Disclosure of Invention
The embodiment of the application aims to provide a wave bit dividing method, a wave bit dividing device, electronic equipment, a storage medium and a program product, which are used for solving the problem that the wave bit division is inaccurate and the beam resource allocation is unreasonable due to the existing wave bit dividing mode.
In a first aspect, an embodiment of the present application provides a method for dividing a wave bit, where the method includes:
acquiring initial position information of each terminal in a satellite coverage area;
Predicting the predicted position information of each terminal at the subsequent service scheduling moment according to the initial position information;
Dividing each terminal according to the predicted position information of the subsequent service scheduling time to obtain a plurality of dividing ranges corresponding to the subsequent service scheduling time;
And determining the wave position corresponding to the service wave beam at the subsequent service scheduling time according to the multiple dividing ranges corresponding to the subsequent service scheduling time.
In the implementation process, the wave position range of the terminal at different service scheduling moments can be accurately judged by predicting the position of the terminal and dividing the wave position in advance, so that the preparation for switching the wave beam is prepared in advance, the wave beam resource can be dynamically adjusted according to the motion condition of the terminal, the wave beam coverage area is more attached to the actual requirement of the terminal, and the utilization rate of satellite communication resources is improved.
Optionally, the predicting, according to the initial position information, the predicted position information of each terminal at the subsequent service scheduling time includes:
and predicting the predicted position information of each terminal at the subsequent service scheduling time according to the initial position information through a machine learning model.
In the implementation process, the machine learning model can learn the motion rule of the terminal, so that accurate prediction of the position can be realized.
Optionally, the initial position information includes position information reported by each terminal when the terminal is randomly accessed and/or position information periodically reported after the terminal is accessed to a satellite network. In this way, a more accurate position can be predicted based on the acquired actual position information of the terminal.
Optionally, the initial position information includes position information of each terminal in a history period, and the predicting, according to the initial position information, predicted position information of each terminal at a subsequent service scheduling time includes:
determining the movement condition of each terminal according to the initial position information;
determining a prediction time window of each terminal according to the motion condition of each terminal;
and predicting the predicted position information of each terminal at the subsequent service scheduling moment according to the initial position information in the predicted time window of each terminal.
In the implementation process, the prediction time window is determined according to the motion condition of each terminal, so that the proper prediction time window can be selected in consideration of the motion condition, the instant motion state of the terminal can be reflected more accurately, and more accurate position prediction is realized.
Optionally, the determining the prediction time window of each terminal according to the motion situation of each terminal includes:
And determining a prediction time window of each terminal according to the movement speed of each terminal, wherein the size of the prediction time window is inversely related to the movement speed of the terminal.
In the implementation process, the position prediction is performed by using the data of different prediction time windows according to different motion conditions, so that the motion conditions of different terminals can be matched conveniently, and the accurate prediction of the position is realized.
Optionally, the dividing each terminal according to the predicted position information of the subsequent service scheduling time to obtain a plurality of dividing ranges corresponding to the subsequent service scheduling time includes:
Dividing each terminal according to the predicted position information of the subsequent service scheduling time to obtain a plurality of initial dividing ranges corresponding to the subsequent service scheduling time;
Determining a target terminal at a boundary position in each initial dividing range;
Predicting the movement track of the target terminal;
Judging whether the target terminal moves from a first initial dividing range to a second initial dividing range in which the target terminal is originally positioned within a set time length according to the moving track;
if yes, the first initial dividing range and the second initial dividing range are adjusted to obtain a final adjusted dividing range.
In the implementation process, the initial division range is adjusted according to the movement track of the terminal, so that the service beam corresponding to the wave position divided after adjustment can serve the target terminal for a long time, and the situation that the service of the target terminal is unstable due to the fact that the beam is switched by fast movement can be reduced.
Optionally, the determining, according to the multiple dividing ranges corresponding to the subsequent service scheduling time, a wave bit corresponding to a service beam at the subsequent service scheduling time includes:
Judging whether each division range corresponding to the subsequent service scheduling time is within the scanning range of the service beam;
if yes, the dividing range is used as a wave position corresponding to the service wave beam;
If not, continuing to divide the dividing range until the divided dividing range is within the scanning range of the service beam.
In the implementation process, the limited beam resources can be intensively distributed to the area with dense users by dividing the dividing range into the scanning range of the service beams, which is helpful for improving the utilization rate of the beam resources and the overall capacity of the system.
Optionally, the determining, according to the multiple dividing ranges corresponding to the subsequent service scheduling time, a wave bit corresponding to a service beam at the subsequent service scheduling time includes:
Judging whether each division range corresponding to the subsequent service scheduling time is within the scanning range of the service beam;
if yes, the dividing range is used as a wave position corresponding to the service wave beam;
if not, a plurality of service beams are distributed to the dividing range, so that the scanning range formed by the service beams can cover the dividing range.
In the implementation process, the limited beam resources can be intensively distributed to the area with dense users by dividing the dividing range into the scanning range of the service beams, which is helpful for improving the utilization rate of the beam resources and the overall capacity of the system.
Optionally, the dividing each terminal according to the predicted position information of the subsequent service scheduling time to obtain a plurality of dividing ranges corresponding to the subsequent service scheduling time includes:
and clustering each terminal according to the predicted position information of the subsequent service scheduling time to obtain a plurality of partition ranges corresponding to the clustered subsequent service scheduling time.
In the implementation manner, through a clustering algorithm, terminals with similar geographic positions can be clustered together to form a plurality of division ranges. This means that each beam can cover more terminals, improving the resource utilization.
In a second aspect, an embodiment of the present application provides a wave-position dividing apparatus, including:
the position acquisition module is used for acquiring initial position information of each terminal in the satellite coverage area;
The position prediction module is used for predicting the predicted position information of each terminal at the subsequent service scheduling moment according to the initial position information;
The range dividing module is used for dividing each terminal according to the predicted position information of the subsequent service scheduling time to obtain a plurality of dividing ranges corresponding to the subsequent service scheduling time;
And the wave position determining module is used for determining the wave position corresponding to the service wave beam at the subsequent service scheduling moment according to the multiple division ranges corresponding to the subsequent service scheduling moment.
In a third aspect, an embodiment of the present application provides an electronic device comprising a processor and a memory storing computer readable instructions which, when executed by the processor, perform the steps of the method as provided in the first aspect above.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of the method as provided in the first aspect above.
In a fifth aspect, embodiments of the present application provide a computer program product comprising computer program instructions which, when read and run by a processor, perform the steps of the method as provided in the first aspect above.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
It should be noted that the terms "system" and "network" in embodiments of the present invention may be used interchangeably. "plurality" means two or more, and "plurality" may also be understood as "at least two" in this embodiment of the present invention. "and/or" describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate that there are three cases of a alone, a and B together, and B alone. The character "/", unless otherwise specified, generally indicates that the associated object is an "or" relationship.
It should be further noted that, in the present application, all actions of acquiring signals, information or data are performed under the condition of conforming to the corresponding data protection rule policy of the country of the location and obtaining the authorization given by the owner of the corresponding device.
The embodiment of the application provides a wave position dividing method, which comprises the steps of obtaining initial position information of each terminal in a satellite coverage area, predicting predicted position information of each terminal at a subsequent service scheduling moment according to the initial position information, dividing each terminal according to the predicted position information to obtain a plurality of dividing ranges corresponding to the subsequent service scheduling moment, and determining wave positions corresponding to service beams according to the dividing ranges. According to the scheme, the wave position range of the terminal at different service scheduling moments can be accurately judged by predicting the position of the terminal and dividing the wave position in advance, so that the preparation for switching the wave beam is finished in advance, the wave beam resource can be dynamically adjusted according to the motion condition of the terminal, the wave beam coverage area is more fit with the actual requirement of the terminal, and the utilization rate of satellite communication resources is improved.
Referring to fig. 1, fig. 1 is a flowchart of a wave-level dividing method according to an embodiment of the present application, where the method includes the following steps:
step S110, initial position information of each terminal in the satellite coverage area is obtained.
The satellite coverage range refers to the range of the corresponding ground wave position of the satellite at a certain position, and in a satellite communication system, the coverage range of each satellite can be determined according to the relevant parameters of the satellite. In some embodiments, during the flight of the satellite, the ground station may send, to the satellite, the ground location range information corresponding to the coverage area of the ground station in real time, for example, the satellite flies to a certain position at a certain moment, at this moment, the satellite may request the ground station to obtain the coverage area information, and after receiving the request, the ground station may determine, according to the current position of the satellite, the coverage area corresponding to the satellite, and then send, to the satellite, the ground location range corresponding to the coverage area. It will be appreciated that satellites may correspond to different coverage areas at different locations, and their correspondence may be preset and stored in the ground station. Of course, the satellite may also store the correspondence, and the satellite may determine the current coverage of the satellite according to the current location.
After determining the current coverage area of the satellite, the position information of each terminal in the coverage area can be acquired.
In some embodiments, the satellite may acquire the position of each terminal under its coverage area in combination with a satellite navigation system, where the navigation system transmits the encoded radio signal to the ground terminal, and after the terminal receives the signal, the terminal may calculate its own position using the signal propagation time and ephemeris information of the satellite and report the calculated position to the navigation system, so that the satellite may acquire the position information of each terminal from the navigation system.
In some other embodiments, the satellite may also acquire the position of the terminal through interaction between the ground station and the terminal, for example, the ground station sends query information to the terminal, and after the terminal responds, the ground station calculates the position of the terminal according to the propagation time of the signal and the feedback information of the terminal, and feeds back the position information to the satellite, so that the satellite can acquire the position information of the terminal.
In some other embodiments, the satellite may acquire the location information of the terminal through a random access procedure of the terminal, that is, the initial location information includes the location information reported by each terminal when the terminal is randomly accessed.
Specifically, the satellite may transmit broadcast system information to each terminal within the coverage area of the satellite through a signaling beam, and then receive position information fed back by each terminal according to the broadcast system information.
The signaling beam is a special beam used for broadcasting system information and control signaling in the satellite communication system, and carries basic information required for communication between the satellite and the terminal, including time synchronization information, frequency calibration parameters, access control parameters and the like.
The satellite will periodically broadcast the signaling beam at preset time intervals. For the signaling beam, the satellite divides the wave positions corresponding to the signaling beams according to the coverage range of the signaling beam and the scanning range of the signaling beam in advance, and when the signaling beam is broadcast, each wave position is periodically polled and scanned to send the broadcast system information. After the terminal is started or enters the coverage area of the satellite, the terminal searches the signaling beam of the satellite and realizes time synchronization by receiving the synchronization signal.
The terminal initiates a random access process under the conditions of state change (such as startup and awakening), position change (such as beam switching and satellite switching), network state change (such as load balancing and system message updating), communication requirement (such as data transmission and emergency call) and the like, and initiates random access by detecting the broadcast system information of the signaling beam. Specifically, the terminal may construct a random access request according to the access parameters in the broadcast system information, where the terminal carries its own geographic location information, where the information may be obtained by a positioning module on the terminal, and then send the random access request to the satellite, where the satellite obtains the location information therein through a random access request interface sent by the acquisition terminal.
And/or, the initial position information may further include position information periodically reported after each terminal accesses the satellite network. That is, after each terminal accesses the satellite network, the position information of each terminal can be reported periodically through signaling, so that the satellite can also obtain the position information of each terminal.
And step S120, predicting the predicted position information of each terminal at the subsequent service scheduling moment according to the initial position information.
Since the initial position information of the terminal acquired by the satellite depends on the active report of the terminal, if the position of the terminal is acquired for a long time before the service scheduling time, if the beam allocation is performed after the wave position division is performed according to the position acquired before the time, inaccuracy may be caused, because the position of the terminal may change, and further, some wave positions may have no terminal or no terminal, and if the beam is allocated for service, the problem of resource waste may be caused. In order to improve the problem, the terminal position information at the service scheduling time can be predicted in advance in the scheme.
The wave bit dividing method in the scheme can be executed at regular time or before each service scheduling time, for example, before the next service scheduling time comes, the wave bit dividing method can be executed firstly based on the position information of each terminal which is currently obtained, namely, initial position information, then the predicted position information of the subsequent service scheduling time is predicted, and the wave bit dividing is carried out, wherein the subsequent service scheduling time can refer to the next service scheduling time or a plurality of service scheduling times after the next service scheduling time, and the like. When the next service scheduling time comes, service scheduling can be performed according to the divided wave positions and the allocated wave beams, and service scheduling efficiency can be improved.
In some embodiments, the initial location information of each terminal may be collected, for example, the initial location information includes a large amount of historical location information reported by the terminal, so that a movement rule and a trend of the terminal may be analyzed according to the historical location information, so that predicted location information at a subsequent service scheduling time may be predicted.
In some embodiments, the position prediction may also be implemented by a Kalman filtering algorithm. If the initial position information is used as an observation value in a Kalman filtering algorithm, then position prediction can be performed to obtain predicted position information.
In some other embodiments, the predicted position information of each terminal at the subsequent service scheduling time can be predicted according to the initial position information through a machine learning model.
In particular, the machine learning model may be a long and short term memory neural network model that may well capture long term dependencies in the time series data, thereby better predicting the future position of the terminal.
Or the machine learning model can be a model generated based on a graph neural network and a long-short-term memory neural network, the graph neural network can effectively process the spatial relationship between the positions, then the dependency relationship between the positions is extracted through the long-short-term memory neural network, and the accuracy of position prediction can be improved.
In the specific implementation process, a large amount of position information can be collected in advance for training a model, and besides the position information, information such as speed, acceleration, direction and the like of a terminal and environmental data such as terrain, traffic state and the like can be collected and used for training the model.
The graph neural network part can be used for capturing the spatial relation between the terminals, inputting the spatial relation into position information and other characteristics, and outputting the spatial relation into node characteristics after graph convolution processing, wherein the node characteristics represent the state of the terminals in space. The long-term and short-term memory network is used for processing time series data, capturing the time dependence, inputting the time dependence as the output characteristic of the graphic neural network, and outputting the time dependence as the predicted position information of the terminal at the future moment. The future time instant here may be a future traffic scheduling time instant and the predicted position information of a plurality of subsequent traffic scheduling time instants may be output.
It will be appreciated that the machine learning model may be implemented using other modes, if short-term prediction may be performed using a long-term memory neural network model, long-term prediction may be performed using a support vector machine or a back-propagation neural network to improve overall prediction accuracy. Alternatively, a mixed model of a convolutional neural network and a long-term memory network can be combined by an attention mechanism to predict. Implementations corresponding to other models are not illustrated herein.
In some embodiments, the actual position reported by the terminal can be used as feedback data for updating and optimizing the machine learning model, that is, the model is subjected to online learning by using the actual position information, so that the model can adapt to the motion mode change of the terminal, and further accurate prediction of the position is realized.
In some embodiments, to compensate for the position error of the model prediction, the predicted position obtained by the kalman filtering algorithm may be integrated with the predicted position obtained by the machine learning model, for example, the two predicted positions are averaged, and the obtained average value is the final predicted position. Or the two predicted positions can be weighted and fused to generate a more accurate predicted position.
And step S130, dividing each terminal according to the predicted position information of the subsequent service scheduling time to obtain a plurality of dividing ranges corresponding to the subsequent service scheduling time.
After the predicted position information is obtained, each terminal can be divided according to the predicted position information, namely, each terminal is divided into a plurality of division ranges, each division range can comprise a plurality of terminals, and thus, the positions of the terminals in the satellite coverage range can be divided to form wave positions.
Step S140, according to the multiple dividing ranges corresponding to the subsequent service scheduling time, determining the wave position corresponding to the service wave beam of the subsequent service scheduling time.
The service beam refers to a beam of a satellite for providing an actual data transmission service to a terminal, and is mainly used for carrying user data (such as voice, video, text information, etc.), unlike the signaling beam. The coverage area of the traffic beam is typically smaller and more accurate than the signaling beam because the traffic beam needs to concentrate energy to improve data transmission efficiency and energy.
After the multiple dividing ranges are obtained, the multiple dividing ranges may be determined as the wave positions corresponding to the service beam, for example, the N dividing ranges may be determined as the N wave positions corresponding to the service beam, as shown in fig. 2.
After determining the wave positions corresponding to the service beams, the satellite can schedule the service beams according to the determined wave positions, namely, adjust the direction of the service beams and the resource allocation, so that the service beams can carry out polling scanning on each wave position (if the number of the service beams is smaller than the number of the wave positions), and if the number of the service beams is larger than the number of the wave positions, one wave position can be scanned by one service beam, so that the service interaction between the satellite and each terminal in the wave positions is realized.
In the implementation process, the wave position range of the terminal at different service scheduling moments can be accurately judged by predicting the position of the terminal and dividing the wave position in advance, so that the preparation for switching the wave beam is prepared in advance, the wave beam resource can be dynamically adjusted according to the motion condition of the terminal, the wave beam coverage area is more attached to the actual requirement of the terminal, and the utilization rate of satellite communication resources is improved.
Based on the above embodiment, in the implementation manner of predicting the location of the terminal, the initial location information may further include location information of each terminal in a previous period, then the motion situation of each terminal may be determined according to the initial location information, a prediction time window of each terminal may be determined according to the motion situation of each terminal, and the predicted location information of each terminal at the subsequent service scheduling time may be predicted according to the initial location information in the prediction time window of each terminal.
The initial position information may represent one movement track condition of the terminal in a previous period, which may refer to all the previous periods, so that the movement condition of each terminal may be determined by the initial position information, for example, by counting the distance between the first position and the last position of each terminal in the previous period, and if the distance is less than or equal to a set distance, the movement condition of the terminal may be considered to be high-speed movement, whereas if the distance is greater than the set distance, the movement condition of the terminal may be considered to be low-speed movement.
The prediction time window of each terminal can be determined according to the motion situation of each terminal, and the prediction time windows corresponding to different motion situations can be different.
In some embodiments, the prediction time window may be dynamically determined according to the motion situation, the prediction time window is determined to be a first prediction time window for a terminal with a motion situation of high speed motion, and the prediction time window is determined to be a second prediction time window with a length greater than that of the first prediction time window for a terminal with a motion situation of low speed motion.
The first prediction time window and the second prediction time window may be preset for two motion conditions, and in this manner, the first prediction time window and the second prediction time window are static and may be flexibly set according to actual requirements. For example, the first prediction time window is set to T1, the second prediction time window is set to T2, and T2 is greater than T1. When the motion condition of a certain terminal is determined to be high-speed motion, the predicted time window is determined to be T1, and when the motion condition of a certain terminal is determined to be low-speed motion, the predicted time window is determined to be T2.
In some other embodiments, the first prediction time window and the second prediction time window may also be intelligently predicted by a model, for example, the information about the terminal that is determined to be moving at high speed in the above-described motion situation, such as speed, acceleration, driving distance, etc., may be input into a machine learning model (such as a long-short-term memory neural network model, etc.) to perform prediction, so that the model may predict the first prediction time window corresponding to the terminal in the high-speed moving scene. The information about the terminal, such as the speed, the acceleration, the driving distance, and the like, of which the motion condition is judged to be low-speed motion can be input into a machine learning model (such as a long-short-term memory neural network model, and the like) for prediction, so that the model can predict a second prediction time window corresponding to the terminal in a low-speed motion scene.
The models used for the high-speed prediction and the low-speed prediction may be the same model or different models. The model can learn the relation between the related information of the terminals and the predicted time window in the high-speed movement and low-speed movement scenes in the training process, so that the information of each terminal can be synthesized to predict a more accurate time window.
It can be understood that the distinction for the motion situation can also be divided into finer granularity, for example, the distinction can also be divided into medium-speed motion situations, and a prediction time window can also be determined for terminals in the medium-speed motion situations, and then for the terminals, initial position information in the corresponding prediction time window is acquired to perform position prediction.
In the implementation process, the prediction time window is determined according to the motion condition of each terminal, so that the proper prediction time window can be selected in consideration of the motion condition, the instant motion state of the terminal can be reflected more accurately, and more accurate position prediction is realized.
On the basis of the above embodiment, in the manner of determining the predicted time window of each terminal, the predicted time window of each terminal may also be determined according to the movement speed of each terminal, where the size of the predicted time window is inversely related to the movement speed of the terminal.
The negative correlation is here understood to be a case that may involve a step change, as in the case of the above-described scheme of determining the prediction time window for high-speed and low-speed movements.
Of course, a negative correlation may also refer to a linear relationship between the speed of motion and the predicted time window, i.e. one variable increases and the other variable is retrieved proportionally, or a non-linear relationship, but generally exhibits a negative correlation.
For example, the predicted time window for each terminal may be determined using a formula like:
t=c/v, C may be a constant, v represents the movement speed of the terminal in the history period, and C may be used to adjust the proportional relationship between the size of the predicted time window and the speed, and may be set according to practical experience. Therefore, the movement speeds of the terminals are different, the determined prediction time windows can be different, and the larger the movement speed is, the shorter the prediction time window is, whereas the smaller the movement speed is, the smaller the prediction time window is.
The initial position information may include some position information when and after the terminal is accessed, so for the terminal under high-speed movement, the position change of the terminal is accelerated, and therefore, a shorter prediction time window can be used for capturing the rapid changes, and the shorter time window can reflect the instant movement state of the terminal more sensitively, so that the prediction accuracy is improved. For the terminal under low-speed movement, the position change of the terminal is slower, so that a longer time window can be used for capturing the trend in a longer time range, the influence of noise can be reduced by the longer time window, and the prediction stability is improved.
It will be appreciated that if the predicted time window of a certain terminal is longer than the duration between the time when the position information of the terminal is first acquired and the current time, the initial position information in the predicted time window of the terminal at this time includes all the position information in the current previous period.
In the implementation process, the position prediction is performed by using the data of different prediction time windows according to different motion conditions, so that the motion conditions of different terminals can be matched conveniently, and the accurate prediction of the position is realized.
On the basis of the above embodiment, since the divided dividing range may not match the scanning range of the service beam, in the manner of determining the wave position corresponding to the service beam, it may be determined whether each dividing range corresponding to the subsequent service scheduling time is within the scanning range of the service beam, if yes, the dividing range is used as the wave position corresponding to the service beam, if not, the dividing range is continued until the divided dividing range is within the scanning range of the service beam.
Wherein the scanning range of the service beam is related to the relevant configuration of the satellite, such as the bandwidth of the configured service beam.
If the division range is within the scanning range of the service beam, it indicates that the division range can be completely covered by the service beam, so the division range can be used as a wave bit corresponding to the service beam. If the dividing range is not within the scanning range of the service beam, it indicates that the dividing range cannot be completely covered by the service beam, and the dividing range may be further divided into a plurality of dividing ranges, for example, until the divided dividing range is within the scanning range of the service beam.
In some embodiments, each of the divided ranges includes some terminals with similar positions, and the boundary of the divided range may be a minimum circumscribed circle, a minimum circumscribed ellipse, a minimum circumscribed rectangle, or the like, and the scanning range of the service beam may be a circle, an ellipse, or the like, so it may be determined whether the divided range is within the scanning range of the service beam by determining whether the scanning range of the service beam can cover the boundary of the divided range.
In the manner of continuing to divide the dividing range, for example, a recursive dividing manner may be adopted, for example, the dividing range is divided into two sub-ranges, then it is determined whether the two sub-ranges are respectively within the scanning range of the service beam, if so, the two sub-ranges are respectively used as the wave bits of the service beam, if not, further division is continued until the condition that the sub-ranges are within the scanning range of the service beam is satisfied, and the division is stopped.
In the implementation process, the limited beam resources can be intensively distributed to the area with dense users by dividing the dividing range into the scanning range of the service beams, which is helpful for improving the utilization rate of the beam resources and the overall capacity of the system.
On the basis of the above embodiment, when judging whether each of the divided ranges is within the scanning range of the service beam, a first parameter of a minimum circumcircle formed by each of the divided ranges may be obtained, where the first parameter may include a radius, a diameter, an area, or a circumference, and then a second parameter of a minimum circumcircle formed by the scanning range of the service beam may be obtained, where the second parameter may also include a radius, a diameter, an area, or a circumference, and then whether the first parameter is less than or equal to the second parameter is judged.
The respective divided ranges may not be in a regular shape, for example, if the divided ranges are divided according to a rectangle, then the first parameter of the minimum circumcircle formed by the divided ranges may be obtained, where the minimum circumcircle of the respective divided ranges may be calculated by using a minimum circumcircle algorithm (such as Welzl algorithm, etc.), and the minimum circumcircle is a minimum circular area including all terminals in the divided ranges. After the minimum circumscribing circle is obtained, parameters such as radius, diameter, area or circumference of the minimum circumscribing circle can be calculated as the first parameter.
When determining the minimum circumcircle corresponding to the scanning range of the service beam, detailed parameters of the service beam including information such as the shape, directivity, bandwidth, scanning angle and the like of the beam can be obtained from the satellite system, then the scanning range of the service beam can be determined according to the parameters of the service beam, the scanning range can be round, elliptical or irregular polygon and the like, if the scanning range is round, the minimum circumcircle of the scanning range is round, if the scanning range is not round, the minimum circumcircle can be obtained by adopting a minimum circumcircle algorithm, and corresponding second parameters such as radius, diameter, area or circumference are calculated.
When comparing the first parameter with the second parameter, the same parameter is compared, for example, the first parameter includes a radius R1, a diameter D1, an area S1 or a circumference Y1, the second parameter includes a radius R2, a diameter D2, an area S2 or a circumference Y2, the radius R1 is compared with the radius R2, the diameters D1 and D2 are compared, the areas S1 and S2 are compared, the circumference Y1 and Y2 are compared, if R1 is less than or equal to R2, or D1 is less than or equal to D2, or S1 is less than or equal to S2, or Y1 is less than or equal to Y2, the division range is considered to be within the scanning range of the service beam, otherwise, the division range is considered not to be within the scanning range of the service beam.
In some other embodiments, the shape formed by the dividing range may be a minimum circumscribed ellipse, a minimum circumscribed rectangle, or the like, and the shape formed by the scanning range of the service beam may be a minimum circumscribed ellipse, a minimum circumscribed rectangle, or the like, so that for convenience of comparison, when determining the shapes formed by the dividing range and the scanning range, the shapes of the dividing range and the scanning range may be unified, for example, the shapes of the dividing range and the scanning range are both the minimum circumscribed rectangle, so that parameters such as an area, a perimeter, or a diagonal length of the minimum circumscribed rectangle may be compared to determine whether the dividing range is within the scanning range, and a specific comparison manner is similar to the above manner, and is not repeated herein.
In the implementation process, the parameters of the minimum circumscribed circles of the division range and the scanning range are calculated, and whether the division range is in the scanning range can be accurately judged through comparison of the parameters.
On the basis of the above embodiment, in other ways of determining the wave positions corresponding to the service beams, it may also be determined whether each of the divided ranges corresponding to the subsequent service scheduling time is within the scanning range of the service beam, if so, the divided range is taken as the wave position corresponding to the service beam, and if not, a plurality of service beams are allocated to the divided range, so that the scanning range formed by the plurality of service beams can cover the divided range.
The manner of determining whether the division range is within the scanning range of the traffic beam may refer to the description related to the above embodiment, and the description is not repeated here.
In this implementation manner, if the division range is not within the scanning range of the service beam, which means that one service beam cannot fully cover the division range, multiple service beams may be respectively allocated to the division range, where a difference between the division range and the scanning range may be compared, for example, a radius of a minimum circumcircle may be used, for example, if a difference between a radius R1 of the minimum circumcircle of the division range minus a radius R2 of the minimum circumcircle of the scanning range is smaller than R2 and greater than 0, two service beams may be allocated to the division range. Of course, when the number of service beams is specifically allocated, if R1 is divided by R2 and can be divided, the number of service beams is the quotient of R1 divided by R2, and if R1 is divided by R2 and can not be divided, the number of service beams is the quotient of R1 divided by R2 and one.
The total scanning range formed by the service beams can completely cover the dividing range, the service beams of the satellite can be all directed to the dividing range when scanning is performed, and the service beams can cover different ranges in the dividing range by adjusting the directing angles of the service beams, so that the total coverage is realized, and the communication between the satellite and the terminals in the dividing range is realized.
In the implementation process, the limited beam resources can be intensively distributed to the area with dense users by dividing the dividing range into the scanning range of the service beams, which is helpful for improving the utilization rate of the beam resources and the overall capacity of the system.
On the basis of the above embodiment, in the manner of dividing each terminal to obtain multiple division ranges, each terminal may be clustered according to the predicted position information of each terminal at the subsequent service scheduling time, so as to obtain multiple division ranges corresponding to the clustered subsequent service scheduling time.
For example, a K-means clustering algorithm can be used for clustering a plurality of terminals, and the specific implementation process is as follows:
a. Initializing a clustering center:
and randomly selecting the predicted position information of K terminals from all the terminals as an initial clustering center.
B. calculating the distance from each terminal to the clustering center:
For each terminal, its distance to all cluster centers is calculated.
C. each terminal is assigned to the nearest cluster center.
And distributing each terminal to the cluster corresponding to the cluster center closest to the terminal.
D. Recalculating the cluster center:
for each cluster, the position mean value of all the terminals in the cluster is recalculated and used as a new cluster center, wherein the new cluster center is the mean value of the predicted position information of all the terminals in the cluster.
E. Judging whether convergence is carried out:
if the positions of all the cluster centers are not changed any more or the change is smaller than a preset threshold value, the algorithm converges, and iteration is stopped.
If the cluster center is changed, returning to the step b, continuously calculating a new cluster center and reassigning the terminal.
Repeating steps b to e, if the cluster center changes, continuing to execute the steps until the cluster center converges, i.e. no significant change exists.
The clustering result is a plurality of dividing ranges, if a certain dividing range is not in the scanning range of the service beam, the dividing range is further required to be clustered, namely the dividing range is further divided into a plurality of subclasses by using a clustering algorithm until the ranges of all the subclasses are in the scanning range of the service beam.
In practical applications, the location of the terminals may change dynamically, in which case the terminals may be periodically re-clustered to update the clustering result.
In some other embodiments, a DBSCAN (Density-Based Spatial Clustering of Applications with Noise based clustering algorithm) may also be used to cluster multiple terminals, which is adapted to handle noisy and irregularly shaped clusters, automatically discover arbitrarily shaped clusters, and effectively identify and handle noisy points. The specific implementation of the clustering algorithm may refer to the implementation in the related art and will not be described in detail herein.
In the implementation process, terminals with similar geographic positions can be gathered together through a clustering algorithm to form a plurality of division ranges. This means that each beam can cover more terminals, improving the resource utilization.
On the basis of the foregoing embodiment, in the foregoing manner of clustering each terminal, in the clustering process, the clustering center may be determined according to the distribution condition of the predicted position information of each terminal.
In the implementation manner, the mean value and standard deviation of all the terminals can be calculated to obtain the distribution range of the terminals, wherein the distribution range is generally in the range of plus or minus 1 to 2 standard deviations of the mean value, so that K points can be uniformly selected as initial clustering centers according to the distribution range, for example, equidistant points in the data range can be selected.
In some other embodiments, the terminal may be further divided into a high-density region and a low-density region according to the predicted position information of the terminal, and then initial cluster centers may be selected from the high-density region and the low-density region, and the number of the selected initial cluster centers may be greater in the high-density region than the number of the selected initial cluster centers in the low-density region.
In some other embodiments, the principal component analysis may be performed on the predicted position information of the plurality of terminals, the predicted position information of the terminals may be projected to a principal component direction, and K points may be uniformly selected as initial cluster centers in the principal component direction.
In some other embodiments, the machine learning model may be further used to extract initial cluster centers corresponding to the predicted position information of the plurality of terminals, for example, the predicted position information of the plurality of terminals may be input into the machine learning model, and the plurality of initial cluster centers may be output through the machine learning model. The machine learning model can learn the relation between the distribution condition of the predicted position information of a large number of terminals and the clustering centers in the training process, so that the clustering centers can be accurately predicted through the machine learning model, and then the clustering algorithm is operated.
The machine learning model may specifically be a deep learning model, such as a random forest model, a convolutional neural network model, a cyclic neural network model, a transducer model, and the like.
In the implementation process, the clustering center is determined according to the distribution condition of the terminal, so that the actual distribution condition of the terminal can be truly reflected, the clustering result is more accurate, the utilization rate of beam resources is improved, and unnecessary coverage areas are reduced.
On the basis of the above embodiment, in the manner of dividing each terminal, in order to simplify the dividing process, the division may also be performed according to the scanning range of the service beam. In the specific implementation process, the minimum circumcircle formed by the terminals can be determined according to the predicted position information of each terminal at the subsequent service scheduling moment, namely, the area without the terminal in the satellite coverage area is eliminated, then the minimum circumcircle is divided according to the scanning range, for example, the minimum circumcircle is divided into a plurality of subareas along the set direction from the circle center of the minimum circumcircle, the diameter of each subarea can not exceed the diameter of the scanning range, and the subarea thus divided can be used as the dividing range and can be used as the wave position corresponding to the service wave beam. Wherein the range of the sub-area can be enlarged appropriately if a certain terminal is located on the boundary of the plurality of sub-areas.
In some embodiments, if the number of terminals in the sub-area divided in this manner is not large, the sub-area may be combined with other sub-areas with high terminal density, and then a plurality of service beams may be allocated to the combined sub-area.
In some embodiments, the terminal density in each sub-area may be calculated, and if the number of terminals in a certain sub-area is too small, the boundary of the sub-area may be adjusted, for example, by merging with other sub-areas with high terminal density in the above scheme.
In the scheme, after the wave positions of the service wave beams are determined, the wave positions can be targeted for the wave positions, and the service wave beams can be subjected to polling scheduling, so that the terminals in each wave position can be served.
On the basis of the embodiment, after the wave position is divided, the boundary and the range of the wave position can be dynamically adjusted according to the position change condition of the terminal.
For example, the current time is divided into the wave positions of the next service scheduling time, if the position information reported by the terminal is received from the current time to the next service scheduling time, the predicted position of the terminal at the next service scheduling time can be adjusted according to the position information of the terminal, and the specific adjustment mode can be a mode of correcting the predicted position according to the latest received position information of the terminal, such as a mode of performing weighted average and the like. And then the divided wave position is adjusted according to the corrected prediction position, for example, the terminal is originally divided into the wave position 1, if the corrected prediction position is closer to the clustering center of the wave position 2, the terminal can be adjusted into the wave position 2, and the boundary and the range of the wave position 1 and the wave position 2 need to be adjusted at the moment.
In some embodiments, to reduce the adjustment amount, it may also be determined whether the difference between the predicted position after the terminal correction and the predicted position before in the above example exceeds a threshold, if the threshold is exceeded, the readjustment of the wave position is performed, and if the threshold is not exceeded, the adjustment of the wave position is not performed.
In some other embodiments, if the wave position of the next service scheduling time (called time 1) is already divided at the current time, and one service scheduling time (called time 2) after the next service scheduling time is divided again according to the method before time 2, and another way is to consider that the position change of some terminals may not be large, so that the terminals with large position change can be clustered again, and the full calculation is avoided.
For example, before time 2, the predicted position of each terminal at time 2 may be predicted, where the predicted position of each terminal at time 2 may be compared with the position of the terminal acquired last time, for example, for terminal 1, the predicted position of terminal 1 at time 2 may be compared with the position of terminal 1 acquired last time (may be the position of terminal 1 that has been reported last time from time 2 between time 1 and time 2, if there is no position reported in the period, the predicted position is at time 1), and if the position difference is greater than the set threshold, the position change is considered to be large, at this time, the terminal 1 may be clustered again, that is, the distance between the terminal 1 and the cluster center of each divided bin is recalculated, and then the terminal 1 may be subdivided into corresponding bins, where the bin at time 2 only needs to be adjusted for the bin at time 1.
It can be understood that in this case, the wave position may be adjusted only for several adjacent service scheduling moments, for example, the wave position is divided by taking 3 service scheduling moments as a period, and at the 4 th service scheduling moment, the wave position is divided again according to the above manner, so as to cope with the position change situation of the terminal in time. Specifically, if the wave position is divided according to the method at the time 1, the wave position adjustment can be performed only for the terminal with large position change at the time 2 and the time 3, and the wave position is divided again according to the method at the time 4.
In some embodiments, in the above manner of dividing the plurality of terminals according to the predicted location information of the terminals, after the plurality of division ranges have been obtained by means of clustering or the like, the plurality of initial division ranges may be referred to as a plurality of initial division ranges, and considering the location variation situation of the subsequent terminals, the target terminals at the boundary positions in each initial division range may be determined first, then the movement tracks of the target terminals may be predicted, the initial division ranges may be adjusted according to the movement tracks, for example, whether the target terminal will move from the first initial division range where the target terminal is originally located to the second initial division range within a set period of time is determined according to the movement tracks, if yes, the first initial division range and the second initial division range are adjusted, so as to obtain the final adjusted division range. And then determining the wave position corresponding to the service wave beam at the subsequent service scheduling moment according to the final dividing range.
The boundary position may be understood as a distance between the terminal and a boundary of a minimum circumscribing circle formed by the initial dividing range being smaller than a set distance. The movement tracks of the target terminals can be predicted for the target terminals at the boundary positions, and the movement tracks of the target terminals can be obtained by inputting the position information of the target terminals and the related parameter information of the terminals (such as the speed, the acceleration, the environment parameters of the terminals, the terminal types and the like) into a deep learning model for prediction.
For example, it may be determined whether the target terminal moves out of the original division range in a short time (for example, a set period of time, for example, 30 s) according to the movement track of the target terminal, if so, it indicates that the position of the target terminal changes rapidly, for example, the target terminal is on a fast moving device such as a high-speed rail, etc., and in this case, the target terminal may be divided into the division ranges to which it is to be moved. For example, if the target terminal is determined to move into the dividing range 2 (i.e. the second initial dividing range) within the set time period through the movement track prediction in the dividing range 1 (i.e. the first initial dividing range) initially, the target terminal can be divided into the dividing range 2, that is, the dividing range 2 is expanded, and the dividing range 1 is reduced, so that the service beam corresponding to the dividing range 2 can serve the target terminal for a long time, and the unstable service caused by the beam switching performed by the rapid movement of the target terminal can be reduced. Of course, if the target terminal does not move out of the original division range within the set period of time, the division range 1 is not adjusted.
It will be appreciated that the target terminals at the boundary for each initial division range may be processed in the manner described above, so that the initial division range may be optimized for a better division of the wave bits.
Referring to fig. 3, fig. 3 is a block diagram of a wave-level dividing apparatus according to an embodiment of the present application, where the apparatus may be a module, a program segment, or a code on an electronic device. It should be understood that the apparatus corresponds to the embodiment of the method of fig. 1 described above, and is capable of performing the steps involved in the embodiment of the method of fig. 1, and specific functions of the apparatus may be referred to in the foregoing description, and detailed descriptions thereof are omitted herein as appropriate to avoid redundancy.
Optionally, the apparatus 200 includes:
A position obtaining module 210, configured to obtain initial position information of each terminal in a satellite coverage area;
A position prediction module 220, configured to predict predicted position information of each terminal at a subsequent service scheduling time according to the initial position information;
the range dividing module 230 is configured to divide each terminal according to the predicted position information of the subsequent service scheduling time, so as to obtain a plurality of dividing ranges corresponding to the subsequent service scheduling time;
The wave position determining module 240 is configured to determine a wave position corresponding to a service beam at the subsequent service scheduling time according to the multiple division ranges corresponding to the subsequent service scheduling time.
Optionally, the location prediction module 220 is configured to predict, according to the initial location information, predicted location information of each terminal at a subsequent service scheduling time by using a machine learning model.
Optionally, the initial position information includes position information reported by each terminal when the terminal is randomly accessed and/or position information periodically reported after the terminal is accessed to a satellite network.
Optionally, the initial position information includes position information of each terminal in a history period, and the position prediction module 220 is configured to determine a movement condition of each terminal according to the initial position information, determine a prediction time window of each terminal according to the movement condition of each terminal, and predict predicted position information of each terminal at a subsequent service scheduling time according to initial position information in the prediction time window of each terminal.
Optionally, the location prediction module 220 is configured to determine a prediction time window of each terminal according to the motion speed of each terminal, where the size of the prediction time window is inversely related to the motion speed of the terminal.
Optionally, the range dividing module 230 is configured to divide each terminal according to the predicted position information of the subsequent service scheduling time to obtain a plurality of initial dividing ranges corresponding to the subsequent service scheduling time, determine a target terminal at a boundary position in each initial dividing range, predict a movement track of the target terminal, determine whether the target terminal will move from a first initial dividing range where the target terminal is originally located to a second initial dividing range within a set duration according to the movement track, and if so, adjust the first initial dividing range and the second initial dividing range to obtain a final adjusted dividing range.
Optionally, the wave position determining module 240 is configured to determine whether each of the divided ranges corresponding to the subsequent service scheduling time is within a scanning range of a service beam, if so, use the divided range as a wave position corresponding to the service beam, and if not, continue to divide the divided range until the divided range is within the scanning range of the service beam.
Optionally, the wave position determining module 240 is configured to determine whether each of the divided ranges corresponding to the subsequent service scheduling time is within a scanning range of a service beam, if so, take the divided range as a wave position corresponding to the service beam, and if not, allocate a plurality of service beams to the divided range, so that the scanning range formed by the plurality of service beams can cover the divided range.
Optionally, the range dividing module 230 is configured to cluster each terminal according to the predicted position information of the subsequent service scheduling time, so as to obtain a plurality of divided ranges corresponding to the clustered subsequent service scheduling time.
It should be noted that, for convenience and brevity, a person skilled in the art will clearly understand that, for the specific working procedure of the apparatus described above, reference may be made to the corresponding procedure in the foregoing method embodiment, and the description will not be repeated here.
Referring to fig. 4, fig. 4 is a schematic structural diagram of an electronic device for performing a bit division method according to an embodiment of the present application, where the electronic device may include at least one processor 310, such as a CPU, at least one communication interface 320, at least one memory 330 and at least one communication bus 340. Wherein the communication bus 340 is used to enable connected communication between these components. The communication interface 320 of the device in the embodiment of the present application is used for performing signaling or data communication with other node devices. The memory 330 may be a high-speed RAM memory or a nonvolatile memory (non-volatile memory), such as at least one disk memory. Memory 330 may also optionally be at least one storage device located remotely from the aforementioned processor. The memory 330 has stored therein computer readable instructions which, when executed by the processor 310, perform the method process described above in fig. 1.
It will be appreciated that the configuration shown in fig. 4 is merely illustrative, and that the electronic device may also include more or fewer components than shown in fig. 4, or have a different configuration than shown in fig. 4. The components shown in fig. 4 may be implemented in hardware, software, or a combination thereof.
Embodiments of the present application provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs a method process performed by an electronic device in the method embodiment shown in fig. 1.
The present embodiment discloses a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, are capable of performing the methods provided by the above-described method embodiments, for example, comprising:
acquiring initial position information of each terminal in a satellite coverage area;
Predicting the predicted position information of each terminal at the subsequent service scheduling moment according to the initial position information;
Dividing each terminal according to the predicted position information of the subsequent service scheduling time to obtain a plurality of dividing ranges corresponding to the subsequent service scheduling time;
And determining the wave position corresponding to the service wave beam at the subsequent service scheduling time according to the multiple dividing ranges corresponding to the subsequent service scheduling time.
In summary, the embodiments of the present application provide a method, an apparatus, an electronic device, a storage medium, and a program product for dividing a wave position, which predict a terminal position and divide the wave position in advance, so that the wave position range of the terminal at different service scheduling moments can be more accurately determined, and thus, the preparation for switching the wave beam is made in advance, and thus, the wave beam resource can be dynamically adjusted according to the motion situation of the terminal, so that the wave beam coverage can be more fit to the actual requirement of the terminal, and the utilization rate of satellite communication resources is improved.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
Further, the units described as separate units may or may not be physically separate, and units displayed as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Furthermore, functional modules in various embodiments of the present application may be integrated together to form a single portion, or each module may exist alone, or two or more modules may be integrated to form a single portion.
In this document, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and variations will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.