CN114095968B - Time advance prediction method, device and communication system - Google Patents

Time advance prediction method, device and communication system Download PDF

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CN114095968B
CN114095968B CN202010857445.7A CN202010857445A CN114095968B CN 114095968 B CN114095968 B CN 114095968B CN 202010857445 A CN202010857445 A CN 202010857445A CN 114095968 B CN114095968 B CN 114095968B
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data
piece
time advance
similarity
feature vector
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CN114095968A (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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China Telecom Corp Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management

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  • Computer Networks & Wireless Communication (AREA)
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  • Mobile Radio Communication Systems (AREA)

Abstract

The disclosure provides a time advance prediction method, a device and a communication system, and relates 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 plurality of pieces of MR data according to the user session ID to obtain a plurality of groups of MR data; sequencing a plurality of pieces of MR data in each set of MR data according to the sending time of each piece of MR data; after sorting, determining whether another piece of MR data and another piece of MR data exist before and after the piece of MR data respectively for one piece of MR data with missing time advance, wherein the other piece of MR data and the another piece of MR data do not have missing time advance; and if another piece of MR data and another piece of MR data exist, predicting the time advance of the missing piece of MR data by adopting a linear interpolation method.

Description

Time advance prediction method, device and communication system
Technical Field
The disclosure relates to the technical field of communication, in particular to a time advance prediction method, a device and a communication system.
Background
In an LTE (Long Term Evolution ) system, different terminals adjust the time of an uplink transmission signal according to a Time Advance (TA), so that the uplink transmission signal arrives at a base station within an error range of the length of one cyclic prefix, thereby maintaining orthogonality between 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 distance estimation between the terminal and the antenna, and the distance characterized by 1 TA is approximately 78.12 meters. The TA is often used as a fingerprint parameter for network location and also in network optimization analysis.
From the MR data statistics of a certain province, 28% of the TA recorded by the MR is missing, which seriously affects the network positioning accuracy of the MR, and may cause inaccurate and incomplete network optimization analysis results.
Disclosure of Invention
The inventors noted that the related art of predicting a TA missing in MR data has a problem of large error.
In order to solve the above-described problems, 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; grouping the plurality of pieces of MR data according to the user session ID to obtain a plurality of groups of MR data; sequencing a plurality of pieces of MR data in each set of MR data according to the sending time of each piece of MR data; after sorting, for one piece of MR data in a set of MR data for which a time advance is missing, determining whether another piece of MR data exists before the one piece of MR data and whether a further piece of MR data exists after the one piece of MR data, wherein neither the another piece of MR data nor the further piece of MR data is missing a time advance; and in the case where it is determined that the other piece of MR data and the further piece of MR data exist, predicting the time advance of the missing piece of MR data by using a linear interpolation method.
In some embodiments, a method of linear interpolation includes: the one piece of MR data is predicted using the other piece of MR data and the further piece of MR data.
In some embodiments, no MR data is present between the other MR data and the one MR data, or the MR data present between the other MR data and the one MR data lacks a time advance.
In some embodiments, no MR data exists between the further piece of MR data and the one piece of MR data, or the MR data existing between the further piece of MR data and the one piece of MR data lacks a time advance.
In some embodiments, the above time advance prediction method further includes: in the case that it is determined that the other piece of MR data or the still another piece of MR data does not exist, a method of feature vector similarity matching is employed to predict the time advance of the one piece of MR data deletion.
In some embodiments, a method of feature vector similarity matching includes: constructing a feature vector of each set of MR data; determining other groups of MR data corresponding to the one piece of MR data and containing other pieces of MR data without missing time advance; calculating the similarity between the feature vector of the set of MR data and the feature vector of each set of MR data in the other sets of MR data respectively; and predicting the time advance of the missing of the one piece of MR data according to the corresponding one piece of 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 eigenvector and a neighboring cell eigenvector corresponding to each set of MR data.
In some embodiments, calculating the similarity between the eigenvectors of the one set of MR data and the eigenvectors of each of the other sets of MR data, respectively, comprises: calculating first similarity between the main cell eigenvectors of the set of MR data and the main cell eigenvectors of each set of MR data in other sets of MR data respectively; calculating second similarity between the neighboring feature vectors of the MR data of the group and the neighboring feature vectors of the MR data of each group in other groups of MR data respectively; similarity between the eigenvectors of the one set of MR data and the eigenvectors of each of the other sets of MR data is calculated from the first and second similarities corresponding to each of the other sets of MR data, respectively.
In some implementations, in each ordered set of MR data, the difference in transmission time of the first and last MR data is less than a second threshold.
According to another aspect of the embodiments of the present disclosure, there is provided a time advance prediction apparatus including: an acquisition module configured to acquire a plurality of pieces of MR data transmitted to one base station by at least one terminal in a set of terminals, wherein a distance between any two terminals in the set of terminals is smaller than a certain threshold; the grouping module is configured to group the plurality of pieces of 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 plurality of pieces of MR data in each group of MR data according to the sending time of each piece of MR data; a determining module configured to determine, after sorting, for one piece of MR data for which a time advance is missing in a set of MR data, whether another piece of MR data exists before the one piece of MR data and whether still another piece of MR data exists after the one piece of MR data, wherein the another piece of MR data and the still another piece of MR data are both free from the time advance; and a first prediction module configured to predict a time advance of the absence of the one piece of MR data using a linear interpolation method in a case where it is determined that the other piece of MR data and the still other piece of MR data exist.
In some embodiments, the time advance prediction apparatus further includes: and a second prediction module configured to predict a time advance of the absence of the one piece of MR data using a feature vector similarity matching method in a case where it is determined that the other piece of MR data or the still other piece of MR data does not exist.
According to still another aspect of the embodiments of the present disclosure, there is provided a time advance prediction apparatus including: a memory; and a processor coupled to the memory, the processor configured to perform the method of any of the embodiments described above based on instructions stored in the memory.
According to still another aspect of the embodiments of the present disclosure, there is provided a communication system including the time advance prediction apparatus of any one of the embodiments described above.
According to yet another aspect of the disclosed embodiments, 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 of the above embodiments.
In the embodiment of the disclosure, the time advance prediction method can solve the problem of inaccurate TA prediction caused by the fact that RSRP under the whole base station is greatly affected by wireless environment, improves the accuracy of TA prediction, and further improves the positioning capability of MR and the network optimization analysis value.
The technical scheme of the present disclosure is described in further detail below through 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 that are required in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and other drawings may be obtained according to these drawings without inventive effort to a person of ordinary skill in the art.
FIG. 1 is a flow chart of a method for predicting a time advance according to the related art;
FIG. 2 is a flow diagram of a method of time advance prediction according to some embodiments of the present disclosure;
FIG. 3 is a flow diagram of a feature vector similarity matching method according to some embodiments of the present disclosure;
FIG. 4 is a schematic diagram of a timing advance prediction apparatus according to some embodiments of the present disclosure;
fig. 5 is a schematic structural view of a time advance prediction apparatus according to other embodiments of the present disclosure.
Detailed Description
The following description of the technical solutions in the embodiments of the present disclosure will be made clearly and completely with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are only some embodiments of the present disclosure, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments in this disclosure without inventive faculty, are intended to fall within the scope of this disclosure.
The relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless it is specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective parts shown in the drawings are not drawn in actual scale for convenience of description.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but should be considered part of the specification where appropriate.
In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
Fig. 1 is a flowchart illustrating a time advance prediction method according to the related art. As shown in FIG. 1, the time advance prediction method includes steps 102-106.
In step 102, MR data, in which neither RSRP (Reference Signal Receiving Power, reference signal received power) nor TA is missing, is extracted from MR (Measurement Report ) data.
In step 104, MR data without missing TA is grouped according to RSRP of the same base station cell, and TA average value corresponding to one RSRP is calculated.
In step 106, the MR data lacking TA is prediction-complemented according to the TA average.
In the related art described above, for a missing TA in MR, a TA average value corresponding to the RSRP value is used to predict and complement the missing TA.
The inventors consider that: since RSRP is greatly affected by the wireless environment, even corresponding to the same TA, RSRP in different scenarios (e.g., presence of a blocker and absence of a blocker) of the same base station cell varies greatly and does not distinguish between time periods and user session IDs, which results in a large predicted TA error.
In view of this, the disclosure proposes a time advance prediction method, which can improve accuracy of TA prediction.
Fig. 2 is a flow diagram of a time advance prediction method according to some embodiments of the present disclosure.
As shown in FIG. 2, in some embodiments, the time advance prediction method includes steps 202-208.
In step 202, a plurality of pieces of MR data transmitted to one base station by at least one terminal in a set of terminals is acquired.
Here, the distance between any two terminals in the set of terminals is smaller than the first threshold, e.g. the distance between any two terminals in the set of terminals is smaller than 50m.
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.
At step 206, the plurality of pieces of MR data in each set of MR data are ordered 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 of the first and last MR data is less than a second threshold. For example, in each set of MR data, the transmission time difference of the first and last pieces of MR data is less than 60 seconds. This can avoid inaccurate prediction of the time advance due to repeated use of the user session ID in the MR data whose time difference is too large.
For example, an example of data obtained by grouping the acquired pieces of MR data and sorting each set of MR data is shown in table 1, in which mme _ue_s1ap_id represents a user session ID. Here, the plurality of MR data are divided into three groups (e.g., group 1, group 2, and group 3), each group of MR data includes 6 pieces of MR data, and the 6 pieces of MR data are ordered in order of transmission time. The primary cell may serve as a primary base station cell for the serving terminal and the neighbors (e.g., the first neighbor, the second neighbor, and the third neighbor) may serve as backup base station cells for the serving terminal.
It should be understood that the numerical values, sequence numbers, number of MR data strips, number of sets, etc. in table 1 are exemplary only and are not intended to limit the scope of the present disclosure. For example, the plurality of 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, the neighbor may also include a fourth neighbor, and so on.
TABLE 1
At step 208, after sorting, for one piece of MR data for which there is a missing time advance in the set of MR data, it is determined whether another piece of MR data is present before the one piece of MR data and whether there is yet another piece of MR data is present after the one piece of MR data, wherein neither the another piece of MR data nor the yet another piece of MR data is missing a time advance.
In other words, after sorting, it is determined whether or not there is MR data without a missing time advance before and after one piece of MR data for the one piece of MR data with the missing time advance in the set of MR data.
In step 210, in the case where it is determined that another piece of MR data and still another piece of MR data exist, a linear interpolation method is employed to predict the time advance of the absence of the one piece of MR data.
In other words, in the case where there is MR data without missing time advance before and after the one piece of MR data, the time advance of the missing piece of MR data is predicted by a linear interpolation method.
In some embodiments, a method of linear interpolation includes: the one piece of MR data is predicted using the other piece of MR data and the further piece of MR data.
In some implementations, there is no MR data between another piece of MR data and the one piece of MR data, and there is no MR data between yet another piece of MR data and the one piece of MR data. For example, in group 1, one piece of MR data lacking the time advance is the fourth piece of MR data, the other piece of MR data is the third piece of MR data, and the other piece of MR data is the fifth piece of MR data. The method of linear interpolation is described below in connection with the example data in table 1.
For example, the fourth piece of MR data can be padded according to the following linear interpolation formula:
linear interpolation padding of the fourth MR data with the third MR data and the fifth MR data, then X 0 ＝3，X 1 ＝3，Y 0 ＝13，Y 1 When the interpolation position x=4, the interpolation value y=13 calculated according to the formula, the timing advance of the fourth piece of MR data missing can be predicted to be 13.
In other implementations, the MR data present between the other piece of MR data and the one piece of MR data lacks a time advance, and the MR data present between the further piece of MR data and the one piece of MR data lacks a time advance.
For example, in group 2, one piece of MR data lacking the time advance is the fourth piece of MR data, the other piece of MR data is the first piece of MR data, and the other piece of MR data is the sixth piece of MR data. From the above linear interpolation formula, X 0 ＝1，X 1 ＝14，Y 0 ＝6，Y 1 When x=4, y=14, the time advance of the fourth piece of MR data deletion can be predicted to be 14.
It should be appreciated that other ways of predicting using linear interpolation exist. For example, there is no MR data between another piece of MR data and the one piece of MR data, and there is no time advance in the MR data between the other piece of MR data and the one piece of MR data. For another example, there is no time advance in the MR data existing between the other piece of MR data and the one piece of MR data, and there is no MR data existing between the other piece of MR data and the one piece of MR data.
Similarly, other pieces of MR data in group 2 that lack a time advance may be linear interpolation predicted using the above-described implementation, and are not repeated here.
By the time advance prediction method, the problem of inaccurate TA prediction caused by the fact that RSRP under the whole base station is greatly affected by a wireless environment can be solved, accuracy of TA prediction is improved, and positioning capability of MR and network optimization analysis value are further improved.
Under the condition that linear interpolation cannot be adopted for predicting the time advance, a feature vector similarity matching method is selected to predict the missing time advance. In some embodiments, the time advance prediction method further includes step 212. In step 208, if it is determined that there is another piece of MR data before the one piece of MR data and there is still another piece of MR data after the one piece of MR data, step 210 is performed, otherwise, step 212 is performed.
In step 212, in the event that it is determined that another piece of MR data or still another piece of MR data does not exist, a method of feature vector similarity matching is employed to predict the time advance of the absence of the one piece of MR data.
In other words, in the case where MR data having no missing time advance does not exist before and after the one piece of MR data, the missing time advance of the one piece of MR data is predicted by the feature vector similarity matching method.
Fig. 3 is a flow diagram of a feature vector similarity matching method according to some embodiments of the present disclosure.
As shown in fig. 3, the method of feature vector similarity matching includes steps 2122-2128. The following is an explanation in connection with the example data of table 1.
In step 2122, a feature vector for each set of MR data is constructed.
In some embodiments, constructing the feature vector for each set of MR data comprises: and respectively constructing a main cell eigenvector and a neighbor cell eigenvector corresponding to each set of MR data. The feature vector may be an RSRP vector.
For example, corresponding to group 1, a primary cell feature vector (-98, -99, -100, -99, -100, -100, -100), a first neighbor feature vector (-100, -100, -99, -99, -99, -99), a second neighbor feature vector (-110-111, -109, -112-110, -109), a third neighbor feature vector (-115, -114, -116, -113, -114-115) is constructed; corresponding to group 2, a primary cell feature vector (-105, -100, -103, -105, -99, -102), a first neighbor feature vector (-108, -112, -111, -109, -111, -111, -111), a second neighbor feature vector (-109, -115, -114, -111, -111, -114), a third neighbor feature vector (-113, -117, -117, -114, -114, -116) are constructed; 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 in which other pieces of MR data corresponding to one piece of MR data lacking the time advance and not lacking the time advance are located are determined.
For example, in group 3, one piece of MR data lacking the time advance is the first piece of MR data. In group 1, the first piece of MR data does not miss the time advance. In group 2, the first piece of MR data does not miss the time advance. Thus, the other sets of MR data where the other pieces of MR data corresponding to the first piece of MR data in set 3 and without missing time advance are located include set 1 and set 2.
In step 2126, the similarity between the eigenvectors of one set of MR data lacking the time advance and the eigenvectors of each of the other sets of MR data, respectively, is calculated. Namely, the similarity between one set of MR data in which the one piece of MR data of the missing time advance is located and each set of MR data in the other sets of MR data is calculated. For example, the similarity between group 3 and group 1 is calculated, and the similarity between group 3 and group 2 is calculated.
In some embodiments, calculating the similarity between the eigenvectors of the one set of MR data and the eigenvectors of each of the other sets of MR data, respectively, comprises the following steps. First, a first similarity between the primary cell eigenvectors of the one set of MR data and the primary cell eigenvectors of each of the other sets of MR data, respectively, is calculated.
For example, let the primary cell eigenvectors of group 3 be a= (-98, -99, -100, -99, -100, -100), let the primary cell eigenvectors of group 1 be b= (-98, -98, -98, -99, -98, -98), according to the cosine similarity formula:
a first similarity between the primary cell feature vector of group 3 and the primary cell feature vector of group 1 is determined 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, a second similarity between the neighboring feature vectors of the one set of MR data and the neighboring feature vectors of each of the other sets of MR data, respectively, is calculated.
Similarly, a second similarity (e.g., 0.999988) between the first neighbor feature vector of group 3 and the first neighbor feature vector of group 1 and a second similarity (e.g., 0.999914 and 0.999943) between the other neighbor feature vectors can be found according to the above formula, as shown in table 2. Similarly, a second similarity between the neighboring feature vectors of group 3 and group 2 is found, respectively, and is not repeated here.
TABLE 2
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 from 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 group 3 and group 2 was 0.999758.
In step 2128, the time advance of the absence of the one piece of MR data is based on the corresponding one piece of MR data in the other set of MR data with the highest similarity.
For example, if another MR data of the highest similarity to the group 3 is the group 1, and the time advance of the first MR data in the group 3 is predicted based on the first MR data in the group 1, the time advance of the first MR data in the group 3 is 13.
Based on the feature vector similarity matching method, other time advances of the deletions in group 3 can be predicted. For example, the fourth piece of MR data in group 3 can be predicted from the fourth piece of MR data in group 1 with the highest similarity. The time advance of the fourth MR data in group 1, which can be obtained according to the above linear interpolation method, is 13, and the time advance of the fourth MR data in group 3 is 13.
In the above embodiment, the judgment is performed according to that the RSRP of the main cell and the RSRP variation trend of the neighboring cell are the most similar, and the missing time advance can be more accurately complemented by combining with finer geographic fingerprints.
Fig. 4 is a schematic structural diagram of a time advance prediction apparatus according to some embodiments of the present disclosure.
As shown in fig. 4, in some embodiments, the time advance prediction apparatus includes an acquisition module 402, a grouping module 404, a ranking module 406, a determination module 408, and a first prediction module 410.
The acquisition module 402 is configured to acquire a plurality of MR data transmitted by at least one terminal of the set of terminals 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 certain threshold.
The grouping module 404 is configured to group the plurality of pieces of MR data according to the user session ID to obtain a plurality of sets of MR data, for example, performing step 204 shown in fig. 2.
The ordering module 406 is configured to order the pieces of MR data in each set of MR data according to the transmission time of each piece of MR data, for example, to perform step 206 shown in fig. 2.
The determining module 408 is configured to determine, after ordering, for one piece of MR data for which the time advance is missing in the set of MR data, whether another piece of MR data exists before the one piece of MR data for which the time advance is missing and whether yet another piece of MR data exists after the one piece of MR data, wherein neither the another piece of MR data nor the yet another piece of MR data is missing the time advance, for example, performing step 208 shown in fig. 2.
The first prediction module 410 is configured to, in case it is determined that another piece of MR data and yet another piece of MR data exist, predict the missing time advance of the piece of MR data for which the missing time advance is missing by using a linear interpolation method, for example, perform step 210 shown in fig. 2.
In some embodiments, as shown in fig. 4, the time advance prediction apparatus further includes a second prediction module 412.
The second prediction module 412 is configured to, in the event that it is determined that there is no other MR data or yet another MR data, employ a feature vector similarity matching method 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 above embodiment, by adopting the time advance prediction device, the problem of inaccurate TA prediction caused by the fact that the RSRP under the whole base station is greatly affected by the wireless environment can be solved, the accuracy of TA prediction is improved, and the positioning capability of MR and the network optimization analysis value are further improved.
Fig. 5 is a schematic structural view of a time advance prediction apparatus according to other embodiments of the present disclosure.
As shown in fig. 5, the time advance prediction apparatus 500 of this embodiment includes a memory 501 and a processor 502 coupled to the memory 501, the processor 502 being configured to perform the method of any of the foregoing embodiments based on instructions stored in the memory 501.
Memory 501 may include, for example, system memory, fixed nonvolatile storage media, and the like. The system memory may store, for example, an operating system, application programs, boot Loader (Boot Loader), and other programs.
The time advance prediction apparatus 500 may further include an input-output interface 503, a network interface 504, a storage interface 505, and the like. These 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, mouse, keyboard, touch screen, etc. 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 SD cards, U discs, and the like.
The embodiment of the disclosure also provides a communication system, which comprises the time advance prediction device of any one embodiment. The communication system adopting the time advance prediction device further improves the positioning capability of the MR and the network optimization analysis value.
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. In order to avoid obscuring the concepts of the present disclosure, some details known in the art are not described. How to implement the solutions disclosed herein will be fully apparent to those skilled in the art from the above description.
It will be appreciated by those skilled in the art that 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, etc.) 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 functions specified in one or more of the flowcharts and/or one or more of the blocks in the block diagrams may 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 above examples are for 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 the foregoing embodiments may be modified and equivalents substituted for elements thereof without departing from the scope and spirit of the disclosure. The scope of the present disclosure is defined by the appended claims.

Claims (14)

1. A time advance prediction method, 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;
grouping the plurality of pieces of MR data according to the user session ID to obtain a plurality of groups of MR data;
sequencing a plurality of pieces of MR data in each set of MR data according to the sending time of each piece of MR data;
after sorting, for one piece of MR data in a set of MR data for which a time advance is missing, determining whether another piece of MR data exists before the one piece of MR data and whether a further piece of MR data exists after the one piece of MR data, wherein neither the another piece of MR data nor the further piece of MR data is missing a time advance; and
in the case where it is determined that the other piece of MR data and the further piece of MR data exist, a linear interpolation method is employed to predict the time advance of the absence of the one piece of MR data.
2. The time advance prediction method of claim 1, wherein the method of linear interpolation comprises:
the one piece of MR data is predicted using the other piece of MR data and the further piece of MR data.
3. The time advance prediction method according to claim 2, wherein no MR data exists between the other piece of MR data and the one piece of MR data, or the MR data existing between the other piece of MR data and the one piece of MR data lacks a time advance.
4. The time advance prediction method according to claim 2, wherein no MR data exists between the further piece of MR data and the one piece of MR data, or no MR data exists between the further piece of MR data and the one piece of MR data.
5. The time advance prediction method of claim 1, further comprising:
in the case that it is determined that the other piece of MR data or the still another piece of MR data does not exist, a method of feature vector similarity matching is employed to predict the time advance of the one piece of MR data deletion.
6. The time advance prediction method of claim 5, wherein predicting the time advance of the one MR data loss 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 one piece of MR data and containing other pieces of MR data without missing time advance;
calculating the similarity between the feature vector of the set of MR data and the feature vector of each set of MR data in the other sets of MR data respectively; and
and predicting the time advance of the missing of one piece of MR data according to the corresponding piece of 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 eigenvector and a neighboring cell eigenvector corresponding to each set of MR data.
8. The time advance prediction method of claim 7, wherein calculating the similarity between the eigenvectors of the one set of MR data and the eigenvectors of each of the other sets of MR data, respectively, comprises:
calculating first similarity between the main cell eigenvectors of the set of MR data and the main cell eigenvectors of each set of MR data in other sets of MR data respectively;
calculating second similarity between the neighboring feature vectors of the MR data of the group and the neighboring feature vectors of the MR data of each group in other groups of MR data respectively;
similarity between the eigenvectors of the one set of MR data and the eigenvectors of each of the other sets of MR data is calculated from the first and second similarities corresponding to each of the other sets of MR data, respectively.
9. The time advance prediction method of claim 1, wherein a transmission time difference of the first MR data and the last MR data is less than a second threshold in each ordered set of MR data.
10. A time advance prediction apparatus comprising:
an acquisition module configured to acquire a plurality of pieces of MR data transmitted to one base station by at least one terminal in a set of terminals, wherein a distance between any two terminals in the set of terminals is smaller than a certain threshold;
the grouping module is configured to group the plurality of pieces of 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 plurality of pieces of MR data in each group of MR data according to the sending time of each piece of MR data;
a determining module configured to determine, after sorting, for one piece of MR data for which a time advance is missing in a set of MR data, whether another piece of MR data exists before the one piece of MR data and whether still another piece of MR data exists after the one piece of MR data, wherein the another piece of MR data and the still another piece of MR data are both free from the time advance; and
a first prediction module configured to predict a time advance of the absence of the one piece of MR data using a linear interpolation method in a case where it is determined that the other piece of MR data and the still other piece of MR data exist.
11. The time advance prediction apparatus of claim 10, further comprising:
and a second prediction module configured to predict a time advance of the absence of the one piece of MR data using a feature vector similarity matching method in a case where it is determined that the other piece of MR data or the still other piece of MR data does not exist.
12. A time advance prediction apparatus comprising:
a memory;
a processor coupled to the memory, the processor configured to perform the time advance prediction method of any of claims 1-9 based on instructions stored in the memory.
13. A communication system, comprising: the time advance prediction apparatus according to 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 of claims 1-9.
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