A dynamic routing method for large-scale low-Earth orbit satellites with adaptive network queuing delay

Abstract

This invention relates to the field of satellite communication technology, specifically to a dynamic routing method for large-scale low-Earth orbit (LEO) satellites that adapts to network queuing delay. Based on a greedy heuristic approach for dynamic routing in large-scale LEO satellite networks, it utilizes parameters such as satellite communication distance and direction, the estimated minimum number of hops from the current satellite to the receiving location, the average queuing delay in the network, and the queuing delay of the next-hop relay satellite. A value function is proposed to find the next-hop satellite, where the function value reflects the distance traveled to the receiving location in a single-hop transmission and the magnitude of the relay satellite's queuing delay. By plotting the change curves of the weights corresponding to the shortest transmission path delay versus the average satellite queuing delay, the optimal weights are found as the average queuing delay changes, thus achieving adaptive network load conditions. Verification shows that, compared to other routing methods that allow non-adjacent satellites to use ISL communication, this invention has the advantage of shorter total delay for networks with different on-board queue load conditions.

Description

Technical Field

[0001] This invention relates to the field of satellite communication technology, and specifically to a large-scale low-Earth orbit satellite dynamic routing method with adaptive network queuing delay. Background Technology

[0002] In large-scale low Earth Orbit (LEO) satellite networks, as the number of satellites launched increases, the distance between adjacent satellites is much smaller than the maximum communication distance of LEO. The routing method that only uses adjacent inter-satellite links (ISL) will lead to an increase in forwarding and queuing times, resulting in a higher total link latency and affecting the user experience of enjoying low-latency services.

[0003] Existing low-Earth orbit (LEO) satellite routing methods can be categorized into static routing and dynamic routing based on whether queuing delays are considered. However, due to the large number of LEO satellites and their high-speed movement relative to the ground, network topologies are highly variable. Static routing cannot adjust promptly when a satellite is overloaded. Dynamic routing methods convert satellite latency into dynamic costs and set minimum hop count paths and flooding areas during route discovery to reduce unnecessary resource waste. As the number of orbital planes and launched satellites increases, routing methods that only allow inter-satellite links between adjacent orbits increase the number of link relays, leading to greater latency. Even by reducing the minimum hop count and shortening link distances, the latency of the constellation at a certain altitude is reduced. Because the queuing delay factor of satellite task store-and-forward is not considered in actual situations, when satellites need to forward a large amount of data simultaneously, queuing delays occur in the forwarding tasks. Summary of the Invention

[0004] The purpose of this invention is to provide a large-scale low-Earth orbit satellite dynamic routing method with adaptive network queuing delay, which maximizes the single-hop transmission distance to reduce the number of relays and minimizes the phase difference between the link and the ideal path to reduce the total link delay while satisfying line-of-sight communication constraints.

[0005] To achieve the above objectives, this invention provides a large-scale low-Earth orbit satellite dynamic routing method with adaptive network queuing delay, comprising the following steps:

[0006] Step 1: Select a constellation network model that matches the satellite position coordinates and link latency;

[0007] Step 2: Determine the coordinates of the sending and receiving locations;

[0008] Step 3: Obtain the communication satellites, and use the current coordinates as the center and the maximum communication distance as the radius to detect the communication range;

[0009] Step 4: Discard relay satellites whose distance from the receiving coordinates is greater than the current coordinates;

[0010] Step 5: Calculate the minimum number of hops K between the current coordinates and the receiving location. min Calculate the communication value function for the remaining communicable satellites;

[0011] Step 6: Select the relay satellite with the highest communication value as the next-hop relay satellite;

[0012] Step 7: If the next-hop satellite cannot communicate directly with the receiving station, change the coordinates of the next-hop relay satellite to the sending location and repeat steps 2 to 6.

[0013] Optionally, the satellite position coordinates in step 1 satisfy the following quantitative relationship:

[0014]

[0015] Among them, C (p,n,t) This is the position parameter matrix of a low-Earth orbit satellite at any time t. The x-axis points from the geocenter O to the vernal equinox; the z-axis is perpendicular to the equatorial plane and points towards the North Pole; the y-axis is perpendicular to the plane formed by the x and z axes and together with the x and z axes, forms a left-handed Cartesian coordinate system. The default satellite is S. 0,0 The initial position is on the positive x-axis;

[0016] The satellite is located at point H, the origin O coincides with the Earth's center, and the satellite's orbital altitude relative to the Earth's center is h, where h = |OH|; point H′ is the projection of point H onto the xOy plane, and HH′ is perpendicular to the xOy plane; angle θ z The angle θ is the angle formed by line segment OH and plane xOy. x ω is the angle formed by line segment OH′ and the x-axis; e Let U(t) be the angular velocity of the satellite relative to the Earth's rotation, and U(t) be the step function.

[0017] Optionally, in step 1, S p1,n1 Send data to S p2,n2 The total delay of a single-hop link consists of three parts: propagation delay D pro Transmission delay D t And queuing delay D queue The expression is as follows:

[0018] Delay(S p1,n1 ,S p2,n2 ) = D pro (S p1,n1 ,S p2,n2 )+D t +D queue

[0019] Where p1, n1, p2, and n2 represent the orbital plane number and satellite number within the orbital plane of satellites S1 and S2, respectively.

[0020] Optionally, in step 3, when the satellite communicates at its maximum communication distance, the following quantitative relationship exists:

[0021]

[0022] θ max This indicates that when the satellite communication distance is min(d) max ,d' max When ), relative to a circle with radius h and center O, the maximum span of the inscribed angle is d. max d′ represents the maximum communication range of the satellite itself. max This represents the maximum satellite communication range when not obstructed by the ground.

[0023] Optionally, the value function for the next hop of the dynamic load satellite network in step 5 is defined as follows:

[0024] f(S pm,nm ,S p(m+1),n(m+1) ,t)=α·f vector +β·f queue

[0025] f vector =cosΔp·(d(S) pm,nm ,Des,t)-d(S p(m+1),n(m+1) ,Des,t)) / c

[0026] f queue =(E[D queue ]-D queue )

[0027] Among them, f vector Δp represents the distance traveled by the low-Earth orbit satellite S along the direction of the receiving point during this hop propagation. p(m+1),n(m+1) The phase difference between the current hop and the ideal relay satellite position, f queue E[D] reflects the relationship between the current satellite queuing delay and the expected value. queue Let α be the expected queuing delay, reflecting the network load status, α be the distance factor, β be the queuing factor, and α ∈ [0,1], β ∈ [0,1], while α + β = 1.

[0028] Optionally, when calculating the specific values ​​of the optimal α and β, the cases of α∈[0,1] will be traversed to find the corresponding value of α with the minimum time delay.

[0029] This invention provides a dynamic routing method for large-scale low-Earth orbit (LEO) satellites that adapts to network queuing delay. First, a large-scale circular polar orbit LEO constellation model is constructed over continuous time. Then, based on a greedy heuristic, dynamic routing for the large-scale LEO satellite network is implemented. This method utilizes parameters such as satellite communication distance and direction, the estimated minimum number of hops from the current satellite to the receiving point, the average queuing delay in the network, and the queuing delay of the next-hop relay satellite. A value function is proposed to find the next-hop satellite, where the function value reflects the distance traveled to the receiving point in a single-hop transmission and the magnitude of the relay satellite's queuing delay. By plotting the change curves of the weights corresponding to the shortest transmission path delay versus the average satellite queuing delay, the optimal weights are found as the average queuing delay changes, thus achieving adaptive network load conditions. Experimental data shows that, compared to other routing methods that allow non-adjacent satellites to use ISL communication, this invention has the advantage of shorter total delay for networks with different on-board queue load conditions. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0031] Figure 1 This is a flowchart illustrating the steps of a large-scale low-Earth orbit satellite dynamic routing method with adaptive network queuing delay according to the present invention.

[0032] Figure 2 This is a schematic diagram of the environmental architecture of the large-scale circular polar low-orbit constellation model of the present invention.

[0033] Figure 3 This is a schematic diagram of the rectangular coordinate system of satellite orbit in a specific embodiment of the present invention.

[0034] Figure 4 This is a schematic diagram of the satellite orbit cross-section in a specific embodiment of the present invention.

[0035] Figure 5 This is a schematic diagram of the maximum transmission angle in a specific embodiment of the present invention.

[0036] Figure 6 This is a schematic diagram comparing the number of iterations with the comparison algorithm in a specific embodiment of the present invention.

[0037] Figure 7 This is a schematic diagram illustrating the relationship between latency and hop count as a function of distance factor in a specific embodiment of the present invention.

[0038] Figure 8This is a schematic diagram illustrating the numerical relationship of the optimal distance factor with an average queuing delay of less than 40ms in a specific embodiment of the present invention.

[0039] Figure 9 This is a schematic diagram comparing propagation delays in a specific embodiment of the present invention.

[0040] Figure 10 This is a schematic diagram comparing queuing delays in a specific embodiment of the present invention.

[0041] Figure 11 This is a schematic diagram of the total latency with an average queuing latency of less than 40ms in a specific embodiment of the present invention.

[0042] Figure 12 This is a schematic diagram illustrating the change in total latency with the number of satellites launched in a specific embodiment of the present invention. Detailed Implementation

[0043] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0044] Please see Figure 1 This invention provides a large-scale low-Earth orbit satellite dynamic routing method with adaptive network queuing delay, comprising the following steps:

[0045] S1: Select a constellation network model that matches the satellite position coordinates and link delay;

[0046] S2: Determine the coordinates of the sending and receiving locations;

[0047] S3: Obtain communicable satellites and detect the communicable range with the current coordinates as the center and the maximum communication distance as the radius;

[0048] S4: Discard relay satellites whose distance from the receiving coordinates is greater than the current coordinates;

[0049] S5: Calculate the minimum hop count K between the current coordinates and the receiving location. min Calculate the communication value function for the remaining communicable satellites;

[0050] S6: Select the relay satellite with the highest communication value as the next-hop relay satellite;

[0051] S7: If the next-hop satellite cannot communicate directly with the receiving station, change the coordinates of the next-hop relay satellite to the sending location and repeat steps S2 to S6.

[0052] The following section provides further explanation regarding the implementation process, model building, and problem modeling:

[0053] Please see Figure 2 In a polar-orbiting constellation of low-Earth orbit (LEO) satellites, inter-orbit links are allowed for communication. A LEO satellite carrying a relay mission calculates a value function for all LEO satellites within its maximum communication range, selecting the satellite with the highest value function as the next-hop LEO satellite. This process is repeated for each selected relay satellite until the satellite can communicate directly with the ground receiving station. This routing method fully utilizes the maximum communication range of satellites, allows inter-orbit satellite communication, considers satellite queuing delays, and finally excludes relay satellites with propagation path distances significantly greater than the ideal path distance, thereby reducing latency.

[0054] A circular polar orbit constellation (N, P, h) is adopted. (p,n) As a constellation configuration, N is the number of satellites in a single orbital plane; P is the number of orbital planes; p is a positive integer index of the orbital plane, and p∈[1,P]; n represents the nth low-Earth orbit satellite in a single orbital plane, and n∈[1,N]; h (p,n) Let S be the altitude of the nth satellite in the p-th orbital plane. Let S be the altitude of the nth low-Earth orbit satellite in the p-th orbital plane. p,n .

[0055] 1.1 Satellite Position Coordinate Model

[0056] like Figure 3 As shown, the satellite orbital plane is defined as perpendicular to the equatorial plane and uniformly distributed within the Earth's hemisphere from the p=0 orbital plane in the direction of Earth's rotation; the n=0 satellites within a single orbital plane are uniformly distributed on the entire orbital circumference in a due south direction on the equatorial plane.

[0057] Let the origin O coincide with the Earth's center, and let the x-axis be the intersection of the equatorial plane and the p=0 orbital plane, pointing towards the satellite S. 0,0 Initial position; the z-axis is perpendicular to the equatorial plane and points towards the North Pole; the y-axis is perpendicular to the plane formed by the x-axis and z-axis and together with the x-axis and z-axis form a left-handed rectangular coordinate system.

[0058] Let the satellite's location be H, and its orbital altitude relative to the Earth's center be h, with h = |OH|; point H′ is the projection of point H onto the xOy plane, and HH′ is perpendicular to the xOy plane; angle θ z The angle θ is the angle formed by line segment OH and plane xOy. x Let OH′ be the angle formed between line segment OH′ and the x-axis.

[0059] Let the period of the low-Earth orbit satellite be T, and h be a constant. When θ is determined... x With θ zBy understanding the relationship between the angle and time, the three-dimensional coordinates of the satellite at any given moment can be determined. Let the position parameter matrix of the low-Earth orbit satellite at any moment t be C. (p,n) (t)=[x (p,n) (t),y (p,n) (t),z (p,n) [t] As shown in the figure, the satellite's coordinates and θ x With θ z The angles have the following quantitative relationships:

[0060]

[0061] Any orbital plane of a polar constellation is always perpendicular to the equatorial plane xOy, through Figure 4 It can be seen that when a satellite on the same orbital plane moves within a hemisphere, θ x It remains constant, only changing when crossing the North and South Poles; while θ z It changes at a constant speed within the period. Therefore, θ can be obtained. x With θ z The expression for t∈[0,T] is:

[0062]

[0063] Where ω e Let U(t) be the angular velocity of the satellite relative to the Earth's rotation, and U(t) be the step function.

[0064] 1.2 Link Delay Model

[0065] Let the maximum communication distance of a low-Earth orbit satellite be d. max The Earth's radius is r, and the satellite's storage and forwarding space is η. Define the satellite phase Φ. p,n ∈[0,π], for low-Earth orbit satellite S p,n The angle formed by the line connecting the Earth's center O and the positive z-axis; define the phase difference ΔΦ(S (p1,n1) ,S (p2,n2) )=Φ (p1,n1) -Φ (p2,n2) Where p1, n1, p2, and n2 represent the orbital plane number and satellite number within the orbital plane of satellites S1 and S2, respectively.

[0066] Defined by S p1,n1 Send data to S p2,n2 The total delay of a single-hop link is:

[0067]

[0068] The total delay of a single-hop link consists of three parts: propagation delay D pro Transmission delay D t And queuing delay D queue.

[0069] For S p1,n1 To S p2,n2 Propagation delay D pro (S p1,n1 ,S p2,n2 D has the following definition: pro (S p1,n1 ,S p2,n2 )=d(S p1,n1 ,S p2,n2 ,t) / c, where d(S p1,n1 ,S p2,n2 (,t) represents the low-orbit satellite S at time t. p1,n1 To S p2,n2 Let be the distance, and c be the speed of light in a vacuum. We also have:

[0070]

[0071] Define D t Let D be the data transmission delay, which is the time it takes for the satellite to transmit mission data from the start to the end. t =pac / R, where pac is the size of the task data and R is the transmission rate, which has the following definition:

[0072]

[0073] ρ t ρ is the transmitted power of the signal. n For noise power, A t For the transmit antenna gain, L(S) p1,n1 ,S p2,n2 ,t) represents the low-Earth orbit satellite S p1,n1 To S p2,n2 In the vacuum at time t, the free space path loss is given, and we have:

[0074]

[0075] Define queuing delay D queue The queue time for mission data before it is transmitted by the current satellite is considered. Before transmitting its own mission data, the satellite needs to relay data received from other satellites. Therefore, while mission data is being relayed by each relay satellite, it must queue and wait for the current relay satellite to process other tasks. In the waiting queue, the mission data that arrives first is transmitted first. Satellite data reception and storage / forwarding follow Poisson and exponential distributions, respectively.

[0076] The arrival of mission data in the satellite network satisfies the following conditions: the arrival of missions is independent and follows a Poisson distribution with parameter λ; there is only one serving satellite and the data processing time is independent; the satellite processing speed is constant and the mission processing time follows a negative exponential distribution with parameter u.

[0077] Therefore, the mission queue on a satellite can be described using the M / M / 1 / K queue model, where the queuing time D... queue Calculated using the M / M / 1 / K queue model. Let the waiting time of mission data in the satellite store-and-forward queue be a random variable D. queue It follows an exponential distribution, so the average waiting time D for data on the satellite... queue The following expressions are available:

[0078]

[0079] 1.3 Problem Modeling

[0080] Let any K-hop transmission path be: path(S) p0,n0 ,S pK,nK ,ξ1,...,ξ k ,...,ξ K |k=1,2,...,K), where S p0,n0 Indicates the launch of a satellite, S pK,nK To ultimately receive satellites, ξ k (k = 1, 2, ..., K) represents the k-th path segment. The task data transmission process is first handled by S. p0,n0 Send, transmitted to S via the first path ξ1. p1,n1 Then it is transmitted to S via the second path ξ2. p2,n2 And so on, in the last segment of the entire link, the data is transferred from S... p(K-1),n(K-1) via the Kth path ξ K Transmitted to the final receiving satellite S pK,nK , transmission path (S p0,n0 ,S pK,nK ,ξ1,...,ξ k ,...,ξ K There are K paths in total (k = 1, 2, ..., K).

[0081] As defined by the single-link delay, the total delay of a K-hop transmission path is the sum of the delays of each individual link. Therefore, the total delay T(path(S) of a K-hop transmission path is... p0,n0 ,S pK,nK ,ξ1,...,ξ k ,...,ξ K |k=1,2,......K) can be represented as:

[0082]

[0083] Therefore, the problem of minimizing the total delay of a transmission path can be expressed as follows:

[0084]

[0085] The following constraints should be met when solving this problem:

[0086] 1) Satellite maximum communication distance limit

[0087] Suppose that at time t, the path path(S) p0,n0 ,S pK,nK ,ξ1,...,ξ k ,...,ξ K The distance of a single-hop low-Earth orbit satellite communication link in |k=1,2,......K) is: d(S pm,nm ,S p(m+1),n(m+1) ,t)m∈N * m < K-1

[0088] At this point, it is necessary to consider whether the signals transmitted by the satellite at its maximum communication range will be blocked by the Earth's surface when low-orbit satellites communicate with each other at a certain altitude.

[0089] Let d′ be the maximum satellite communication range when it is not blocked by the earth's surface. max At this point, the line-of-sight path of the signal is exactly tangent to the Earth's surface, and the length of line segment OH is h. Obviously d′ max The actual operating altitude h of the low-Earth orbit satellite is related to this, so it is necessary to consider not only the satellite's maximum communication range d. max It is also necessary to consider the maximum satellite communication range d′ under unobstructed terrain. max Therefore, for a single link distance d(S) pm,nm ,S p(m+1),n(m+1) The following constraints exist for (t):

[0090] d(S pm,nm ,S p(m+1),n(m+1) ,t)<min(d max ,d′ max (10)

[0091] 2) Maximum waiting queue length limit for satellites

[0092] Let η be the maximum storage and forwarding space of a low-Earth orbit (LEO) satellite. When the size of the data to be forwarded exceeds the maximum storage and forwarding space, the LEO satellite will no longer accept new forwarding tasks until the remaining forwarding space can allow the forwarding of the current task. This requires satisfying: D queue ·R+pac<η, therefore for the queuing waiting time Dqueue The following limitations exist:

[0093]

[0094] In P1, the decision variable is ξ. k ξ k This can reflect the path(S) p0,n0 ,S pK,nK ,ξ k The selection of each relay low-Earth orbit satellite in the process involves considering the propagation delay between adjacent nodes, queuing time, and the propagation latency of the current link. k The evaluation is based on the distance the data travels along the direction of the final receiving location after completion.

[0095] Therefore, the problem of finding the shortest latency link is:

[0096]

[0097] When a low-Earth orbit satellite transmits over its maximum communication distance, it may select a next-hop relay satellite with a high queuing delay; conversely, a relay satellite with a low queuing delay may be far from the ideal path. To address this problem, this invention combines a greedy approach with consideration that on-board queuing delay and satellite position are independent, and that the satellite position variable is continuous over continuous time, while the queuing delay is a discrete variable with integer values. Therefore, the corresponding problem is a mixed-integer nonlinear programming problem.

[0098] Based on this, the subsequent steps of the present invention consider a low-latency low-Earth orbit satellite routing method that maximizes the communication distance of each hop of the relay satellite under different satellite queue delays.

[0099] Steps S2 to S6 also involve determining the theoretical minimum number of hops, the evaluation function, and the optimal distance factor.

[0100] 1) Theoretical minimum number of hops

[0101] In a low-Earth orbit (LEO) satellite network, there is only one least-delay path (LDP) (or two if the constellation is perfectly symmetrical and network load is not considered). However, there is more than one least-hop path (LHP). The goal is to prove that the least-delay path is a subset of the least-hop path, i.e.: It has been proven that the lowest latency link is not a subset of the lowest hop count link. However, the counterexample exhibits severely uneven satellite coverage. Considering the overall coverage effect of a constellation, the counterexample scenario is unlikely to occur in reality. Therefore, we can still assume... This theory holds true in reality. For example... Figure 5As shown, when satellites communicate at their maximum communication distance, the following quantitative relationships exist:

[0102]

[0103] θ max This indicates that when the satellite communication distance is min(d) max ,d' max When θ is the maximum span of the inscribed angle relative to a circle with radius h and center O, let θ be the inscribed angle formed by the transmitting and receiving gateways on Earth and the Earth's center O. centre Assuming there is a relay satellite at any ideal location, the theoretical minimum number of hops is... However, gaps exist in real constellations, so it's impossible for every jump to reach the maximum communication distance. This means the actual number of jumps may exceed the theoretical minimum number of jumps, K. min .

[0104] 2) Evaluation function

[0105] Low Earth orbit satellite S pm,nm When searching for the next-hop relay satellite, the evaluation function should include the single-hop communication distance d(S). pm,nm ,S p(m+1),n(m+1) ,t), queuing time D for the next hop satellite queue And considering the consistency between the direction of the single-hop receiving satellite and the direction of the final ground receiving station, the value function for the next hop in a dynamic load satellite network is defined as follows:

[0106] f(S pm,nm ,S p(m+1),n(m+1) ,t)=α·f vector +β·f queue (14)

[0107] For formula (14) f vector with f queue It has the following definition:

[0108]

[0109] f vector It can reflect the distance that the mission data travels along the direction of the receiving point during this hop propagation; Δp represents the distance traveled by the low-Earth orbit satellite S. p(m+1),n(m+1) The phase difference Δp between the current hop and the ideal relay satellite position is related to the theoretical minimum number of hops.

[0110]

[0111] f queue =(E[D queue ]-D queueThis reflects the relationship between the current satellite queuing delay and the expected value. E[D] queue Let f be the expected queuing delay, which can be expressed in terms of network load. Let α be the distance factor and β be the queuing factor, where α ∈ [0,1], β ∈ [0,1], and α + β = 1. The value function is measured in milliseconds and can provide a reference for selecting the next-hop relay satellite. Based on the value function's numerical result, the next-hop relay satellite with the highest value is selected within the communication range. vector with f queue The maximum values ​​are all on the order of milliseconds, so the value function will not be significantly affected by either of them being too large. The distance factor α and the queuing factor β can be adjusted according to the network load. Different values ​​of α and β will also affect the selection of relay satellites, thereby affecting the final delay result of the entire link. This function value reflects the distance traveled to the receiving location in single-hop transmission and the queuing delay of the relay satellite. At the same time, it can be seen from formulas (15) and (16) that the larger the distance traveled to the receiving location in single-hop transmission, the greater the value of f. vector The larger the value of f, the smaller the current relay satellite forwarding queuing experiment is compared to the overall satellite queuing delay in the network. queue The larger the value, the more likely the satellite with the highest value function will be selected as the next-hop relay satellite.

[0112] α and β represent the degree of emphasis a satellite places on finding its next-hop satellite. When queuing delay exists, the total transmission delay can be reduced by decreasing the total number of hops or selecting a relay satellite with a smaller queuing delay. As the single-hop distance increases, the number of hops in the transmission process can be reduced, thereby reducing queuing and lowering the total link delay. When directly selecting a relay satellite with a smaller queuing delay, the satellite's position may not be within the maximum transmission distance from the satellite to the destination. Therefore, the values ​​of α and β represent two strategies for reducing queuing delay. A larger α value tends to reduce queuing and thus lower delay, while a smaller α value tends to reduce delay by directly selecting a relay satellite with a lower queuing delay.

[0113] 3) Determination of the optimal distance factor

[0114] By changing the values ​​of α and β, under a certain average queuing delay in the overall network, when α and β are optimal factors, the total delay of a K-hop path should be minimized. However, since the amount of tasks received by the satellite in the future is uncertain, the average queuing delay of the network varies, and the queuing delay of each satellite also varies at different times, making it impossible to directly calculate the total delay. When calculating the specific values ​​of the optimal α and β, the cases of α∈[0,1] are traversed to find the value of α corresponding to the minimum delay. After the optimal distance factor α in the current network load is determined, considering the applicability under other queuing delay conditions, it is also necessary to change the average queuing delay E[D]. queue The value of α is used to calculate the optimal distance factor under other common network queuing delay conditions, and the function curve of the average queuing delay versus the optimal distance factor α is obtained, thereby meeting the need to find the minimum delay path under different network queuing delay conditions.

[0115] The method of this invention is then transformed into an algorithm, and its complexity is compared and analyzed with other algorithms:

[0116] The algorithm steps are as follows:

[0117] 1. Input: Transmit position Sta, Receive position Des, Maximum transmission rate R, Transmission task size pac, Optimal distance factor α

[0118] 2. Search for all communicable satellites of the current node.

[0119] 3. Calculate the minimum hop count from the current node to the receiving location.

[0120] 4. Select satellite nodes that are closer to the receiving location than the current satellite node, and calculate the value function of these nodes.

[0121] 5. Select the satellite node with the highest value function as the relay satellite.

[0122] 6. Replace the selected relay satellite node with the current node. If the current node cannot communicate directly with the receiving location, proceed to step 27. The routing algorithm ends.

[0123] The algorithm contains two loop bodies: a `While` loop and a `For` loop within the `While` loop. The `While` loop iterates once after each hop of data transmission. Given an optimal distance factor α, the hop count converges to the theoretical minimum hop count K. min Therefore, the maximum number of iterations in the While loop body at this point is equal to the minimum number of jumps, K. min .

[0124] The maximum number of iterations in the For loop should be equal to the number of satellites within the satellite's maximum communication range. Satellite nodes are most densely packed in the polar regions, so the number of satellites within the communication range is greatest at these locations. Let L be the number of satellites that can communicate within the communication range at the poles. Therefore, the maximum number of iterations for the body of a For loop should be L.

[0125] Therefore, the maximum number of iterations for the algorithm of this invention is K. min In summary, the algorithm's complexity can be calculated to be O(K). min L).

[0126] The low-latency routing algorithm based on stochastic geometry stipulates that the number of hops in the entire transmission link must be consistent with the minimum theoretical hop count. Based on this, the transmission distance is divided into equal intervals, and a relay satellite is searched within a given range around each division point. The time complexity is O(K). min Therefore, the time complexity of the algorithm in this invention is greater than that of the comparative algorithm, but it achieves a lower total link latency.

[0127] Furthermore, to demonstrate the advantages of the present invention, specific embodiments are proposed, and comparative analysis of different methods is conducted through simulation results.

[0128] 1) Simulation scene settings

[0129] There are polar orbital constellations (N, P, h) (p,n) ), where N=50, P=72, a total of 3600 low-Earth orbit satellites, all of which operate at an altitude of approximately 550km, that is, for p∈[1,72], n∈[1,50], h (p,n) ≈550. Assume the task data size is 50MB and the transmission rate is 10GB / s. The average queuing time is 25ms. The minimum hop count between the launch and receiving locations is 7, calculated using formula (12). The comparative experiment uses existing low-Earth orbit satellite routing algorithms that allow cross-orbit communication. All delays are in milliseconds.

[0130] 2) Simulation Results Explanation

[0131] Figure 6 This paper compares the time complexity of the method of this invention with that of a low-latency routing algorithm based on random geometry. Given an optimal distance factor, the link hop count obtained by the method of this invention can still reach the theoretical minimum hop count. However, because the method of this invention adds a step of finding the node with the maximum value function in each hop process, the time complexity is greater than that of the low-latency routing algorithm based on random geometry.

[0132] Figure 7This demonstrates that changes in the distance factor can affect the total number of hops and the final delay of the link. From formula (14), it can be seen that as the distance factor gradually increases, f... vector The increased weight of the distance factor leads to a greater emphasis on single-hop propagation distance when selecting the next-hop relay satellite, thus reducing the number of relays in the entire transmission link and causing the average number of hops to gradually converge towards the minimum number of hops. It is evident that under a network load environment with an average queuing delay of 25ms, the minimum delay occurs just as the number of hops converges to the minimum. If the distance factor continues to increase after the total number of hops in the link has converged, it will cause f... queue The weight of the relay satellite is reduced, and the queuing delay on the node is no longer considered during the selection of relay satellites. However, the number of hops has already reached the theoretical minimum number of hops, which will lead to an increase in the actual total delay.

[0133] according to Figure 8 This paper illustrates the trend of the optimal distance factor as a function of the average queuing delay when the average queuing delay in the network is below 40ms. It shows that as the network load increases, reflected in an increase in average queuing delay, the optimal distance factor increases with the increase in average queuing delay, but the slope gradually decreases. This is because the ratio of the distance factor to the queuing delay factor does not change linearly as the distance factor increases; the derivative of α / β increases with the increase of α. Therefore, for the same increase in α, a larger α value will have a greater impact on the value of α / β.

[0134] In practical applications, the optimal distance factor value can be selected based on the fitting curve of the optimal distance factor and the average queuing delay, according to the current state of the network, so as to achieve the effect of adaptive average queuing delay.

[0135] Figure 9 and Figure 10 This study demonstrates that, compared to cross-track link routing algorithms that do not consider queuing delay, the method of this invention significantly reduces queuing delay in the link while sacrificing minimal propagation delay, ultimately reducing the total link delay. Furthermore, the reduction in queuing delay becomes more pronounced as the average queuing delay in the network increases, while the propagation delay remains relatively stable without an upward trend.

[0136] Figure 11 The total transmission path delay obtained by the method of this invention is compared with the total delay of the cross-rail link routing algorithm that does not consider queuing delay. Figure 8 and Figure 9 The results are consistent. The main way the method of this invention reduces latency is by reducing queuing latency in the link. The additional queuing latency is about 1-2ms, which has a negligible impact on the total latency. Therefore, the total latency comparison chart basically inherits the trend of the queuing latency comparison chart. The latency advantage of the method of this invention increases as the average queuing latency increases.

[0137] As the number of satellite launches increases, satellites become denser, resulting in more satellites in ideal locations to choose from. In this case, satellites with lower queuing delays can be selected. Since queuing delays on satellites are random, blindly selecting the node with the longest single-hop transmission distance as a relay satellite is inappropriate. The average queuing delay of the selected node should be close to the expected queuing delay E(D) of all nodes in the current network. queue Consistent. (By) Figure 12 As can be seen, the shortest latency calculated by the method of this invention gradually decreases as the number of satellites increases, so it can adapt well to the situation of increasing number of satellite nodes and demonstrates good robustness.

[0138] The above description discloses only one preferred embodiment of the present invention, and should not be construed as limiting the scope of the present invention. Those skilled in the art will understand that all or part of the processes of the above embodiments can be implemented, and equivalent changes made in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. A method for large-scale low earth orbit satellite dynamic routing with adaptive network queuing latency, characterized in that, The method comprises the following steps: Step 1: selecting a constellation network model meeting satellite position coordinates and link time delay; Step 2: determining coordinate positions of a sending site and a receiving site; Step 3: obtaining a communicable satellite, and performing communicable range detection with the current coordinates as a center and the maximum communication distance as a radius; Step 4: discarding a relay satellite with a distance greater than the current coordinates from the receiving coordinates; Step 5: Calculate the minimum hop number K from the current coordinate to the receiving location min Calculate the communication value function for the remaining communicable satellites; The value function of the next hop of the dynamic load satellite network is defined as follows: f(S pm,nm ,S p(m+1),n(m+1) ,t) = a - f vector + β - f queue f vector = cos Δp · (d(S pm,nm , Des, t) - d(S p(m+1),n(m+1) , Des, t)) / c f queue = (E[D queue ]- D queue ) where f vector reflects the distance of the current hop propagation, Δp represents the low-orbit satellite S p(m+1),n(m+1) phase difference with the ideal relay satellite position of the current hop, d(S pm,nm , Des, t) is the distance from the current satellite S pm,nm to the receiving location Des at time t, d(S p(m+1),n(m+1) , Des, t) is the distance from the next-hop candidate satellite S p(m+1),n(m+1) to the receiving location Des at time t, c is the propagation speed of light in a vacuum, f queue reflects the size relationship between the queuing delay of the current satellite and the expected value, E[D queue ] is the expectation of the queuing delay, reflects the network load state, α is the distance factor, β is the queuing factor, and α ∈ [0, 1], β ∈ [0, 1], and α + β = 1. Step 6: selecting a relay satellite with the maximum communication value as the next hop relay satellite; Step 7: if the next hop satellite cannot directly communicate with the receiving station, the coordinates of the next hop relay satellite are changed into the sending site, and steps 2 to 6 are repeatedly executed.

2. The large-scale low-orbit satellite dynamic routing method of adaptive network queuing delay according to claim 1, wherein the satellite position coordinates in step 1 meet the following quantity relationship:

3. The large-scale low-orbit satellite dynamic routing method of adaptive network queuing delay according to claim 2, wherein the satellite position coordinates in step 1 meet the following quantity relationship: Wherein, a circular polar orbit constellation (N, P, h (p,n) ) is adopted as the constellation configuration, N is the number of satellites on a single orbit plane; P is the number of orbit planes; p is the orbit plane number sequence of a positive integer, and p∈[1, P]; n represents the nth low-orbit satellite in a single orbit plane, and n∈[1, N]; T is the period of the low-orbit satellite, t is an arbitrary moment of the satellite; C (p,n,t) is the position parameter matrix of the low-orbit satellite at an arbitrary moment t, the x-axis is the direction of the vernal equinox point from the center of the earth O, the z-axis is perpendicular to the equatorial plane and points to the north pole; the y-axis is perpendicular to the plane formed by the x-axis and the z-axis and forms a left-handed rectangular coordinate system with the x-axis and the z-axis, and the default satellite S 0,0 initial position is on the positive half of the x-axis; The satellite is located at point H, the origin O coincides with the center of the earth, the satellite runs at a height h from the center of the earth, and h = |OH|; point H' is the projection of point H on the xOy plane, and HH' is perpendicular to the xOy plane; angle θ z is the angle between line segment OH and the xOy plane; angle θ x is the angle between line segment OH' and the x axis; ω e is the angular velocity of the satellite relative to the rotation of the earth, and U(t) is a step function. Wherein p1, n1, p2, n2 represent the orbit plane number and the satellite number in the orbit plane of satellites S1 and S2 respectively. In step 1, S p1,n1 Send data to S p2,n2 The total delay of a single-hop link consists of three parts: propagation delay D pro Transmission delay D t And queuing delay D queue The expression is as follows: Delay(S p1,n1 ,S p2,n2 ) = D pro (S p1,n1 ,S p2,n2 ) + D t + D queue 4. The large-scale low-orbit satellite dynamic routing method of adaptive network queuing delay according to claim 3, wherein when the satellite communicates under the condition of the maximum communication distance in step 3, the following quantity relationship exists:

5. The large-scale low-orbit satellite dynamic routing method of adaptive network queuing delay according to claim 1, wherein when the specific values of optimal α and β are calculated, α ∈ [0, 1] is traversed, and the corresponding α value under the minimum time delay is found. ​ θ max represents the maximum angular span of the circumference of a circle with radius h and center O when the satellite communication distance is min(d max ,d' max ). max d is the maximum communication range of the satellite itself, and d' is the maximum satellite communication range without being blocked by the ground. max d is the maximum communication range of the satellite itself, and d' is the maximum satellite communication range without being blocked by the ground. ​ ​

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