CN113259950B - Low-orbit satellite spot beam closing method based on service prediction - Google Patents

Low-orbit satellite spot beam closing method based on service prediction Download PDF

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CN113259950B
CN113259950B CN202110523767.2A CN202110523767A CN113259950B CN 113259950 B CN113259950 B CN 113259950B CN 202110523767 A CN202110523767 A CN 202110523767A CN 113259950 B CN113259950 B CN 113259950B
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CN113259950A (en
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唐伦
朱丹青
陈前斌
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Chongqing University of Post and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/24—Cell structures
    • H04W16/28—Cell structures using beam steering
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/02—Hierarchically pre-organised networks, e.g. paging networks, cellular networks, WLAN [Wireless Local Area Network] or WLL [Wireless Local Loop]
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Abstract

The invention relates to a low orbit satellite spot beam closing method based on service prediction, which belongs to the field of low orbit satellite communication and comprises the following steps: s1: acquiring historical service space-time distribution information from a low-orbit satellite network, and processing service volume data into a two-dimensional service distribution thermodynamic diagram arranged according to a time sequence; s2: inputting a service prediction model, predicting the service distribution condition of the next time slot, and outputting a service distribution thermodynamic diagram; s3: based on the service prediction result, ensuring the coverage of the whole network service, and solving a closable beam set in the network by taking closing of as many satellite beams as possible as a target; s4: all satellite beams in the set are turned off. The invention can improve the ping-pong switching effect of the low-orbit satellite communication system, reduce the network blocking probability and improve the service quality of the system while saving the beam resources.

Description

Low-orbit satellite spot beam closing method based on service prediction
Technical Field
The invention belongs to the field of low-orbit satellite communication, and relates to a low-orbit satellite spot beam closing method based on service prediction
Background
Currently, there is less research related to the beam-off strategy in the existing low-earth orbit satellite system. Because the multi-beam low-orbit satellite network has the characteristics of dynamic change of network topology, uneven time-space distribution of service volume in the network, overlapping and covering of beams in partial areas and the like, serious ping-pong switching effect and a great deal of beam resource waste can be caused. Therefore, the low-earth satellite beam-off related art becomes the key to solve these problems.
When designing a closing strategy of satellite beams, firstly, the characteristics of global coverage of a satellite communication system, dynamic change of network topology and the like need to be fully considered, coverage holes are avoided to the greatest extent after partial beams are closed, the service quality of the system is ensured, the frequent switching phenomenon of users is improved, the ping-pong switching rate of the system is reduced, and unnecessary beam resource waste is reduced. Secondly, in a low-orbit satellite communication scene, due to the obvious difference between the distribution conditions of users in a continental territory and a marine territory and the difference between the day and night conditions of east-west hemispheres, the distribution of the traffic in the network has certain space-time correlation. In order to ensure the full coverage of network users and reduce the network blocking rate, the service distribution condition in a future network can be predicted based on historical service data, service beams are reserved in advance, and the service quality of a system is improved. And further, service requirements possibly existing in the future in different regions are included in the constraint of solving the closable beam, and a beam closing method based on service prediction is provided.
Disclosure of Invention
The invention aims to provide a beam closing method suitable for a low-orbit satellite communication system by combining the characteristics of the low-orbit satellite communication system, aiming at the phenomena of beam overlapping coverage in a high-latitude area, high system ping-pong switching rate and the like of the low-orbit satellite communication system adopting a near polar orbit constellation and considering the influence factor of uneven space-time distribution of service volume in the low-orbit satellite network.
In order to achieve the purpose, the invention provides the following technical scheme:
a low orbit satellite spot beam closing method based on service prediction comprises the following steps:
s1: acquiring historical service space-time distribution information from a low-orbit satellite network, and processing service volume data into a two-dimensional service distribution thermodynamic diagram arranged according to a time sequence;
s2: inputting a service prediction model, predicting the service distribution condition of the next time slot, and outputting a service distribution thermodynamic diagram;
s3: based on the service prediction result, ensuring the coverage of the whole network service, and solving a closable beam set in the network by taking closing of as many satellite beams as possible as a target;
s4: all satellite beams in the set are turned off.
Further, the processing of the traffic data into the two-dimensional traffic distribution thermodynamic diagram arranged in time series in step S1 specifically includes the following steps:
s11: dividing a network coverage area into a plurality of grid areas by adopting a grid method;
s12: defining the latitude of the center of each grid as thetacGrid longitude length llonLong latitude llatRadius of the earth ReTraffic N within each griduObtaining grid service density rho; taking the average value of each service quantity positioned in the same grid as the service quantity value of the grid to represent the service quantity distribution of the region;
s13: and generating a two-dimensional service distribution thermodynamic diagram by taking the grid longitude and latitude data as coordinates and the grid service density data as values of all areas in the thermodynamic diagram.
Further, the predicting the traffic distribution of the next slot in step S2 is based on the convolution LSTM model, and specifically includes the following steps:
s21: taking a time-continuous service distribution thermodynamic diagram as an input of a service prediction model, and extracting spatial features of the thermodynamic diagram by using a two-dimensional convolutional neural network;
s22: adding a pooling layer, wherein the size of a pooling window is 2 multiplied by 2, and the dimension of the generated characteristic sequence is reduced to half of the original dimension through pooling;
s23: splicing the spatial features into a time sequence vector on a time dimension, using the time sequence vector as the input of a Long Short-Term Memory (LSTM) model, and extracting time dimension features through the LSTM model;
s24: the nonlinear expression capability of the model is improved through two fully-connected layers;
s25: and performing deconvolution on the output one-dimensional vector matrix, and outputting a predicted service distribution thermodynamic diagram.
Further, step S3 specifically includes the following steps:
s31: establishing a problem model aiming at minimizing the number of beams turned on in the low-orbit satellite communication system, which is described in detail as follows:
Figure BDA0003065039970000021
wherein the content of the first and second substances,
Figure BDA0003065039970000022
the coverage requirement of service users is met, and after partial wave beams are closed in the network, each user in the system
Figure BDA0003065039970000023
In other words, there is at least one satellite beam serving the user
Figure BDA0003065039970000024
Figure BDA0003065039970000031
Wherein the content of the first and second substances,
Figure BDA0003065039970000032
in order to be a coverage factor,
Figure BDA0003065039970000033
for users in the network, the beams of the ongoing traffic transmission service cannot be turned off, and the set of beams of such ongoing service users is denoted as BsThen the constraint is:
Figure BDA0003065039970000034
if there is a traffic demand in the coverage area of a certain beam in the next time slot, the beam is not turned off, and the set of such beams is marked as BpThen the constraint is:
Figure BDA0003065039970000035
wherein, for BsThe solution of (1) is obtained by predicting the services of different areas of the next time slot by using a service prediction model; based on the service demand prediction result, the coverage factor between the area and the beam with service for the next time slot is obtained
Figure BDA0003065039970000036
The calculation of (A) yields a set of beams B for which there is a traffic demands；
S32: establishing a matrix A to characterize the coverage of beams and users in the network, the beams
Figure BDA0003065039970000037
Expressed by rows of the matrix, user
Figure BDA0003065039970000038
Expressed in columns of the matrix:
Figure BDA0003065039970000039
wherein a isijAs coverage factors:
Figure BDA00030650399700000310
the row vector of matrix A is represented by aiRepresenting that a new matrix composed of partial row vectors is a sub-matrix A' of A;
s33: and solving a low-orbit satellite beam closing problem based on flow prediction.
Further, step S33 specifically includes the following steps:
s331: initializing a matrix A, and enabling A' to be A;
s332: selecting a beam i, and connecting the row a corresponding to the beam i in the submatrix AiAll the middle elements are set to be 0; simultaneously enabling the iteration number n to be 0;
s333: checking whether the matrix A 'meets the constraint condition for covering all services, and if so, making the matrix A equal to A', and jumping to the step S332; if the constraint is not met, the iteration times n +1 are skipped to the step S334;
s334: checking whether the iteration times exceed the total number of the full-network wave beams for N times, and if the iteration times do not reach the total number of the full-network wave beams for N times, jumping to the step S332; if the number of times exceeds N, jumping to step S335;
s335: and outputting the matrix A.
The invention has the beneficial effects that: the invention provides a beam closing method based on service prediction by combining the space-time characteristics of service distribution in a low-earth orbit satellite network, and the method can improve the ping-pong switching effect of a low-earth orbit satellite communication system, reduce the network blocking probability and improve the service quality of the system while saving beam resources.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.
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For purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a schematic diagram of a method for dividing a satellite coverage area by using a grid method according to the present invention;
FIG. 2 is a schematic diagram of a service distribution thermodynamic diagram in the method of the present invention;
FIG. 3 is a schematic diagram of a network structure of a service prediction model in the method of the present invention;
fig. 4 is a schematic diagram of a beam closing process in the method of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not intended to indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and are not to be construed as limiting the present invention, and it is possible for a person having ordinary skill in the art to understand the specific meaning of the above terms according to specific circumstances.
Referring to fig. 1 to 4, a method for closing a low-earth orbit satellite beam based on service prediction includes the following steps:
1) acquiring historical service space-time distribution information from a low-orbit satellite network, and processing service volume data into a two-dimensional service distribution thermodynamic diagram arranged according to a time sequence;
2) inputting a service prediction model, predicting the service distribution condition of the next time slot, and outputting a service distribution thermodynamic diagram;
3) and based on the service prediction result, ensuring the coverage of the whole network service, and solving a closable beam set in the network by taking closing of as many satellite beams as possible as a target.
4) All satellite beams in the set are turned off.
The method for generating the service distribution thermodynamic diagram comprises the following steps:
the method comprises the following steps: and dividing the coverage area by adopting a grid method. Taking the service distribution situation of a certain area as an example, the latitude and longitude range of the area is about 10 degrees 41' to 43 degrees 39 degrees south latitude, and 112 degrees to 154 degrees east longitude. Dividing the region into a plurality of grid areas, wherein the longitude and the latitude of each grid are respectively 1.5 degrees and 1.389 degrees as step lengths, as shown in figure 1;
step two: and taking the average value of each service quantity positioned in the same grid as the service quantity value of the grid so as to represent the service quantity distribution of the region. Defining the latitude of the center of each grid as thetacGrid longitude length llonLong latitude llatThe radius of the earth is ReTraffic in each grid using NuDenoted by p, traffic density. Assuming that the traffic of each grid is uniformly distributed, the grid traffic density can be obtained:
Figure BDA0003065039970000051
Figure BDA0003065039970000052
Figure BDA0003065039970000053
Figure BDA0003065039970000054
step three: definition of lambdajIs the average arrival rate, T, of service jjmIs the average duration of service j, S (i) is the set of areas covered by beam i, NjkIs the total amount of traffic j in region k, AgIs the area of the grid, NiFor the number of grids covered by beam i, the traffic density of the ith beam can be obtained:
Figure BDA0003065039970000055
and further generating a two-dimensional service distribution thermodynamic diagram by taking the grid longitude and latitude data as coordinates and the grid service density data as values of all the areas in the thermodynamic diagram, as shown in fig. 2.
The method for predicting the traffic distribution of the next time slot comprises the following steps:
the method comprises the following steps: taking a time-continuous service distribution thermodynamic diagram with the size of 100 multiplied by 100 as the input of a service prediction model, and extracting the spatial characteristics of the thermodynamic diagram by using a two-dimensional convolution neural network;
step two: adding a pooling layer, wherein the size of a pooling window is 2 multiplied by 2, and the dimension of the generated characteristic sequence is reduced to half of the original dimension through pooling;
step three: splicing the spatial features into a time sequence vector on a time dimension, using the time sequence vector as an input of an LSTM, and extracting time dimension features through an LSTM model;
step four: the nonlinear expression capability of the model is improved through two fully-connected layers;
step five: and performing deconvolution on the output one-dimensional vector matrix, and outputting a predicted service distribution thermodynamic diagram. The network structure of the overall service prediction model is shown in fig. 3.
The low-orbit satellite beam closing method based on the service prediction comprises the following steps:
the method comprises the following steps: establishing a problem model, wherein the research goal of the beam closing scheme is to minimize the number of beams turned on in the low-earth orbit satellite communication system, which is specifically described as follows:
Figure BDA0003065039970000061
wherein the content of the first and second substances,
Figure BDA0003065039970000062
the coverage requirement of service users is met, and after partial wave beams are closed in the network, each user in the system
Figure BDA0003065039970000069
In other words, there should be at least one satellite beam that can serve the user
Figure BDA0003065039970000064
Figure BDA0003065039970000065
Wherein the content of the first and second substances,
Figure BDA0003065039970000066
in order to be a coverage factor,
Figure BDA0003065039970000067
for users in the network, the beams of the ongoing traffic transmission service cannot be turned off, and the set of beams of such ongoing service users is denoted as BsThen the constraint is:
Figure BDA0003065039970000068
if there is a traffic demand in the coverage area of a certain beam in the next time slot, the beam can not be turned off, and the wave is transmittedSet of bundles denoted BpThen the constraint is:
Figure BDA0003065039970000071
wherein, for BsThe solution of (2) can be obtained by predicting the services of different areas of the next time slot by using a service prediction model. Based on the service demand prediction result, the coverage factor between the area and the beam with service for the next time slot is obtained
Figure BDA0003065039970000072
The calculation of (A) yields a set of beams B for which there is a traffic demands。
Thus, the low earth orbit satellite beam off optimization problem can be expressed as:
Figure BDA0003065039970000073
Figure BDA0003065039970000074
Figure BDA0003065039970000075
Figure BDA0003065039970000076
step two: establishing a matrix A to characterize the coverage of beams and users in the network, the beams
Figure BDA0003065039970000077
Expressed by rows of a matrix, user
Figure BDA0003065039970000078
Expressed in columns of the matrix:
Figure BDA0003065039970000079
wherein the content of the first and second substances,
Figure BDA00030650399700000710
the row vector of matrix A is represented by aiRepresenting that the new matrix composed of partial row vectors is a sub-matrix a' of a, the problem of optimization of beam closing for low orbit satellites can be transformed into:
Figure BDA00030650399700000711
Figure BDA00030650399700000712
Figure BDA00030650399700000713
Figure BDA00030650399700000714
step three: the method for solving the low orbit satellite beam closing problem based on flow prediction comprises the following steps:
1) initializing a matrix A, and enabling A' to be A;
2) selecting a beam i, and connecting the row a corresponding to the beam i in the submatrix AiAll the middle elements are set to be 0; meanwhile, making the iteration number n equal to 0;
3) checking whether the matrix A 'meets three constraint conditions, if so, making the matrix A equal to A', and jumping to the step 2; if the constraint is not met, the iteration times n +1 are enabled to jump to the step 4;
4) checking whether the iteration times exceed N (total number of full-network beams), if the iteration times do not reach N times, jumping to the step 2; if the number of times exceeds N, jumping to the step 5;
5) and outputting the matrix A.
The overall beam off procedure is shown in fig. 4.
Finally, the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all that should be covered by the claims of the present invention.

Claims (1)

1. A low orbit satellite spot beam closing method based on service prediction is characterized in that: the method comprises the following steps:
s1: acquiring historical service space-time distribution information from a low-orbit satellite network, and processing service volume data into a two-dimensional service distribution thermodynamic diagram arranged according to a time sequence; the method specifically comprises the following steps:
s11: dividing a network coverage area into a plurality of grid areas by adopting a grid method;
s12: defining the latitude of the center of each grid as thetacGrid longitude length llonLong latitude llatRadius of the earth ReTraffic N within each griduObtaining grid service density rho; taking the average value of each service quantity positioned in the same grid as the service quantity value of the grid to represent the service quantity distribution of the region; setting the services of each grid to be uniformly distributed, wherein the grid service density rho is obtained by the following formula:
Figure FDA0003486119010000011
Figure FDA0003486119010000012
Figure FDA0003486119010000013
Figure FDA0003486119010000014
s13: generating a two-dimensional service distribution thermodynamic diagram by taking the grid longitude and latitude data as coordinates and the grid service density data as values of all areas in the thermodynamic diagram;
s2: inputting a service prediction model, predicting the service distribution condition of the next time slot, and outputting a service distribution thermodynamic diagram; the method specifically comprises the following steps:
s21: taking a time-continuous service distribution thermodynamic diagram as an input of a service prediction model, and extracting spatial features of the thermodynamic diagram by using a two-dimensional convolutional neural network;
s22: adding a pooling layer, wherein the size of a pooling window is 2 multiplied by 2, and the dimension of the generated characteristic sequence is reduced to half of the original dimension through pooling;
s23: splicing the spatial features into a time sequence vector on a time dimension, taking the time sequence vector as the input of a long-short term memory (LSTM) model, and extracting time dimension features through the LSTM model;
s24: the nonlinear expression capability of the model is improved through two fully-connected layers;
s25: carrying out deconvolution on the output one-dimensional vector matrix, and outputting a predicted service distribution thermodynamic diagram;
s3: based on the service prediction result, ensuring the coverage of the whole network service, and solving a closable beam set in the network by taking closing of as many satellite beams as possible as a target; the method specifically comprises the following steps:
s31: establishing a problem model aiming at minimizing the number of beams turned on in the low-orbit satellite communication system, which is specifically described as follows:
Figure FDA0003486119010000021
wherein the content of the first and second substances,
Figure FDA0003486119010000022
the coverage requirement of service users is met, and after partial wave beams are closed in the network, each user in the system
Figure FDA0003486119010000023
In other words, there is at least one satellite beam serving the user
Figure FDA0003486119010000024
Figure FDA0003486119010000025
Wherein the content of the first and second substances,
Figure FDA0003486119010000026
in order to be a coverage factor,
Figure FDA0003486119010000027
for users in the network, the beams of the ongoing traffic transmission service cannot be turned off, and the set of beams of such ongoing service users is denoted as BsThen the constraint is:
Figure FDA0003486119010000028
if there is a traffic demand in the coverage area of a certain beam in the next time slot, the beam cannot be turned off, and such beam set is marked as BpThen the constraint is:
Figure FDA0003486119010000029
wherein, for BsThe solution of (1) is obtained by predicting the services of different areas of the next time slot by using a service prediction model; based on the service demand prediction result, the coverage factor between the area and the beam with service for the next time slot is obtained
Figure FDA00034861190100000210
The calculation of (A) yields a set of beams B for which there is a traffic demands；
S32: establishing a matrix A to characterize the coverage of beams and users in the network, the beams
Figure FDA00034861190100000211
Expressed by rows of a matrix, user
Figure FDA00034861190100000212
Expressed in columns of the matrix:
Figure FDA00034861190100000213
wherein a isijAs coverage factors:
Figure FDA00034861190100000214
the row vector of matrix A is represented by aiRepresenting that a new matrix composed of partial row vectors is a sub-matrix A' of A;
s33: solving a low-orbit satellite beam closing problem based on flow prediction; step S33 specifically includes the following steps:
s331: initializing a matrix A, and enabling A' to be A;
s332: selecting a beam i, and connecting the row a corresponding to the beam i in the submatrix AiAll the middle elements are set to be 0; meanwhile, making the iteration number n equal to 0;
s333: checking whether the matrix A 'meets the constraint condition for covering all services, and if so, making the matrix A equal to A', and jumping to the step S332; if the constraint is not met, the iteration times n +1 are skipped to the step S334;
s334: checking whether the iteration times exceed the total number of the full-network wave beams for N times, and if the iteration times do not reach the total number of the full-network wave beams for N times, jumping to the step S332; if the number of times exceeds N, jumping to step S335;
s335: outputting a matrix A;
s4: all satellite beams in the set are turned off.
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