A method for path selection of a mega constellation network based on rolling optimization

Abstract

The application discloses a kind of based on rolling optimization's giant constellation network path selection method, this method includes: constructing giant constellation network and static network;Mapping giant constellation network and static network obtains mapping information;According to mapping information, each LEO satellite periodically exchanges queuing delay information with each LEO satellite within three hops connected LEO satellite, and the queuing delay information after exchange is saved in link state data table;Link state data table is handled, and the shortest path information is obtained;According to the shortest path information, the next hop LEO satellite is selected, and the rolling optimization's giant constellation network path selection result based on is obtained.The application adopts rolling optimization idea, and global optimal path calculation is decomposed into multiple local optimal path calculation, can reduce calculation complexity, accelerate routing convergence speed;At the same time, link state information exchange is limited in local area, and signaling overhead can be reduced.

Description

Technical Field

[0001] This invention relates to the field of satellite communication network technology, and in particular to a path selection method for mega-constellation networks based on rolling optimization. Background Technology

[0002] Low Earth Orbit (LEO) satellite networks have become a research hotspot in the field of satellite communication networks due to their advantages such as low latency, wide coverage, and flexible networking. Routing technology is key to maximizing the performance of LEO satellite networks. In recent years, the emergence of mega-constellation projects, represented by Starlink and OneWeb, has sparked a surge in the construction of mega-constellations in LEO. These mega-constellations consist of hundreds to thousands of LEO satellites, and their massive network scale and highly dynamic topology pose significant challenges to routing design. Over the past few decades, researchers have proposed various routing algorithms for LEO satellite networks, such as virtual topology-based routing and coverage domain-based routing. However, these algorithms suffer from high computational complexity, outdated topology information, and high overhead from link state flooding, making them unsuitable for networks composed of mega-constellations in LEO.

[0003] Rolling optimization is an optimization concept derived from Model Predictive Control (MPC). In MPC, optimization is not performed over the entire time domain, but rather over a finite-length optimization time domain. Once the finite-time optimization problem at a certain moment is solved, MPC applies the control signal corresponding to that moment to the controlled object, then shifts the optimization time domain to the next moment to solve the next finite-time optimization problem. Through rolling optimization, MPC adapts to changes in the parameters of the controlled object. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a path selection method for mega-constellation networks based on rolling optimization. The idea of ​​rolling optimization is applied to the routing path selection problem. When calculating the routing path, each satellite node only uses the link state information within a three-hop range to calculate the optimal path, thereby reducing the path calculation overhead and link state flooding overhead. At the same time, since the exchange range of link state information is limited to within three hops, the real-time nature of the link state information can be ensured, and it can adapt to changes in link state.

[0005] To address the aforementioned technical problems, this invention discloses a path selection method for giant constellation networks based on rolling optimization, the method comprising:

[0006] S1, constructing a giant constellation network and a static network;

[0007] S2, Map the giant constellation network and the static network to obtain mapping information;

[0008] S3, according to the mapping information, each LEO satellite periodically exchanges queuing delay information with LEO satellites connected within three hops to each LEO satellite, and saves the exchanged queuing delay information in the link status data table;

[0009] The link status data table includes geographic address and queuing delay information;

[0010] S4, process the link status data table to obtain the shortest path information;

[0011] S5. Select the next-hop LEO satellite based on the shortest path information to obtain the path selection result of the giant constellation network based on rolling optimization.

[0012] As an optional implementation, in this embodiment of the invention, the mega-constellation network includes N LEO satellite constellations at different altitudes; the orbit type of the LEO satellite constellations is polar orbit.

[0013] The static network comprises M virtual nodes with fixed geographic coordinates.

[0014] As an optional implementation, in this embodiment of the invention, mapping the mega-constellation network and the static network to obtain mapping information includes:

[0015] Based on the principle of minimum distance, the giant constellation network and the static network are mapped to obtain mapping information;

[0016] The mapping information is the geographic coordinates of a virtual node obtained by the satellite closest to it at any given time; the geographic coordinates are geographic addresses.

[0017] As an optional implementation, in this embodiment of the invention, processing the link state data table to obtain the shortest path information includes:

[0018] S41, perform shortest path calculation on the first satellite node M1, the second satellite node M2, the third satellite node M3 and the fourth satellite node M4 that are connected to any LEO satellite in one hop, and obtain the first shortest path P1 from the first satellite node M1 to the destination address, the second shortest path P2 from the second satellite node M2 ​​to the destination address, the third shortest path P3 from the third satellite node M3 to the destination address and the fourth shortest path P4 from the fourth satellite node M4 to the destination address;

[0019] S42, sort the first shortest path P1 from the first satellite node M1 to the destination address, the second shortest path P2 from the second satellite node M2 ​​to the destination address, the third shortest path P3 from the third satellite node M3 to the destination address, and the fourth shortest path P4 from the fourth satellite node M4 to the destination address to obtain the path with the shortest distance.

[0020] S43, add the next hop of the shortest path to the routing table to obtain the shortest path information;

[0021] The routing table includes the destination satellite geographic address and the next-hop satellite geographic address.

[0022] As an optional implementation, in this embodiment of the invention, the method for calculating the shortest path is as follows:

[0023] Process the LEO satellites connected to any LEO satellite within three hops and the LEO satellite itself to obtain graph G(V,E), where V is the set of vertices and E is the set of edges;

[0024] Weights are calculated for the graph G(V,E) to obtain the edge weights located in the graph G(V,E) and the edge weights located outside the graph G(V,E);

[0025] The edge weights located in graph G(V,E) and the edge weights located outside graph G(V,E) are processed to obtain the shortest path.

[0026] As an optional implementation, in the first aspect of the present invention, the edge weight expression in graph G(V,E) is:

[0027]

[0028] Where e(i,j) is the edge weight between satellite i and satellite j in graph G(V,E), τ(i,j) is the link propagation delay between satellite i and satellite j, n(t) is the number of packets in the queue at time t, and L avg It is the average group length.

[0029] As an optional implementation, in this embodiment of the invention, the edge weight located outside graph G(V,E) is the link propagation delay τ(i,j) between satellite i and satellite j.

[0030] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0031] (1) This invention adopts the idea of ​​rolling optimization, which transforms the shortest path calculation from traditional global optimization to local optimization within a three-hop range, which can reduce computational overhead, improve the real-time performance of path calculation, and accelerate the route convergence speed.

[0032] (2) This invention takes into account the large number of nodes in a giant constellation network. By limiting the range of link state exchange to within three hops, the overhead required for link state flooding can be significantly reduced. At the same time, the real-time nature of link state information can be ensured.

[0033] (3) In the path calculation process, the weights of edges other than three hops are determined by the link propagation delay, which can ensure the consistency of path calculation and avoid the generation of routing loops. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a flowchart illustrating a path selection method for a giant constellation network based on rolling optimization disclosed in an embodiment of the present invention.

[0036] Figure 2 This is a flowchart illustrating another method for selecting paths in a giant constellation network based on rolling optimization, as disclosed in an embodiment of the present invention. Detailed Implementation

[0037] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0039] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0040] This invention discloses a path selection method for a mega-constellation network based on rolling optimization. The method includes: constructing a mega-constellation network and a static network; mapping the mega-constellation network and the static network to obtain mapping information; according to the mapping information, each LEO satellite periodically exchanges queuing delay information with LEO satellites connected to it within three hops, and storing the exchanged queuing delay information in a link state data table; the link state data table includes geographical addresses and queuing delay information; processing the link state data table to obtain shortest path information; and selecting the next-hop LEO satellite based on the shortest path information to obtain the path selection result for the mega-constellation network based on rolling optimization. This invention employs the rolling optimization concept, decomposing the calculation of the globally optimal path into multiple segments of locally optimal path calculation, which can reduce computational complexity and accelerate route convergence; simultaneously, restricting the exchange of link state information to a local area can reduce signaling overhead. These are described in detail below.

[0041] Example 1

[0042] Please see Figure 1 , Figure 1 This is a flowchart illustrating a path selection method for giant constellation networks based on rolling optimization, as disclosed in an embodiment of the present invention. Figure 1 The described rolling optimization-based path selection method for mega-constellation networks is applied in the field of satellite communication network technology to achieve routing path selection. This invention does not limit the specific implementation of the method. Figure 1 As shown, this path selection method for giant constellation networks based on rolling optimization can include the following operations:

[0043] S1, constructing a giant constellation network and a static network;

[0044] S2, Map the giant constellation network and the static network to obtain mapping information;

[0045] S3, according to the mapping information, each LEO satellite periodically exchanges queuing delay information with LEO satellites connected within three hops to each LEO satellite, and saves the exchanged queuing delay information in the link status data table;

[0046] The link status data table includes geographic address and queuing delay information;

[0047] S4, process the link status data table to obtain the shortest path information;

[0048] S5. Select the next-hop LEO satellite based on the shortest path information to obtain the path selection result of the giant constellation network based on rolling optimization.

[0049] Optionally, the mega-constellation network comprises N LEO satellite constellations at different altitudes; the orbit type of the LEO satellite constellations is polar orbit.

[0050] The static network comprises M virtual nodes with fixed geographic coordinates.

[0051] Optionally, the mapping of the giant constellation network and the static network to obtain mapping information includes:

[0052] Based on the principle of minimum distance, the giant constellation network and the static network are mapped to obtain mapping information;

[0053] The mapping information is the geographic coordinates of a virtual node obtained by the satellite closest to it at any given time; the geographic coordinates are geographic addresses.

[0054] Optionally, processing the link state data table to obtain the shortest path information includes:

[0055] S41, perform shortest path calculation on the first satellite node M1, the second satellite node M2, the third satellite node M3 and the fourth satellite node M4 that are connected to any LEO satellite in one hop, and obtain the first shortest path P1 from the first satellite node M1 to the destination address, the second shortest path P2 from the second satellite node M2 ​​to the destination address, the third shortest path P3 from the third satellite node M3 to the destination address and the fourth shortest path P4 from the fourth satellite node M4 to the destination address;

[0056] S42, sort the first shortest path P1 from the first satellite node M1 to the destination address, the second shortest path P2 from the second satellite node M2 ​​to the destination address, the third shortest path P3 from the third satellite node M3 to the destination address, and the fourth shortest path P4 from the fourth satellite node M4 to the destination address to obtain the path with the shortest distance.

[0057] S43, add the next hop of the shortest path to the routing table to obtain the shortest path information;

[0058] The routing table includes the destination satellite geographic address and the next-hop satellite geographic address.

[0059] Optionally, the method for calculating the shortest path is as follows:

[0060] Process the LEO satellites connected to any LEO satellite within three hops and the LEO satellite itself to obtain graph G(V,E), where V is the set of vertices and E is the set of edges;

[0061] Weights are calculated for the graph G(V,E) to obtain the edge weights located in the graph G(V,E) and the edge weights located outside the graph G(V,E);

[0062] The edge weights located in graph G(V,E) and the edge weights located outside graph G(V,E) are processed to obtain the shortest path.

[0063] The shortest path is the path with the smallest sum of weights on all edges.

[0064] Optionally, the edge weight expression in graph G(V,E) is:

[0065]

[0066] Where e(i,j) is the edge weight between satellite i and satellite j in graph G(V,E), τ(i,j) is the link propagation delay between satellite i and satellite j, n(t) is the number of packets in the queue at time t, and L avg It is the average group length.

[0067] Optionally, the edge weight located outside the graph G(V,E) is the link propagation delay τ(i,j) between satellite i and satellite j.

[0068] As can be seen, this invention adopts the rolling optimization concept, transforming the shortest path calculation from traditional global optimization to local optimization within a three-hop range. This reduces computational overhead, improves the real-time performance of path calculation, and accelerates route convergence. Considering the large number of nodes in mega-constellation networks, this invention significantly reduces the overhead of link state flooding by limiting link state exchange to within three hops. Simultaneously, it ensures the real-time performance of link state information. Furthermore, this invention uses link propagation delay to determine the weights of edges outside the three-hop range during path calculation, guaranteeing consistency in path calculation and preventing routing loops.

[0069] Example 2

[0070] Please see Figure 2 , Figure 2 This is a flowchart illustrating another method for path selection in a giant constellation network based on rolling optimization, as disclosed in an embodiment of the present invention. Figure 2The described rolling optimization-based path selection method for mega-constellation networks is applied in the field of satellite communication network technology to achieve routing path selection. This invention does not limit the specific implementation of the method. Figure 2 As shown, this path selection method for giant constellation networks based on rolling optimization can include the following operations:

[0071] Step S1: The mega-constellation network includes LEO satellite constellations at different altitudes, with polar orbits. A virtual node design is employed, mapping the mega-constellation network to a static network. The static network consists of virtual nodes with fixed geographic coordinates. The mega-constellation network and the static network are mapped according to the minimum distance principle. At any given time, the satellite closest to a virtual node obtains the node's geographic coordinates, which are referred to as its geographic address.

[0072] Each LEO satellite periodically exchanges queuing delay information with its LEO satellites connected within three hops or less, and stores this information in a link state data table. The link state data table contains geographic addresses and queuing delay information.

[0073] Step S2: The graph formed by LEO satellites (including the LEO satellite) connected to a LEO satellite within three hops in step S1 is denoted as G(V,E), as shown below. Figure 2 As shown; where V is the set of vertices and E is the set of edges. The weight of an edge is determined by the sum of the link propagation delay and the queuing delay; the weight of edge e(i,j) is...

[0074]

[0075] Where τ(i,j) is the link propagation delay between satellite i and satellite j, n(t) is the number of packets in the queue at time t, and L avg It is the average group length;

[0076] Step S3: The satellite nodes connected to the LEO satellite in one hop are denoted as M1, M2, M3, and M4. The LEO satellite calculates the shortest paths from M1, M2, M3, and M4 to the destination address, and these four shortest paths are denoted as P1, P2, P3, and P4. When calculating the shortest paths, the edge weights within graph G(V,E) are calculated using the method described in step S2, while the edge weights outside graph G(V,E) are determined by the link propagation delay.

[0077]

[0078] Step S4: Select the shortest path among P1, P2, P3, and P4, and add the next hop of that path to the routing table.

[0079] The routing table contains destination satellite geographic address and next-hop satellite geographic address information.

[0080] As can be seen, this invention adopts the rolling optimization concept, transforming the shortest path calculation from traditional global optimization to local optimization within a three-hop range. This reduces computational overhead, improves the real-time performance of path calculation, and accelerates route convergence. Considering the large number of nodes in mega-constellation networks, this invention significantly reduces the overhead of link state flooding by limiting link state exchange to within three hops. Simultaneously, it ensures the real-time performance of link state information. Furthermore, this invention uses link propagation delay to determine the weights of edges outside the three-hop range during path calculation, guaranteeing consistency in path calculation and preventing routing loops.

[0081] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0082] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0083] Finally, it should be noted that the method and apparatus for path selection in a giant constellation network based on rolling optimization disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A path selection method for giant constellation networks based on rolling optimization, characterized in that, The method includes: S1, constructing a giant constellation network and a static network; The mega-constellation network comprises N LEO satellite constellations at different altitudes; the orbital type of the LEO satellite constellations is polar orbit. The static network comprises M virtual nodes with fixed geographic coordinates, where N and M are positive integers; S2, Map the giant constellation network and the static network to obtain mapping information, including: Based on the principle of minimum distance, the giant constellation network and the static network are mapped to obtain mapping information; The mapping information is the geographic coordinates of a virtual node obtained by the satellite closest to it at any given time; the geographic coordinates are geographic addresses. S3, according to the mapping information, each LEO satellite periodically exchanges queuing delay information with LEO satellites connected within three hops to each LEO satellite, and saves the exchanged queuing delay information in the link status data table; The link status data table includes geographic address and queuing delay information; S4, process the link status data table to obtain the shortest path information, including: S41, perform shortest path calculation on the first satellite node M1, the second satellite node M2, the third satellite node M3 and the fourth satellite node M4 that are connected to any LEO satellite in one hop, and obtain the first shortest path P1 from the first satellite node M1 to the destination address, the second shortest path P2 from the second satellite node M2 ​​to the destination address, the third shortest path P3 from the third satellite node M3 to the destination address and the fourth shortest path P4 from the fourth satellite node M4 to the destination address; S42, sort the first shortest path P1 from the first satellite node M1 to the destination address, the second shortest path P2 from the second satellite node M2 ​​to the destination address, the third shortest path P3 from the third satellite node M3 to the destination address, and the fourth shortest path P4 from the fourth satellite node M4 to the destination address to obtain the path with the shortest distance. S43, add the next hop of the shortest path to the routing table to obtain the shortest path information; The routing table includes the destination satellite geographic address and the next-hop satellite geographic address; The method for calculating the shortest path is as follows: Process the LEO satellites connected to any LEO satellite within three hops and the LEO satellite itself to obtain graph G(V,E), where V is the set of vertices and E is the set of edges; Weights are calculated for the graph G(V,E) to obtain the edge weights located in the graph G(V,E) and the edge weights located outside the graph G(V,E); The edge weights located in graph G(V,E) and the edge weights located outside graph G(V,E) are processed to obtain the shortest path; The edge weight expression in graph G(V,E) is: Where e(i,j) is the edge weight between satellite i and satellite j in graph G(V,E), τ(i,j) is the link propagation delay between satellite i and satellite j, n(t) is the number of packets in the queue at time t, and L avg is the average group length, and T is the calculation time interval; The edge weight located outside the graph G(V,E) is the link propagation delay τ(i,j) between satellite i and satellite j; S5. Select the next-hop LEO satellite based on the shortest path information to obtain the path selection result of the giant constellation network based on rolling optimization.

Images