Time-varying channel modeling method oriented to low-orbit satellite scene

Abstract

The invention relates to a time-varying channel modeling method oriented to a low-orbit satellite scene, and belongs to the technical field of low-orbit satellite communication. In order to solve the problem that low-orbit satellite channel modeling accurately represents time-varying characteristics, a propagation model is provided, and a channel is fitted into a process that a signal reaches a receiving end from a satellite terminal through multiple times of cluster bounce; a parameter drift mechanism is introduced to simulate the trend of channel parameters changing along with time, natural transition and connection between newly generated parameters and parameters in the previous stage are guaranteed, and the continuity of channel parameter change is improved; the dynamic process of generation, development and extinction of the cluster is considered from the perspective of the channel cluster through the birth and death process of the dynamic change of the cluster, and the dynamic change of the cluster in the channel is reflected more truly. On the basis of the method, the characterization capability of a channel simulation framework on the time-varying characteristic can be remarkably enhanced, and a more reliable and more accurate channel model is provided for a low-orbit satellite scene.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of low-orbit satellite communication and relates to a time-varying channel modeling method for a low-orbit satellite scenario. BACKGROUND

[0002] With the gradual popularization of 5G networks, future 6G networks have attracted widespread attention, and satellite networks are one of the key directions of 6G wireless network research. Compared with 5G ground network communication, low-orbit satellite networks (LEO) have the advantages of wide coverage and large communication capacity, but also face technical challenges such as large time delay, multipath effect and complex signal attenuation. These factors directly affect the performance and reliability of the communication system. As the basis of information transmission, the characteristics of the wireless channel have a decisive influence on the overall performance of the communication system. Therefore, in-depth research on the wireless channel characteristics in the low-orbit satellite communication scenario and the construction of a high-precision dynamic channel model are prerequisite conditions for deploying an efficient wireless communication system. At present, the Non-Terrestrial Network (NTN) wideband channel model defined in the 3GPP TR38.811 standard provides a basic simulation framework for low-orbit satellite communication systems, but the model uses fixed channel parameters at different elevation angles and does not consider the continuous change of channel parameters with time caused by satellite motion, lacking a dynamic evolution mechanism, which leads to large jumps in channel parameters (such as time delay, angle, etc.) at adjacent locations, affecting the applicability of the model in dynamic scenarios. In the prior art, a low-orbit satellite propagation channel modeling method based on the RRT algorithm and the Corazza model constructs a channel model by generating the optimal signal propagation path and simulating the propagation characteristics of the direct path and multipath components, but this method still uses static parameters for modeling and does not consider the dynamic changes of the channel cluster, and the precision is insufficient in high-speed dynamic scenarios, making it difficult to meet the modeling needs of 6G networks in complex dynamic scenarios. Therefore, there is an urgent need for a channel model that can accurately represent the dynamic change characteristics of low-orbit satellite channels, support cluster dynamic modeling and have high precision and generalization ability, in order to improve the applicability of low-orbit satellite communication systems in complex dynamic scenarios. SUMMARY

[0003] Therefore, the purpose of the present application is to provide a time-varying channel modeling method for a low-orbit satellite scenario, which, under the same elevation angle range, calculates the channel parameters at the next time point based on the channel parameters at the initial time point through the method of parameter drift, and uses the Multi-Path Component Distance (MCD) as the method of replacing the old cluster with a new cluster to solve the continuous change of the parameters of the cluster between different elevation angle ranges.

[0004] To achieve the above purpose, the present application provides the following technical solutions:

[0005] A time-varying channel modeling method for a low-orbit satellite scenario, the method comprising:

[0006] S1, establishing a propagation scenario, initializing network parameters, and generating large-scale parameters at the same time;

[0007] S2, fitting the channel into a propagation model in which a signal bounces through multiple clusters from a satellite terminal to a receiving end; generating corresponding small-scale parameters at an initial time, updating the cluster set at each subsequent time through parameter drift or cluster birth-death process, and then generating small-scale parameters at the current time;

[0008] S3, generating channel coefficients based on the small-scale parameters generated at each time, and establishing a channel model;

[0009] In step S2, at each subsequent time, the cluster set is updated according to the following steps:

[0010] S21, determining whether the elevation angle range has changed, if not, going to step S22; if changed, going to step S23;

[0011] S22, updating the cluster set through a parameter drift process; wherein the parameter drift updates the cluster set by dynamically adjusting the properties of the clusters;

[0012] S23, updating the cluster set through a birth-death process; wherein the birth-death process of the clusters measures the similarity between new clusters and old clusters by minimizing the distance of multipath components, and uses the new cluster with the highest similarity to replace the old cluster, gradually updating the cluster set.

[0013] Further, in the process of the network scenario established in S1, the following steps are included:

[0014] Defining the network layout, including satellite orbit parameters and mobile speed of user equipment (UE); and configuring antenna parameters, including antenna type and polarization mode;

[0015] Allocating propagation conditions, including line of sight (LOS) and non-line of sight (NLOS);

[0016] Calculating path loss, including free space loss (FSPL), atmospheric attenuation, and rain attenuation;

[0017] Generating relevant large-scale parameters, including delay spread (DS), Rician factor (K), shadow fading (SF), and angle spread, including angle of arrival spread (ASA), zenith angle of arrival spread (ZSA), angle of departure spread (ASD), and zenith angle of departure spread (ZSD).

[0018] Further, in S2, the process of generating small-scale parameters at the initial time is as follows:

[0019] A cluster delay is generated, and a delay sequence of a multipath cluster is generated based on the delay spread DS:

[0020] τ′ n = -r τ DS·ln(X n )

[0021] τ n = sort(τ′ n -min(τ′ n ))

[0022] where r τ denotes the correlation coefficient of the delay distribution, DS denotes the delay spread, X n denotes a discrete uniform distribution subject to {-1, 1}, sort(·) is a descending order sorting, τ′ n and τ n are the delay and the normalized delay, respectively;

[0023] Based on an exponential decay model, the power of each cluster is generated according to the cluster delay:

[0024]

[0025] where Z n ~N(0, ξ 2 ) is a shadow term of each cluster, with a unit of [dB];

[0026] A multipath departure angle and an arrival angle are generated, and combined with the line-of-sight angle of the satellite and the UE, a Gaussian or Laplace distribution angle AOA, EOA, AOD, EOD is generated based on the angle spread, AOA, EOA, AOD, EOD are the arrival azimuth angle, the arrival elevation angle, the transmission azimuth angle, and the transmission elevation angle, respectively;

[0027] Then, the path power ratio of vertical polarization and horizontal polarization is generated.

[0028] Further, in S22, the attributes of the cluster of parameter drift update include power, delay, and arrival angle; at a subsequent moment, in the case where the elevation angle range does not change, the process of parameter drift is:

[0029] The propagation distance d n,t0 of the nth cluster is calculated according to the delay τ n at the initial moment t0 as:

[0030] d n,t0 = τ n ·c+|d los,t0 |

[0031] where c is the speed of light; d los,t0 is a vector from the transmitting end to the receiving end, which is expressed as:

[0032] d los,t0 = RX t0 -TX t0

[0033] where RX t0 and TX t0 denote the position vectors of the receiver and the transmitter at the initial time t0, respectively;

[0034] In the nth cluster, the direction vector from the receiver to the center of the last scatterer LS n and the direction vector from the transmitter to the center of the first scatterer FS n are denoted as a and b n , respectively.

[0035]

[0036] where a n,t0 is the vector from the receiver to the center of LS n , and b n,t0 is the vector from the transmitter to the center of FS n .

[0037] Let the vector from the center of the first scatterer to the center of the last scatterer be c n,t0 , then:

[0038] |c n,t0 | = d n,t0 - |a n,t0 | - |b n,t0 |

[0039] Then, by fixing the additional degree of freedom by minimizing |c n,t0 |, where an extra minimum distance d min between the phase center of the antenna array and the nearest scatterer is introduced, |a n,t0 | and |b n,t0 | are calculated by solving the following optimization problem:

[0040]

[0041] |a n,t0 | ≥ d min

[0042] |b n,t0 | ≥ d min

[0043] A triangle is formed by the transmitter, the first scatterer, and the last scatterer, and the initial value of |a n,t0 | is set to d min , and the first scatterer FSn and the last scatterer LS n position vector.

[0044] Further, in the process of solving the position vector of the first scatterer FS n and the last scatterer LS n from the transmitting end, the vector d n of the pointing LS T,L,n is expressed as:

[0045] d T,L,n = d los,t0 + a n,t0

[0046] |a n,t0 |+|c n,t0 |the path length d′ n is expressed as:

[0047] d′ n = d n,t0 -|a n,t0 |

[0048] Then the path length |b n |from the transmitting end to the first scatterer FS n,t0 is expressed as:

[0049]

[0050] Through cyclic iteration, the size of |a n,t0 |is dynamically adjusted to optimize the intermediate path |c n,t0 |with the minimum value as the goal;

[0051] After the cyclic iteration optimization is completed, the position vectors of FS n and LS m are determined as:

[0052]

[0053] Then the updated propagation distance at the next time t1 is:

[0054] d m,t1 =|TX t1 -FS n |+|FS n -LS n |+|LS n -RX t1 |

[0055] The updated propagation delay at the next time t1 is:

[0056] τ n,t1 =d n,t1 / c

[0057] The updated cluster is thus calculated to point from the transmitter to the first scatterer by vector b n,t1 and from the receiver to the last scatterer by vector a n,t1 is:

[0058] b n,t1 = FS n -TX t1

[0059] a n,t1 = LS n -RX t1

[0060] The updated transmit and arrival angles are:

[0061]

[0062] The updated cluster power is:

[0063]

[0064] The cluster set updated by parameter drift is thus obtained.

[0065] Further, the loop iteration optimization process in S22 is:

[0066] S221, initialize step size: according to the maximum path length d max calculate the initial value of step size step_size; set the value of d max to take;

[0067] S222, iteratively update the length of |a n,t0 | in each iteration, |a n,t0 | is increased by a step size;

[0068] S223, if |a n,t0 | is less than the minimum distance d min , adjust it to d min , and shorten the value of step size step_size; if |a n,t0 | is greater than the maximum distance d max , adjust it to d max , and reverse the value of step size step_size;

[0069] S224, then recalculate the lengths of |b n,t0 | and |c n,t0 | according to the corresponding formula;

[0070] S225, check the current |c n,t0whether the length is reduced, if smaller than before, receiving the current solution, otherwise reversing the step direction, and reducing the step; and updating |a n,t0 |, |b n,t0 | and |c n,t0 | value;

[0071] S226, stop iteration until the step is smaller than a threshold or reaches a maximum iteration number.

[0072] Further, in S23, the birth and death process of the cluster is:

[0073] S231, for each pair (i, j) of new clusters and old clusters, calculate the value of MCD: S[i, j] = MCD ij :

[0074] Minimizing the multipath component distance includes time delay distance and angle distance, wherein,

[0075] Time delay distance:

[0076]

[0077] In the formula, τ i , τ j is the time delay of the new cluster and the old cluster, Δτ max is the maximum value of all time delay differences, and ζ is a delay scaling factor for adjusting the weight in the total distance of the time delay;

[0078] Angle distance:

[0079]

[0080] In the formula, (x, y, z) is the spherical coordinate expression of the angle;

[0081] Total distance expression:

[0082] MCD ij = ω τ ·MCD τ,ij + ω AOA / AOD ·MCD AOA / AOD,ij

[0083] Wherein, ω τ and ω AOA / AOD are weight coefficients;

[0084] S232, select the minimum S[i, j] row by row, replace the old cluster j with the new cluster i step by step, and remove the i row and j column of S to obtain a new S;

[0085] S233, continue to select the minimum S[i, j] row by row, replace the old cluster j with the new cluster i step by step, and the remaining new cluster or old cluster uses the gradually generated or extinct method to smooth the transition;

[0086] In the replacement process, the power of the new cluster and the old cluster is adjusted step by step:

[0087]

[0088] Thus, the cluster set updated by the birth and death process is obtained.

[0089] Further, in S3, the process of generating a channel coefficient based on the small-scale parameter generated at each time to establish a channel model includes:

[0090] Generating a random initial phase, assigning a random initial phase to each multipath component;

[0091] Mapping the delay, angle, polarization and phase parameters into the channel matrix;

[0092] Adding path loss and shadow fading, superimposing the path loss and shadow fading into the channel matrix.

[0093] On the other hand, a computer readable storage medium is also proposed, and the storage medium stores a computer program, when the computer program is executed by a processor, a time-varying channel modeling method for a low-orbit satellite scenario is realized.

[0094] On the other hand, a computer program product is also proposed, characterized by comprising a computer program, which is executed by a processor to realize a time-varying channel modeling method for a low-orbit satellite scenario as described above.

[0095] The beneficial effects of the present application are:

[0096] The present application introduces parameter drift and the birth and death process of clusters to generate parameters of new clusters. The parameter drift mechanism can simulate the trend of slow and continuous change of channel parameters over time. It is not a sudden change of parameters, but by setting reasonable drift rules and rates, there is a natural transition and connection between the newly generated parameters and the parameters of the previous stage, thereby ensuring the continuity of the change of channel parameters.

[0097] The birth and death process of clusters considers the dynamic process of the generation, development and disappearance of clusters in the channel from the perspective of channel clusters. In the process of channel change, new clusters will be born with the change of the environment, and old clusters may disappear due to the change of signal propagation conditions. By accurately modeling the birth and death process of clusters, the dynamic change of clusters in the channel can be more realistically reflected, and then the new cluster parameters conforming to the actual situation are generated.

[0098] Through the synergistic effect of parameter drift and the birth and death process of clusters, the present application can significantly enhance the representation ability of the channel simulation framework to the time-varying characteristics, and provide a more reliable and more accurate channel model for the low-orbit satellite scenario.

[0099] Additional advantages, objects, and features of the application will be apparent to those skilled in the art upon examination of the following specification. It is intended that the application not be limited by any of the details of the specification. Instead, the true scope of the application is to be determined by the following claims. BRIEF DESCRIPTION OF DRAWINGS

[0100] In order to make the objects, technical solutions and advantages of the present application clearer, the preferred embodiments of the present application will be described in detail below with reference to the drawings, in which:

[0101] Figure 1 is the establishment process of the conventional 3GPP model;

[0102] Figure 2 is a cluster update process diagram after the introduction of parameter drift and the birth and death process of the cluster under the embodiment of the present application;

[0103] Figure 3 is a propagation model diagram of the bounce of the multiple clusters of the signal under the fitting of the embodiment of the present application;

[0104] Figure 4 is a cyclic iteration optimization process diagram in the parameter drift process under the embodiment of the present application. DETAILED DESCRIPTION

[0105] The embodiments of the present application will be described below through specific concrete examples, and those skilled in the art can easily understand other advantages and effects of the present application from the content disclosed in the specification. The present application can also be implemented or applied through other different specific embodiments, and each detail in the specification can be modified or changed based on different views and applications without departing from the spirit of the present application. It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present application in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0106] The drawings are only used for exemplary illustration, and the representation is only a schematic diagram, not a physical diagram, and cannot be understood as a limitation on the present application; in order to better illustrate the embodiments of the present application, some components in the drawings will be omitted, enlarged or reduced, and do not represent the size of the actual product; it can be understood by those skilled in the art that some known structures and their descriptions in the drawings can be omitted.

[0107] The same or similar reference numerals in the drawings of the embodiments of the present application correspond to the same or similar components; in the description of the present application, it is understood that if the orientations or positional relationships indicated by the terms "upper", "lower", "left", "right", "front", "back" and the like are based on the orientations or positional relationships shown in the drawings, they are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, therefore the terms describing the positional relationship in the drawings are only used for exemplary illustration, and cannot be understood as a limitation on the present application. For those skilled in the art, the specific meanings of the above terms can be understood according to the specific circumstances.

[0108] Please refer to Figures 1-4 , a time-varying channel modeling method for a low earth orbit satellite scenario.

[0109] In the traditional channel standard simulation architecture, the non-terrestrial network (NTN) channel model constructed according to the 3GPP TR 38.811 standard adopts a discretization cycle ("drop") architecture design in the simulation process. In each simulation cycle, the geometric position relationship between the low earth orbit satellite (LEO) and the user equipment (UE) is determined, and static channel parameters (including path loss, multipath delay, angle spread, etc.) are generated, and then the channel impulse response (CIR) is synthesized. When switching to a new simulation cycle, the system will reinitialize the random seed to generate new channel parameter geometry in a completely independent manner, such as Figure 1 In the 3GPP model part shown in, the whole includes the following steps:

[0110] (1) Generate general parameters (steps 1-4):

[0111] Step 1: Define the network layout, including satellite orbit parameters, user equipment UE moving speed. Configure antenna parameters (antenna type, polarization mode, etc.).

[0112] Step 2: Assign propagation conditions (Line of Sight, LOS or Non Line of Sight, NLOS).

[0113] Step 3: Calculate the path loss, including free space loss (FSPL), atmospheric attenuation, rain attenuation, etc.

[0114] Step 4: Generate relevant large-scale parameters, including delay spread (DS), angle spread (ASA, ZSA, ASD, ZSD), Rician factor (K) and shadow fading (SF).

[0115] (2) Generate small-scale parameters (steps 5-8):

[0116] Step 5: Generate cluster delay, generate a time delay sequence of multipath clusters based on DS

[0117] τ' n = -r τ DS·ln(X n ) (1)

[0118] τ n = sort(τ' n -min(τ' n )) (2)

[0119] In the formula, r τ indicates the correlation coefficient of the delay distribution, DS indicates the delay spread, X n indicates a discrete uniform distribution subject to {-1, 1}, sort(·) is descending order sorting, τ' n and τ n are delay and normalized delay respectively.

[0120] Step 6: Generate cluster power, generate the power of each cluster according to the cluster delay based on the exponential decay model.

[0121]

[0122] Step 7: Generate multi-path departure angle and arrival angle, combine the line-of-sight angle of the satellite and the UE, and generate the angle AOA, EOA, AOD, EOD of Gaussian or Laplace distribution based on angle spread. AOA, EOA, AOD, EOD are the arrival azimuth angle, the arrival elevation angle, the transmission azimuth angle and the transmission elevation angle respectively.

[0123] Step 8: Generate the path power ratio of vertical polarization and horizontal polarization.

[0124] (3) Generate channel coefficients (steps 9-11):

[0125] Step 9: Generate random initial phase, assign a random initial phase to each multi-path component.

[0126] Step 10: Map the delay, angle, polarization and phase parameters into the channel matrix.

[0127] Step 11: Add path loss and shadow fading, superimpose the path loss and shadow fading into the channel matrix.

[0128] However, since the new and old parameters are completely independent between each drop, the channel does not change continuously with the movement of the satellite, so it is difficult for the standard channel simulation framework to represent the time-varying characteristics of the channel. In order to solve this problem, the parameter drift and the birth-death process of the cluster introduced in the application are used to generate the parameters of the new cluster to ensure the continuity of the channel change.

[0129] In this embodiment, a time-varying channel modeling method for low-orbit satellite scenarios is described in detail, which improvesFigure 1 The generation of the channel in the standard channel simulation framework shown, the introduction of parameter drift and the birth and death process of the cluster to generate the next time delay, angle and power parameters, as shown in Figure 2 , wherein,

[0130] Parameter drift refers to: in the satellite channel model, because the satellite time is in motion, in order to ensure the continuity of the change of the channel, the newly generated channel parameters should maintain continuity with the channel parameters at the last time. As shown in Figure 3 The present application fits the channel into the propagation model of the signal from the satellite terminal through multiple clusters of bouncing to the receiving end. At the initial time t0, the position vectors of the receiving end and the transmitting end are RX t0 and TX t0 . The departure elevation angle and azimuth angle of the nth cluster from the transmitting end to the center of the first scatterer FS n are θ n,ZOD and φ n,AOD , the elevation angle and azimuth angle of the cluster from the center of the last scatterer LS n to the receiving end are θ n,ZOA and φ n,AOA , v sat (t0) and v(t0) represent the velocity vectors of the satellite terminal and the ground terminal at time t, d los,t0 represents the distance vector from the satellite terminal to the ground terminal at t0. τ n represents the time delay of the nth cluster.

[0131] First, according to the time delay at the initial time t0, the propagation distance d n,t0 of the nth cluster can be calculated as:

[0132] d n,t0 = τ n · c + |d los,t0 | (4)

[0133] Where c is the speed of light, d los,t0 is the vector from the transmitting end to the receiving end, and is represented as:

[0134] d los,t0 = RX t0 -TX t0 (5)

[0135] In the nth cluster, the direction vector from the receiving end to the center of the last scatterer LS n is , and the direction vector from the transmitting end to the center of the first scatterer FS n is , which are represented by equations (6) and (7) as follows:

[0136]

[0137] Where a n,t0 Pointing the receiver to LS n The center vector, h n,t0 The sender points to FS n The vector at the center, the vector from the center of the first scatterer to the center of the last scatterer, is c. n,t0 So c n,t0 Should meet:

[0138] |c n,t0 |=d n,t0 -|a n,t0 |-|b n,t0 | (8)

[0139] Due to vector a n,t0 and b n,t0 The modulus is unknown, causing c n,t0 Given an indeterminate vector, one approach to fixing the additional degrees of freedom is to minimize |c|. n,t0 To obtain more realistic results, an additional minimum distance d between the phase center of the antenna array and the nearest scatterer is introduced. min The calculation of |a| is achieved by solving the following optimization problem. n,t0 | and |b n,t0 |

[0140]

[0141] |a n,t0 |≥d min (10)

[0142] |b n,t0 |≥d min (11)

[0143] |a n,t0 | is initially set to d min Using trigonometric functions, we solve for the triangle formed by the emitter, the first scatterer, and the last scatterer, and then solve for the first scatterer FS. n and the last scatterer LS n The position vector. Where, from the transmitter, LS... n vector d T,L,n Represented as:

[0144] d T,L,n =d los,t0 +a n,t0 (12)

[0145] |a n,t0 |+|c n,t0 | Path length d′ nmay be expressed as:

[0146] d' n = d n,t0 - |a n,t0 | (13) Then the path length |b n | from the transmitter to the first scatterer Fs n,t0 may be expressed as:

[0147]

[0148] And through the iterative loop, the size of |a n,t0 | is dynamically adjusted so that the intermediate path |c n,t0 | is as small as possible.

[0149] The specific iteration process is shown in Figure 4 , and the process is as follows:

[0150] (1) Initialize the step size: calculate the initial value of the step size step_size according to the maximum path length d max to speed up the convergence. Set the value of d max to be 0.5·(d n,t0 + |d los,t0 |).

[0151] (2) Iteratively update the length of |a n,t0 |, and increase the length of |a n,t0 | by a step size in each iteration.

[0152] (3) If |a n,t0 | is less than the minimum distance d min , adjust it to d min , and shorten the value of the step size step_size. If |a n,t0 | is greater than the maximum distance d max , adjust it to d max , and shorten the value of the step size step_size in the opposite direction.

[0153] (4) Then recalculate the lengths of |b n,t0 | and |c n,t0 | according to equations (8) and (12)-(14)

[0154] (5) Check if the current length of |c n,t0 | is smaller than before. If it is, accept the current solution, otherwise reverse the step size direction and reduce the step size. Update the values of |a n,t0 |, |b n,t0 | and |c n,t0 |.

[0155] (6) stop the iteration until the step size is smaller than a threshold or the maximum number of iterations is reached.

[0156] At this time, the position vector of FS n and LS n can be determined as:

[0157]

[0158] The position vectors of the receiver and the transmitter at the next time are updated as:

[0159] RX t1 = RX t0 + v(t0)·Δt (17)

[0160] TX t1 = TX t0 + v sat (t0)·Δt (18)

[0161] The propagation distance at the next time is updated as:

[0162] d n,t1 = |TX t1 - FS n | + |FS n - LS n | + |LS n - RX t1 | (19)

[0163] The propagation delay is updated as:

[0164] τ n,t1 = d n,t1 / c (20)

[0165] From (15)-(17), the vector b n,t1 pointing from the transmitter to the first scatterer and the vector a n,t1 pointing from the receiver to the last scatterer after the cluster update are:

[0166] b n,t1 = FS n - TX t1 (21)

[0167] a n,t1 = LS n - RX t1 (22)

[0168] The transmit angle and the arrival angle are updated from (21)-(19) as:

[0169]

[0170] Update the power of the cluster by substituting formula (20) into (3):

[0171]

[0172] In this embodiment, the pseudo code of the parameter drift algorithm is also given, as shown in Table 1 below:

[0173] Table 1

[0174]

[0175]

[0176] For the birth and death process of the cluster, the birth and death process of the cluster will appear between different elevation ranges. The present application introduces the minimum multipath component distance (MCD) to realize the birth and death process of the cluster, so as to maintain the continuous change of the channel between different elevation ranges.

[0177] MCD is a measurement method for measuring the distance between multipath components (MPCs), which can consider multi-dimensional characteristics such as time delay and angle, and realize unified measurement between different physical quantities through normalization processing. MCD is used to measure the similarity between new clusters and old clusters, and the smaller it is, the more similar the two clusters are. MCD includes the following two parts:

[0178] Time delay distance:

[0179]

[0180] Where τ i , τ j is the time delay of the new cluster and the old cluster, Δτ max is the maximum value of all time delay differences, and ζ is a delay scaling factor for adjusting the weight of the time delay in the total distance.

[0181] Angle distance:

[0182]

[0183] Where (x, y, z) is the spherical coordinate expression of the angle.

[0184] Total distance expression:

[0185] MCD ij = ω τ ·MCD τ,ij + ω AOA / AOD ·MCD AOA / AOD,ij (30)

[0186] Where ω τ and ω AOA / AOD are weight coefficients.

[0187] First, according to the above steps, the value of MCD is calculated for each pair (i, j) of new clusters and old clusters: S[i, j] = MCD ij S[i, j] represents the similarity between the new cluster i and the old cluster j.

[0188] The smallest S[i, j] is selected row by row, and the new cluster i is gradually replaced by the old cluster j. The i row and j column of S are removed to obtain a new S, and the smallest S[i, j] is selected row by row, and the new cluster i is gradually replaced by the old cluster j. The remaining new clusters or old clusters are gradually generated or eliminated to smooth the transition.

[0189] During the replacement process, the power of the new cluster and the old cluster is gradually adjusted:

[0190]

[0191] Pnew,it is the power of the i-th new cluster at time t, Pold,j is the initial power of the i-th old cluster. x is a normalized time variable, ranging from 0, 1.

[0192] In this embodiment, the pseudo code of the birth and death process of the cluster is also given, as shown in Table 2:

[0193] Table 2

[0194]

[0195] In more detail, the pseudo code of the above overall process is also given in this embodiment, as shown in Table 3:

[0196] Table 3

[0197]

[0198] In the pseudo code of the above Table 3, the overall process is as follows:

[0199] S1, establish the propagation scenario, initialize the network parameters, and generate the large-scale parameters at the same time;

[0200] S2, fit the channel into a propagation model in which the signal from the satellite terminal reaches the receiving end through multiple clusters of bouncing; generate the corresponding small-scale parameters at the initial moment, and update the cluster set through parameter drift or cluster birth and death process at each subsequent moment, and then generate the small-scale parameters at the current moment;

[0201] S3, generate the channel coefficient based on the small-scale parameters generated at each moment, and establish the channel model.

[0202] In step S2, at each subsequent time instant, it is determined whether the elevation angle range has changed, and if not, the cluster set is updated through a parameter drift process, and if so, the cluster set is updated through a birth-death process; wherein the parameter drift is implemented by dynamically adjusting the properties of the clusters (such as power, delay, angle of arrival, etc.) and updating the cluster set, and the birth-death process of the clusters is implemented by minimizing the distance between the multi-path components to measure the similarity between the new clusters and the old clusters, and the new cluster with the highest similarity is used to replace the old cluster, and the cluster set is gradually updated.

[0203] In the field of wireless communication, channel simulation framework is a key tool for evaluating and optimizing the performance of communication systems. However, the standard channel simulation framework has significant limitations in characterizing the time-varying characteristics of the channel. The traditional framework is often based on fixed parameter settings or simple statistical models, and there is a lack of effective correlation mechanism between new and old parameters in each independent simulation drop. This makes the channel characteristics unable to present continuous and smooth changes over time, environment and movement of communication terminals (such as satellites, etc.), making it difficult to accurately simulate the complex scenarios of dynamic changes of channels in the real world, and thus affecting the accuracy of communication system design and performance evaluation.

[0204] In order to effectively overcome this problem, the present application innovatively introduces parameter drift and birth-death process of clusters to generate parameters of new clusters. The parameter drift mechanism can simulate the trend of slow and continuous change of channel parameters over time. It is not a sudden change of parameters, but by setting reasonable drift rules and rates, there is a natural transition and connection between the newly generated parameters and the parameters of the previous stage, thereby ensuring the continuity of the change of channel parameters.

[0205] The birth-death process of clusters considers the dynamic process of the generation, development and disappearance of clusters in the channel from the perspective of channel clusters. In the process of channel change, new clusters will be born with the change of environment, and old clusters may disappear due to the change of signal propagation conditions. By accurately modeling the birth-death process of clusters, the dynamic changes of clusters in the channel can be more realistically reflected, and thus the actual new cluster parameters can be generated.

[0206] Through the synergistic effect of parameter drift and birth-death process of clusters, the present application can significantly enhance the characterization ability of the channel simulation framework for time-varying characteristics, and provide a more reliable and accurate channel model for low-orbit satellite scenarios.

[0207] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced by equivalents without departing from the spirit and scope of the present application, and all should be covered in the scope of the claims of the present application.

Claims

1. A time-varying channel modeling method for low-Earth orbit satellite scenarios, characterized in that: The method includes: S1. Establish the propagation scenario, initialize network parameters, and generate large-scale parameters simultaneously; S2. Fit the channel into a propagation model of the signal from the satellite terminal through multiple cluster bounces to the receiver; generate the corresponding small-scale parameters at the initial moment, update the cluster set at each subsequent moment through parameter drift or the process of cluster birth and death, and then generate the small-scale parameters at the current moment. S3. Generate channel coefficients based on the small-scale parameters generated at each time step, and establish a channel model; In step S2, at each subsequent time point, the cluster set is updated according to the following steps: S21. Determine if the elevation angle range has changed. If it has not changed, proceed to step S22; if it has changed, proceed to step S23. S22. Update the cluster set through a parameter drift process; wherein, parameter drift updates the cluster set by dynamically adjusting the cluster attributes. S23. The cluster set is updated through the birth and death process; wherein, the birth and death process of the cluster measures the similarity between the new cluster and the old cluster by minimizing the multipath component distance, and uses the new cluster with the highest similarity to replace the old cluster, and the cluster set is updated step by step.

2. The time-varying channel modeling method for low-Earth orbit satellite scenarios according to claim 1, characterized in that: The process of establishing a network scenario in S1 includes the following steps: Define the network layout, including satellite orbit parameters and user equipment (UE) movement speed; and configure antenna parameters, including antenna type and polarization. Assign propagation conditions, including line-of-sight (LOS) and non-line-of-sight (NLOS); Calculate path loss, including free space loss (FSPL), atmospheric attenuation, and precipitation attenuation; Generate relevant large-scale parameters, including delay spread DS, Rice factor K, shadowing fading SF, and angle spread, including arrival azimuth spread ASA, arrival elevation spread ZSA, launch azimuth spread ASD, and launch elevation spread ZSD.

3. The time-varying channel modeling method for low-Earth orbit satellite scenarios according to claim 1, characterized in that: In S2, the process of generating small-scale parameters at the initial time is as follows: Generate cluster delays, and generate multipath cluster delay sequences based on delay spread DS: the n =-r τ DS·ln(X n ) t n =sort(τ′ n -min(τ′ n )) In the formula, r τ The correlation coefficient represents the time delay distribution, DS represents the time delay spread, and X represents the time delay spread. n τ′ represents a discrete uniform distribution following {-1, 1}, sort(·) sorts in descending order, and τ′ n and τ n These are the time delay and the normalized time delay, respectively. Based on the exponential decay model, the power of each cluster is generated according to the cluster delay: In the formula, Z n ~N(0,ξ 2 ) is the shaded item for each cluster, in [dB]; The multipath departure angle and arrival angle are generated. Combined with the line-of-sight angles of the satellite and the UE, the angles AOA, EOA, AOD, and EOD are generated based on the angle extension and a Gaussian or Laplace distribution. AOA, EOA, AOD, and EOD are the arrival azimuth angle, arrival elevation angle, launch azimuth angle, and launch elevation angle, respectively. Then, the path power ratio of vertical polarization and horizontal polarization is generated.

4. The time-varying channel modeling method for low-Earth orbit satellite scenarios according to claim 3, characterized in that: In S22, the cluster attributes updated by parameter drift include power, delay, and angle of arrival; in subsequent time steps, assuming the elevation angle range remains unchanged, the parameter drift process is as follows: Based on the time delay τ of the initial time t0 n The propagation distance d of the nth cluster was calculated. n,t0 for: d n,t0 =t n ·c+|d los,t0 | In the formula, c is the speed of light; d los,t0 It is a vector from the transmitter to the receiver, represented as: d los,t0 =RX t0 -TX t0 In the formula, RX t0 and TX t0 These represent the position vectors of the receiver and transmitter at the initial time t0, respectively. In the nth cluster, the distance from the receiver to the last scatterer LS n The direction vector of the center and from the emitter to the first scatterer FS n The direction vector of the center Represented as: In the formula, a n,t0 Pointing the receiver to LS n The vector at the center, b n,t0 The sender points to FS n The vector at the center; Let c be the vector from the center of the first scatterer to the center of the last scatterer. n,t0 ,but: |c n,t0 |=d n,t0 -|a n,t0 |-|b n,t0 | Then, by minimizing |c n,t0 |To fix the additional degrees of freedom, where an extra minimum distance d is introduced between the phase center of the antenna array and the nearest scatterer. min The calculation of |a| is achieved by solving the following optimization problem. n,t0 | and |b n,t0 |: |a n,t0 |≥d min |b n,t0 |≥d min Form a triangle with the transmitter, the first scatterer, and the last scatterer, and define |a| n,t0 The initial value of | is d min The first scatterer FS is solved using trigonometric functions. n and the last scatterer LS n The position vector.

5. The time-varying channel modeling method for low-Earth orbit satellite scenarios according to claim 4, characterized in that: The first scatterer FS is solved using trigonometric functions. n and the last scatterer LS n During the position vector process, from the transmitter to the LS n vector d T,L,n Represented as: d T,L,n =d los,t0 +a n,t0 |a n,t0 |+|c n,t0 | Path length d′ n Represented as: d′ n =d n,t0 -|a n,t0 | Then from the emitter to the first scatterer FS n Path length | b n,t0 | is represented as: By iteratively adjusting |a n,t0 The size of |, with the intermediate path|c n,t0 The goal is to minimize the target. After the iterative optimization is completed, the FS is determined. n and LS n The position vectors are as follows: Then the propagation distance updated at the next time step t1 is: d n,t1 =|TX t1 -FS n |+|FS n -LS n |+|LS n -RX t1 | The propagation delay of the next update at time t1 is: t n,t1 =d n,t1 / c Therefore, the updated cluster vector b, pointing from the emitter to the first scatterer, is calculated. n,t1 and the vector a from the receiver pointing to the last scatterer n,t1 for: b n,t1 =FS n -TX t1 a n,t1 =LS n -RX t1 The updated launch angle and arrival angle are: The updated cluster power is: This yields the cluster set updated through parameter drift.

6. The time-varying channel modeling method for low-Earth orbit satellite scenarios according to claim 5, characterized in that: The iterative optimization process in S22 is as follows: S221. Initialize step size: based on the maximum path length d. max Calculate the initial value of step_size; set d max The value is taken as follows: S222, Iterative Update |a n,t0 The length of |a| in each iteration n,t0 |Increase the step size; S223, If |a n,t0 | Less than the minimum distance d min Adjust it to d min And shorten the step size value; if |a n,t0 | Greater than the maximum distance d max Adjust it to d max And in the opposite direction, shorten the step_size value; S224. Then recalculate |b according to the corresponding formula. n,t0 | and |c n,t0 The length of |; S225, Check the current |c n,t0 If the length of |a has decreased, and it is smaller than before, then the current solution is accepted; otherwise, the step direction is reversed, the step size is decreased, and |a is updated. n,t0 |、|b n,t0 | and |c n,t0 The value of |; S226. Stop iterating until the step size is less than the threshold or the maximum number of iterations is reached.

7. The time-varying channel modeling method for low-Earth orbit satellite scenarios according to claim 3, characterized in that: In S23, the cluster's birth and death process is as follows: S231. For each pair (i,j) between the new cluster and the old cluster, calculate the value of MCD: S[i,j] = MCD ij : Minimizing multipath component distance includes time delay distance and angular distance, where, Delay distance: In the formula, τ i , τ j It is the time delay between the new cluster and the old cluster, Δτ max It is the maximum value among all delay differences, and ζ is the delay scaling factor used to adjust the weight of delay in the total distance; Angular distance: In the formula, (x,y,z) is the spherical coordinate expression of the angle; Total distance expression: McDonald's ij =ω τ ·MCD τ,ij +ω AOA / AOD ·MCD AOA / AOD,ij Where, ω τ and ω AOa / aOD These are weighting coefficients; S232. Select the smallest S[i,j] row by row, replace the old cluster j with the new cluster i, and remove the i-th row and j-th column of S to obtain the new S; S233. Continue to select the smallest S[i,j] row by row, and gradually replace the old cluster j with the new cluster i. The remaining new clusters or old clusters are smoothly transitioned by gradually generating or eliminating them. During the replacement process, the power of the new and old clusters is gradually adjusted: Thus, we obtain the cluster set updated through the birth and death process.

8. The time-varying channel modeling method for low-Earth orbit satellite scenarios according to claim 1, characterized in that: In S3, the process of generating channel coefficients and establishing a channel model based on the small-scale parameters generated at each time step includes: Generate a random initial phase and assign a random initial phase to each multipath component; Map the time delay, angle, polarization, and phase parameters to the channel matrix; Add path loss and shadow fading, and superimpose them onto the channel matrix.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements a time-varying channel modeling method for low-Earth orbit satellite scenarios as described in any one of claims 1-8.

10. A computer program product, characterized in that: It includes a computer program that, when executed by a processor, implements a time-varying channel modeling method for low-Earth orbit satellite scenarios as described in any one of claims 1-8.

Images