal group, showing similarity; the difference between classes means that the cluster center time series feature vectors of terminal groups of different classes show difference.

[0059] It is worth noting that when the terminals are grouped by a preset clustering algorithm, the relationship between the timing feature vectors corresponding to the terminal group can be the cluster center timing feature vector of the cluster center corresponding to the terminal group, and the cluster center can represent the center position or average position of the timing feature vectors of all terminals in its terminal group.

[0060] Reference Figure 3 In some embodiments, in a high-speed rail communication scenario, a base station collects timing feature vectors of multiple user terminals within its coverage area, constructs a timing feature vector data set (i.e., a set of timing feature vectors), and clusters the user terminals according to the number of high-speed rail carriages k through a preset clustering algorithm to obtain k terminal groups; there are differences in the cluster-center timing feature vectors corresponding to the cluster centers of the k terminal groups, and there are similarities between the cluster-center timing feature vectors of each terminal in each terminal group and its corresponding cluster center, that is, the terminal characteristics within the class are similar, and the terminal characteristics between classes are different.

[0061] In some embodiments, determining at least one terminal group according to the set of timing feature vectors includes:

[0062] According to the set of time series feature vectors, the terminals are classified by a preset classifier to obtain at least one terminal group.

[0063] The embodiments of the present disclosure do not limit the preset classifier, which may be a recursive neural network classifier, an attention mechanism classifier, etc. In some examples, the preset classifier is obtained by neural network training.

[0064] The basis for classification processing is the similarity or difference between the timing feature vectors of each terminal in the timing feature vector set, or the similarity or difference between the timing feature vector of each terminal in the timing feature vector set and a preset number of reference feature vectors. In some examples, the similarity or difference can be quantified by calculating the distance or similarity between the two. There are differences between the terminal groups obtained by classification, and the terminals contained in each terminal group are similar.

[0065] The classification rules followed by the terminal grouping are: the feature vectors of each terminal in the same class are similar, and the feature vectors of each terminal between classes are different. The similarity within a class refers to the similarity between the time series feature vectors of each terminal in the terminal group of the same class, or the similarity between the time series feature vectors of each terminal in the terminal group of the same class and the preset reference feature vector; the difference between classes refers to the statistical value of the reference feature vectors of terminal groups of different classes, which show differences. This statistical value can be used to characterize the central value or average value of the time series feature vectors of each terminal in the terminal group.

[0066] In some embodiments, before clustering the terminals using a preset clustering algorithm according to the set of time series feature vectors to obtain at least one terminal group, the method further includes:

[0067] Obtaining a preset number of terminal groups;

[0068] According to the set of time series feature vectors, clustering the terminals by a preset clustering algorithm to obtain at least one terminal group includes:

[0069] According to the set of time series feature vectors, the terminals are clustered into a preset number of terminal groups by using a preset clustering algorithm.

[0070] In this embodiment, the preset number of terminal groups is pre-set, and may be fixed or dynamically changed. In some examples, in a high-speed rail communication scenario, the preset number is set to the total number of carriages of the high-speed rail. In other examples, the base station determines the preset number of terminal groups in response to an input instruction, and the input instruction may be used to indicate the preset number of terminal groups.

[0071] In some embodiments, when the terminal is located on a high-speed rail, the preset number is the number of carriages of the high-speed rail.

[0072] In an embodiment of the present disclosure, in a high-speed rail communication scenario, each terminal is located on the high-speed rail, the preset number is determined as the number of carriages of the high-speed rail, and each terminal in each terminal group can be considered to be a terminal located in the same carriage.

[0073] In some embodiments, reference Figure 4, S3 includes:

[0074] S31, determining a cluster center timing feature vector corresponding to the cluster center of the terminal group as the timing feature vector corresponding to the terminal group;

[0075] S32: Determine the relative positions between the terminal groups according to the relationship between the cluster center time series feature vectors corresponding to the terminal groups.

[0076] In this embodiment, the cluster center of the terminal group is the center of the terminal group obtained by the preset clustering algorithm, which can represent the average or center characteristics of the terminal group. The characteristics of each cluster (i.e., the terminal group) can be understood through the cluster center, and can be used as a representative of each cluster for subsequent analysis or calculation. Here, the cluster center time series feature vector of the cluster center represents the time series feature vector corresponding to its terminal group, and the relationship between the cluster center time series feature vectors of each cluster center is used as the relationship between the time series feature vectors corresponding to each terminal group to determine the relative positions between the terminal groups;

[0077] The embodiments of the present disclosure do not limit the type of relationship between the cluster center time series feature vectors, which may be a numerical value relationship or a numerical value absolute value relationship, etc., which can be determined based on the actual situation of the KPI.

[0078] In some embodiments, reference Figure 5 , S32 includes:

[0079] S321, determining the order of the plurality of terminal groups according to the relationship between the cluster core timing feature vectors corresponding to the respective terminal groups, wherein the relationship between the cluster core timing feature vectors represents the position relationship of the terminal groups relative to the base station;

[0080] S322: Determine relative positions between the terminal groups according to the ranking of the plurality of terminal groups.

[0081] In this embodiment, the base station is used as a reference, and the position relationship of the terminal group relative to the base station is determined in units of terminal groups. The sorting basis can be the numerical value, positive and negative value, etc. of the cluster center time series feature vector. The sorting of multiple terminal groups represents the position order of the terminal groups, and then the relative position between the terminal groups can be determined. In some examples, the sorting can represent the level order of the terminal groups.

[0082] The principle of sorting terminal groups is: taking clusters as units, counting the time series feature vectors of the cluster centers of each cluster, using the eigenvalues ​​of the constructed time series feature vectors, sorting the clusters, and determining the relative position relationship of the terminal groups in each cluster.

[0083] Reference Figure 6In some examples, in the high-speed rail communication scenario, k clusters are obtained by clustering, each cluster corresponds to a terminal group, and the terminal corresponding to each cluster is analyzed in turn to obtain the cluster level of the terminal group, and the cluster levels are sorted to determine the relative position relationship of the terminal group, thereby determining the relative position between the terminal groups.

[0084] In some embodiments, reference Figure 7 , S321 includes:

[0085] S3211, comparing the cluster center time sequence feature vectors corresponding to each of the terminal groups to obtain a comparison result, wherein the comparison result reflects the orientation and distance of the terminal group relative to the base station;

[0086] S3212: Determine the order of the plurality of terminal groups according to the comparison result.

[0087] In this embodiment, the base station is used as a reference, and the direction and distance of each terminal relative to the base station are determined by comparison. The direction here can be determined according to the displacement direction of each terminal, such as the moving direction away from the base station on one side and the moving direction close to the base station on the other side.

[0088] In some embodiments, determining the order of the plurality of terminal groups according to the comparison result includes:

[0089] Determine, according to the comparison result, a displacement direction of the terminal group relative to the base station, and obtain a first number of terminal groups whose displacement directions are close to the base station and a second number of terminal groups whose displacement directions are far from the base station;

[0090] According to the distance of the comparison result, the first number of terminal groups are sorted according to a first preset order, and the second number of terminal groups are sorted according to a second preset order; the first preset order reflects that the distances of the terminal groups relative to the base station decrease in sequence, and the second preset order reflects that the distances of the terminal groups relative to the base station increase in sequence.

[0091] In an embodiment of the present disclosure, the cluster core time series feature vector includes feature values ​​of the KPI of the terminal group corresponding to multiple time periods. According to the feature values ​​at multiple time periods, the displacement direction of the terminal group can be determined, and the terminal group is divided according to the displacement direction, and the displacement direction is determined to be a first number of terminal groups far away from the base station, and a second number of terminal groups close to the base station;

[0092] According to the displacement direction of the terminal groups, the terminal groups are sorted. The first number of terminal groups far away from the base station are sorted from large to small according to the distance. The smaller the distance, the closer to the base station; the second number of terminal groups close to the base station are sorted from small to large according to the distance. The smaller the distance, the closer to the base station; the minimum distance value of the sorted first number of terminal groups and the minimum distance value of the sorted first number of terminal groups are spliced ​​to obtain the sorting of multiple terminal groups.

[0093] In some examples, the total number of terminal groups is N, the positive or negative value of the frequency deviation can reflect the displacement direction of the terminal group, and the value of the frequency deviation can reflect the distance of the terminal relative to the base station. When the KPI corresponding to the cluster center timing feature vector is the frequency deviation, the sorting process of the terminal group is:

[0094] According to the N terminal groups, k terminal groups whose cluster center timing characteristic vectors corresponding to the frequency offset are positive numbers, and (Nk) terminal groups whose cluster center timing characteristic vectors corresponding to the frequency offset are negative numbers are determined; wherein the k terminal groups whose frequency offset is positive number indicates that the terminal groups are close to the base station, and the (Nk) terminal groups whose frequency offset is negative number indicates that the terminal groups are far away from the base station;

[0095] Arrange the k terminal groups in the i-th position according to the descending order of the cluster center time series feature vectors, and obtain the ranking of the k terminal groups, where i is from 1 to k;

[0096] Arrange the (Nk) terminal groups in the jth position according to the absolute value of the cluster center time series feature vector from small to large, and obtain the ranking of the (Nk) terminal groups, where j is taken from (k+1) to N, i, j, k, and N are all positive integers, 0 <k≤N。

[0097] In one example, taking the high-speed rail with 10 carriages all traveling in the direction close to the base station as an example, the relative position of each terminal is determined as follows:

[0098] Based on the time series feature vector set, through the preset clustering algorithm, the preset number of clusters is the number of carriages (ie, 10), and the clustering result is determined to be 10 terminal groups. The terminal groups have similarities within each cluster and differences between clusters.

[0099] Determine the cluster core timing feature vector of the cluster core of each terminal group, and analyze the KPI (time, frequency deviation, CP, Ta) constituting the cluster core timing feature vector.

[0100] Traverse the 10 terminal groups, sort the terminal groups according to the cluster core timing feature vectors, and sort the 10 terminal groups in descending order of frequency deviation and Ta, that is, the frequency deviation of the cluster core timing feature vector of the a-th terminal group is greater than the frequency deviation of the cluster core timing feature vector of the (a+1)-th terminal group, and the Ta feature value of the cluster core timing feature vector of the a-th terminal group is greater than the Ta feature value of the cluster core timing feature vector of the (a+1)-th terminal group. In this way, the position of the leftmost carriage of the high-speed rail corresponds to the position of the terminal in the first terminal group.

[0101] The above process is repeated until all 10 terminal groups are sorted, and the sorting status of each terminal group is determined, which reflects the position of the terminal in the terminal group corresponding to the position of the carriage from left to right.

[0102] According to the sorting situation, the relative positions of the terminals on the high-speed rail are determined in terminal grouping units.

[0103] In another example, taking a high-speed rail with 10 carriages crossing the base station (i.e., the displacement direction of some carriages is away from the base station, and the displacement direction of other carriages is close to the base station) as an example, the relative position of each terminal is determined as follows:

[0104] Based on the time series feature vector set, through the preset clustering algorithm, the preset number of clusters is the number of carriages (ie, 10), and the clustering result is determined to be 10 terminal groups. The terminal groups have similarities within each cluster and differences between clusters.

[0105] Determine the cluster core timing feature vector of the cluster core of each terminal group, and analyze the KPI (time, frequency deviation, CP, Ta) constituting the cluster core timing feature vector.

[0106] Traverse 10 terminal groups and sort the terminal groups according to the cluster center timing feature vector. The frequency deviation corresponding to the terminal group on one side of the base station on the high-speed rail is a positive number, and the frequency deviation corresponding to the terminal group on the other side of the base station on the high-speed rail is a negative number. That is, the position of the terminal group relative to the base station is divided according to the cluster center timing feature vector;

[0107] Sort the x terminal groups with positive frequency deviations in descending order of frequency deviation and Ta, that is, the frequency deviation of the cluster-center timing feature vector of the first terminal group is greater than that of the cluster-center timing feature vectors of other terminal groups, and the Ta feature quantity of the cluster-center timing feature vector of the first terminal group is greater than the Ta feature quantity of the cluster-center timing feature vectors of other terminal groups; at the same time, the frequency deviation of the cluster-center timing feature vector of the bth terminal group is greater than the frequency deviation of the cluster-center timing feature vector of the (b+1)th terminal group, and the Ta feature quantity of the cluster-center timing feature vector of the bth terminal group is greater than the Ta feature quantity of the cluster-center timing feature vector of the (b+1)th terminal group, so as to determine the position of the terminal in the 1st to xth terminal groups corresponding to the position of one side of the high-speed rail carriage.

[0108] The (10-x) terminal groups with negative frequency deviation are sorted in order of the absolute value of the frequency deviation and Ta from small to large, that is, the absolute value of the frequency deviation of the cluster core timing feature vector of the 10th terminal group is greater than the absolute value of the frequency deviation of the cluster core timing feature vector of other terminal groups, and the Ta feature quantity of the cluster core timing feature vector of the 10th terminal group is greater than the Ta feature quantity of the cluster core timing feature vector of other terminal groups. At the same time, the absolute value of the frequency deviation of the cluster core timing feature vector of the (c+1)th terminal group is greater than the absolute value of the frequency deviation of the cluster core timing feature vector of the cth terminal group, and the Ta feature quantity of the cluster core timing feature vector of the (c+1)th terminal group is greater than the Ta feature quantity of the cluster core timing feature vector of the cth terminal group. In this way, the position of the terminal in the (x+1)th to 10th terminal groups corresponding to the position of the carriage on the other side of the high-speed rail is determined.

[0109] The above process is repeated until all 10 terminal groups are sorted, and the sorting status of each terminal group is determined, which reflects the position of the terminal in the terminal group corresponding to the position of the carriage from left to right.

[0110] According to the sorting situation, the relative positions of the terminals on the high-speed rail are determined in terminal grouping units.

[0111] In another example, taking the high-speed rail with 10 carriages all traveling away from the base station as an example, the relative positions of the terminals are determined as follows:

[0112] Based on the time series feature vector set, through the preset clustering algorithm, the preset number of clusters is the number of carriages (ie, 10), and the clustering result is determined to be 10 terminal groups. The terminal groups have similarities within each cluster and differences between clusters.

[0113] Determine the cluster core timing feature vector of the cluster core of each terminal group, and analyze the KPI (time, frequency deviation, CP, Ta) constituting the cluster core timing feature vector.

[0114] Traverse 10 terminal groups and sort the terminal groups according to the cluster core timing feature vectors, that is, the absolute value of the frequency deviation of the cluster core timing feature vector of the 10th terminal group is greater than the absolute value of the frequency deviation of the cluster core timing feature vectors of other terminal groups, and the Ta feature quantity of the cluster core timing feature vector of the 10th terminal group is greater than the Ta feature quantity of the cluster core timing feature vectors of other terminal groups, and at the same time, the absolute value of the frequency deviation of the cluster core timing feature vector of the (d+1)th terminal group is greater than the absolute value of the frequency deviation of the cluster core timing feature vector of the dth terminal group, and the Ta feature quantity of the cluster core timing feature vector of the (d+1)th terminal group is greater than the Ta feature quantity of the cluster core timing feature vector of the dth terminal group, so as to determine the positions of the terminals in the 1st to 10th terminal groups corresponding to the positions of the high-speed rail cars.

[0115] The above process is repeated until all 10 terminal groups are sorted, and the sorting status of each terminal group is determined, which reflects the position of the terminal in the terminal group corresponding to the position of the carriage from left to right.

[0116] According to the sorting situation, the relative positions of the terminals on the high-speed rail are determined in terminal grouping units.

[0117] In some embodiments, after determining the relative positions between the terminal groups according to the relationship between the timing feature vectors corresponding to the terminal groups, the method further includes:

[0118] Channel resources are configured according to the relative positions and service requirements between the terminal groups.

[0119] In this embodiment, the network is optimized based on the relative positions between the terminal groups, and channel resources are configured according to different service requirements, including service requirements of uplink channels and downlink channels, and the channel resources include uplink channel resources, downlink channel resources, and service channel resources. The channel resources can be adaptively configured, and in some examples, the adaptive configuration is performed through a network optimization module, and the input of the module is the relative positions between the terminal groups.

[0120] Based on the accurate determination of the relative positions between the terminal groups, the uplink and downlink service demands of the terminals in the scenario are accurately identified, and the uplink and downlink control channel resources and service channel resources are adaptively configured, thereby improving the user experience in the scenario.

[0121] In the embodiments of the present disclosure, key performance indicators include not only necessary indicators but also auxiliary indicators, all of which are associated with determining relative positions, and can identify the relative positions of terminals from multiple dimensions. In addition, after constructing a set of timing feature vectors, the relative positions of the terminals are determined based on the positional relationship of each terminal relative to the base station through analysis of the key performance indicators, thereby improving the accuracy of relative position determination.

[0122] The embodiments of the present disclosure analyze and determine the KPI associated with the relative position based on the scheduling data collected by the base station, group the terminals using clustering or classifiers, perceive the relative position relationship of the terminals in the scenario, and use this as input to identify uplink and downlink service needs and adaptively configure control channel resources and service channel resources.

[0123] The method for determining the relative position of a terminal includes a data collection and analysis module, a grouping module and a network optimization module, wherein the data collection and analysis module is used to analyze and select KPIs related to user positions from the scheduling data collected by the base station, and construct a set of time series feature vectors; the grouping module is used to group the terminals using clustering or classifiers, etc., with the number of high-speed rail carriages as the total number of groups, and analyze the time series feature vectors of each terminal group based on the grouping to determine the relative position relationship of the terminals; the network optimization module is used to configure channel resources based on relative positions and business requirements.

[0124] Second, refer to Figure 8 , an embodiment of the present disclosure provides a base station, comprising:

[0125] One or more processors 801;

[0126] A memory 802, on which one or more programs are stored, and when the one or more programs are executed by one or more processors, the one or more processors implement any one of the above methods for determining the relative position of a terminal;

[0127] One or more I / O interfaces 803 are connected between the processor and the memory and are configured to implement information exchange between the processor and the memory.

[0128] Among them, the processor 801 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 802 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory (FLASH); the I / O interface (read-write interface) 803 is connected between the processor 801 and the memory 802, and can realize information interaction between the processor 801 and the memory 802, including but not limited to a data bus (Bus), etc.

[0129] In some embodiments, the processor 801 , the memory 802 , and the I / O interface 803 are connected to each other via a bus 804 , and further connected to other components of the computing device.

[0130] Figure 8 The structure shown is only for illustration and does not limit the structure of the above base station. Figure 8 More or fewer components as shown, or with Figure 8 Different configurations shown.

[0131] Thirdly, refer to Fig. 9 The embodiment of the present disclosure provides a computer-readable medium on which a computer program is stored. When the program is executed by a processor, any of the above-mentioned methods for determining the relative position of a terminal is implemented.

[0132] In order to enable those skilled in the art to more clearly understand the technical solutions provided by the embodiments of the present disclosure, the technical solutions provided by the embodiments of the present disclosure are described in detail below through specific embodiments:

[0133] Embodiment 1, refer to Fig.10 and Fig.11 , the base station performs adaptive configuration for network optimization in the high-speed rail communication scenario:

[0134] The base station collects high-speed rail wireless dispatching data, analyzes and selects KPIs related to the relative position of the terminal, and uses KPIs to construct timing feature vectors at different time sequences. The timing feature vectors constitute a timing feature vector set.

[0135] According to the set of time series feature vectors, the terminals are clustered to obtain k terminal groups. The features within each terminal group are similar, but the features between classes are different.

[0136] A feature analysis is performed on each terminal group to determine the ranking of each terminal group, and based on the ranking of the terminal groups, the relative position relationship of each terminal group is determined.

[0137] Channel resources are configured according to the relative position relationship of each terminal group to optimize the network.

[0138] In the second embodiment, in a high-speed rail communication scenario, the base station determines the relative position relationship between terminals through the k-means algorithm, thereby adaptively configuring the network optimization:

[0139] First, a set of timing feature vectors is constructed. The base station collects high-speed rail wireless scheduling data, analyzes and selects KPIs related to the relative position of the terminal, and uses KPIs to construct timing feature vectors at different time sequences. The timing feature vectors constitute a set of timing feature vectors.

[0140] Then, refer to Fig.12 and Figure 6 Through the k-means algorithm, each terminal is clustered according to the set of time series feature vectors. The number of clusters is the number of carriages. Terminals with similar feature vectors are clustered into one category, and the terminal characteristic vectors in each cluster show differences. According to the clustering results of the k-means algorithm, the time series feature vector characteristics of each type of terminal are analyzed, and the relative position relationship of each type of terminal is determined based on the clustering cluster, and then the relative position relationship of each type of terminal group is sorted.

[0141] Finally, the relative position relationship of each type of terminal grouping is input into the network optimization module as one of the network optimization inputs. According to different uplink and downlink service requirements, uplink and downlink control channel resources and service channel resources are adaptively configured.

[0142] In the third embodiment, in a high-speed rail communication scenario, the base station determines the relative position relationship between terminals through the GMM algorithm, thereby adaptively configuring the network optimization:

[0143] First, a set of timing feature vectors is constructed. The base station collects high-speed rail wireless scheduling data, analyzes and selects KPIs related to the relative position of the terminal, and uses KPIs to construct timing feature vectors at different time sequences. The timing feature vectors constitute a set of timing feature vectors.

[0144] Then, refer to Fig.13 and Figure 6 Through the GMM algorithm, each terminal is clustered according to the set of time series feature vectors. The number of clusters is the number of carriages. Terminals with similar feature vectors are clustered into one category, and the terminal characteristic vectors in each cluster show differences. According to the clustering results of the GMM algorithm, the time series feature vector characteristics of each type of terminal are analyzed, and the relative position relationship of each type of terminal is determined based on the clustering cluster, and then the relative position relationship of each type of terminal group is sorted.

[0145] Finally, the relative position relationship of each type of terminal grouping is input into the network optimization module as one of the network optimization inputs. According to different uplink and downlink service requirements, uplink and downlink control channel resources and service channel resources are adaptively configured.

[0146] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0147] Example embodiments have been disclosed herein, and although specific terms are employed, they are used and should be interpreted only in a general illustrative sense and not for limiting purposes. In some instances, it will be apparent to those skilled in the art that, unless otherwise expressly noted, features, characteristics, and / or elements described in conjunction with a particular embodiment may be used alone or in combination with features, characteristics, and / or elements described in conjunction with other embodiments. Therefore, those skilled in the art will appreciate that various changes in form and detail may be made without departing from the scope of the present disclosure as set forth in the appended claims.

Claims

1. A method for determining a relative position of a terminal, comprising: Acquire a set of time series feature vectors, wherein the set of time series feature vectors includes at least one time series feature vector of the terminal, and the time series feature vector is composed of feature values ​​of at least one key performance indicator of the terminal corresponding to multiple moments; Determining at least one terminal group according to the set of timing feature vectors; The relative positions between the terminal groups are determined according to the relationship between the time sequence feature vectors corresponding to the terminal groups.

2. The method for determining the relative position of a terminal according to claim 1, wherein: Get the terminal's timing feature vector set, including: Acquire characteristic values ​​of at least one key performance indicator of each of the terminals corresponding to multiple time periods, and determine a time series characteristic vector of each of the terminals; A set of timing feature vectors is constructed according to the timing feature vector of at least one terminal.

3. The method for determining the relative position of a terminal according to claim 1, wherein: Determining at least one terminal group according to the set of timing feature vectors includes: According to the set of time series feature vectors, clustering processing is performed on the terminals by using a preset clustering algorithm to obtain at least one terminal group.

4. The method for determining the relative position of a terminal according to claim 3, wherein: Before clustering the terminals according to the set of time series feature vectors by using a preset clustering algorithm to obtain at least one terminal group, the method further includes: Obtaining a preset number of terminal groups; According to the set of time series feature vectors, clustering the terminals by a preset clustering algorithm to obtain at least one terminal group includes: According to the set of time series feature vectors, the terminals are clustered into a preset number of terminal groups by using a preset clustering algorithm.

5. The method for determining the relative position of a terminal according to claim 4, wherein: When the terminal is located on a high-speed rail, the preset number is the number of carriages of the high-speed rail.

6. The method for determining the relative position of a terminal according to claim 1, wherein: Determining at least one terminal group according to the set of timing feature vectors includes: According to the set of time series feature vectors, the terminals are classified by a preset classifier to obtain at least one terminal group.

7. The method for determining the relative position of a terminal according to any one of claims 3 to 6, wherein: Determining relative positions between the terminal groups according to the relationship between the time series feature vectors corresponding to the terminal groups includes: Determine a cluster center time series feature vector corresponding to the cluster center of the terminal group as the time series feature vector corresponding to the terminal group; The relative positions between the terminal groups are determined according to the relationship between the cluster-center time series feature vectors corresponding to the terminal groups.

8. The method for determining the relative position of a terminal according to claim 7, wherein: Determining the relative positions between the terminal groups according to the cluster core timing feature vector includes: Determine the order of the plurality of terminal groups according to the relationship between the cluster core timing feature vectors corresponding to the respective terminal groups, wherein the relationship between the cluster core timing feature vectors represents the position relationship of the terminal groups relative to the base station; The relative positions between the terminal groups are determined according to the ranking of the plurality of terminal groups.

9. The method for determining the relative position of a terminal according to claim 7, wherein: Determining the order of the plurality of terminal groups according to the relationship between the cluster center time sequence feature vectors corresponding to the respective terminal groups includes: Comparing the cluster center time sequence feature vectors corresponding to each of the terminal groups to obtain a comparison result, wherein the comparison result reflects the orientation and distance of the terminal group relative to the base station; According to the comparison result, the order of the plurality of terminal groups is determined.

10. The method for determining the relative position of a terminal according to claim 9, wherein: Determining the order of the plurality of terminal groups according to the comparison result includes: Determine, according to the comparison result, a displacement direction of the terminal group relative to the base station, and obtain a first number of terminal groups whose displacement directions are close to the base station and a second number of terminal groups whose displacement directions are far from the base station; According to the distance of the comparison result, the first number of terminal groups are sorted according to a first preset order, and the second number of terminal groups are sorted according to a second preset order; the first preset order reflects that the distances of the terminal groups relative to the base station decrease in sequence, and the second preset order reflects that the distances of the terminal groups relative to the base station increase in sequence.

11. The method for determining the relative position of a terminal according to any one of claims 1 to 6, wherein: The key performance indicator includes at least one of frequency deviation, partial cell, timing advance, transmit power and super cell identifier.

12. The method for determining the relative position of a terminal according to any one of claims 1 to 6, wherein: After determining the relative positions between the terminal groups according to the relationship between the time series feature vectors corresponding to the terminal groups, the method further includes: Channel resources are configured according to the relative positions and service requirements between the terminal groups.

13. A base station, comprising: one or more processors; A memory having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the relative position determination method of the terminal according to any one of claims 1 to 12.

14. A computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method for determining the relative position of a terminal according to any one of claims 1 to 12 is implemented.

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