CN107172637A - A kind of method and apparatus classified to calling - Google Patents
A kind of method and apparatus classified to calling Download PDFInfo
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Abstract
The embodiment of the present application discloses a kind of method and apparatus classified to calling, and methods described includes：Packet is obtained, packet includes N number of data acquisition system corresponding with N number of calling difference, and the corresponding data acquisition systems of any calling Hi in N number of calling include all measurement report MR that communication terminal is sent to base station successively during the calling Hi；Cutting is carried out respectively with w preset time window to each calling in N number of calling, it is determined that each calling the corresponding feature set after each preset time window cutting, feature set includes the value of cell number corresponding with the calling after cutting, switching number and at least one user-defined feature；According to each calling corresponding feature set after each preset time window cutting, N number of calling is classified according to the classification of default mobile status.The embodiment of the present application can be conducive to improving precision when classifying to calling according to the classification that corresponding cell number, switching number and user-defined feature determine to call is called.
Description
Technical Field
The present application relates to the field of communications technologies, and in particular, to a method and an apparatus for classifying a call.
Background
In network optimization, an operator often needs to classify the moving state of each call in an acquired packet by using a processing device such as a server. It should be noted that, when a communication terminal such as a mobile phone or a tablet computer (Pad) is used for a call, it is considered that one call process corresponds to two calls, where a communication terminal initiating the call corresponds to one call and a communication terminal receiving the call also corresponds to one call. In addition, when portable communication equipment such as a mobile phone and a pad surf the internet through the base station, one-time internet surfing process also corresponds to one-time calling. In the data packet acquired by the server, each call corresponds to one data set, and each data set includes all Measurement Reports (MRs) sent by the communication terminal to the base station in one call process.
The inventor of the present application finds that, when classifying a call, the prior art simply processes a data set corresponding to one call, and classifies the call according to two parameters, namely, the number of acquired cells and the number of handovers. Taking a call corresponding to a scenario in which a user uses a communication terminal to walk back and forth in a certain area as an example, the number of cells and the number of handovers in a data set may be relatively large, and the prior art may classify the call into a category of high-speed motion, which is obviously inconsistent with the actual moving state of the call. Thus, the prior art techniques are inaccurate when classifying calls.
Disclosure of Invention
The embodiment of the application provides a method and a device for classifying calls, which are used for improving the precision of classifying the calls.
In a first aspect, an embodiment of the present application provides a method for classifying a call, where the method includes:
acquiring a data packet, wherein the data packet comprises N data sets respectively corresponding to N calls, and the data set corresponding to any call Hi in the N calls comprises all measurement reports MR which are sequentially sent to a base station by a communication terminal in the process of calling Hi, wherein N is an integer greater than 1, and i is greater than or equal to 1 and less than or equal to N;
segmenting each call in the N calls by w preset time windows respectively, and determining a feature set corresponding to each call after each preset time window is segmented, wherein the feature set comprises the number of cells, the switching number and the value of at least one custom feature corresponding to the segmented call, and the custom feature is related to the moving state of the call;
and classifying the N calls according to the classes of the preset moving state according to the feature set corresponding to each call after being segmented by each preset time window.
In embodiments of the present application, the at least one customized feature includes at least one of the following customized features: receiving signal strength standard deviation, switching entropy, outdoor cell ratio and inter-station distance speed; wherein,
the received signal strength standard deviation is the standard deviation of the received signal strength value of the call Hj;
the switching entropy is used for representing the uncertainty of the call Hj access cell;
the outdoor cell occupation ratio is the percentage of the number of the outdoor type cells accessed by the call Hj to the total number of all the accessed cells;
and the inter-station distance speed is the farthest distance between the average value of all the position information obtained by the call Hj and all the positions of the call.
In some possible embodiments, the switching entropy is calculated according to the following formula:
wherein, the entropy is the switching entropy corresponding to the call Hj, and the entropy is the switching entropy corresponding to the call Hj
Wherein N represents the total number of cells accessed by the call Hj, i represents the ith cell accessed by the call Hj, # i represents the number of times the call Hj accesses the ith cell, T represents the total number of times the call Hj accesses different cells, and pi represents the probability of accessing the ith cell in the period of time.
It can be seen that, in the above technical solution, since the user-defined feature related to the mobile state of the call is set, when the mobile state is judged, the judgment can be performed by combining the user-defined feature in addition to the number of cells and the number of handovers corresponding to the call, which is beneficial to improving the accuracy when classifying the call.
In some possible embodiments, the access terminal may be configured to, depending on the nature of the access cell,
the received signal strength standard deviation in a preset feature set corresponding to the call Hj includes: the received signal strength standard deviation of the main serving cell, the received signal strength standard deviations of the neighboring cells, and the received signal strength standard deviations of all cells:
the switching entropy in the preset feature set corresponding to the call Hj includes: entropy of the main serving cell, entropy of the neighboring cells, and entropy of all cells.
In some possible embodiments, when the data set corresponding to N 'of the N calls includes accurate geographic location information, the preset feature set corresponding to any of the N' calls further includes: average speed, wherein N' is ≧ 1.
In some possible embodiments, the precise geographical location information comprises location information obtained by AGPS or OTT location services.
In some possible embodiments, when the data set corresponding to N' calls of the N calls includes accurate geographic location information, the classifying the N calls according to a category of a preset moving state includes:
any one of the N' calls HkDetermining the call H according to the corresponding average speeds and the corresponding average speeds of the types of the preset moving stateskWherein 1. ltoreq. K.ltoreq.N';
determining the boundary of any two preset mobile state categories in an M-dimensional space by using a supervised learning algorithm according to the category corresponding to each call in the N 'calls and the feature set corresponding to each call in the N' calls, and obtaining the region range of any one preset mobile state category in the M-dimensional space according to the boundary, wherein M is the call HkThe number of the corresponding features in the feature set;
for the (N-N ') calls in the N calls, calling the (N-N') calls which do not include the accurate geographic position information in the corresponding data set, obtaining the mapping position of the call in the M-dimensional space according to the (N-N ') calls and determining the moving state corresponding to the (N-N') calls according to the mapping position and the regional range of the M-dimensional space distribution of any preset moving state.
In some possible embodiments, classifying the N calls according to a preset category of mobility state includes:
when Geographic Information System (GIS) Information is included in the N' calls,
acquiring the position of the specified ground feature information in the GIS information, wherein the specified ground feature information is the ground feature information which is strongly related to the preset moving state; the feature information may be, for example: residential, shopping mall, park, road, intersection, highway or railway, etc.
Any one of the N' calls HkDetermining the call H according to the corresponding average speed and the speed corresponding to the preset type of the moving statekWherein 1. ltoreq. K.ltoreq.N';
determining said any call HkMatching ground feature information;
determining a set J of feature information corresponding to the N' calls;
determining the boundary of any two preset moving state categories in an M-dimensional space according to the category corresponding to each call in the N 'calls, the feature set corresponding to each call in the N' calls and the feature information set J by using a supervised learning algorithm, obtaining the region range of any one preset moving state category in the M-dimensional space according to the boundary, wherein M is the call HkThe number of the corresponding features in the feature set;
for the (N-N ') calls in the N calls, calling the (N-N') calls which do not include the accurate geographic position information in the corresponding data set, obtaining the mapping position of the call in the M-dimensional space according to the (N-N ') calls and determining the moving state corresponding to the (N-N') calls according to the mapping position and the regional range of the M-dimensional space distribution of any preset moving state.
In some possible embodiments, a set Ji of calls in the N ' calls corresponding to any feature information in the set J is determined, if the set Ji includes N "calls, a set of types of moving states corresponding to the N" calls is Ji ', a number of types of moving states corresponding to the set Ji ' is N ' ″, and if the number of calls corresponding to a type of a certain moving state in the set Ji ' is smaller than
The vector F corresponding to the call corresponding to the type of the mobile state is copied. In this way, the classification accuracy of the class with the smaller probability can be improved.
According to the category of the mobile state corresponding to each call in the N' calls, the vector F and the vector corresponding to the preset feature set corresponding to each call, determining the boundary of any two categories of the preset mobile state in an M-dimensional space by using a supervised learning algorithm, and obtaining the regional range of any one category of the preset mobile state in the M-dimensional space according to the boundary, wherein M is the call HkThe number of the corresponding features in the preset feature set;
for the (N-N ') calls in the N calls, the data set corresponding to the calls does not include accurate geographic position information, any one of the (N-N ') calls obtains the mapping position of the call in the M-dimensional space according to the corresponding preset feature set, and the moving state corresponding to any one of the (N-N ') calls is determined according to the mapping position and the regional range of any one of the preset moving states in the M-dimensional space distribution.
In some possible embodiments, when the precise geographical location information is not included in the N calls, the classifying the N calls according to a category of a preset moving state includes:
obtaining N vectors corresponding to the preset feature set and respectively corresponding to the N calls according to the set of the preset feature set corresponding to the N calls;
dividing the N calls into M sets according to the N vectors and an unsupervised learning algorithm, wherein M is larger than the number of the categories of the preset moving state;
and classifying the M sets according to the preset moving state category according to expert rules, wherein the category of any call is the same as that of the set to which the call belongs.
In a second aspect, an embodiment of the present application provides an apparatus for classifying a call, where the apparatus includes:
an obtaining unit, configured to obtain a data packet, where the data packet includes N data sets corresponding to N calls, respectively, and a data set corresponding to any call Hi in the N calls includes all measurement reports MR that are sequentially sent by a communication terminal to a base station in the process of calling Hi, where N is an integer greater than 1, and i is greater than or equal to 1 and less than or equal to N;
the first processing unit is used for segmenting each call in the N calls by w preset time windows respectively, and determining a feature set corresponding to each call after each preset time window is segmented, wherein the feature set comprises the number of cells, the switching number and at least one value of a user-defined feature corresponding to the segmented call, and the user-defined feature is related to the moving state of the call;
and the classification unit is used for classifying the N calls according to the classes of the preset moving states according to the feature set corresponding to each call after being segmented by each preset time window.
In embodiments of the present application, the at least one customized feature includes at least one of the following customized features: receiving signal strength standard deviation, switching entropy, outdoor cell ratio and inter-station distance speed; wherein,
the received signal strength standard deviation is the standard deviation of the received signal strength value of the call Hj;
the switching entropy is used for representing the uncertainty of the call Hj access cell;
the outdoor cell occupation ratio is the percentage of the number of the outdoor type cells accessed by the call Hj to the total number of all the accessed cells;
and the inter-station distance speed is the farthest distance between the average value of all the position information obtained by the call Hj and all the positions of the call.
In some possible embodiments, the switching entropy may be calculated according to the following formula:
wherein, the entropy is the switching entropy corresponding to the call Hj, and the entropy is the switching entropy corresponding to the call Hj
Wherein N represents the total number of cells accessed by the call Hj, i represents the ith cell accessed by the call Hj, # i represents the number of times the call Hj accesses the ith cell, T represents the total number of times the call Hj accesses different cells, and pi represents the probability of accessing the ith cell in the period of time.
It can be seen that, in the above technical solution, since the user-defined feature related to the mobile state of the call is set, when the mobile state is judged, the judgment can be performed by combining the user-defined feature in addition to the number of cells and the number of handovers corresponding to the call, which is beneficial to improving the accuracy when classifying the call.
In some possible embodiments, the access terminal may be configured to, depending on the nature of the access cell,
the received signal strength standard deviation in a preset feature set corresponding to the call Hj includes: the received signal strength standard deviation of the main serving cell, the received signal strength standard deviations of the neighboring cells, and the received signal strength standard deviations of all cells:
the switching entropy in the preset feature set corresponding to the call Hj includes: entropy of the main serving cell, entropy of the neighboring cells, and entropy of all cells.
In some possible embodiments, when the data set corresponding to N 'of the N calls includes accurate geographic location information, the preset feature set corresponding to any of the N' calls further includes: average speed, wherein N' is ≧ 1.
In some possible embodiments, the precise geographical location information comprises location information obtained by AGPS or OTT location services.
In some possible embodiments, when the data sets corresponding to N' of the N calls include accurate geographical location information, the classification unit is specifically configured to,
any one of the N' calls HkDetermining the call H according to the corresponding average speeds and the corresponding average speeds of the types of the preset moving stateskWherein 1. ltoreq. K.ltoreq.N';
determining the boundary of any two preset mobile state categories in an M-dimensional space by using a supervised learning algorithm according to the category corresponding to each call in the N 'calls and the feature set corresponding to each call in the N' calls, and obtaining the region range of any one preset mobile state category in the M-dimensional space according to the boundary, wherein M is the call HkThe number of the corresponding features in the feature set;
for the (N-N ') calls in the N calls, calling the (N-N') calls which do not include the accurate geographic position information in the corresponding data set, obtaining the mapping position of the call in the M-dimensional space according to the (N-N ') calls and determining the moving state corresponding to the (N-N') calls according to the mapping position and the regional range of the M-dimensional space distribution of any preset moving state.
In some possible embodiments, the classification unit is specifically adapted to,
when GIS information is included in the N' calls,
acquiring the position of the specified ground feature information in the GIS information, wherein the specified ground feature information is the ground feature information which is strongly related to the preset moving state;
any one of the N' calls HkDetermining the call H according to the corresponding average speed and the speed corresponding to the preset type of the moving statekWherein 1. ltoreq. K.ltoreq.N';
determining said any call HkMatching ground feature information;
determining a set J of feature information corresponding to the N' calls;
determining the boundary of any two preset moving state categories in an M-dimensional space according to the category corresponding to each call in the N 'calls, the feature set corresponding to each call in the N' calls and the feature information set J by using a supervised learning algorithm, obtaining the region range of any one preset moving state category in the M-dimensional space according to the boundary, wherein M is the call HkThe number of the corresponding features in the feature set;
for the (N-N ') calls in the N calls, calling the (N-N') calls which do not include the accurate geographic position information in the corresponding data set, obtaining the mapping position of the call in the M-dimensional space according to the (N-N ') calls and determining the moving state corresponding to the (N-N') calls according to the mapping position and the regional range of the M-dimensional space distribution of any preset moving state.
In some possible embodiments, when the precise geographical location information is not included in the N calls, the classification unit is specifically configured to,
obtaining N vectors corresponding to the preset feature set and respectively corresponding to the N calls according to the set of the preset feature set corresponding to the N calls;
dividing the N calls into M sets according to the N vectors and an unsupervised learning algorithm, wherein M is larger than the number of the categories of the preset moving state;
and classifying the M sets according to the preset moving state category according to expert rules, wherein the category of any call is the same as that of the set to which the call belongs.
In a third aspect, embodiments of the present application provide a storage medium, which is a non-volatile computer-readable storage medium storing at least one program, where each program includes instructions that include some or all of the steps of any one of the methods for classifying a call provided by the embodiments of the present application that can be executed by an apparatus having a processor.
In a fourth aspect, an embodiment of the present application provides an apparatus for classifying a call, including:
a processor and a memory component coupled to each other; wherein the processor is configured to perform the method of any one of claims 1 to 9.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments or the background art of the present application, the drawings required to be used in the embodiments or the background art of the present application will be described below.
FIG. 1 is a schematic diagram of a scenario in which an embodiment of the present application is applied;
fig. 2 is a flow chart illustrating a method of classifying a call according to an embodiment of the present application;
FIG. 3 is a diagram illustrating an actual moving track of each communication terminal and a distance and a speed between corresponding stations in FIG. 1;
fig. 4 is a schematic structural diagram of an apparatus for classifying a call according to another embodiment of the present application.
Detailed Description
The embodiments of the present application will be described below with reference to the drawings.
Referring to fig. 1, fig. 1 is a schematic view of a scenario applied in the embodiment of the present application. As shown in fig. 1, the device 101 that classifies calls acquires a data packet from a plurality of base stations (BTS1, BTS2, BTS3, BTS4, …, BTSn), where the data packet includes data sets corresponding to the plurality of calls, the data set corresponding to any call includes all Measurement Reports (MRs) that are sequentially transmitted by the communication terminal to the base stations during the call, the MR is information fed back to the device 101 that classifies calls by the communication terminal, and the MR includes information such as serving cells, neighboring cells, and signal strengths corresponding to these cells, which are received by the terminal to be used in the embodiment of the present application. The communication terminal a in fig. 1 is located on a car, and moves at a high speed as the car moves. The communication terminal B is located indoors and is in a stationary state. The communication terminal C moves at a low speed as the user walks. As can be seen from fig. 1, at different times T1, T2, and T3, the different communication terminals move at different distances, where communication terminal a moves farthest, communication terminal C moves second, and communication terminal B does not move. Conventionally, when a moving state of a communication terminal is determined, the moving state is generally determined based on the number of cells and the number of handovers corresponding to the communication terminal when calling. It should be noted that, when the communication terminal moves fast in a small range, according to the prior art, since the number of cells and the number of handovers are small, the obtained moving state of the communication terminal may be slow or static, and thus the prior art is inaccurate in classifying the call.
In order to improve the precision of classifying the mobile states of the communication terminal, the embodiment of the present application introduces a custom feature related to the mobile states of the calls, and the classifying device 101 obtains data packets corresponding to a plurality of calls and classifies the mobile states of the plurality of calls.
Specifically, as shown in fig. 2, a method for classifying a call provided in an embodiment of the present application includes the following steps:
s201, obtaining a data packet, where the data packet includes N data sets corresponding to N calls respectively, and a data set corresponding to any call Hi in the N calls includes all measurement reports MR sent by a communication terminal to a base station in sequence in the process of calling Hi, where N is an integer greater than 1, and i is greater than or equal to 1 and less than or equal to N.
S202, each call in the N calls is respectively segmented by w preset time windows, and a feature set corresponding to each call after each preset time window segmentation is determined, wherein the feature set comprises the number of cells, the switching number and at least one value of a custom feature corresponding to the segmented call, and the custom feature is related to the moving state of the call.
Wherein the number of cells is the number of cells accessed in total within a period of time (such as 30 seconds, or 1 minute, or 5 minutes, etc.).
Wherein the number of switching is the total number of switching occurring in a period of time (such as 30 seconds, 1 minute, 5 minutes, etc.)
Wherein the at least one custom feature comprises at least one of the following custom features: receiving signal strength standard deviation, switching entropy, outdoor cell ratio and inter-station distance speed; wherein,
the received signal strength standard deviation is the standard deviation of the received signal strength value of the call Hj. Specifically, it may be a standard deviation of received signal intensity values obtained by measuring a plurality of times over a period of time (such as 30 seconds, or 1 minute, or 5 minutes, etc.). It can be understood that the received signal strength standard deviation can be divided into the received signal strength standard deviation of the main serving cell, the received signal strength standard deviation of the neighboring cell and the received signal strength standard deviation of all cells according to the different properties of the transmitting end of the received signal. Generally, the faster the communication terminal moves, the larger the value of the received signal strength standard deviation.
And the switching entropy is used for expressing the uncertainty of the call Hj access cell. Specifically, the uncertainty of accessing different cells by the communication terminal within a period of time (e.g. 30 seconds, or 1 minute, or 5 minutes, etc.) may be set to be between 0 and 1. For example, if the communication terminal is in a static state, it has only one access cell during the period, and its handover entropy is 0. It can be understood that the faster the communication terminal moves, the larger the number of access cells and the number of cell handovers in a period of time, the more the handover entropy approaches 1. For example, if the handover entropy is 0.2, it can be inferred that the communication terminal is in a low-speed moving state. If the handover entropy is 0.9, it can be inferred that the communication terminal is in a high-speed moving state.
In some possible embodiments, the switching entropy may be calculated according to the following formula:
wherein, the entropy is the switching entropy corresponding to the call Hj, and the entropy is the switching entropy corresponding to the call Hj
Wherein N represents the total number of cells accessed by the call Hj, i represents the ith cell accessed by the call Hj, # i represents the number of times the call Hj accesses the ith cell, T represents the total number of times the call Hj accesses different cells, and pi represents the probability of accessing the ith cell in the period of time.
According to different access cell properties, the switching entropy includes: entropy of the main serving cell, entropy of the neighboring cells, and entropy of all cells.
And the outdoor cell occupation ratio is the percentage of the number of the outdoor type cells accessed by the call Hj to the total number of all the accessed cells. It can be understood that if the outdoor cell occupancy is less than 50%, the communication terminal may be considered to be located indoors and in a stationary state.
And the inter-station distance speed is the farthest distance between the average value of all the position information obtained by the call Hj and all the positions of the call. In particular, it may be the farthest distance from all positions of the mean of all position information (including MR positioning, AGPS positioning, OTT positioning) obtained by the user over a period of time. The greater the inter-station distance speed, the greater the moving speed of the communication terminal is considered. For the condition that the communication terminal starts from a position and returns to the position within a period of time, the distance and the speed between the stations can represent the movement speed of the user better than a simple speed calculation model, namely a ratio of the distance between the starting point and the ending point to the time difference. Referring to fig. 3, where the actual movement locus of each communication terminal is shown by a dotted line in fig. 3, the start position of the solid line segment with an arrow in the figure indicates the mean value of the position information, the position indicated by the arrow of the line segment with an arrow is the point farthest from the mean value within a specified window time, and the distance of the line segment with an arrow is the inter-station speed by way of example. As can be seen from fig. 3, the distance speed between stations of the left communication terminal is the largest, the distance speed between stations of the middle communication terminal is the next largest, and the distance speed between stations of the right communication terminal is the smallest.
S203, classifying the N calls according to the classes of the preset moving states according to the feature sets corresponding to the calls after the calls are segmented by the preset time windows.
It should be noted that feature sets respectively corresponding to a plurality of calls obtained by segmentation in any preset time window are calculated, and then an average value of the feature sets is determined to serve as the feature set corresponding to the preset time window. For example, a call H1 is divided into 6 calls in a preset 30-second time window, feature sets corresponding to the 6 calls are calculated, and then the average value of each feature in the 6 obtained feature sets is used as the feature set of the call H1.
It can be seen that, in the above technical solution, since the user-defined feature related to the mobile state of the call is set, when the mobile state is judged, the judgment can be performed by combining the user-defined feature in addition to the number of cells and the number of handovers corresponding to the call, which is beneficial to improving the accuracy when classifying the call.
In some possible embodiments, when the data set corresponding to N' calls of the N calls includes accurate geographic location information, the classifying the N calls according to a category of a preset moving state includes:
and determining the category of any call Hk in the N 'calls according to the corresponding average speed and the corresponding average speed of the call Hk according to the category of the preset moving state, wherein K is more than or equal to 1 and less than or equal to N'. For example, if N is 1000 and N' is 120, the data set corresponding to 120 calls out of 1000 calls includes the precise geographic location information. The precise geographical location information may be obtained through an Assisted Global Positioning System (AGPS) or through an internet application service (Over The Top, OTT). According to the geographical location information of the 120 calls with accurate geographical location information, the average speed of each of the 120 calls can be obtained, and the category of the moving state of the calls is determined according to the evaluation speed corresponding to the category of the preset moving state.
Determining the boundary of any two preset mobile state categories in an M-dimensional space by using a supervised learning algorithm according to the category corresponding to each call in the N 'calls and the feature set corresponding to each call in the N' calls, and obtaining the region range of any one preset mobile state category in the M-dimensional space according to the boundary, wherein M is the call HkThe number of the corresponding features in the feature set;
for the (N-N ') calls in the N calls, calling the (N-N') calls which do not include the accurate geographic position information in the corresponding data set, obtaining the mapping position of the call in the M-dimensional space according to the (N-N ') calls and determining the moving state corresponding to the (N-N') calls according to the mapping position and the regional range of the M-dimensional space distribution of any preset moving state.
In some possible embodiments, when GIS information is included in the N' calls,
acquiring the position of the specified ground feature information in the GIS information, wherein the specified ground feature information is the ground feature information which is strongly related to the preset moving state; the feature information may be, for example: residential, shopping mall, park, road, intersection, highway or railway, etc.
Any one of the N' calls HkDetermining the call H according to the corresponding average speed and the speed corresponding to the preset type of the moving statekWherein 1. ltoreq. K.ltoreq.N';
determining said any call HkMatching ground feature information;
determining a set J of feature information corresponding to the N' calls;
determining the boundary of any two preset moving state categories in an M-dimensional space according to the category corresponding to each call in the N 'calls, the feature set corresponding to each call in the N' calls and the feature information set J by using a supervised learning algorithm, obtaining the region range of any one preset moving state category in the M-dimensional space according to the boundary, wherein M is the call HkThe number of corresponding features in the feature set. For example, in some of the possible embodiments,
in some possible embodiments, a set Ji of calls in the N ' calls corresponding to any feature information in the set J is determined, if the set Ji includes N "calls, a set of types of moving states corresponding to the N" calls is Ji ', a number of types of moving states corresponding to the set Ji ' is N ' ″, and if the number of calls corresponding to a type of a certain moving state in the set Ji ' is smaller than
The vector F corresponding to the call corresponding to the type of the mobile state is copied. In this way, the classification accuracy of the class with the smaller probability can be improved.
According to the category of the mobile state corresponding to each call in the N' calls, the vector F and the vector corresponding to the preset feature set corresponding to each call, determining the boundary of any two categories of the preset mobile state in an M-dimensional space by using a supervised learning algorithm, and obtaining the regional range of any one category of the preset mobile state in the M-dimensional space according to the boundary, wherein M is the call HkThe number of the corresponding features in the preset feature set;
for the (N-N ') calls in the N calls, the data set corresponding to the calls does not include accurate geographic position information, any one of the (N-N ') calls obtains the mapping position of the call in the M-dimensional space according to the corresponding preset feature set, and the moving state corresponding to any one of the (N-N ') calls is determined according to the mapping position and the regional range of any one of the preset moving states in the M-dimensional space distribution.
For example, if the geographic information corresponding to the GIS is a park, 150 calls including precise geographic locations are passed through the park, wherein 92 calls correspond to slow movement, 51 calls correspond to stationary movement, and 7 calls correspond to high-speed movement. Since the number of calls corresponding to the high-speed motion is less than 150/3 ═ 50, the vector corresponding to the call of the high-speed motion can be copied, the vector corresponding to the high-speed motion is made to reach 50 or more than 50 after copying, then the vector corresponding to the preset feature set corresponding to the 150 calls and the vectors corresponding to the 43 copied (taking the number of calls corresponding to the high-speed motion after copying as an example) calls with the high-speed motion, the boundary of any two classes of the preset motion state in the M-dimensional space is determined by using a supervised learning algorithm, and the region range of any one class of the preset motion state in the M-dimensional space is obtained according to the boundary.
For the (N-N ') calls in the N calls, calling the (N-N') calls which do not include the accurate geographic position information in the corresponding data set, obtaining the mapping position of the call in the M-dimensional space according to the (N-N ') calls and determining the moving state corresponding to the (N-N') calls according to the mapping position and the regional range of the M-dimensional space distribution of any preset moving state.
In some possible embodiments, when the N calls do not include accurate geographical location information, obtaining, according to the set of the preset feature set corresponding to the N calls, N vectors corresponding to the preset feature set and respectively corresponding to the N calls;
dividing the N calls into M sets according to the N vectors and an unsupervised learning algorithm, wherein M is larger than the number of the categories of the preset moving state; for example, if the predetermined moving state includes 4 types: static, low speed motion, medium speed motion, and high speed motion. If N is 1000, in some possible embodiments of the present invention, 1000 calls may be divided into 20 sets according to an unsupervised learning algorithm, and then the moving state of each of the 20 sets may be determined according to an expert algorithm such as a factor analysis method, an iterative algorithm, a principal component analysis method, and the like. Such as determining the moving state of a set with a switching entropy value of 0 as stationary. And determining the moving state of the call in the set with the switching entropy of more than 0.7 and the switching number of more than 5 as high-speed movement and the like. The category of any call is the same as the category of the set to which it belongs.
Referring to fig. 4, an apparatus 400 for classifying a call according to an embodiment of the present application is provided, and specifically, the apparatus 400 for classifying a call shown in fig. 4 may include: an acquisition unit 401, a first processing unit 402, and a classification unit 403.
The obtaining unit 401 is configured to execute the method in step S201 in fig. 2 in the embodiment of the method of the present invention, and the embodiment of the obtaining unit 401 may refer to the description corresponding to step S201 in fig. 2 in the embodiment of the method of the present invention, which is not described herein again.
The first processing unit 402 is configured to execute the method of step S202 in fig. 2 in the method embodiment of the present invention, and the implementation manner of the first processing unit 402 may refer to the description corresponding to step S202 in fig. 2 in the method embodiment of the present invention, which is not described herein again.
The classifying unit 403 is configured to execute the method of step S203 in fig. 2 in the embodiment of the method of the present invention, and the implementation manner of the classifying unit 403 may refer to the description corresponding to step S203 in fig. 2 in the embodiment of the method of the present invention, which is not described herein again.
Optionally, in some possible embodiments of the present invention, the at least one customized feature includes at least one of the following customized features: receiving signal strength standard deviation, switching entropy, outdoor cell ratio and inter-station distance speed; wherein,
the received signal strength standard deviation is the standard deviation of the received signal strength value of the call Hj;
the switching entropy is used for representing the uncertainty of the call Hj access cell;
the outdoor cell occupation ratio is the percentage of the number of the outdoor type cells accessed by the call Hj to the total number of all the accessed cells;
and the inter-station distance speed is the farthest distance between the average value of all the position information obtained by the call Hj and all the positions of the call.
Optionally, in some possible embodiments of the present invention, the switching entropy is calculated according to the following formula:
wherein, the entropy is the switching entropy corresponding to the call Hj, and the entropy is the switching entropy corresponding to the call Hj
Wherein N represents the total number of cells accessed by the call Hj, i represents the ith cell accessed by the call Hj, # i represents the number of times the call Hj accesses the ith cell, T represents the total number of times the call Hj accesses different cells, and pi represents the probability of accessing the ith cell in the period of time.
Optionally, in some possible embodiments of the present invention, the access terminal may determine, according to the nature of the access cell,
the received signal strength standard deviation in a preset feature set corresponding to the call Hj includes: the received signal strength standard deviation of the main serving cell, the received signal strength standard deviations of the neighboring cells, and the received signal strength standard deviations of all cells:
the switching entropy in the preset feature set corresponding to the call Hj includes: entropy of the main serving cell, entropy of the neighboring cells, and entropy of all cells.
Optionally, in some possible embodiments of the present invention, when the data set corresponding to N 'calls in the N calls includes accurate geographic location information, the preset feature set corresponding to any one of the N' calls further includes: average speed, wherein N' is ≧ 1.
The precise geographical location information includes location information obtained through AGPS or OTT location services.
Optionally, in some possible embodiments of the present invention, when the data sets corresponding to N' calls out of the N calls include accurate geographic location information, the classifying unit is specifically configured to,
any one of the N' calls HkDetermining the call H according to the corresponding average speeds and the corresponding average speeds of the types of the preset moving stateskWherein 1. ltoreq. K.ltoreq.N';
determining the boundary of any two preset mobile state categories in an M-dimensional space by using a supervised learning algorithm according to the category corresponding to each call in the N 'calls and the feature set corresponding to each call in the N' calls, and obtaining the region range of any one preset mobile state category in the M-dimensional space according to the boundary, wherein M is the call HkThe number of the corresponding features in the feature set;
for the (N-N ') calls in the N calls, calling the (N-N') calls which do not include the accurate geographic position information in the corresponding data set, obtaining the mapping position of the call in the M-dimensional space according to the (N-N ') calls and determining the moving state corresponding to the (N-N') calls according to the mapping position and the regional range of the M-dimensional space distribution of any preset moving state.
Optionally, in some possible embodiments of the invention, the classification unit is specifically configured to,
when GIS information is included in the N' calls,
acquiring the position of the specified ground feature information in the GIS information, wherein the specified ground feature information is the ground feature information which is strongly related to the preset moving state;
any one of the N' calls HkDetermining the call H according to the corresponding average speed and the speed corresponding to the preset type of the moving statekWherein 1. ltoreq. K.ltoreq.N';
determining said any call HkMatching ground feature information;
determining a set J of feature information corresponding to the N' calls;
determining the boundary of any two preset moving state categories in an M-dimensional space according to the category corresponding to each call in the N 'calls, the feature set corresponding to each call in the N' calls and the feature information set J by using a supervised learning algorithm, obtaining the region range of any one preset moving state category in the M-dimensional space according to the boundary, wherein M is the call HkThe number of the corresponding features in the feature set;
for the (N-N ') calls in the N calls, calling the (N-N') calls which do not include the accurate geographic position information in the corresponding data set, obtaining the mapping position of the call in the M-dimensional space according to the (N-N ') calls and determining the moving state corresponding to the (N-N') calls according to the mapping position and the regional range of the M-dimensional space distribution of any preset moving state.
Optionally, in some possible embodiments of the present invention, when the precise geographical location information is not included in the N calls, the classifying unit is specifically configured to,
obtaining N vectors corresponding to the preset feature set and respectively corresponding to the N calls according to the set of the preset feature set corresponding to the N calls;
dividing the N calls into M sets according to the N vectors and an unsupervised learning algorithm, wherein M is larger than the number of the categories of the preset moving state;
and classifying the M sets according to the preset moving state category according to expert rules, wherein the category of any call is the same as that of the set to which the call belongs.
Embodiments of the present application further provide a computer storage medium, where the computer storage medium may store a program, and the program includes, when executed, some or all of the steps of any one of the methods for classifying a call described in the above method embodiments.
An embodiment of the present application further provides a device for classifying a call, including: a processor and a memory component coupled to each other; wherein the processor is configured to perform any one of the methods of classifying a call described in the above method embodiments.
The steps in the method of the embodiment of the application can be sequentially adjusted, combined and deleted according to actual needs.
The units in the device of the embodiment of the application can be combined, divided and deleted according to actual needs.
One of ordinary skill in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by hardware related to instructions of a computer program, which may be stored in a computer-readable storage medium, and when executed, may include the processes of the above method embodiments. And the aforementioned storage medium includes: various media capable of storing program codes, such as Read-Only Memory (ROM) or Random Access Memory (RAM), magnetic disks, or optical disks.
Claims (20)
1. A method of classifying a call, the method comprising:
acquiring a data packet, wherein the data packet comprises N data sets respectively corresponding to N calls, and the data set corresponding to any call Hi in the N calls comprises all measurement reports MR which are sequentially sent to a base station by a communication terminal in the process of calling Hi, wherein N is an integer greater than 1, and i is greater than or equal to 1 and less than or equal to N;
segmenting each call in the N calls by w preset time windows respectively, and determining a feature set corresponding to each call after each preset time window is segmented, wherein the feature set comprises the number of cells, the switching number and the value of at least one custom feature corresponding to the segmented call, and the custom feature is related to the moving state of the call;
and classifying the N calls according to the classes of the preset moving state according to the feature set corresponding to each call after being segmented by each preset time window.
2. The method of claim 1,
the at least one custom feature comprises at least one of the following custom features: receiving signal strength standard deviation, switching entropy, outdoor cell ratio and inter-station distance speed; wherein,
the received signal strength standard deviation is the standard deviation of the received signal strength value of the call Hj;
the switching entropy is used for representing the uncertainty of the call Hj access cell;
the outdoor cell occupation ratio is the percentage of the number of the outdoor type cells accessed by the call Hj to the total number of all the accessed cells;
and the inter-station distance speed is the farthest distance between the average value of all the position information obtained by the call Hj and all the positions of the call.
3. The method of claim 2,
the switching entropy is calculated according to the following formula:
<mrow> <mi>e</mi> <mi>n</mi> <mi>t</mi> <mi>r</mi> <mi>o</mi> <mi>p</mi> <mi>y</mi> <mo>=</mo> <mo>-</mo> <mrow> <mo>(</mo> <msup> <mi>lg</mi> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> <mi>N</mi> <mo>)</mo> </mrow> <mo>*</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msub> <mi>p</mi> <mi>i</mi> </msub> <mo>*</mo> <mi>lg</mi> <mi> </mi> <msub> <mi>p</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> </mrow>
wherein, the entropy is the switching entropy corresponding to the call Hj, and the entropy is the switching entropy corresponding to the call Hj
Wherein N represents the total number of cells accessed by the call Hj, i represents the ith cell accessed by the call Hj, # i represents the number of times the call Hj accesses the ith cell, T represents the total number of times the call Hj accesses different cells, and pi represents the probability of accessing the ith cell in the period of time.
4. The method of claim 3,
depending on the nature of the access cell,
the received signal strength standard deviation in a preset feature set corresponding to the call Hj includes: the received signal strength standard deviation of the main serving cell, the received signal strength standard deviations of the neighboring cells, and the received signal strength standard deviations of all cells:
the switching entropy in the preset feature set corresponding to the call Hj includes: entropy of the main serving cell, entropy of the neighboring cells, and entropy of all cells.
5. The method of claim 4,
when the data set corresponding to N 'calls in the N calls includes accurate geographical location information, the preset feature set corresponding to any one of the N' calls further includes: average speed, wherein N' is ≧ 1.
6. The method of claim 5,
the precise geographical location information includes location information obtained through AGPS or OTT location services.
7. The method of claim 6,
when the data sets corresponding to N' calls in the N calls include accurate geographical location information, classifying the N calls according to a preset category of a mobile state includes:
any one of the N' calls HkDetermining the call H according to the corresponding average speeds and the corresponding average speeds of the types of the preset moving stateskWherein 1. ltoreq. K.ltoreq.N';
determining the boundary of any two preset mobile state categories in an M-dimensional space by using a supervised learning algorithm according to the category corresponding to each call in the N 'calls and the feature set corresponding to each call in the N' calls, and obtaining the region range of any one preset mobile state category in the M-dimensional space according to the boundary, wherein M is the call HkThe number of the corresponding features in the feature set;
for the (N-N ') calls in the N calls, calling the (N-N') calls which do not include the accurate geographic position information in the corresponding data set, obtaining the mapping position of the call in the M-dimensional space according to the (N-N ') calls and determining the moving state corresponding to the (N-N') calls according to the mapping position and the regional range of the M-dimensional space distribution of any preset moving state.
8. The method of claim 6, wherein classifying the N calls according to a predetermined category of mobility state comprises:
when GIS information is included in the N' calls,
acquiring the position of the specified ground feature information in the GIS information, wherein the specified ground feature information is the ground feature information which is strongly related to the preset moving state;
any one of the N' calls HkDetermining the call H according to the corresponding average speed and the speed corresponding to the preset type of the moving statekWherein 1. ltoreq. K.ltoreq.N';
determining said any call HkMatching ground feature information;
determining a set J of feature information corresponding to the N' calls;
determining the boundary of any two preset moving state categories in an M-dimensional space according to the category corresponding to each call in the N 'calls, the feature set corresponding to each call in the N' calls and the feature information set J by using a supervised learning algorithm, obtaining the region range of any one preset moving state category in the M-dimensional space according to the boundary, wherein M is the call HkThe number of the corresponding features in the feature set;
for the (N-N ') calls in the N calls, calling the (N-N') calls which do not include the accurate geographic position information in the corresponding data set, obtaining the mapping position of the call in the M-dimensional space according to the (N-N ') calls and determining the moving state corresponding to the (N-N') calls according to the mapping position and the regional range of the M-dimensional space distribution of any preset moving state.
9. The method of claim 2,
when the accurate geographical location information is not included in the N calls, classifying the N calls according to a preset category of a moving state includes:
obtaining N vectors corresponding to the preset feature set and respectively corresponding to the N calls according to the set of the preset feature set corresponding to the N calls;
dividing the N calls into M sets according to the N vectors and an unsupervised learning algorithm, wherein M is larger than the number of the categories of the preset moving state;
and classifying the M sets according to the preset moving state category according to expert rules, wherein the category of any call is the same as that of the set to which the call belongs.
10. An apparatus for classifying a call, the apparatus comprising:
an obtaining unit, configured to obtain a data packet, where the data packet includes N data sets corresponding to N calls, respectively, and a data set corresponding to any call Hi in the N calls includes all measurement reports MR that are sequentially sent by a communication terminal to a base station in the process of calling Hi, where N is an integer greater than 1, and i is greater than or equal to 1 and less than or equal to N;
the first processing unit is used for segmenting each call in the N calls by w preset time windows respectively, and determining a feature set corresponding to each call after each preset time window is segmented, wherein the feature set comprises the number of cells, the switching number and at least one value of a user-defined feature corresponding to the segmented call, and the user-defined feature is related to the moving state of the call;
and the classification unit is used for classifying the N calls according to the classes of the preset moving states according to the feature set corresponding to each call after being segmented by each preset time window.
11. The apparatus of claim 10,
the at least one custom feature comprises at least one of the following custom features: receiving signal strength standard deviation, switching entropy, outdoor cell ratio and inter-station distance speed; wherein,
the received signal strength standard deviation is the standard deviation of the received signal strength value of the call Hj;
the switching entropy is used for representing the uncertainty of the call Hj access cell;
the outdoor cell occupation ratio is the percentage of the number of the outdoor type cells accessed by the call Hj to the total number of all the accessed cells;
and the inter-station distance speed is the farthest distance between the average value of all the position information obtained by the call Hj and all the positions of the call.
12. The apparatus of claim 11,
the switching entropy is calculated according to the following formula:
<mrow> <mi>e</mi> <mi>n</mi> <mi>t</mi> <mi>r</mi> <mi>o</mi> <mi>p</mi> <mi>y</mi> <mo>=</mo> <mo>-</mo> <mrow> <mo>(</mo> <msup> <mi>lg</mi> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> <mi>N</mi> <mo>)</mo> </mrow> <mo>*</mo> <munderover> <mo>&Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>N</mi> </munderover> <mrow> <mo>(</mo> <msub> <mi>p</mi> <mi>i</mi> </msub> <mo>*</mo> <mi>lg</mi> <mi> </mi> <msub> <mi>p</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> </mrow>
wherein, the entropy is the switching entropy corresponding to the call Hj, and the entropy is the switching entropy corresponding to the call Hj
Wherein N represents the total number of cells accessed by the call Hj, i represents the ith cell accessed by the call Hj, # i represents the number of times the call Hj accesses the ith cell, T represents the total number of times the call Hj accesses different cells, and pi represents the probability of accessing the ith cell in the period of time.
13. The apparatus of claim 12,
depending on the nature of the access cell,
the received signal strength standard deviation in a preset feature set corresponding to the call Hj includes: the received signal strength standard deviation of the main serving cell, the received signal strength standard deviations of the neighboring cells, and the received signal strength standard deviations of all cells:
the switching entropy in the preset feature set corresponding to the call Hj includes: entropy of the main serving cell, entropy of the neighboring cells, and entropy of all cells.
14. The apparatus of claim 13,
when the data set corresponding to N 'calls in the N calls includes accurate geographical location information, the preset feature set corresponding to any one of the N' calls further includes: average speed, wherein N' is ≧ 1.
15. The apparatus of claim 14,
the precise geographical location information includes location information obtained through AGPS or OTT location services.
16. The apparatus of claim 15,
when the data sets corresponding to N' of the N calls include accurate geographical location information, the classifying unit is specifically configured to,
any one of the N' calls HkDetermining the call H according to the corresponding average speeds and the corresponding average speeds of the types of the preset moving stateskWherein 1. ltoreq. K.ltoreq.N';
determining the boundary of any two preset mobile state categories in an M-dimensional space by using a supervised learning algorithm according to the category corresponding to each call in the N 'calls and the feature set corresponding to each call in the N' calls, and obtaining the region range of any one preset mobile state category in the M-dimensional space according to the boundary, wherein M is the call HkThe number of the corresponding features in the feature set;
for the (N-N ') calls in the N calls, calling the (N-N') calls which do not include the accurate geographic position information in the corresponding data set, obtaining the mapping position of the call in the M-dimensional space according to the (N-N ') calls and determining the moving state corresponding to the (N-N') calls according to the mapping position and the regional range of the M-dimensional space distribution of any preset moving state.
17. The apparatus according to claim 15, wherein the classification unit is specifically configured to,
when GIS information is included in the N' calls,
acquiring the position of the specified ground feature information in the GIS information, wherein the specified ground feature information is the ground feature information which is strongly related to the preset moving state;
any one of the N' calls HkDetermining the call H according to the corresponding average speed and the speed corresponding to the preset type of the moving statekWherein 1. ltoreq. K.ltoreq.N';
determining said any call HkMatching ground feature information;
determining a set J of feature information corresponding to the N' calls;
determining the boundary of any two preset moving state categories in an M-dimensional space according to the category corresponding to each call in the N 'calls, the feature set corresponding to each call in the N' calls and the feature information set J by using a supervised learning algorithm, obtaining the region range of any one preset moving state category in the M-dimensional space according to the boundary, wherein M is the call HkThe number of the corresponding features in the feature set;
for the (N-N ') calls in the N calls, calling the (N-N') calls which do not include the accurate geographic position information in the corresponding data set, obtaining the mapping position of the call in the M-dimensional space according to the (N-N ') calls and determining the moving state corresponding to the (N-N') calls according to the mapping position and the regional range of the M-dimensional space distribution of any preset moving state.
18. The apparatus of claim 11,
when the precise geographical location information is not included in the N calls, the classification unit is specifically configured to,
obtaining N vectors corresponding to the preset feature set and respectively corresponding to the N calls according to the set of the preset feature set corresponding to the N calls;
dividing the N calls into M sets according to the N vectors and an unsupervised learning algorithm, wherein M is larger than the number of the categories of the preset moving state;
and classifying the M sets according to the preset moving state category according to expert rules, wherein the category of any call is the same as that of the set to which the call belongs.
19. A storage medium, characterized in that it is a non-volatile computer-readable storage medium storing at least one program, each of said programs comprising instructions which, when executed by an apparatus having a processor, cause the apparatus to carry out the method of classifying a call according to any one of claims 1-9.
20. An apparatus for classifying a call, comprising:
a processor and a memory component coupled to each other; wherein the processor is configured to perform the method of any one of claims 1 to 9.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101453770A (en) * | 2007-12-07 | 2009-06-10 | 华为技术有限公司 | Measurement control method and apparatus |
| US20100189013A1 (en) * | 2002-01-11 | 2010-07-29 | Broadcom Corporation | Plug-In-Playable Wireless Communication System |
| CN102026309A (en) * | 2009-09-10 | 2011-04-20 | 大唐移动通信设备有限公司 | Method, system and device for detecting mobile state of terminal |
| CN103167551A (en) * | 2011-12-15 | 2013-06-19 | 华为技术有限公司 | User equipment (UE) measurement result reporting method and user equipment |
| CN103907384A (en) * | 2012-09-26 | 2014-07-02 | 华为技术有限公司 | Method and device for estimating moving status and measurement report method and device |
| CN104080135A (en) * | 2013-03-29 | 2014-10-01 | 电信科学技术研究院 | Network selection method and device |
| CN104094649A (en) * | 2011-08-12 | 2014-10-08 | 华为技术有限公司 | Method for estimating mobility state |
| CN105101247A (en) * | 2015-09-01 | 2015-11-25 | 重庆邮电大学 | Mobility state estimate enhancement method and device based on switching type specific weight |
| WO2016033249A1 (en) * | 2014-08-28 | 2016-03-03 | Apple Inc. | User equipment transmit duty cycle control |
-
2017
- 2017-05-17 CN CN201710348411.3A patent/CN107172637B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100189013A1 (en) * | 2002-01-11 | 2010-07-29 | Broadcom Corporation | Plug-In-Playable Wireless Communication System |
| CN101453770A (en) * | 2007-12-07 | 2009-06-10 | 华为技术有限公司 | Measurement control method and apparatus |
| CN102026309A (en) * | 2009-09-10 | 2011-04-20 | 大唐移动通信设备有限公司 | Method, system and device for detecting mobile state of terminal |
| CN104094649A (en) * | 2011-08-12 | 2014-10-08 | 华为技术有限公司 | Method for estimating mobility state |
| CN103167551A (en) * | 2011-12-15 | 2013-06-19 | 华为技术有限公司 | User equipment (UE) measurement result reporting method and user equipment |
| CN103907384A (en) * | 2012-09-26 | 2014-07-02 | 华为技术有限公司 | Method and device for estimating moving status and measurement report method and device |
| CN104080135A (en) * | 2013-03-29 | 2014-10-01 | 电信科学技术研究院 | Network selection method and device |
| WO2016033249A1 (en) * | 2014-08-28 | 2016-03-03 | Apple Inc. | User equipment transmit duty cycle control |
| CN105101247A (en) * | 2015-09-01 | 2015-11-25 | 重庆邮电大学 | Mobility state estimate enhancement method and device based on switching type specific weight |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022242447A1 (en) * | 2021-05-19 | 2022-11-24 | 中兴通讯股份有限公司 | Road user recognition method and device, storage medium, and electronic device |
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|---|---|
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