CN114390605A - Switching method, device, equipment and storage medium - Google Patents

Switching method, device, equipment and storage medium Download PDF

Info

Publication number
CN114390605A
CN114390605A CN202011133860.4A CN202011133860A CN114390605A CN 114390605 A CN114390605 A CN 114390605A CN 202011133860 A CN202011133860 A CN 202011133860A CN 114390605 A CN114390605 A CN 114390605A
Authority
CN
China
Prior art keywords
terminal
cluster
matrix
switching
clustered
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202011133860.4A
Other languages
Chinese (zh)
Other versions
CN114390605B (en
Inventor
常世元
李高盛
李玉诗
张斌
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Datang Mobile Communications Equipment Co Ltd
Original Assignee
Datang Mobile Communications Equipment Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Datang Mobile Communications Equipment Co Ltd filed Critical Datang Mobile Communications Equipment Co Ltd
Priority to CN202011133860.4A priority Critical patent/CN114390605B/en
Publication of CN114390605A publication Critical patent/CN114390605A/en
Application granted granted Critical
Publication of CN114390605B publication Critical patent/CN114390605B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0083—Determination of parameters used for hand-off, e.g. generation or modification of neighbour cell lists
    • H04W36/0085—Hand-off measurements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/318—Received signal strength
    • H04B17/327—Received signal code power [RSCP]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0083—Determination of parameters used for hand-off, e.g. generation or modification of neighbour cell lists
    • H04W36/00837—Determination of triggering parameters for hand-off
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/24—Reselection being triggered by specific parameters
    • H04W36/30—Reselection being triggered by specific parameters by measured or perceived connection quality data
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Artificial Intelligence (AREA)
  • Quality & Reliability (AREA)
  • Electromagnetism (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Probability & Statistics with Applications (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The embodiment of the application provides a switching method, a switching device, switching equipment and a storage medium, wherein the method comprises the following steps: respectively acquiring RSRP measurement data samples of terminals in each cluster; determining a target cluster with the highest similarity to the RSRP measurement data of the target terminal based on the RSRP measurement data samples of the terminals in each cluster; configuring a switching parameter threshold of a target terminal based on a switching parameter threshold corresponding to the target cluster to complete switching of the target terminal; wherein, the switching parameter thresholds corresponding to different clusters are different. According to the embodiment of the application, the clustering is carried out on the users, the switching parameter thresholds corresponding to different clusters are different, the cluster to which the target terminal belongs is determined, and the switching is carried out after the corresponding switching parameter threshold is determined, so that different switching parameter configurations are carried out on the terminals under different scenes, ping-pong switching is reduced, switching is avoided when the terminal signals are poor, call drop is reduced, and meanwhile, the system performance is improved through switching in advance or delayed switching.

Description

Switching method, device, equipment and storage medium
Technical Field
The present application relates to the field of communications technologies, and in particular, to a handover method, apparatus, device, and storage medium.
Background
In a 5G system, handover is employed to ensure the link quality of a UE (User Equipment) in a mobile situation.
The existing switching method sets a uniform threshold for the switching parameters of all user terminals in the same cell, and a base station reports a measurement report according to UE and performs switching control according to the uniform parameter setting threshold. However, under the condition that the user terminals covered by the same cell have complex scenes, the scheme of using the uniform switching parameters easily causes that part of the user terminals are switched too late or too early, thereby affecting the user perception and affecting the system performance.
Therefore, how to provide a method for switching a ue with a complex scenario and adapted to the coverage of the same cell becomes an urgent problem to be solved
Disclosure of Invention
The embodiment of the application provides a switching method, a switching device and a storage medium, which are used for solving the defects that in the prior art, the switching method is not suitable for the situation that the scenes of user terminals covered by the same cell are relatively complex, and the switching of part of the user terminals is too late or too early, and realizing the complex switching suitable for the scenes of the user terminals covered by the same cell.
In a first aspect, an embodiment of the present application provides a handover method, including:
respectively obtaining RSRP (Reference Signal Receiving Power) measurement data samples of terminals in each cluster;
determining a target cluster with the highest similarity to the RSRP measurement data of the target terminal based on the RSRP measurement data samples of the terminals in each cluster;
configuring a switching parameter threshold of the target terminal based on a switching parameter threshold corresponding to the target cluster to complete the switching of the target terminal;
wherein, the switching parameter thresholds corresponding to different clusters are different.
Optionally, according to a handover method of an embodiment of the present application, before the obtaining RSRP measurement data samples of terminals in each cluster respectively, the method further includes:
acquiring Reference Signal Received Power (RSRP) measurement data of all cells covering the terminal to be clustered by the terminal to be clustered;
clustering all terminals to be clustered based on RSRP measurement data of all terminals to be clustered in a preset area;
and determining a switching parameter threshold corresponding to each cluster based on the receiving level quantity of the terminal in each cluster.
Optionally, according to the handover method of an embodiment of the present application, the determining the handover parameter threshold corresponding to each cluster based on the reception level amount of the terminal in each cluster specifically includes:
acquiring the receiving level quantity of all terminals in each cluster;
comparing the receiving level quantity of all the terminals of each cluster, and determining a switching parameter threshold corresponding to each cluster; the handover parameter threshold is inversely proportional to the amount of receive levels of all the terminals.
Optionally, according to a handover method of an embodiment of the present application, the obtaining reference signal received power, RSRP, measurement data of a terminal to be clustered to all cells covering the terminal to be clustered includes:
acquiring a first matrix formed by Reference Signal Received Power (RSRP) measurement data of all cells covering a terminal to be clustered by the terminal to be clustered;
based on the first matrix, obtaining a second matrix according to a TSNE (T-Stochastic neighbor Embedding, T distribution and random neighbor Embedding) algorithm, wherein the second matrix comprises sample data of each terminal to be clustered; and the sample data of the terminal to be clustered is used for describing RSRP measurement data of the terminal to be clustered.
Optionally, according to a switching method according to an embodiment of the present application, the obtaining a second matrix according to a TSNE algorithm based on the first matrix includes:
initializing sample data of each terminal to be clustered in the second matrix;
calculating to obtain the probability distribution p of the first matrix based on the RSRP measurement data of each terminal to be clustered in the first matrix;
iteratively updating the second matrix according to a TSNE algorithm based on the probability distribution p;
and after the second matrix is determined to be updated iteratively, acquiring the second matrix in the last updating process.
Optionally, according to a switching method according to an embodiment of the present application, iteratively updating the second matrix according to a TSNE algorithm based on the probability distribution p includes:
in each updating process, calculating and obtaining the probability distribution q of a second matrix based on sample data of each terminal in the second matrix obtained in the previous updating process;
calculating to obtain KL divergence between the probability distribution p and the probability distribution q;
updating the second matrix based on the KL (Kullback-Leibler Divergence, relative entropy) Divergence.
Optionally, according to a switching method of an embodiment of the present application, the determining that the iterative updating of the second matrix is finished includes:
the KL divergence is lower than a preset value; or the iterative update times exceed the preset iterative update times.
Optionally, according to a handover method of an embodiment of the present application, the method further includes:
clustering all the terminals to be clustered again every other first preset time, and determining a switching parameter threshold corresponding to each cluster; and/or
And re-determining the switching parameter threshold corresponding to each cluster every second preset time.
In a second aspect, an embodiment of the present application further provides a network device, including a memory, a transceiver, a processor:
a memory for storing a computer program; a transceiver for transceiving data under control of the processor; a processor for reading the computer program in the memory and performing the following operations:
respectively acquiring RSRP measurement data samples of terminals in each cluster;
determining a target cluster with the highest similarity to the RSRP measurement data of the target terminal based on the RSRP measurement data samples of the terminals in each cluster;
configuring a switching parameter threshold of the target terminal based on a switching parameter threshold corresponding to the target cluster to complete the switching of the target terminal;
wherein, the switching parameter thresholds corresponding to different clusters are different.
Optionally, according to the network device in an embodiment of the present application, before the obtaining RSRP measurement data samples of terminals in each cluster respectively, the operations further include:
acquiring Reference Signal Received Power (RSRP) measurement data of all cells covering the terminal to be clustered by the terminal to be clustered;
clustering all terminals to be clustered based on RSRP measurement data of all terminals to be clustered in a preset area;
and determining a switching parameter threshold corresponding to each cluster based on the receiving level quantity of the terminal in each cluster.
Optionally, according to the network device in an embodiment of the present application, the determining, based on the reception level amount of the terminal in each cluster, a handover parameter threshold corresponding to each cluster specifically includes:
acquiring the receiving level quantity of all terminals in each cluster;
comparing the receiving level quantity of all the terminals of each cluster, and determining a switching parameter threshold corresponding to each cluster; the handover parameter threshold is inversely proportional to the amount of receive levels of all the terminals.
Optionally, according to the network device in an embodiment of the present application, the obtaining reference signal received power, RSRP, measurement data of the terminal to be clustered to all cells covering the terminal to be clustered includes:
acquiring a first matrix formed by Reference Signal Received Power (RSRP) measurement data of all cells covering a terminal to be clustered by the terminal to be clustered;
based on the first matrix, obtaining a second matrix according to a TSNE algorithm, wherein the second matrix comprises sample data of each terminal to be clustered; and the sample data of the terminal to be clustered is used for describing RSRP measurement data of the terminal to be clustered.
Optionally, according to the network device according to an embodiment of the present application, the obtaining a second matrix according to a TSNE algorithm based on the first matrix includes:
initializing sample data of each terminal to be clustered in the second matrix;
calculating to obtain the probability distribution p of the first matrix based on the RSRP measurement data of each terminal to be clustered in the first matrix;
iteratively updating the second matrix according to a TSNE algorithm based on the probability distribution p;
and after the second matrix is determined to be updated iteratively, acquiring the second matrix in the last updating process.
Optionally, according to the network device according to an embodiment of the present application, the iteratively updating the second matrix according to the TSNE algorithm based on the probability distribution p includes:
in each updating process, calculating and obtaining the probability distribution q of a second matrix based on sample data of each terminal in the second matrix obtained in the previous updating process;
calculating to obtain KL divergence between the probability distribution p and the probability distribution q;
updating the second matrix based on the KL divergence.
Optionally, according to the network device in an embodiment of the present application, the determining that the iterative updating of the second matrix is finished includes:
the KL divergence is lower than a preset value; or the iterative update times exceed the preset iterative update times.
Optionally, according to the network device of an embodiment of the present application, the operations further include:
clustering all the terminals to be clustered again every other first preset time, and determining a switching parameter threshold corresponding to each cluster; and/or
And re-determining the switching parameter threshold corresponding to each cluster every second preset time.
In a third aspect, an embodiment of the present application further provides a switching device, including:
the acquisition module is used for respectively acquiring RSRP measurement data samples of the terminals in each cluster;
the determining module is used for determining a target cluster with the highest similarity to the RSRP measurement data of the target terminal based on the RSRP measurement data samples of the terminals in each cluster;
the switching module is used for configuring a switching parameter threshold of the target terminal based on the switching parameter threshold corresponding to the target cluster to complete the switching of the target terminal;
wherein, the switching parameter thresholds corresponding to different clusters are different.
In a fourth aspect, this application embodiment further provides a processor-readable storage medium, where the processor-readable storage medium stores a computer program, where the computer program is configured to cause the processor to execute the steps of the handover method according to the first aspect.
According to the switching method, the device, the equipment and the storage medium provided by the embodiment of the application, users are clustered, the switching parameter thresholds corresponding to different clusters are different, the cluster to which the target terminal belongs is determined, and the switching is performed after the switching parameter threshold corresponding to the cluster is determined, so that different switching parameter configurations can be performed on the terminals in different scenes, ping-pong switching is reduced, switching is avoided when the signal of the terminal is poor, call drop is reduced, and meanwhile, the system performance is improved through switching in advance or switching in a delayed mode.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic flowchart of a handover method according to an embodiment of the present application;
fig. 2 is a schematic flow chart of another handover method provided in the embodiment of the present application;
fig. 3 is a schematic structural diagram of a switching device according to an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a network device according to an embodiment of the present application.
Detailed Description
In the embodiment of the present application, the term "and/or" describes an association relationship of associated objects, and means that there may be three relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
In the embodiments of the present application, the term "plurality" means two or more, and other terms are similar thereto.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The embodiment of the application provides a switching method and a switching device, which are used for reducing ping-pong switching, avoiding switching when signals are poor, reducing call drop and improving user perception through switching in advance or switching in a delayed mode.
The method and the device are based on the same application concept, and because the principles of solving the problems of the method and the device are similar, the implementation of the device and the method can be mutually referred, and repeated parts are not repeated.
The NR (New Radio, New air interface) system handover algorithm employs a UE-assisted network control method, where a base station configures measurement parameters for a UE, the UE performs measurement according to the parameters configured by the base station and sends a measurement report, and the base station determines when to perform handover and to which target cell to handover. The switching scheme sets a uniform threshold for the switching parameters of all user terminals in the same cell, and does not perform distinguishing control on the users in the cell. In practice, the user terminal covered by the same cell has a complex scene, for example, for a situation where a certain cell covers both the trunk and the surrounding villages, the terminal with the fast moving trunk should be configured to switch as soon as possible under the situation, so that switching after more signal deterioration is avoided, and the risk of radio link failure is increased; meanwhile, for the access terminal at the coverage edge of the surrounding residential area, the switching parameters are expected to be more conservative, and the frequent occurrence of ping-pong switching is avoided, and the two requirements are contradictory. Similar handover scenarios are many, and for a scheme using uniform handover parameters, it is easy to happen in some scenarios that some user terminals are handed over too late or too early, which affects user perception and system performance.
In order to solve the above problem, embodiments of the present application provide a multi-scenario recognition method, which is characterized in that all terminal clusters that belong to the same cell but have different handover requirements in a target area are separable based on Receiving RSRP values (Reference Signal Receiving powers) of all cells in a user measurement report. Then, the characteristics of the RSRP in each cluster and among different clusters are statistically analyzed to determine the switching requirement level of the terminal in different clusters under the coverage of a certain cell. Therefore, the network equipment can carry out different switching parameter configurations on the terminals with different types of characteristics. The method can reduce ping-pong switching, avoid switching when the signal is poor, reduce call drop, and improve user perception by switching in advance or switching in a delayed manner.
The present application is described in detail below with reference to several embodiments:
fig. 1 is a schematic flowchart of a handover method provided in an embodiment of the present application, and as shown in fig. 1, the method includes the following steps:
step 100, respectively obtaining RSRP measurement data samples of terminals in each cluster;
specifically, in this embodiment, users in the target area are clustered, all users which belong to the same cell but have different handover requirements are separated, and then RSRP characteristics of terminals in each cluster are statistically analyzed, so as to configure different handover parameter thresholds for terminals in different clusters.
Therefore, when a target terminal is newly accessed to a network, in order to determine a handover parameter threshold of the target terminal, RSRP measurement data samples of terminals in each cluster may be first obtained respectively;
specifically, RSRP measurement data samples may be extracted according to the number proportion of terminals in each cluster; for example, the ratio of the number of terminals in clusters a, B, C and D is 2:3:4:5, the ratio of extracting RSRP measurement data samples in clusters a, B, C and D, respectively, may be 2:3:4: 5.
Specifically, the RSRP measurement data samples extracted from each cluster may remain unchanged for a certain time, and when a newly added target terminal needs to be switched, the subsequent steps may be directly completed based on the existing RSRP measurement data samples, and the RSRP measurement data samples extracted from each cluster may also be updated irregularly.
Specifically, the present embodiment does not limit the manner and number of obtaining RSRP measurement data samples of terminals in each cluster.
Step 110, determining a target cluster with highest similarity to the RSRP measurement data of the target terminal based on the RSRP measurement data samples of the terminals in each cluster;
specifically, after the RSRP measurement data samples of the terminals in each cluster are obtained, the RSRP measurement data of the target terminal may be compared with the RSRP measurement data samples of the terminals in each cluster, a cluster with the highest similarity to the RSRP measurement data of the target terminal is determined as the target cluster, and the handover parameter threshold of the target cluster is correspondingly obtained as the handover parameter threshold of the target terminal.
Specifically, when the RSRP measurement data of the target terminal is compared with the RSRP measurement data samples of the terminals in each cluster, the RSRP measurement data of the target terminal and the RSRP measurement data samples of the terminals in each cluster may be subjected to average similarity calculation based on gaussian similarity or euclidean distance, and the cluster with the highest RSRP measurement data similarity with the target terminal is determined as the target cluster.
In this embodiment, a linear or nonlinear similarity calculation method, including but not limited to a gaussian similarity method and an euclidean distance method, is used to calculate the difference between the RSRP measurement data of the target terminal and the RSRP measurement data samples of the terminals in each cluster, determine the attribute of the samples, and accordingly complete the personalized handover of the target terminal.
And 120, configuring a switching parameter threshold of the target terminal based on the switching parameter threshold corresponding to the target cluster, and completing the switching of the target terminal.
Specifically, after determining that the cluster with the highest RSRP measurement data similarity with the target terminal is the target cluster, the switching parameter threshold of the target cluster may be correspondingly obtained as the switching parameter threshold of the target terminal, and the switching parameter threshold of the target terminal is configured, so as to complete the switching operation of the target terminal based on the switching parameter threshold of the target terminal.
The embodiment can distinguish multiple scenes to carry out flexible personalized switching operation on the access terminal, and improves user perception.
Taking an access terminal as an example, the terminal triggers an A3 event and reports the event in the process of moving from a main cell to a neighboring cell, and the network side extracts RSRP characteristic data and compares the RSRP characteristic data with RSRP measurement data samples of terminals in each cluster, and completes switching work according to a preset switching parameter threshold corresponding to each cluster. Therefore, not only can ping-pong switching be reduced, switching is avoided when signals are poor, and user perception can be improved by switching in advance or switching in a delayed mode while call drop is reduced.
According to the switching method provided by the embodiment of the application, the cluster to which the target terminal belongs is determined and the corresponding switching parameter threshold is determined to be switched by clustering the users, wherein the switching parameter thresholds corresponding to different clusters are different, so that different switching parameter configurations can be performed on the terminals under different scenes, ping-pong switching is reduced, switching is avoided when the terminal signals are poor, and the system performance is improved by switching in advance or switching in a delayed manner while call drop is reduced.
Optionally, based on any of the above embodiments, before the obtaining RSRP measurement data samples of terminals in each cluster respectively, the method further includes:
acquiring Reference Signal Received Power (RSRP) measurement data of all cells covering the terminal to be clustered by the terminal to be clustered;
clustering all terminals to be clustered based on RSRP measurement data of all terminals to be clustered in a preset area;
and determining a switching parameter threshold corresponding to each cluster based on the receiving level quantity of the terminal in each cluster.
Specifically, before determining the cluster to which the target terminal belongs, all clusters and the handover parameter threshold corresponding to each cluster need to be determined first.
Therefore, Reference Signal Received Power (RSRP) measurement data of all cells covering the terminal to be clustered by the terminal to be clustered can be obtained firstly, all the terminals to be clustered are clustered based on the RSRP measurement data of all the terminals to be clustered in a preset area, and all users covering the same cell and having different switching requirements are separated.
Specifically, in order to obtain the handover parameter threshold corresponding to each cluster, different handover requirements of terminals in different clusters may be obtained based on a relationship between the received levels of the terminals in each cluster and a time variation of the received levels of the terminals in each cluster, and different handover parameter thresholds (e.g., different hysteresis times and/or different cell offsets) may be further set.
Specifically, when all the terminals to be clustered are clustered, clustering manners such as DBSCAN clustering and K-means clustering may be adopted, and all the manners that can realize clustering of the terminals to be clustered in this embodiment are applicable to this embodiment, which is not limited in this embodiment.
Taking the Clustering of DBSCAN (Density-Based Spatial Clustering of Applications with Noise, Density-Based Clustering algorithm) as an example, the principle of DBSCAN Clustering is the class Clustering of high-Density population. And if more than or equal to epsilon data points corresponding to the terminal to be clustered exist in the r neighborhood of the data point corresponding to a certain terminal to be clustered, the data point corresponding to the terminal to be clustered is called a core object. The parameter r describes a neighborhood distance threshold for a certain data point, and epsilon describes a threshold for the number of samples in a neighborhood where the distance of a certain sample is r. All points within this r neighborhood of the core object continue in this way until there are no more data points that satisfy the condition.
Optionally, based on any one of the above embodiments, the determining, based on the reception level amount of the terminal in each cluster, a handover parameter threshold corresponding to each cluster specifically includes:
acquiring the receiving level quantity of all terminals in each cluster;
comparing the receiving level quantity of all the terminals of each cluster, and determining a switching parameter threshold corresponding to each cluster; the handover parameter threshold is inversely proportional to the amount of receive levels of all the terminals.
Specifically, after the clustering of the terminals with different switching requirements is determined, the corresponding data characteristics can be calculated. By comparing the terminal level variation of different clusters, the OMC (Operation and Maintenance Center) can flexibly set different threshold thresholds of the handover parameters for different scenes.
Specifically, within a fixed time range, the variation of the level of the terminal in each cluster along with the time can be counted, and the variation of the level is in direct proportion to the switching requirement of the terminal in the cluster, so that the switching requirement degree of the access terminal in each cluster can be determined, and the switching parameter threshold can be distributed to different clusters according to the corresponding proportion by comparing the variation of the level, such as parameters of switching delay time and the like.
For example, the larger the level change, the shorter the hysteresis time, and the faster the switching needs to be completed.
For example, the larger the level change amount, the smaller the cell offset amount, and the faster the handover needs to be completed.
Therefore, the reception level amounts of all terminals in each cluster may be acquired first; and determining a switching parameter threshold corresponding to each cluster based on the receiving level quantity of all the terminals of each cluster.
It can be understood that, when determining the handover parameter threshold corresponding to each cluster, it is sufficient to set a condition that the larger the level change amount is, the shorter the delay time is, and the smaller the cell offset is.
For example, the handover parameter threshold may be set to be inversely proportional to the receiving level amount of all terminals, that is, the larger the receiving level amount is, the smaller the handover parameter threshold is; the handover parameter threshold may also be set to be inversely proportional to the receiving level amounts of all terminals, for example, the receiving level amounts corresponding to clusters a, B, C and D are 1:2:3:4, and the handover parameter threshold corresponding to clusters a, B, C and D may be set to be 12:6:4: 3.
Optionally, based on any one of the above embodiments, the obtaining reference signal received power, RSRP, measurement data of the terminal to be clustered, which covers all cells of the terminal to be clustered, includes:
acquiring a first matrix formed by Reference Signal Received Power (RSRP) measurement data of all cells covering a terminal to be clustered by the terminal to be clustered;
based on the first matrix, obtaining a second matrix according to a TSNE algorithm, wherein the second matrix comprises sample data of each terminal to be clustered; and the sample data of the terminal to be clustered is used for describing RSRP measurement data of the terminal to be clustered.
Specifically, due to the complexity of the wireless communication environment, when a terminal to be clustered measures reference signal received power RSRP of all cells covering the terminal to be clustered, interference of different degrees is caused, and data generated by the same terminal to be clustered at different times in the same place is different. In order to eliminate redundant information and reduce interference in a complex environment, collected measurement data of a terminal to be clustered needs to be preprocessed. The purpose of preprocessing can not only remove noise, but also can make the same type of data closer and different types of data far away from each other in the measured data of the terminal to be clustered after data preprocessing.
In this embodiment, the sample data of the high-dimensional space can be mapped to the low-dimensional space with the minimum data structure loss through the TSNE algorithm, and meanwhile, the data of the same type in the sample data of the terminal to be clustered is closer, and the data of different types are far away from each other.
Specifically, a first matrix, i.e., sample data of a high-dimensional space, formed by reference signal received power RSRP measurement data of all cells covering the terminal to be clustered by the terminal to be clustered may be first obtained, and then a second matrix, i.e., pre-processed measurement data of the terminal to be clustered may be obtained according to a TSNE algorithm.
It can be understood that the elements in the first matrix and the second matrix are used for describing the RSRP measurement data of the terminal to be clustered, the first matrix is directly composed of the RSRP measurement data of the terminal to be clustered, and the second matrix is used for describing the preprocessed RSRP measurement data.
In this embodiment, after the measured data is preprocessed through the TSNE algorithm, the measured data in the high-dimensional space is mapped to the low-dimensional space, and the same type of data is closer to the data structure while the data structure is still maintained, and the heterogeneous data is further away from the data structure. And further, clustering analysis is carried out on the data of the low-dimensional space, so that clear multi-scene classification can be given.
Specifically, in this embodiment, when preprocessing the measurement data, other algorithms or models than the TSNE algorithm may be used, as long as the preprocessing processes or algorithms similar to dimension compression or redundant information removal can be implemented, which are applicable to this embodiment, and this embodiment is not limited thereto.
Optionally, based on any one of the foregoing embodiments, the obtaining, based on the first matrix, the second matrix according to the TSNE algorithm includes:
initializing sample data of each terminal to be clustered in the second matrix;
calculating to obtain the probability distribution p of the first matrix based on the RSRP measurement data of each terminal to be clustered in the first matrix;
iteratively updating the second matrix according to a TSNE algorithm based on the probability distribution p;
and after the second matrix is determined to be updated iteratively, acquiring the second matrix in the last updating process.
Specifically, sample data of each terminal to be clustered in the second matrix may be initialized first;
specifically, the probability distribution p of the first matrix can be calculated and obtained based on RSRP measurement data of each terminal to be clustered in the first matrix; and iteratively updating the second matrix according to a TSNE algorithm based on the probability distribution p, and acquiring the second matrix in the last updating process after the process of determining that the second matrix is updated iteratively is completely finished, and performing subsequent clustering processing on the second matrix as the preprocessed measurement data.
Specifically, it is assumed that input data of the TSNE algorithm is expressed as x ═ x (x)1,x2,x3,...,xn) I.e. a first matrix, wherein the ith measurement data is represented as xi＝(xi1,xi2,...,xim). In the high-dimensional space, the data structure is represented using a joint probability distribution of the respective measurement data, as follows:
Figure BDA0002736033990000141
wherein p isijRepresenting data x in a high dimensional spaceiAnd xjThe nearest neighbor probability between, σ is determined according to the maximum entropy principle,
Figure BDA0002736033990000142
representing a given data point xiProbability distribution of all other data. σ centered on each sample point is required to make the entropy of the final distribution small, usually bounded by log (k), which is the number of determined neighborhood points. In TSNE, a binary search is used to find an optimal σ iteratively, using the confusion as a measure.
Wherein x is1,x2,x3,...,xnIt is indicated that the 1 st terminal,2 nd terminal, 3 rd terminal, …, nth terminal; the ith measurement data represents the measurement data of the ith terminal, and m represents the coverage terminal xiNumber of cells, xi1,xi2,...,ximFinger terminal xiRSRP measurement data in 1 st cell, terminal xiRSRP measurement data in cell 2, …, terminal xiRSRP measurement data at the mth cell.
In the embodiment, the switching requirement degree of the terminal in each cluster can be divided and discharged by using but not limited to a time sequence statistical method, that is, the switching requirement degree is sequenced on the characteristics of the cluster data according to the time sequence data and the scene recognition result, and a corresponding switching parameter threshold is configured to improve the user perception.
Optionally, based on any one of the above embodiments, the iteratively updating the second matrix according to the TSNE algorithm based on the probability distribution p includes:
in each updating process, calculating and obtaining the probability distribution q of a second matrix based on sample data of each terminal in the second matrix obtained in the previous updating process;
calculating to obtain KL divergence between the probability distribution p and the probability distribution q;
updating the second matrix based on the KL divergence.
Specifically, the data is represented as y ═ (y) in the low-dimensional space1,y2,y3,...,yn) I.e. a second matrix, where yi＝(yi1,yi2,...,yit) T is the dimension of the sample i after being processed; y is1,y2,y3,...,ynDenotes the 1 st terminal, the 2 nd terminal, the 3 rd terminal, …, the nth terminal; y isi1,yi2,...,yitFinger terminal yiSample data in cell 1, terminal yiSample data in cell 2, …, terminal yiSample data in the t-th cell.
Specifically, in each updating process, based on sample data of each terminal in the second matrix obtained in the previous updating process, the probability distribution q of the second matrix is calculated; the lower dimensional spatial distribution is represented by a more general T distribution, as follows:
Figure BDA0002736033990000151
wherein q isijRepresenting data y in a low-dimensional spaceiAnd yjA neighbor probability therebetween;
specifically, in the current updating process, after the probability distribution q of the second matrix is determined, the KL divergence between the probability distribution p and the probability distribution q may be calculated.
Specifically, in this embodiment, the TSNE algorithm may measure the similarity between the probability distributions p and q using KL divergence, which is expressed as follows:
Figure BDA0002736033990000152
wherein Q isiRepresenting a given data point yiProbability distribution with all other data; in this embodiment, in each updating process, the optimized objective loss function needs to be calculated, and it can be understood that the smaller the KL divergence is, the closer the data structures of the high-dimensional space and the low-dimensional space are, the better the data processing effect is.
Specifically, in each updating process, after calculating the optimization objective loss function to obtain the degree of similarity between p and q, i.e. KL divergence, a gradient descent method may be adopted in the optimization process, where the gradient is expressed as follows:
Figure BDA0002736033990000153
in the low-dimensional space, the data updating mode is as follows:
Figure BDA0002736033990000161
wherein, ytRepresents data in the low-dimensional space at time t, and α is the learning rate.
In this embodiment, each update process updates y to obtain an updated second matrix.
Optionally, based on any of the above embodiments, the determining that the iterative updating of the second matrix is finished includes:
the KL divergence is lower than a preset value; or the iterative update times exceed the preset iterative update times.
Specifically, when the iterative update process of the second matrix is determined to be ended, the KL divergence may be determined based on the KL divergence, for example, when the KL divergence is determined to be lower than a preset value, the iterative update process of the second matrix is determined to be ended;
specifically, when the iterative update process of the second matrix is determined to be finished, the iterative update process may be determined based on a preset update time, for example, if the preset update time is 50 times, the update is finished after the second matrix is updated 50 times, and the second matrix in the 50 th update process is used for the subsequent clustering process based on the preprocessed measurement data.
Optionally, based on any one of the above embodiments, the method further includes:
clustering all the terminals to be clustered again every other first preset time, and determining a switching parameter threshold corresponding to each cluster; and/or
And re-determining the switching parameter threshold corresponding to each cluster every second preset time.
Specifically, the cluster may be updated irregularly, and correspondingly the handover parameter threshold may also be updated irregularly; when the cluster is not updated, the switching parameter threshold corresponding to the cluster can be updated irregularly. The target of flexible handover is met, and accordingly, the RSRP measurement data samples extracted from each cluster each time can be updated.
According to the switching method provided by the embodiment of the application, the cluster to which the target terminal belongs is determined and the corresponding switching parameter threshold is determined to be switched by clustering the users, wherein the switching parameter thresholds corresponding to different clusters are different, so that different switching parameter configurations can be performed on the terminals under different scenes, ping-pong switching is reduced, switching is avoided when the terminal signals are poor, and the system performance is improved by switching in advance or switching in a delayed manner while call drop is reduced.
Fig. 2 is a schematic flow chart of another handover method provided in an embodiment of the present application, and as shown in fig. 2, the method includes the following steps:
step 200, preprocessing data;
specifically, after the RSRP measurement data of the terminal to be clustered is obtained, the sample data of the high-dimensional space can be mapped to the low-dimensional space with the minimum data structure loss through the TSNE algorithm, and meanwhile, the data of the same type in the sample data of the terminal to be clustered are closer, and the data of different types are far away from each other.
Specifically, a first matrix, namely sample data of a high-dimensional space, formed by reference signal received power RSRP measurement data of all cells covering the terminal to be clustered by the terminal to be clustered may be first obtained, and then a second matrix may be obtained according to a TSNE algorithm, so that noise points may be removed, and after data preprocessing, the same type of data in the measurement data of the terminal to be clustered may be closer, and different types of data may be far away from each other.
Specifically, it is assumed that input data of the TSNE algorithm is expressed as x ═ x (x)1,x2,x3,...,xn) I.e. a first matrix, wherein the ith measurement data is represented as xi＝(xi1,xi2,...,xim). In the high-dimensional space, the data structure is represented using a joint probability distribution of the respective measurement data, as follows:
Figure BDA0002736033990000171
wherein p isijRepresenting data x in a high dimensional spaceiAnd xjThe nearest neighbor probability between, σ is determined according to the maximum entropy principle,
Figure BDA0002736033990000172
Pirepresenting a given data point xiProbability distribution of all other data. σ centered on each sample point is required to make the entropy of the final distribution small, usually bounded by log (k), which is the number of determined neighborhood points. In TSNE, a binary search is used to find an optimal σ iteratively, using the confusion as a measure.
Wherein x is1,x2,x3,...,xnDenotes the 1 st terminal, the 2 nd terminal, the 3 rd terminal, …, the nth terminal; the ith measurement data represents the measurement data of the ith terminal, and m represents the coverage terminal xiNumber of cells, xi1,xi2,...,ximFinger terminal xiRSRP measurement data in 1 st cell, terminal xiRSRP measurement data in cell 2, …, terminal xiRSRP measurement data at the mth cell.
And then, iteratively updating the second matrix according to a TSNE algorithm based on the probability distribution p of the first matrix, and after the process of determining that the second matrix is updated iteratively is completely finished, acquiring the second matrix in the last updating process as the preprocessed measurement data to perform subsequent clustering.
Specifically, the data is represented as y ═ (y) in the low-dimensional space1,y2,y3,...,yn) I.e. a second matrix, where yi＝(yi1,yi2,...,yit) T is the dimension of the sample i after being processed; y is1,y2,y3,...,ynDenotes the 1 st terminal, the 2 nd terminal, the 3 rd terminal, …, the nth terminal; y isi1,yi2,...,yitFinger terminal yiSample data in cell 1, terminal yiSample data in cell 2, …, terminal yiSample data in the t-th cell.
Specifically, in each updating process of y, based on sample data of each terminal in the second matrix obtained in the previous updating process, the probability distribution q of the second matrix is calculated; the lower dimensional spatial distribution is represented by a more general T distribution, as follows:
Figure BDA0002736033990000181
wherein q isijRepresenting data y in a low-dimensional spaceiAnd yjThe neighbor probability in between. Specifically, in the current updating process, after the probability distribution q of the second matrix is determined, the KL divergence between the probability distribution p and the probability distribution q may be calculated.
Specifically, in this embodiment, the TSNE algorithm may measure the similarity between the probability distributions p and q using KL divergence, which is expressed as follows:
Figure BDA0002736033990000182
wherein Q isiRepresenting a given data point yiProbability distribution with all other data. In this embodiment, in each updating process, the optimized objective loss function needs to be calculated, and it can be understood that the smaller the KL divergence is, the closer the data structures of the high-dimensional space and the low-dimensional space are, the better the data processing effect is.
Specifically, in each updating process, after calculating the optimization objective loss function to obtain the degree of similarity between p and q, i.e. KL divergence, a gradient descent method may be adopted in the optimization process, where the gradient is expressed as follows:
Figure BDA0002736033990000191
in the low-dimensional space, the data updating mode is as follows:
Figure BDA0002736033990000192
wherein, ytRepresents data in the low-dimensional space at time t, and α is the learning rate.
In this embodiment, each update process updates y to obtain an updated second matrix.
Step 210, clustering samples;
in particular, the preprocessed measurement data samples may be clustered.
Taking DBSCAN clustering as an example, the principle of DBSCAN clustering is the class clustering of high-density groups. And if more than or equal to epsilon data points corresponding to the terminal to be clustered exist in the r neighborhood of the data point corresponding to a certain terminal to be clustered, the data point corresponding to the terminal to be clustered is called a core object. The parameter r describes a neighborhood distance threshold for a certain data point, and epsilon describes a threshold for the number of samples in a neighborhood where the distance of a certain sample is r. All points within this r neighborhood of the core object continue in this way until there are no more data points that satisfy the condition.
Step 220, multi-scene analysis;
specifically, after the clusters of multiple different scenes are obtained, different handover requirements of the terminals in different clusters can be obtained based on the relationship between the terminal reception levels in each cluster and the time variation and the comparison between the level strength variation strengths among the clusters, and different handover parameter thresholds (such as different delay times and/or different cell offsets) are further set.
Step 230, switching management;
specifically, when a target terminal is newly added, after an RSRP measurement data sample of a terminal in each cluster is obtained, the RSRP measurement data of the target terminal may be compared with the RSRP measurement data sample of the terminal in each cluster, a cluster with the highest RSRP measurement data similarity to the target terminal is determined as the target cluster, and a handover parameter threshold of the target cluster is correspondingly obtained as the handover parameter threshold of the target terminal. The base station will complete its switching according to the switching parameter threshold.
And step 240, personalized switching.
Specifically, in the process that an access terminal moves from a main cell to a neighboring cell, an a3 event is triggered and reported, and the network extracts RSRP characteristic data of the access terminal and compares the RSRP characteristic data with RSRP measurement data samples of terminals in each cluster, and completes switching work according to a switching parameter threshold corresponding to each cluster.
The multi-scene recognition method based on TSNE combined with DBSCAN provided in the embodiment of the present application is characterized by Receiving RSRP values (Reference Signal Receiving Power) of all cells in a user measurement report, and can separate all terminals in a target area that belong to the same cell but have different handover requirements. And then, the variation of the RSRP in each cluster and among different clusters along with time is analyzed through time series data statistics to determine the switching requirement level of different clusters under the coverage of a certain cell. Therefore, the base station can carry out different switching parameter configurations on the terminals with different types of characteristics. The method can reduce ping-pong switching, avoid switching when the signal is poor, reduce call drop, and improve user perception by switching in advance or switching in a delayed manner.
The technical scheme provided by the embodiment of the application can be suitable for various systems, particularly 5G systems. For example, the applicable system may be a global system for mobile communication (GSM) system, a Code Division Multiple Access (CDMA) system, a Wideband Code Division Multiple Access (WCDMA) General Packet Radio Service (GPRS) system, a long term evolution (long term evolution, LTE) system, an LTE Frequency Division Duplex (FDD) system, an LTE Time Division Duplex (TDD) system, an LTE-a (long term evolution) system, a universal mobile system (universal mobile telecommunications system, UMTS), a Worldwide Interoperability for Mobile Access (WiMAX) system, a New Radio network (NR 5) system, etc. These various systems include terminal devices and network devices. The System may further include a core network portion, such as an Evolved Packet System (EPS), a 5G System (5GS), and the like.
The terminal device referred to in the embodiments of the present application may refer to a device providing voice and/or data connectivity to a user, a handheld device having a wireless connection function, or another processing device connected to a wireless modem. In different systems, the names of the terminal devices may be different, for example, in a 5G system, the terminal device may be called a User Equipment (UE). A wireless terminal device, which may be a mobile terminal device such as a mobile telephone (or "cellular" telephone) and a computer having a mobile terminal device, for example, a portable, pocket, hand-held, computer-included, or vehicle-mounted mobile device, may communicate with one or more Core Networks (CNs) via a Radio Access Network (RAN). Examples of such devices include Personal Communication Service (PCS) phones, cordless phones, Session Initiation Protocol (SIP) phones, Wireless Local Loop (WLL) stations, and Personal Digital Assistants (PDAs). The wireless terminal device may also be referred to as a system, a subscriber unit (subscriber unit), a subscriber station (subscriber station), a mobile station (mobile), a remote station (remote station), an access point (access point), a remote terminal device (remote terminal), an access terminal device (access terminal), a user terminal device (user terminal), a user agent (user agent), and a user device (user device), which are not limited in this embodiment of the present application.
The network device according to the embodiment of the present application may be a base station, and the base station may include a plurality of cells for providing services to a terminal. A base station may also be referred to as an access point, or a device in an access network that communicates over the air-interface, through one or more sectors, with wireless terminal devices, or by other names, depending on the particular application. The network device may be configured to exchange received air frames with Internet Protocol (IP) packets as a router between the wireless terminal device and the rest of the access network, which may include an Internet Protocol (IP) communication network. The network device may also coordinate attribute management for the air interface. For example, the network device according to the embodiment of the present application may be a Base Transceiver Station (BTS) in a Global System for Mobile communications (GSM) or a Code Division Multiple Access (CDMA), may be a network device (NodeB) in a Wideband Code Division Multiple Access (WCDMA), may be an evolved Node B (eNB or e-NodeB) in a Long Term Evolution (LTE) System, may be a 5G Base Station (gbb) in a 5G network architecture (next evolution System), may be a Home evolved Node B (HeNB), a relay Node (relay Node), a Home Base Station (femto), a pico Base Station (pico Base Station), and the like, which are not limited in the embodiments of the present application. In some network architectures, a network device may include a Centralized Unit (CU) node and a Distributed Unit (DU) node, which may also be geographically separated.
Fig. 3 is a schematic structural diagram of a switching device according to an embodiment of the present application, and as shown in fig. 3, the switching device includes: an obtaining module 310, a determining module 320 and a switching module 330; wherein,
the obtaining module 310 is configured to obtain RSRP measurement data samples of terminals in each cluster respectively;
the determining module 320 is configured to determine, based on the RSRP measurement data samples of the terminals in each cluster, a target cluster with the highest RSRP measurement data similarity to a target terminal;
the switching module 330 is configured to configure a switching parameter threshold of the target terminal based on the switching parameter threshold corresponding to the target cluster, so as to complete switching of the target terminal;
wherein, the switching parameter thresholds corresponding to different clusters are different.
Specifically, after the acquiring module 310 acquires the RSRP measurement data samples of the terminals in each cluster, the switching device determines, by the determining module 320, a target cluster with the highest RSRP measurement data similarity to the target terminal based on the RSRP measurement data samples of the terminals in each cluster; and finally, configuring a switching parameter threshold of the target terminal through a switching module based on the switching parameter threshold corresponding to the target cluster, and completing the switching of the target terminal.
It should be noted that the apparatus provided in the embodiment of the present application can implement all the method steps implemented by the method embodiment and achieve the same technical effect, and detailed descriptions of the same parts and beneficial effects as the method embodiment in this embodiment are omitted here.
The switching device provided by the embodiment of the application can realize different switching parameter configurations for the terminals under different scenes by clustering users and determining the cluster to which the target terminal belongs and switching after determining the corresponding switching parameter threshold, thereby reducing ping-pong switching, avoiding switching when the signal of the terminal is poor, reducing call drop and improving the system performance by switching in advance or switching in a delayed manner.
Fig. 4 is a schematic structural diagram of a network device according to an embodiment of the present application, where as shown in fig. 4, the network device includes a memory 420, a transceiver 400, and a processor 410:
a memory 420 for storing a computer program; a transceiver 400 for transceiving data under the control of the processor 410; a processor 410 for reading the computer program in the memory and performing the following operations:
respectively acquiring RSRP measurement data samples of terminals in each cluster;
determining a target cluster with the highest similarity to the RSRP measurement data of the target terminal based on the RSRP measurement data samples of the terminals in each cluster;
configuring a switching parameter threshold of the target terminal based on a switching parameter threshold corresponding to the target cluster to complete the switching of the target terminal;
wherein, the switching parameter thresholds corresponding to different clusters are different.
A transceiver 400 for receiving and transmitting data under the control of a processor 410.
Where in fig. 4, the bus architecture may include any number of interconnected buses and bridges, with one or more processors represented by processor 410 and various circuits of memory represented by memory 420 being linked together. The bus architecture may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. The bus interface provides an interface. The transceiver 400 may be a plurality of elements including a transmitter and a receiver, and a single unit 410 provided for communication with various other devices over a transmission medium is responsible for managing the bus architecture and general processing, and a memory 420 may store data used by the processor 410 in performing operations.
The processor 410 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or a Complex Programmable Logic Device (CPLD), and may also have a multi-core architecture.
It should be noted that, the apparatus provided in the embodiment of the present invention can implement all the method steps implemented by the method embodiment and achieve the same technical effect, and detailed descriptions of the same parts and beneficial effects as the method embodiment in this embodiment are omitted here.
Optionally, based on any of the above embodiments, before the obtaining RSRP measurement data samples of terminals in each cluster respectively, the operations further include:
acquiring Reference Signal Received Power (RSRP) measurement data of all cells covering the terminal to be clustered by the terminal to be clustered;
clustering all terminals to be clustered based on RSRP measurement data of all terminals to be clustered in a preset area;
and determining a switching parameter threshold corresponding to each cluster based on the receiving level quantity of the terminal in each cluster.
Specifically, the device provided in the embodiment of the present application can implement all the method steps implemented by the method embodiment and achieve the same technical effects, and details of the same parts and beneficial effects as those of the method embodiment in this embodiment are not repeated herein.
Optionally, based on any one of the above embodiments, the determining, based on the reception level amount of the terminal in each cluster, a handover parameter threshold corresponding to each cluster specifically includes:
acquiring the receiving level quantity of all terminals in each cluster;
comparing the receiving level quantity of all the terminals of each cluster, and determining a switching parameter threshold corresponding to each cluster; the handover parameter threshold is inversely proportional to the amount of receive levels of all the terminals.
Specifically, the device provided in the embodiment of the present application can implement all the method steps implemented by the method embodiment and achieve the same technical effects, and details of the same parts and beneficial effects as those of the method embodiment in this embodiment are not repeated herein.
Optionally, based on any one of the above embodiments, the obtaining reference signal received power, RSRP, measurement data of the terminal to be clustered, which covers all cells of the terminal to be clustered, includes:
acquiring a first matrix formed by Reference Signal Received Power (RSRP) measurement data of all cells covering a terminal to be clustered by the terminal to be clustered;
based on the first matrix, obtaining a second matrix according to a TSNE algorithm, wherein the second matrix comprises sample data of each terminal to be clustered; and the sample data of the terminal to be clustered is used for describing RSRP measurement data of the terminal to be clustered.
Specifically, the device provided in the embodiment of the present application can implement all the method steps implemented by the method embodiment and achieve the same technical effects, and details of the same parts and beneficial effects as those of the method embodiment in this embodiment are not repeated herein.
Optionally, based on any one of the foregoing embodiments, the obtaining, based on the first matrix, the second matrix according to the TSNE algorithm includes:
initializing sample data of each terminal to be clustered in the second matrix;
calculating to obtain the probability distribution p of the first matrix based on the RSRP measurement data of each terminal to be clustered in the first matrix;
iteratively updating the second matrix according to a TSNE algorithm based on the probability distribution p;
and after the second matrix is determined to be updated iteratively, acquiring the second matrix in the last updating process.
Specifically, the device provided in the embodiment of the present application can implement all the method steps implemented by the method embodiment and achieve the same technical effects, and details of the same parts and beneficial effects as those of the method embodiment in this embodiment are not repeated herein.
Optionally, based on any one of the above embodiments, the iteratively updating the second matrix according to the TSNE algorithm based on the probability distribution p includes:
in each updating process, calculating and obtaining the probability distribution q of a second matrix based on sample data of each terminal in the second matrix obtained in the previous updating process;
calculating to obtain KL divergence between the probability distribution p and the probability distribution q;
updating the second matrix based on the KL divergence.
Specifically, the device provided in the embodiment of the present application can implement all the method steps implemented by the method embodiment and achieve the same technical effects, and details of the same parts and beneficial effects as those of the method embodiment in this embodiment are not repeated herein.
Optionally, based on any of the above embodiments, the determining that the iterative updating of the second matrix is finished includes:
the KL divergence is lower than a preset value; or the iterative update times exceed the preset iterative update times.
Specifically, the device provided in the embodiment of the present application can implement all the method steps implemented by the method embodiment and achieve the same technical effects, and details of the same parts and beneficial effects as those of the method embodiment in this embodiment are not repeated herein.
Optionally, based on any one of the above embodiments, the operations further include:
clustering all the terminals to be clustered again every other first preset time, and determining a switching parameter threshold corresponding to each cluster; and/or
And re-determining the switching parameter threshold corresponding to each cluster every second preset time.
Specifically, the device provided in the embodiment of the present application can implement all the method steps implemented by the method embodiment and achieve the same technical effects, and details of the same parts and beneficial effects as those of the method embodiment in this embodiment are not repeated herein.
It should be noted that the division of the unit in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation. In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented as a software functional unit and sold or used as a stand-alone product, may be stored in a processor readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
It should be noted that, the apparatus provided in the embodiment of the present invention can implement all the method steps implemented by the method embodiment and achieve the same technical effect, and detailed descriptions of the same parts and beneficial effects as the method embodiment in this embodiment are omitted here.
On the other hand, an embodiment of the present application further provides a processor-readable storage medium, where the processor-readable storage medium stores a computer program, where the computer program is configured to cause the processor to execute the method provided in each of the above embodiments, and the method includes:
respectively acquiring RSRP measurement data samples of terminals in each cluster;
determining a target cluster with the highest similarity to the RSRP measurement data of the target terminal based on the RSRP measurement data samples of the terminals in each cluster;
configuring a switching parameter threshold of the target terminal based on a switching parameter threshold corresponding to the target cluster to complete the switching of the target terminal;
wherein, the switching parameter thresholds corresponding to different clusters are different.
In the processor-readable storage medium provided in this embodiment, the computer program stored thereon enables the processor to implement all the method steps implemented by the foregoing method embodiments, and achieve the same technical effects, and detailed descriptions of the same parts and beneficial effects as those of the method embodiments are omitted here.
The processor-readable storage medium can be any available medium or data storage device that can be accessed by a processor, including, but not limited to, magnetic memory (e.g., floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc.), optical memory (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor memory (e.g., ROMs, EPROMs, EEPROMs, non-volatile memory (NAND FLASH), Solid State Disks (SSDs)), etc.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer-executable instructions. These computer-executable instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These processor-executable instructions may also be stored in a processor-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the processor-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These processor-executable instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.

Claims (18)

1. A method of handover, comprising:
respectively acquiring RSRP measurement data samples of terminals in each cluster;
determining a target cluster with the highest similarity to the RSRP measurement data of the target terminal based on the RSRP measurement data samples of the terminals in each cluster;
configuring a switching parameter threshold of the target terminal based on a switching parameter threshold corresponding to the target cluster to complete the switching of the target terminal;
wherein, the switching parameter thresholds corresponding to different clusters are different.
2. The handover method according to claim 1, wherein before the separately obtaining RSRP measurement data samples for terminals in each cluster, the method further comprises:
acquiring Reference Signal Received Power (RSRP) measurement data of all cells covering the terminal to be clustered by the terminal to be clustered;
clustering all terminals to be clustered based on RSRP measurement data of all terminals to be clustered in a preset area;
and determining a switching parameter threshold corresponding to each cluster based on the receiving level quantity of the terminal in each cluster.
3. The handover method according to claim 2, wherein the determining the handover parameter threshold corresponding to each cluster based on the reception level amount of the terminal in each cluster specifically comprises:
acquiring the receiving level quantity of all terminals in each cluster;
comparing the receiving level quantity of all the terminals of each cluster, and determining a switching parameter threshold corresponding to each cluster; the handover parameter threshold is inversely proportional to the amount of receive levels of all the terminals.
4. The handover method according to claim 2, wherein the obtaining reference signal received power, RSRP, measurement data of the terminal to be clustered for all cells covering the terminal to be clustered comprises:
acquiring a first matrix formed by Reference Signal Received Power (RSRP) measurement data of all cells covering a terminal to be clustered by the terminal to be clustered;
based on the first matrix, obtaining a second matrix according to a TSNE algorithm, wherein the second matrix comprises sample data of each terminal to be clustered; and the sample data of the terminal to be clustered is used for describing RSRP measurement data of the terminal to be clustered.
5. The switching method according to claim 4, wherein said obtaining a second matrix according to a TSNE algorithm based on the first matrix comprises:
initializing sample data of each terminal to be clustered in the second matrix;
calculating to obtain the probability distribution p of the first matrix based on the RSRP measurement data of each terminal to be clustered in the first matrix;
iteratively updating the second matrix according to a TSNE algorithm based on the probability distribution p;
and after the second matrix is determined to be updated iteratively, acquiring the second matrix in the last updating process.
6. The switching method according to claim 5, wherein said iteratively updating said second matrix according to a TSNE algorithm based on said probability distribution p comprises:
in each updating process, calculating and obtaining the probability distribution q of a second matrix based on sample data of each terminal in the second matrix obtained in the previous updating process;
calculating to obtain KL divergence between the probability distribution p and the probability distribution q;
updating the second matrix based on the KL divergence.
7. The handover method of claim 5, wherein the determining that the iteration updating the second matrix is complete comprises:
the KL divergence is lower than a preset value; or the iterative update times exceed the preset iterative update times.
8. The handover method according to claim 2, wherein the method further comprises:
clustering all the terminals to be clustered again every other first preset time, and determining a switching parameter threshold corresponding to each cluster; and/or
And re-determining the switching parameter threshold corresponding to each cluster every second preset time.
9. A network device comprising a memory, a transceiver, a processor:
a memory for storing a computer program; a transceiver for transceiving data under control of the processor; a processor for reading the computer program in the memory and performing the following operations:
respectively acquiring RSRP measurement data samples of terminals in each cluster;
determining a target cluster with the highest similarity to the RSRP measurement data of the target terminal based on the RSRP measurement data samples of the terminals in each cluster;
configuring a switching parameter threshold of the target terminal based on a switching parameter threshold corresponding to the target cluster to complete the switching of the target terminal;
wherein, the switching parameter thresholds corresponding to different clusters are different.
10. The network device of claim 9, wherein prior to the separately obtaining RSRP measurement data samples for terminals in each cluster, the operations further comprise:
acquiring Reference Signal Received Power (RSRP) measurement data of all cells covering the terminal to be clustered by the terminal to be clustered;
clustering all terminals to be clustered based on RSRP measurement data of all terminals to be clustered in a preset area;
and determining a switching parameter threshold corresponding to each cluster based on the receiving level quantity of the terminal in each cluster.
11. The network device of claim 10, wherein the determining the threshold of the handover parameter corresponding to each cluster based on the received level amount of the terminal in each cluster specifically comprises:
acquiring the receiving level quantity of all terminals in each cluster;
comparing the receiving level quantity of all the terminals of each cluster, and determining a switching parameter threshold corresponding to each cluster; the handover parameter threshold is inversely proportional to the amount of receive levels of all the terminals.
12. The network device of claim 10, wherein the obtaining reference signal received power, RSRP, measurement data of the terminal to be clustered for all cells covering the terminal to be clustered comprises:
acquiring a first matrix formed by Reference Signal Received Power (RSRP) measurement data of all cells covering a terminal to be clustered by the terminal to be clustered;
based on the first matrix, obtaining a second matrix according to a TSNE algorithm, wherein the second matrix comprises sample data of each terminal to be clustered; and the sample data of the terminal to be clustered is used for describing RSRP measurement data of the terminal to be clustered.
13. The network device of claim 12, wherein obtaining the second matrix according to the TSNE algorithm based on the first matrix comprises:
initializing sample data of each terminal to be clustered in the second matrix;
calculating to obtain the probability distribution p of the first matrix based on the RSRP measurement data of each terminal to be clustered in the first matrix;
iteratively updating the second matrix according to a TSNE algorithm based on the probability distribution p;
and after the second matrix is determined to be updated iteratively, acquiring the second matrix in the last updating process.
14. The network device of claim 13, wherein iteratively updating the second matrix according to a TSNE algorithm based on the probability distribution p comprises:
in each updating process, calculating and obtaining the probability distribution q of a second matrix based on sample data of each terminal in the second matrix obtained in the previous updating process;
calculating to obtain KL divergence between the probability distribution p and the probability distribution q;
updating the second matrix based on the KL divergence.
15. The network device of claim 13, wherein the determining that the iteration of updating the second matrix is complete comprises:
the KL divergence is lower than a preset value; or the iterative update times exceed the preset iterative update times.
16. The network device of claim 10, wherein the operations further comprise:
clustering all the terminals to be clustered again every other first preset time, and determining a switching parameter threshold corresponding to each cluster; and/or
And re-determining the switching parameter threshold corresponding to each cluster every second preset time.
17. A switching device, comprising:
the acquisition module is used for respectively acquiring RSRP measurement data samples of the terminals in each cluster;
the determining module is used for determining a target cluster with the highest similarity to the RSRP measurement data of the target terminal based on the RSRP measurement data samples of the terminals in each cluster;
the switching module is used for configuring a switching parameter threshold of the target terminal based on the switching parameter threshold corresponding to the target cluster to complete the switching of the target terminal;
wherein, the switching parameter thresholds corresponding to different clusters are different.
18. A processor-readable storage medium, characterized in that the processor-readable storage medium stores a computer program for causing a processor to perform the method of any one of claims 1 to 8.
CN202011133860.4A 2020-10-21 2020-10-21 Switching method, device, equipment and storage medium Active CN114390605B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011133860.4A CN114390605B (en) 2020-10-21 2020-10-21 Switching method, device, equipment and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011133860.4A CN114390605B (en) 2020-10-21 2020-10-21 Switching method, device, equipment and storage medium

Publications (2)

Publication Number Publication Date
CN114390605A true CN114390605A (en) 2022-04-22
CN114390605B CN114390605B (en) 2023-07-14

Family

ID=81193255

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011133860.4A Active CN114390605B (en) 2020-10-21 2020-10-21 Switching method, device, equipment and storage medium

Country Status (1)

Country Link
CN (1) CN114390605B (en)

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102833806A (en) * 2012-08-10 2012-12-19 大唐移动通信设备有限公司 Method and device for cell switching
CN103974350A (en) * 2014-05-20 2014-08-06 上海交通大学 Switching method for self-adaptive threshold adjustment
CN104486784A (en) * 2014-12-19 2015-04-01 东南大学 Method for reducing switch failures in ultra-dense wireless network
CN105027618A (en) * 2013-03-12 2015-11-04 高通股份有限公司 Method and apparatus for UE measurement assisted handover classification
CN109151930A (en) * 2017-06-28 2019-01-04 大唐移动通信设备有限公司 cell measuring method and device
US20200119879A1 (en) * 2017-07-31 2020-04-16 Guangdong Oppo Mobile Telecommunications Corp., Ltd. Data transmission method and terminal device
CN111372293A (en) * 2018-12-26 2020-07-03 华为技术有限公司 Communication method and communication device
CN111698738A (en) * 2019-12-27 2020-09-22 中国电信股份有限公司 Method and device for switching boundary of shared carrier

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102833806A (en) * 2012-08-10 2012-12-19 大唐移动通信设备有限公司 Method and device for cell switching
CN105027618A (en) * 2013-03-12 2015-11-04 高通股份有限公司 Method and apparatus for UE measurement assisted handover classification
CN103974350A (en) * 2014-05-20 2014-08-06 上海交通大学 Switching method for self-adaptive threshold adjustment
CN104486784A (en) * 2014-12-19 2015-04-01 东南大学 Method for reducing switch failures in ultra-dense wireless network
CN109151930A (en) * 2017-06-28 2019-01-04 大唐移动通信设备有限公司 cell measuring method and device
US20200119879A1 (en) * 2017-07-31 2020-04-16 Guangdong Oppo Mobile Telecommunications Corp., Ltd. Data transmission method and terminal device
CN111372293A (en) * 2018-12-26 2020-07-03 华为技术有限公司 Communication method and communication device
CN111698738A (en) * 2019-12-27 2020-09-22 中国电信股份有限公司 Method and device for switching boundary of shared carrier

Also Published As

Publication number Publication date
CN114390605B (en) 2023-07-14

Similar Documents

Publication Publication Date Title
US11109283B1 (en) Handover success rate prediction and management using machine learning for 5G networks
US20220210706A1 (en) Method and apparatus for identifying target neighbor cell for handover of user equipment (ue)
Ghosh et al. Coverage and rate analysis in two‐tier heterogeneous networks under suburban and urban scenarios
CN103686895B (en) Switching control method, wireless network controller and access node
CN113994731A (en) Method and apparatus for wireless communication
US11337131B1 (en) Method and apparatus for recommending real-time handover to a target cell in open-radio access network (O-RAN) environment
US11947575B2 (en) Geohash based auto-segmentation
US20230071942A1 (en) Cellular network indoor traffic auto-detection
US20220210709A1 (en) Method and apparatus for initiating handover (ho) procedure in an open-radio access network (o-ran) environment
CN114390605B (en) Switching method, device, equipment and storage medium
CN108243467B (en) Cell switching method and system based on LTE network
Zhohov et al. Reducing latency: Improving handover procedure using machine learning
EP3466143B1 (en) Method and network node for providing an rf model of a telecommunications system
CN114885368A (en) Method, device and network equipment for optimizing neighbor cell configuration
CN113498070B (en) AP prediction method, device and system
EP3178251B1 (en) Radio network node and method for determining whether a wireless device is a suitable candidate for handover to a target cell for load balancing reasons
CN109327854B (en) Track user identification method and device
CN115942403A (en) Method and device for reporting beam measurement result
CN116097752A (en) Switching method, device, equipment and storage medium
CN114071531B (en) Processing method, device, terminal and network equipment for MDT (minimization of drive test)
CN115623572A (en) Uplink interference processing method and device
WO2023143200A1 (en) Beam switching method and apparatus, and processor readable storage medium
CN114363983B (en) Method for directionally acquiring terminal information
EP4068855A1 (en) Method for recommending target cell for handover in open-radio access network environment
CN111434138B (en) Cell signal quality determination method, device and system

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant