CN115037351A - Hyperbolic space embedding representation method of satellite communication network - Google Patents

Hyperbolic space embedding representation method of satellite communication network Download PDF

Info

Publication number
CN115037351A
CN115037351A CN202210515229.3A CN202210515229A CN115037351A CN 115037351 A CN115037351 A CN 115037351A CN 202210515229 A CN202210515229 A CN 202210515229A CN 115037351 A CN115037351 A CN 115037351A
Authority
CN
China
Prior art keywords
satellite
node
nodes
model
probability
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202210515229.3A
Other languages
Chinese (zh)
Other versions
CN115037351B (en
Inventor
何元智
付华珺
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Institute of Network Engineering Institute of Systems Engineering Academy of Military Sciences
Original Assignee
Institute of Network Engineering Institute of Systems Engineering Academy of Military Sciences
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Institute of Network Engineering Institute of Systems Engineering Academy of Military Sciences filed Critical Institute of Network Engineering Institute of Systems Engineering Academy of Military Sciences
Priority to CN202210515229.3A priority Critical patent/CN115037351B/en
Publication of CN115037351A publication Critical patent/CN115037351A/en
Application granted granted Critical
Publication of CN115037351B publication Critical patent/CN115037351B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
    • H04B7/1851—Systems using a satellite or space-based relay
    • H04B7/18519—Operations control, administration or maintenance
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/12—Discovery or management of network topologies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • 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]
    • H04W84/04—Large scale networks; Deep hierarchical networks
    • H04W84/06—Airborne or Satellite Networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The invention discloses a hyperbolic space embedded representation method of a satellite communication network, which comprises the following steps: constructing a Poincare sphere model; constructing a satellite communication network; representing a satellite communication network by using a first satellite network model in Euclidean space; calculating the probability of edges existing between two satellite nodes in the first satellite network model by using the meta path, and solving the maximum value of the probability; in a hyperbolic space, mapping a first satellite network model into a second satellite network model by using a Poincar sphere model; screening satellite nodes and neighbor satellite nodes thereof in the second satellite network model to obtain negative type nodes of the satellite nodes; and optimizing a probability maximization model of edges between the satellite nodes and the neighboring satellite nodes by using a Riemann gradient descent method to obtain a hyperbolic embedded representation updating model of the satellite communication network. The method improves the accuracy and the rapidity of the representation of the topological structure of the satellite communication network, and is particularly suitable for the efficient representation of the large-scale complex satellite communication network under the multi-satellite high-dynamic topology.

Description

Hyperbolic space embedding representation method of satellite communication network
Technical Field
The invention relates to the technical field of satellite communication, in particular to a hyperbolic space embedded representation method of a satellite communication network.
Background
Compared with a ground communication system, the satellite communication system has the remarkable advantages of wide coverage range and no limitation of terrain conditions, and plays an irreplaceable role in serving air, sea, desert, mountain land and remote unmanned area users and dealing with ground communication infrastructure damage caused by natural disasters such as earthquake, flood and the like; with the rapid development of the low-orbit constellation and the rapid development of the technology, the number of satellite nodes of the space low-orbit constellation satellite is increasing day by day, which leads to the exponential increase of the number of links in the satellite communication of each constellation. The traditional Euclidean space can only adapt to a growth mode of a polynomial hierarchy, and a satellite communication network embedded model under the traditional Euclidean space is difficult to meet the current situation of the satellite communication network which is rapidly developed nowadays. The non-Euclidean space belongs to the expansion of Euclidean geometric space, the hyperbolic space is a constant negative curvature space, compared with the Euclidean space, the capacity of the hyperbolic space is larger, the hyperbolic space is more suitable for analysis modeling of a large-scale complex network, and a model which is more suitable for being established by complex data expressed by low-dimensional embedding is easier to be established.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a hyperbolic space embedding expression method of a satellite communication network, which can obtain a model more fitting an actual system by mapping the satellite communication network into the hyperbolic space, better adapt to the explosive growth that the number of the satellites which are increasing nowadays and the network connectivity of the satellites are exponential, and provide a low-dimensional, efficient and accurate embedding mode for a large-scale multi-satellite complex satellite communication network, thereby reducing the calculated amount of route optimization, capacity optimization and the like of the large-scale multi-satellite complex satellite communication network and shortening the calculation time.
In order to solve the technical problem, the embodiment of the invention discloses a hyperbolic space embedded representation method of a satellite communication network, which comprises the following steps:
s1, constructing a Poincare sphere model; the Poincare sphere model is represented by a plurality of open d-dimensional unit spheres and Riemann metric tensors; presetting a first satellite network model and a second satellite network model;
s2, constructing a satellite communication network; the satellite communication network is composed of a plurality of satellites, and each satellite is set as a satellite node;
s3, representing the satellite communication network by the first satellite network model in Euclidean space; calculating two satellite nodes in the first satellite network model by using a meta path to obtain a first probability of edges existing between the two satellite nodes;
s4, mapping the first satellite network model into the second satellite network model by using the Poincar sphere model in the hyperbolic space;
calculating a second probability of an edge existing between two satellite nodes in the second satellite network model to obtain a second probability maximum value of the edge;
s5, presetting a time delay threshold value and an error rate threshold value;
screening the satellite nodes in the second satellite network model and neighbor satellite nodes thereof to obtain negative type nodes and non-negative type nodes of the satellite nodes;
processing the satellite nodes, and the negative type nodes and the non-negative type nodes thereof to obtain a probability maximization model of edges existing between the satellite nodes and the neighbor satellite nodes thereof;
and S6, optimizing the probability maximization model by using a Riemann gradient descent method to obtain a hyperbolic embedded representation updating model of the satellite communication network.
As an optional implementation manner, in an embodiment of the present invention, a representation method of the Poincar é sphere model includes:
the Poincare sphere model uses a plurality of open D-dimensional unit spheres D d And Riemann metric tensor
Figure BDA0003639245280000023
Expressed, the calculation formula is:
D d ＝{x∈R d :||x||＜1}
Figure BDA0003639245280000021
Figure BDA0003639245280000022
in the formula, R d Is d-dimensional real number field, λ x Is tensor g E Constant multiple of, D d For D dimension unit sphere, x ∈ D d ，g E For the Euclidean metric tensor, let g E ＝I，
Figure BDA0003639245280000024
Is the riemann metric tensor.
As an optional implementation manner, in an embodiment of the present invention, the first satellite network model includes:
the satellite communication network is composed of satellite nodes v i N, n being the number of satellite nodes in the satellite communication network; the satellite node v i Forming a satellite node set V;
in the satellite communication network, directional connection between any two satellite nodes is set as an edge;
satellite node v i To satellite node v j Is a directed edge e ij I, j ═ 1, 2.. n, the directed edge e ij Forming a directed edge set E;
in Euclidean space, the satellite communication network is denoted G (V, E), V i ∈V，e ij E, i, j ═ 1, 2.. n, G (V, E) is the first satellite network model.
As an optional implementation manner, in an embodiment of the present invention, the calculating two satellite nodes in the first satellite network model by using a meta path to obtain a first probability that an edge exists between the two satellite nodes includes:
the meta path is:
Figure BDA0003639245280000031
satellite node v in the satellite communication network i And satellite node v j Presence of edge e ij First probability p of ij Comprises the following steps:
Figure BDA0003639245280000032
in the formula, gamma ij As a satellite node v i And satellite node v j The channel delay between them, Γ is the delay threshold, ξ ij For satellite nodes v i And satellite node v j Xi is the bit error rate threshold, W (gamma) ij ,ξ ij ) Is related to the channel delay gamma ij And channel error rate xi ij A function of (a); when p is ij When the node is greater than 0, connecting the satellite node v j Referred to as satellite node v i The neighbor satellite node of (2).
As an optional implementation manner, in an embodiment of the present invention, the mapping the first satellite network model to the second satellite network model includes:
the first satellite network model G (V, E) is mapped to the second satellite network model Θ in the hyperbolic space, and the method includes:
Figure BDA0003639245280000041
in the formula, theta i Representing a satellite node in the second satellite network model, i ═ 1,2 i The number of the cells.
As an optional implementation manner, in this embodiment of the present invention, the processing the satellite node, and the negative type node and the non-negative type node thereof, to obtain a probability maximization model of edges existing between the satellite node and neighboring satellite nodes thereof includes:
minimizing a probability of an edge existing between the satellite node and a negative type node of the satellite node;
maximizing the probability of edges existing between the satellite nodes and non-negative type nodes of the satellite nodes;
and obtaining a probability maximization model of edges existing between the satellite node and the neighbor satellite nodes.
As an optional implementation manner, in an embodiment of the present invention, the calculating a second probability that an edge exists between two satellite nodes in the second satellite network model to obtain a second maximum probability of the edge includes:
s41, the satellite node theta i With its neighbor satellite node c (theta) i ) j A distance d therebetween D (θ i ,c(θ i ) j ) Comprises the following steps:
Figure BDA0003639245280000042
in the formula, the satellite node theta i The neighbor satellite node set of (a) is C (theta), C (theta) i ) j ∈C(Θ),j∈{1,2,...|V|-1}；
S42, the satellite node theta i With its neighbor satellite node c (theta) i ) j Second probability p (theta) of edge existing therebetween i |c(θ i ) j (ii) a Θ) is:
p(θ i |c(θ i ) j ；Θ)＝1/1+exp[d D (θ i ,c(θ i ) j )]
s43, the satellite node theta i With its neighbor satellite node c (theta) i ) j Second probability p (theta) of edge existing therebetween i |c(θ i ) j (ii) a Θ) maximum value is:
Figure BDA0003639245280000043
and obtaining a second probability maximum value of the edge.
As an optional implementation manner, in an embodiment of the present invention, the screening a satellite node in the second satellite network model and a neighboring satellite node thereof to obtain a negative type node and a non-negative type node of the satellite node includes:
the negative type node of the satellite node is a neighbor satellite node of the satellite node, wherein the channel time delay is greater than a preset time delay threshold value, and the error rate is greater than a preset error rate threshold value;
the non-negative type node of the satellite node is a neighbor satellite node of the satellite node, wherein the channel delay is smaller than a preset delay threshold value, and the error rate is smaller than a preset error rate threshold value.
As an optional implementation manner, in this embodiment of the present invention, the processing the satellite node, and the negative type node and the non-negative type node thereof, to obtain a probability maximization model of edges existing between the satellite node and neighboring satellite nodes thereof includes:
s51, in the second satellite network model, the satellite node theta i The negative type node of is Q l ，Q l ∈Θ，l＝1,2,...m,m＜|V|-1；
The negative type node Q l And the satellite node theta i The probability of an edge existing in between is:
Figure BDA0003639245280000051
s52, connecting the satellite node theta i With said negative type node Q l The probability of having an edge in between is minimized;
connecting the satellite node theta i Neighbor satellite node c with the nonnegative type node g (θ i ) j The probability of edges existing between them is maximized;
obtaining the satellite node theta i With its neighbor satellite node c (theta) i ) j A probability maximization model S (Θ) with edges in between, the method comprising:
Figure BDA0003639245280000052
s (Θ) is the probability maximization model.
As an optional implementation manner, in an embodiment of the present invention, the probability maximization model is optimized by using a riemann gradient descent method to obtain an updated hyperbolic embedded representation model of a satellite communication network, where the method includes:
s61, make
Figure BDA0003639245280000061
Represented as the satellite node theta i ∈D d An embedded tangent space;
computing the Riemann gradient of the probability maximization model S (theta)
Figure BDA0003639245280000062
Using the Riemann gradient
Figure BDA0003639245280000063
Updating the satellite node θ i The method comprises the following steps:
Figure BDA0003639245280000064
in the formula (I), the compound is shown in the specification,
Figure BDA0003639245280000065
is an exponential mapping function on the Poincare sphere, η is a constant,
Figure BDA0003639245280000066
s62, in non-euclidean space,
Figure BDA0003639245280000067
in hyperbolic space, using the Riemann gradient
Figure BDA0003639245280000068
Updating the satellite node θ i ，
Figure BDA0003639245280000069
θ i The updating is as follows:
Figure BDA00036392452800000610
to obtain theta i And updating the result.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
(1) according to the method, the Poincar é sphere is selected as the equivalent model, the problem that equidistant embedding cannot be achieved between the hyperbolic space and the Euclidean space is solved, the specific angle-keeping performance of the Poincar sphere is utilized, the calculated amount of network embedding is simplified, and the accuracy and the rapidity of network topological structure analysis are improved;
(2) according to the method, the infinite extensibility and the radial index extensibility of the Poincare sphere equivalent model are utilized, the expression of a large-scale complex network form under a multi-satellite high-dynamic topology is solved, the embedding in the Euclidean space is put into the hyperbolic space by utilizing the characteristic that the hyperbolic space has a potential hierarchical structure, the accuracy and the rapidity of information transmission are greatly improved, and a foundation is laid for embedding of a future large-scale low-orbit satellite communication network;
(3) through the research on the network embedding method of the satellite communication network in the hyperbolic space, the method can better adapt to the current situation that the number of the satellites which are increasing day by day and the network connectivity of the satellites are exponentially increased; by mapping the satellite communication network to the hyperbolic space, a model representation more fitting an actual system is obtained, and a technical and theoretical foundation is laid for greatly improving the forwarding efficiency among the satellite nodes.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flowchart of a hyperbolic space embedding representation method of a satellite communication network according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
The terms "first," "second," and the like in the description and claims of the present invention and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or apparatus that comprises a list of steps or elements is not limited to those listed but may alternatively include other steps or elements not listed or inherent to such process, method, product, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Fig. 1 is a schematic flowchart of a hyperbolic space embedding representation method of a satellite communication network, disclosed in an embodiment of the present invention, and the method shown in fig. 1 includes the following steps:
s1, making a plurality of open D-dimensional unit balls D d And Riemann metric tensor
Figure BDA0003639245280000081
To express the Poincar sphere model, which is specifically:
D d ＝{x∈R d :||x||＜1}
Figure BDA0003639245280000082
Figure BDA0003639245280000083
wherein R is d Is d-dimensional real number field, λ x Is tensor g E Constant multiple of (D) d For D dimension unit sphere, x ∈ D d ，g E For the Euclidean metric tensor, let g E ＝I，
Figure BDA0003639245280000084
Is the riemann metric tensor.
S2, using graph theory expression method, setting each satellite in the satellite communication network as a satellite node, denoted as v i N, n is the number of satellite nodes in the satellite communication network; the directed connection between any two satellite nodes is set as an edge, using e ij Representing slave satellite nodes v i To satellite node v j The edge of (1); the satellite communication network is represented in Euclidean space as G (V, E), V i ∈V，e ij E, i, j is 1, 2.. n, V is a set of satellite nodes of the satellite communication network in the Euclidean space, E is a set of directed edges between the satellite nodes of the satellite communication network in the Euclidean space, and G (V, E) is called as a first satellite network model;
s3, giving the meta path as
Figure BDA0003639245280000085
Defining satellite nodes v in a satellite communications network i And satellite node v j Presence of edge e ij Has a probability of
Figure BDA0003639245280000086
Wherein, γ ij For satellite nodes v i And satellite node v j Channel delay between, Γ is delay threshold, ξ ij As a satellite node v i And satellite node v j Xi is the threshold of the error rate, W vij (γ ij ,ξ ij )＝a|γ ij |+(1-a)|ξ ij I, a is an element (0, 1); when p is ij When the node is greater than 0, connecting the satellite node v j Referred to as satellite node v i The neighbor satellite node of (1);
s4, the representation of the satellite node set on the Poincar sphere in the hyperbolic space is set as
Figure BDA0003639245280000091
Wherein, theta i Representing satellite nodes in the satellite communication network, i ═ 1, 2., | V |, where | V | is a satellite node θ where the node set V is mapped on the Poincar sphere of the hyperbolic space i Calculating the probability of edges existing between two satellite nodes, and solving the maximum value of the probability, wherein theta is a second satellite network model;
s41, let the satellite node theta i And neighbor satellite node c (θ) i ) j Is a distance of
Figure BDA0003639245280000092
Wherein, the satellite node theta i The neighbor satellite node set of (a) is C (theta), C (theta) i ) j ∈C(Θ),j∈{1,2,...|V|-1}；
S42, satellite node theta i With neighbor satellite node c (theta) i ) j Has a probability of having an edge therebetween of
p(θ i |c(θ i ) j ；Θ)＝1/1+exp[d D (θ i ,c(θ i ) j )]
S43, calculating the satellite node theta i And its neighbor satellite sectionPoint c (theta) i ) j Maximum value of probability of edge existing therebetween
Figure BDA0003639245280000093
S5, for satellite node theta i All the neighbor satellite nodes are screened, and the originating neighbor satellite nodes with the channel time delay larger than the time delay threshold and the error rate higher than the error rate threshold are defined as satellite nodes theta j A negative type node of (2); by minimizing the satellite node theta i And the probability of edges existing between the nodes of the negative type to maximize the satellite node theta i The probability of edges existing between the satellite node and the neighbor satellite node of the nonnegative type node is obtained to obtain the satellite node theta i A probability maximization model of edges existing between the satellite nodes and the neighbor satellite nodes;
s51, aiming at the satellite node theta i Screening all the neighbor satellite nodes to obtain the negative type node marked as Q l ，Q l E Θ, l 1, 2.. m, m < | V | -1, then the negative type node Q l And satellite node theta i Has a probability of an edge existing therebetween of
Figure BDA0003639245280000094
S52, by minimizing the satellite node theta i And a negative type node Q l The probability of edges existing between the two to maximize the satellite node theta i Neighbor satellite node c of nonnegative type node g (θ i ) j There is a probability of an edge between them, so that the satellite node θ in S43 i With its neighbor satellite node c (theta) i ) j The probability maximization model with edges in between is optimized as
Figure BDA0003639245280000101
And S6, optimizing the probability maximization model with edges between any satellite node and the neighboring satellite nodes by using a Riemannian gradient descent method to obtain a satellite node representation updating model.
S61, order
Figure BDA0003639245280000102
Denoted as satellite node theta i ∈D d Embedded tangent space, computing satellite node theta i With its neighbor satellite node c (theta) i ) j Riemann gradient of probability maximization model S (theta) with edges in between
Figure BDA0003639245280000103
The satellite node theta corresponding to the maximization of S (theta) i The representation is updated to
Figure BDA0003639245280000104
Wherein
Figure BDA0003639245280000105
Is an exponential mapping function on the Poincare sphere, and eta is a constant;
s62, in non-European space, exp θi (. satisfy)
Figure BDA0003639245280000106
Satellite node theta in hyperbolic space i With its neighbor satellite node c (theta) i ) j Satellite node theta corresponding to probability maximization model S (theta) with edges in between i The representation is updated as:
Figure BDA0003639245280000107
through the above detailed description of the embodiments, those skilled in the art will clearly understand that the embodiments may be implemented by software plus a necessary general hardware platform, and may also be implemented by hardware. Based on such understanding, the above technical solutions may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, where the storage medium includes a Read-Only Memory (ROM), a Random Access Memory (RAM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a Compact Disc-Read-Only Memory (CD-ROM), or other disk memories, CD-ROMs, or other magnetic disks, A tape memory, or any other medium readable by a computer that can be used to carry or store data.
Finally, it should be noted that: the hyperbolic space embedding representation method of the satellite communication network disclosed in the embodiment of the present invention is only disclosed as a preferred embodiment of the present invention, and is only used for illustrating the technical solution of the present invention, rather than limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art; the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. A hyperbolic space embedded representation method of a satellite communication network, the method comprising:
s1, constructing a Poincare sphere model; the Poincare sphere model is represented by a plurality of open d-dimensional unit spheres and Riemann metric tensors; presetting a first satellite network model and a second satellite network model;
s2, constructing a satellite communication network; the satellite communication network is composed of a plurality of satellites, and each satellite is set as a satellite node;
s3, representing the satellite communication network by the first satellite network model in Euclidean space; calculating two satellite nodes in the first satellite network model by using a meta path to obtain a first probability of edges existing between the two satellite nodes;
s4, mapping the first satellite network model into the second satellite network model by using the Poincar sphere model in the hyperbolic space;
calculating a second probability of an edge existing between two satellite nodes in the second satellite network model to obtain a second probability maximum value of the edge;
s5, presetting a time delay threshold and an error rate threshold;
screening satellite nodes and neighbor satellite nodes thereof in the second satellite network model to obtain negative type nodes and non-negative type nodes of the satellite nodes;
processing the satellite nodes, and the negative type nodes and the non-negative type nodes thereof to obtain a probability maximization model of edges existing between the satellite nodes and neighbor satellite nodes thereof;
and S6, optimizing the probability maximization model by using a Riemann gradient descent method to obtain a hyperbolic embedded representation updating model of the satellite communication network.
2. The hyperbolic space embedding representation method of a satellite communication network according to claim 1, wherein the representation method of the poincare sphere model is as follows:
the Poincare sphere model uses a plurality of open D-dimensional unit spheres D d And Riemann metric tensor g x D Expressed, the calculation formula is:
D d ＝{x∈R d :||x||＜1}
Figure FDA0003639245270000021
Figure FDA0003639245270000022
in the formula, R d Is d-dimensional real number field, λ x Is tensor g E Constant multiple of, D d For D dimension unit sphere, x ∈ D d ，g E For the Euclidean metric tensor, let g E ＝I，
Figure FDA0003639245270000023
Is the riemann metric tensor.
3. The hyperbolic spatial embedding representation method of a satellite communication network as claimed in claim 1, wherein the first satellite network model comprises:
the satellite communication network is formed by satellite nodes v i N, n being the number of satellite nodes in the satellite communication network; the satellite node v i Forming a satellite node set V;
in the satellite communication network, directional connection between any two satellite nodes is set as an edge;
satellite node v i To satellite node v j Is a directed edge e ij N, said directed edge e being 1,2 ij Forming a directed edge set E;
in Euclidean space, the satellite communication network is denoted G (V, E), V i ∈V，e ij E, i, j ═ 1, 2.. n, G (V, E) is the first satellite network model.
4. The hyperbolic spatial embedding representation method of a satellite communication network according to claim 1, wherein the calculating two satellite nodes in the first satellite network model by using a meta-path to obtain a first probability of an edge existing between the two satellite nodes comprises:
the meta-path is:
Figure FDA0003639245270000024
satellite node v in the satellite communication network i And satellite node v j Presence of edge e ij First probability p of ij Comprises the following steps:
Figure FDA0003639245270000025
in the formula, gamma ij As a satellite node v i And satellite node v j Channel delay between, Γ is delay threshold, ξ ij For satellite nodes v i And satellite node v j Xi is the bit error rate threshold, W (gamma) ij ,ξ ij ) Is related to channel delay y ij And channel error rate ξ ij A function of (a); when p is ij When the node is greater than 0, connecting the satellite node v j Referred to as satellite node v i Of the neighboring satellite node.
5. The hyperbolic spatial embedded representation method of a satellite communication network according to claim 1, wherein said first satellite network model is mapped to a second satellite network model comprising:
the first satellite network model G (V, E) is mapped to the second satellite network model Θ in the hyperbolic space, and the method includes:
Figure FDA0003639245270000031
in the formula, theta i Representing a satellite node in the second satellite network model, i ═ 1, 2. | V, | V | being a mapped satellite node θ i The number of (2).
6. The hyperbolic spatial embedding representation method of a satellite communication network according to claim 1, wherein the processing the satellite nodes, and their negative type nodes and non-negative type nodes, to obtain a probability maximization model of edges existing between the satellite nodes and their neighboring satellite nodes, comprises:
minimizing a probability that an edge exists between the satellite node and a negative type node of the satellite node;
maximizing the probability of edges existing between the satellite nodes and non-negative type nodes of the satellite nodes;
and obtaining a probability maximization model of edges existing between the satellite node and the neighbor satellite nodes.
7. The hyperbolic spatial embedding representation method of a satellite communication network according to claim 1, wherein a second probability of an edge existing between two satellite nodes in the second satellite network model is calculated to obtain a second probability maximum of the edge, the method comprising:
s41, the satellite node theta i With its neighbor satellite node c (theta) i ) j A distance d between D (θ i ,c(θ i ) j ) Comprises the following steps:
Figure FDA0003639245270000041
in the formula, the satellite node theta i The neighbor satellite node set of (a) is C (theta), C (theta) i ) j ∈C(Θ),j∈{1,2,...|V|-1}；
S42, the satellite node theta i With its neighbor satellite node c (theta) i ) j Second probability p (theta) of edge existing therebetween i |c(θ i ) j (ii) a Θ) is:
p(θ i |c(θ i ) j ；Θ)＝1/1+exp[d D (θ i ,c(θ i ) j )]
s43, the satellite node theta i With its neighbor satellite node c (theta) i ) j Second probability p (theta) of edge existing therebetween i |c(θ i ) j (ii) a Θ) maximum value is:
Figure FDA0003639245270000042
and obtaining a second probability maximum value of the edge.
8. The hyperbolic space embedded representation method of a satellite communication network according to claim 1, wherein screening the satellite nodes in the second satellite network model and their neighboring satellite nodes to obtain negative type nodes and non-negative type nodes of the satellite nodes comprises:
the negative type node of the satellite node is a neighbor satellite node of the satellite node, wherein the channel time delay is greater than a preset time delay threshold value, and the error rate is greater than a preset error rate threshold value;
the non-negative type node of the satellite node is a neighbor satellite node of the satellite node, wherein the channel time delay is smaller than a preset time delay threshold value, and the error rate is smaller than a preset error rate threshold value.
9. The hyperbolic space embedding representation method of a satellite communication network as claimed in claim 1, wherein the processing the satellite nodes, and their negative type nodes and non-negative type nodes, to obtain a probability maximization model of edges existing between the satellite nodes and their neighboring satellite nodes comprises:
s51, in the second satellite network model, the satellite node theta i The negative type node of is Q l ，Q l ∈Θ，l＝1,2,...m,m＜|V|-1；
The negative type node Q l And the satellite node theta i The probability of an edge existing in between is:
Figure FDA0003639245270000051
s52, connecting the satellite node theta i And the negative type node Q l The probability of having an edge in between is minimized;
connecting the satellite node theta i With said nodes of non-negative typeNeighbor satellite node c g (θ i ) j The probability of edges existing between them is maximized;
obtaining the satellite node theta i With its neighbor satellite node c (theta) i ) j A probability maximization model S (Θ) with edges in between, the method comprising:
Figure FDA0003639245270000052
s (Θ) is the probability maximization model.
10. The hyperbolic space embedded representation method of a satellite communication network according to claim 1, wherein the probability maximization model is optimized by using a riemann gradient descent method to obtain an updated hyperbolic embedded representation model of the satellite communication network, and the method comprises:
s61, order
Figure FDA0003639245270000053
Represented as the satellite node theta i ∈D d An embedded tangent space;
computing the Riemann gradient of the probability maximization model S (theta)
Figure FDA0003639245270000054
Using the Riemann gradient
Figure FDA0003639245270000055
Updating the satellite node θ i The method comprises the following steps:
Figure FDA0003639245270000056
in the formula (I), the compound is shown in the specification,
Figure FDA0003639245270000057
is an exponential mapping function on the Poincare sphere, η is a constant,
Figure FDA0003639245270000058
s62, in non-euclidean space,
Figure FDA0003639245270000061
in hyperbolic space, using the Riemann gradient
Figure FDA0003639245270000062
Updating the satellite node θ i ，
Figure FDA0003639245270000063
θ i The updating is as follows:
Figure FDA0003639245270000064
to obtain theta i And updating the result.
CN202210515229.3A 2022-05-11 2022-05-11 Hyperbolic space embedded representation method of satellite communication network Active CN115037351B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210515229.3A CN115037351B (en) 2022-05-11 2022-05-11 Hyperbolic space embedded representation method of satellite communication network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210515229.3A CN115037351B (en) 2022-05-11 2022-05-11 Hyperbolic space embedded representation method of satellite communication network

Publications (2)

Publication Number Publication Date
CN115037351A true CN115037351A (en) 2022-09-09
CN115037351B CN115037351B (en) 2023-05-16

Family

ID=83120415

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210515229.3A Active CN115037351B (en) 2022-05-11 2022-05-11 Hyperbolic space embedded representation method of satellite communication network

Country Status (1)

Country Link
CN (1) CN115037351B (en)

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6591084B1 (en) * 1998-04-27 2003-07-08 General Dynamics Decision Systems, Inc. Satellite based data transfer and delivery system
US20160099770A1 (en) * 2013-05-07 2016-04-07 Elbit Systems Land And C4I Ltd. Mobile ad-hoc network with satellite node
CN111193540A (en) * 2020-04-08 2020-05-22 北京大学深圳研究生院 Hyperbolic geometry-based sky and land information network unified routing method
CN111526090A (en) * 2020-07-06 2020-08-11 北京大学深圳研究生院 Routing method of heaven-earth integrated information network

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6591084B1 (en) * 1998-04-27 2003-07-08 General Dynamics Decision Systems, Inc. Satellite based data transfer and delivery system
US20160099770A1 (en) * 2013-05-07 2016-04-07 Elbit Systems Land And C4I Ltd. Mobile ad-hoc network with satellite node
CN111193540A (en) * 2020-04-08 2020-05-22 北京大学深圳研究生院 Hyperbolic geometry-based sky and land information network unified routing method
CN111526090A (en) * 2020-07-06 2020-08-11 北京大学深圳研究生院 Routing method of heaven-earth integrated information network

Also Published As

Publication number Publication date
CN115037351B (en) 2023-05-16

Similar Documents

Publication Publication Date Title
US20090123088A1 (en) Tiled projections for planar processing of round earth data
CN112235384A (en) Data transmission method, device, equipment and storage medium in distributed system
CN107133398B (en) River runoff prediction method based on complex network
CN110288118B (en) Unmanned aerial vehicle-based air rescue response method
CN103593400A (en) Lightning activity data statistics method based on modified Apriori algorithm
CN115358487A (en) Federal learning aggregation optimization system and method for power data sharing
CN114064627A (en) Knowledge graph link completion method and system for multiple relations
CN107609694B (en) Structure optimization method for offshore wind power cluster power transmission system and storage medium
CN115037351A (en) Hyperbolic space embedding representation method of satellite communication network
CN113207101A (en) Information processing method based on 5G city component sensor and Internet of things cloud platform
Ajayi et al. Africa 3: A continental network model to enable the African fourth industrial revolution
Qatawneh New efficient algorithm for mapping linear array into hex-cell network
US10823881B2 (en) Cloud forecast using sequential images
CN108897820B (en) Parallelization method of DENCLUE algorithm
CN107623595B (en) Network server deployment method and system
CN116030255A (en) System and method for three-dimensional point cloud semantic segmentation
CN115171433A (en) Vehicle fog-assisted post-disaster rescue task unloading method for unmanned aerial vehicle
CN115630514A (en) Unmanned aerial vehicle cluster cooperative task allocation method and device
CN107659501A (en) Efficiency optimized transmission method and system based on complicated gradient network
CN112020085B (en) Node failure sweep effect analysis method for aviation ad hoc network
CN109120438B (en) Data cooperative transmission method and system under opportunity network
CN113507413A (en) Route optimization method and device and computing equipment
CN112615736A (en) Delay optimal distributed NNs collaborative optimization method facing linear edge network
CN117171628B (en) Graph structure data node classification method and device in heterogeneous federal environment
CN114697221B (en) Reliability mapping algorithm based on snapshot tree model in space-earth integrated network

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant