CN114095968A - Time advance prediction method, device and communication system - Google Patents

Time advance prediction method, device and communication system Download PDF

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CN114095968A
CN114095968A CN202010857445.7A CN202010857445A CN114095968A CN 114095968 A CN114095968 A CN 114095968A CN 202010857445 A CN202010857445 A CN 202010857445A CN 114095968 A CN114095968 A CN 114095968A
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data
piece
similarity
missing
timing advance
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CN114095968B (en
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许盛宏
宫云平
李力卡
曹磊
王敏
王兵
武巍
冯云喜
马泽雄
王谦
黄云飞
余育青
罗伟华
范家杰
姚彦强
许群路
郑博
张慧嫦
原思平
郑三强
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China Telecom Corp Ltd
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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Abstract

The present disclosure provides a method, an apparatus and a communication system for predicting a timing advance, which relate to the technical field of communication, wherein the method comprises the following steps: acquiring a plurality of pieces of measurement report MR data sent to a base station by at least one terminal in a terminal set; grouping the multiple pieces of MR data according to the user session ID to obtain multiple sets of MR data; sequencing the multiple pieces of MR data in each set of MR data according to the sending time of each piece of MR data; after sequencing, determining whether another piece of MR data and another piece of MR data respectively exist before and after the MR data for the MR data which lacks the time lead in the group of MR data, wherein the another piece of MR data and the another piece of MR data do not lack the time lead; and if another piece of MR data and another piece of MR data exist, predicting the missing time advance of one piece of MR data by adopting a linear interpolation method.

Description

Time advance prediction method, device and communication system
Technical Field
The present disclosure relates to the field of communications technologies, and in particular, to a method, an apparatus, and a communication system for predicting a timing advance.
Background
In an LTE (Long Term Evolution) system, different terminals adjust the time of an uplink transmission signal according to a Timing Advance (TA) so that the uplink transmission signal reaches a base station within an error range of the length of a cyclic prefix, thereby maintaining orthogonality among uplink transmissions of a plurality of terminals and ensuring uplink synchronization. The TA is carried in the user measurement report MR, and can be used for estimating the distance between the terminal and the antenna, and the distance represented by 1 TA is about 78.12 meters. TA is often used as a fingerprint parameter for network location and also in network optimization analysis.
From MR data statistics of a certain province, 28% of the recorded TAs of the MR are missing, which seriously affects the network positioning accuracy of the MR, and may also cause inaccurate and incomplete network optimization analysis results.
Disclosure of Invention
The inventors have noted that the related art of predicting TA missing in MR data has a problem of large error.
In order to solve the above problem, the embodiments of the present disclosure propose the following solutions.
According to an aspect of the embodiments of the present disclosure, there is provided a time advance prediction method, including: acquiring a plurality of pieces of measurement report MR data sent to a base station by at least one terminal in a terminal set, wherein the distance between any two terminals in the terminal set is smaller than a first threshold value; grouping the multiple pieces of MR data according to the user session ID to obtain multiple sets of MR data; sequencing the multiple pieces of MR data in each set of MR data according to the sending time of each piece of MR data; after sorting, for a piece of MR data missing a timing advance in a set of MR data, determining whether another piece of MR data exists before the piece of MR data and whether another piece of MR data exists after the piece of MR data, wherein neither the another piece of MR data nor the another piece of MR data missing a timing advance; and in the case that the other piece of MR data and the another piece of MR data are determined to exist, predicting the missing time advance of the piece of MR data by adopting a linear interpolation method.
In some embodiments, a method of linear interpolation includes: predicting the piece of MR data using the other piece of MR data and the further piece of MR data.
In some embodiments, there is no MR data between the other MR data and the one piece of MR data, or there is no MR data between the other MR data and the one piece of MR data that lacks a timing advance.
In some embodiments, no MR data exists between the further piece of MR data and the piece of MR data, or no MR data exists between the further piece of MR data and the piece of MR data.
In some embodiments, the method for predicting timing advance further comprises: in the case that the other piece of MR data or the another piece of MR data is determined not to exist, predicting the missing time advance of the piece of MR data by adopting a feature vector similarity matching method.
In some embodiments, the method of feature vector similarity matching comprises: constructing a feature vector of each set of MR data; determining other groups of MR data corresponding to the MR data and where other MR data without the time lead is located; calculating the similarity between the feature vectors of the one set of MR data and the feature vectors of each set of MR data in the other sets of MR data respectively; and predicting the missing time lead of the MR data according to the corresponding MR data in the other group of MR data with the highest similarity.
In some embodiments, constructing the feature vector for each set of MR data comprises: and respectively constructing a main cell characteristic vector and an adjacent cell characteristic vector corresponding to each group of MR data.
In some embodiments, calculating the similarity between the feature vectors of the one set of MR data and the feature vectors of each of the other sets of MR data, respectively, comprises: calculating first similarity between the characteristic vector of the main cell of the MR data and the characteristic vector of the main cell of each MR data in other MR data; calculating second similarity between the neighboring region feature vectors of the group of MR data and the neighboring region feature vectors of each group of MR data in other groups of MR data; calculating the similarity between the feature vector of the one set of MR data and the feature vector of each of the other sets of MR data according to the first similarity and the second similarity corresponding to each of the other sets of MR data, respectively.
In some implementations, the difference in transmission times of the first piece of MR data and the last piece of MR data in each ordered set of MR data is less than a second threshold.
According to another aspect of the embodiments of the present disclosure, there is provided a timing advance prediction apparatus, including: an obtaining module, configured to obtain multiple pieces of MR data sent by at least one terminal in a terminal set to one base station, wherein a distance between any two terminals in the terminal set is smaller than a certain threshold; the grouping module is configured to group the MR data according to the user session ID to obtain a plurality of groups of MR data; the ordering module is configured to order the MR data in each group of MR data according to the sending time of each MR data; the judging module is configured to judge whether another piece of MR data exists before a piece of MR data and whether another piece of MR data exists after the piece of MR data for the piece of MR data which lacks the time advance after sorting, wherein the another piece of MR data and the another piece of MR data do not lack the time advance; and a first prediction module configured to adopt a linear interpolation method to predict the missing time advance of the piece of MR data under the condition that the other piece of MR data and the another piece of MR data are determined to exist.
In some embodiments, the timing advance predicting apparatus further includes: a second prediction module configured to, in the case that it is determined that the other piece of MR data or the another piece of MR data does not exist, predict a time advance of the missing piece of MR data by a method of eigenvector similarity matching.
According to another aspect of the embodiments of the present disclosure, there is provided a timing advance prediction apparatus, including: a memory; and a processor coupled to the memory, the processor configured to perform the method of any of the above embodiments based on instructions stored in the memory.
According to still another aspect of the embodiments of the present disclosure, a communication system is provided, which includes the timing advance predicting apparatus of any of the above embodiments.
According to a further aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method of any one of the embodiments described above.
In the embodiment of the disclosure, by adopting the time advance prediction method, the problem of inaccurate TA prediction caused by the fact that the RSRP under the whole base station is greatly influenced by a wireless environment can be solved, the TA prediction accuracy is improved, and the MR positioning capability and the network optimization analysis value are further improved.
The technical solution of the present disclosure is further described in detail by the accompanying drawings and examples.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart illustrating a timing advance prediction method according to the related art;
fig. 2 is a flow diagram of a method of timing advance prediction, according to some embodiments of the present disclosure;
FIG. 3 is a schematic flow diagram of a feature vector similarity matching method according to some embodiments of the present disclosure;
fig. 4 is a schematic structural diagram of a timing advance prediction apparatus according to some embodiments of the present disclosure;
fig. 5 is a schematic diagram of a timing advance prediction apparatus according to further embodiments of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
The relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
Fig. 1 is a flowchart illustrating a timing advance prediction method according to the related art. As shown in fig. 1, the timing advance prediction method includes step 102-106.
In step 102, MR data not missing from both RSRP (Reference Signal Receiving Power) and TA is extracted from MR (Measurement Report) data.
In step 104, MR data without TA missing is grouped according to RSRP in the same base station cell, and a TA mean corresponding to one RSRP is calculated.
In step 106, predictive completion of the missing TA MR data is performed based on the TA mean.
In the related art, for a missing TA in the MR, the missing TA is compensated by prediction using a TA mean corresponding to an RSRP value.
The inventor thinks that: since RSRP is greatly affected by the radio environment, even corresponding to the same TA, RSRP under different scenarios (e.g., presence of a barrier and absence of a barrier) in the same base station cell is greatly different and does not distinguish between time periods and user session IDs, which results in a large predicted TA error.
In view of this, the present disclosure provides a method for predicting a timing advance, which can improve the accuracy of TA prediction.
Fig. 2 is a flow diagram of a method of timing advance prediction, according to some embodiments of the present disclosure.
As shown in fig. 2, in some embodiments, the timing advance prediction method includes steps 202-208.
In step 202, a plurality of pieces of MR data transmitted by at least one terminal in a terminal set to one base station is acquired.
Here, the distance between any two terminals in the set of terminals is smaller than the first threshold, for example, the distance between any two terminals in the set of terminals is smaller than 50 m.
In step 204, the plurality of pieces of MR data are grouped according to the user session ID to obtain a plurality of sets of MR data.
In step 206, the pieces of MR data in each set of MR data are sorted according to the transmission time of each piece of MR data.
In some embodiments, in each ordered set of MR data, the difference in transmission time between the first piece of MR data and the last piece of MR data is less than a second threshold. For example, in each set of MR data, the difference between the transmission time of the first piece of MR data and the transmission time of the last piece of MR data is less than 60 seconds. This can avoid inaccurate prediction of the timing advance due to repeated use of the user session ID in the MR data with too large a difference in time.
For example, a data example obtained by grouping a plurality of pieces of MR data and sorting each set of MR data is shown in table 1, where mme _ ue _ s1ap _ ID represents a user session ID. Here, the pieces of MR data are divided into three groups (e.g., group 1, group 2, and group 3), each of which includes 6 pieces of MR data, and the 6 pieces of MR data are sorted in order of transmission time. The primary cell may serve as a primary base station cell for the serving terminal, and the neighbor cells (e.g., the first neighbor cell, the second neighbor cell, and the third neighbor cell) may serve as backup base station cells for the serving terminal.
It should be understood that the numerical values, serial numbers, number of MR data pieces, number of groups, etc. in table 1 are merely illustrative and are not intended to limit the scope of the present disclosure. For example, the multiple sets of MR data may also include 6 sets of MR data, each set of MR data may also include 10 pieces of MR data, and the neighboring region may also include a fourth neighboring region and the like.
TABLE 1
Figure BDA0002646936580000071
In step 208, after sorting, for a piece of MR data missing a timing advance in a set of MR data, it is determined whether another piece of MR data exists before the piece of MR data and whether another piece of MR data exists after the piece of MR data, wherein neither the another piece of MR data nor the another piece of MR data missing a timing advance.
In other words, after sorting, for a piece of MR data missing a timing advance in a set of MR data, it is determined whether MR data not missing a timing advance exists before and after the piece of MR data.
In step 210, in the case that it is determined that there is another piece of MR data and another piece of MR data, a linear interpolation method is used to predict the missing timing advance of the piece of MR data.
In other words, when there is MR data without missing timing advance before and after the piece of MR data, the linear interpolation method is used to predict the missing timing advance of the piece of MR data.
In some embodiments, a method of linear interpolation includes: predicting the piece of MR data using the other piece of MR data and the further piece of MR data.
In some implementations, there is no MR data between the other piece of MR data and the piece of MR data, and there is no MR data between the other piece of MR data and the piece of MR data. For example, in group 1, one piece of MR data missing a timing advance is the fourth piece of MR data, another piece of MR data is the third piece of MR data, and another piece of MR data is the fifth piece of MR data. The method of linear interpolation is described below in conjunction with the example data in table 1.
For example, the fourth MR data may be padded according to the following linear interpolation formula:
Figure BDA0002646936580000081
and performing linear interpolation filling on the fourth MR data by using the third MR data and the fifth MR data, wherein X is0＝3，X1＝3，Y0＝13，Y1When the interpolated position X is 4, the interpolated value Y is 13 calculated according to the formula, and the missing timing advance of the fourth MR data can be predicted to be 13.
In other implementations, MR data present between another piece of MR data and the piece of MR data is absent of the timing advance, and MR data present between another piece of MR data and the piece of MR data is absent of the timing advance.
For example, in group 2, one piece of MR data missing a timing advance is the fourth piece of MR data, another piece of MR data is the first piece of MR data, and another piece of MR data is the sixth piece of MR data. From the above linear interpolation formula, X0＝1，X1＝14，Y0＝6，Y1When X is 4 and Y is 14, the timing advance of the missing fourth MR data can be predicted to be 14.
It should be understood that other approaches exist for prediction methods that employ linear interpolation. For example, there is no MR data between another piece of MR data and the piece of MR data, and there is no timing advance in the MR data between another piece of MR data and the piece of MR data. For another example, MR data existing between another piece of MR data and the piece of MR data lacks a timing advance, and MR data does not exist between another piece of MR data and the piece of MR data.
Similarly, the other pieces of MR data in group 2 without the time advance can be subjected to linear interpolation prediction by using the above implementation manner, and are not repeated here.
By the time advance prediction method, the problem of inaccurate TA prediction caused by the fact that the RSRP under the whole base station is greatly influenced by a wireless environment can be solved, the TA prediction accuracy is improved, and the MR positioning capability and the network optimization analysis value are further improved.
And under the condition that the time lead prediction can not be carried out by adopting linear interpolation, selecting a method of similar matching of the characteristic vectors to predict the missing time lead. In some embodiments, the timing advance prediction method further comprises step 212. In step 208, if it is determined that there is another piece of MR data before the piece of MR data and there is another piece of MR data after the piece of MR data, step 210 is performed, otherwise, step 212 is performed.
In step 212, in the case that it is determined that there is no other piece of MR data or another piece of MR data, the method of feature vector similarity matching is used to predict the missing timing advance of the piece of MR data.
In other words, when MR data without missing timing advance does not exist before and after the piece of MR data, the missing timing advance of the piece of MR data is predicted by adopting a feature vector similarity matching method.
Fig. 3 is a schematic flow diagram of a feature vector similarity matching method according to some embodiments of the present disclosure.
As shown in fig. 3, the method for matching similarity of feature vectors includes steps 2122 and 2128. The following is explained in conjunction with the example data of table 1.
At step 2122, feature vectors for each set of MR data are constructed.
In some embodiments, constructing the feature vector for each set of MR data comprises: and respectively constructing a main cell characteristic vector and a neighboring cell characteristic vector corresponding to each group of MR data. The feature vector may here be an RSRP vector.
For example, corresponding to group 1, a primary cell feature vector (-98, -99, -100, -99, -100, -100), a first neighbor feature vector (-100, -100, -99, -99, -99, -99), a second neighbor feature vector (-110-; corresponding to group 2, construct a primary cell feature vector (-105, -100, -103, -105, -99, -102), a first neighbor feature vector (-108, -112, -111, -109, -111, -111), a second neighbor feature vector (-109, -115, -114, -111, -111, -114), a third neighbor feature vector (-113, -117, -117, -114, -114, -116); corresponding to group 3, a primary cell feature vector (-98, -99, -100, -99, -100, -100), a first neighbor feature vector (-103, -103, -102, -103, -103, -103), a second neighbor feature vector (-118, -117, -115, -115, -116, -114), a third neighbor feature vector (-121, -120, -119, -118, -119, -118) are constructed.
In step 2124, other sets of MR data corresponding to the one MR data lacking the timing advance and in which other pieces of MR data not lacking the timing advance are located are determined.
For example, in group 3, one piece of MR data missing a timing advance is the first piece of MR data. In group 1, the first piece of MR data does not lack a timing advance. In group 2, the first piece of MR data does not lack a timing advance. Therefore, the other sets of MR data in which the other pieces of MR data corresponding to the first piece of MR data in set 3 do not lack the timing advance include set 1 and set 2.
In step 2126, similarities between the feature vectors of the one set of MR data missing the timing advance and the feature vectors of each of the other sets of MR data are calculated. Namely, calculating the similarity between one set of MR data in which the one piece of MR data missing the time advance is located and each set of MR data in the other sets of MR data. For example, the similarity between group 3 and group 1 and the similarity between group 3 and group 2 are calculated.
In some embodiments, calculating the similarity between the feature vectors of the one set of MR data and the feature vectors of each of the other sets of MR data, respectively, comprises the following steps. First, a first similarity between the primary cell feature vector of the one set of MR data and the primary cell feature vectors of each of the other sets of MR data is calculated.
For example, let a be (-98, -99, -100, -99, -100, -100) for the primary cell feature vector of group 3, and B be (-98, -98, -99, -98, -98) for the primary cell feature vector of group 1, according to the cosine similarity formula:
Figure BDA0002646936580000111
a first similarity between the primary cell feature vector of group 3 and the primary cell feature vector of group 1 is found to be 0.999959. Similarly, a first similarity between the primary cell feature vector of group 3 and the primary cell feature vector of group 2 is found.
Next, second similarities between the neighboring feature vectors of the group of MR data and the neighboring feature vectors of each group of MR data in other groups of MR data are calculated.
Similarly, a second similarity (e.g., 0.999988) between the first neighboring feature vector of group 3 and the first neighboring feature vector of group 1 and a second similarity (e.g., 0.999914 and 0.999943) between other neighboring feature vectors can be obtained according to the above formula, as shown in table 2. Similarly, the second similarity between the feature vectors of the neighboring regions of group 3 and group 2 is found, and is not repeated here.
TABLE 2
Figure BDA0002646936580000112
Next, the similarity between the feature vector of the one set of MR data and the feature vector of each of the other sets of MR data is calculated according to the first similarity and the second similarity corresponding to each of the other sets of MR data, respectively.
For example, the similarity between group 3 and group 1 may be obtained by calculating an average of the first similarity (e.g., 0.999959) and the second similarity (e.g., 0.999988, 0.999914, and 0.999943). As shown in table 2, the similarity between group 3 and group 1 was 0.999951. Similarly, the similarity between the available group 3 and group 2 is 0.999758.
In step 2128, the time advance of the missing MR data is determined according to the corresponding MR data in the other MR data group with the highest similarity.
For example, if another set of MR data with the highest similarity to the set 3 is set 1, and the time advance of the missing first piece of MR data in the set 3 is predicted according to the first piece of MR data in the set 1, the time advance of the first piece of MR data in the set 3 is 13.
Based on the above-mentioned feature vector similarity matching method, other time advances missing in group 3 can be predicted. For example, the fourth MR data in group 3 can be predicted from the fourth MR data in group 1 with the highest similarity. The time advance amount of the fourth MR data in the set 1, which can be obtained according to the above linear interpolation method, is 13, and the time advance amount of the fourth MR data in the set 3 is 13.
In the above embodiment, the judgment is performed according to the most similar RSRP variation trend of the main cell and the RSRP variation trend of the neighboring cell, and the missing timing advance can be more accurately complemented by combining with the finer geographical fingerprint.
Fig. 4 is a schematic structural diagram of a timing advance prediction apparatus according to some embodiments of the present disclosure.
As shown in fig. 4, in some embodiments, the timing advance prediction apparatus includes an obtaining module 402, a grouping module 404, a sorting module 406, a determining module 408, and a first prediction module 410.
The obtaining module 402 is configured to obtain pieces of MR data transmitted by at least one terminal in the terminal set to one base station, for example, to perform step 202 shown in fig. 2. The distance between any two terminals in the set of terminals is less than a threshold.
The grouping module 404 is configured to group the plurality of pieces of MR data into a plurality of sets of MR data according to the user session ID, for example, to perform step 204 shown in fig. 2.
The sorting module 406 is configured to sort the pieces of MR data in each set of MR data according to the transmission time of each piece of MR data, such as performing step 206 shown in fig. 2.
The determining module 408 is configured to determine, after sorting, whether another piece of MR data exists before the piece of MR data missing the time advance and whether another piece of MR data exists after the piece of MR data missing the time advance, where the another piece of MR data and the another piece of MR data do not miss the time advance, for the piece of MR data missing the time advance in the set of MR data, for example, step 208 shown in fig. 2 is executed.
The first prediction module 410 is configured to employ a linear interpolation method to predict the missing timing advance of one MR data missing a timing advance, for example, to perform step 210 shown in fig. 2, in case it is determined that there is another MR data and another MR data.
In some embodiments, as shown in fig. 4, the timing advance prediction apparatus further comprises a second prediction module 412.
The second prediction module 412 is configured to, in the case that it is determined that there is no other MR data or another MR data, adopt a method of feature vector similarity matching to predict the time advance of the absence of the one MR data, for example, to perform step 212 shown in fig. 2.
In the embodiment, by adopting the time advance predicting device, the problem of inaccurate TA prediction caused by the fact that the RSRP under the whole base station is greatly influenced by a wireless environment can be solved, the accuracy of TA prediction is improved, and the positioning capability and the network optimization analysis value of the MR are further improved.
Fig. 5 is a schematic diagram of a timing advance prediction apparatus according to further embodiments of the present disclosure.
As shown in fig. 5, the timing advance prediction apparatus 500 of this embodiment includes a memory 501 and a processor 502 coupled to the memory 501, and the processor 502 is configured to execute the method of any one of the foregoing embodiments based on instructions stored in the memory 501.
The memory 501 may include, for example, a system memory, a fixed non-volatile storage medium, and the like. The system memory may store, for example, an operating system, application programs, a Boot Loader (Boot Loader), and other programs.
The timing advance predicting apparatus 500 may further include an input-output interface 503, a network interface 504, a storage interface 505, and the like. The interfaces 503, 504, 505 and the memory 501 and the processor 502 may be connected by a bus 506, for example. The input/output interface 503 provides a connection interface for input/output devices such as a display, a mouse, a keyboard, and a touch screen. The network interface 504 provides a connection interface for various networking devices. The storage interface 505 provides a connection interface for external storage devices such as an SD card and a usb disk.
The embodiment of the present disclosure further provides a communication system, including the timing advance predicting apparatus in any of the above embodiments. The communication system adopting the time advance predicting device further improves the positioning capability and the network optimization analysis value of the MR.
The disclosed embodiments also provide a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method of any of the above embodiments.
Thus, various embodiments of the present disclosure have been described in detail. Some details that are well known in the art have not been described in order to avoid obscuring the concepts of the present disclosure. It will be fully apparent to those skilled in the art from the foregoing description how to practice the presently disclosed embodiments.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that the functions specified in one or more of the flows in the flowcharts and/or one or more of the blocks in the block diagrams can be implemented by computer program instructions. These computer program 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 computer program instructions may also be stored in a computer-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 computer-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 computer program 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.
Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the foregoing examples are for purposes of illustration only and are not intended to limit the scope of the present disclosure. It will be understood by those skilled in the art that various changes may be made in the above embodiments or equivalents may be substituted for elements thereof without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims (14)

1. A method of time advance prediction, comprising:
acquiring a plurality of pieces of measurement report MR data sent to a base station by at least one terminal in a terminal set, wherein the distance between any two terminals in the terminal set is smaller than a first threshold value;
grouping the multiple pieces of MR data according to the user session ID to obtain multiple sets of MR data;
sequencing the multiple pieces of MR data in each set of MR data according to the sending time of each piece of MR data;
after sorting, for a piece of MR data missing a timing advance in a set of MR data, determining whether another piece of MR data exists before the piece of MR data and whether another piece of MR data exists after the piece of MR data, wherein neither the another piece of MR data nor the another piece of MR data missing a timing advance; and
in the case that the other piece of MR data and the another piece of MR data are determined to exist, a linear interpolation method is adopted to predict the missing time advance of the one piece of MR data.
2. The method of time advance prediction according to claim 1, wherein the method of linear interpolation comprises:
predicting the piece of MR data using the other piece of MR data and the further piece of MR data.
3. The method of time advance prediction according to claim 2, wherein no MR data exists between the other MR data and the one piece of MR data, or MR data existing between the other MR data and the one piece of MR data lacks a time advance.
4. The method of time advance prediction according to claim 2, wherein no MR data exists between the further piece of MR data and the piece of MR data, or none of the MR data existing between the further piece of MR data and the piece of MR data has a time advance.
5. The timing advance prediction method of claim 1, further comprising:
in the case that the other piece of MR data or the another piece of MR data is determined not to exist, predicting the missing time advance of the piece of MR data by adopting a feature vector similarity matching method.
6. The timing advance prediction method according to claim 5, wherein predicting the missing timing advance of the piece of MR data by using a feature vector similarity matching method comprises:
constructing a feature vector of each set of MR data;
determining other groups of MR data corresponding to the MR data and where other MR data without the time lead is located;
calculating the similarity between the feature vectors of the one set of MR data and the feature vectors of each set of MR data in the other sets of MR data respectively; and
and predicting the missing time lead of the MR data according to the corresponding MR data in the other group of MR data with the highest similarity.
7. The time advance prediction method of claim 6, wherein constructing the feature vector for each set of MR data comprises:
and respectively constructing a main cell characteristic vector and an adjacent cell characteristic vector corresponding to each group of MR data.
8. The method of timing advance prediction according to claim 7, wherein calculating the similarity between the feature vectors of the set of MR data and the feature vectors of each of the other sets of MR data, respectively, comprises:
calculating first similarity between the characteristic vector of the main cell of the MR data and the characteristic vector of the main cell of each MR data in other MR data;
calculating second similarity between the neighboring region feature vectors of the group of MR data and the neighboring region feature vectors of each group of MR data in other groups of MR data;
calculating the similarity between the feature vector of the one set of MR data and the feature vector of each of the other sets of MR data according to the first similarity and the second similarity corresponding to each of the other sets of MR data, respectively.
9. The method of time advance prediction according to claim 1, wherein in each ordered set of MR data, a difference in transmission time between a first piece of MR data and a last piece of MR data is less than a second threshold.
10. A time advance prediction apparatus comprising:
an obtaining module, configured to obtain multiple pieces of MR data sent by at least one terminal in a terminal set to one base station, wherein a distance between any two terminals in the terminal set is smaller than a certain threshold;
the grouping module is configured to group the MR data according to the user session ID to obtain a plurality of groups of MR data;
the ordering module is configured to order the MR data in each group of MR data according to the sending time of each MR data;
the judging module is configured to judge whether another piece of MR data exists before a piece of MR data and whether another piece of MR data exists after the piece of MR data for the piece of MR data which lacks the time advance after sorting, wherein the another piece of MR data and the another piece of MR data do not lack the time advance; and
a first prediction module configured to adopt a linear interpolation method to predict the missing time advance of the piece of MR data in the case that the other piece of MR data and the another piece of MR data are determined to exist.
11. The timing advance prediction apparatus of claim 10, further comprising:
a second prediction module configured to, in the case that it is determined that the other piece of MR data or the another piece of MR data does not exist, predict a time advance of the missing piece of MR data by a method of eigenvector similarity matching.
12. A time advance prediction apparatus comprising:
a memory;
a processor coupled to the memory, the processor configured to perform the timing advance prediction method of any of claims 1-9 based on instructions stored in the memory.
13. A communication system, comprising: the timing advance prediction apparatus of any one of claims 10 to 12.
14. A computer readable storage medium having stored thereon computer program instructions, wherein the instructions when executed by a processor implement the time advance prediction method of any one of claims 1-9.
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