Non-terrestrial network data scheduling and resource orchestration based on edge information hub

Abstract

The present disclosure provides a non-terrestrial network data scheduling and resource orchestration method. The method comprises: acquiring first spectral efficiency during communication between each of a plurality of user sides and an edge information hub, and second spectral efficiency during communication between the edge information hub and a satellite; on the basis of the first spectral efficiency, the second spectral efficiency and a preset optimization model, acquiring optimal bandwidth allocation of the communication between each of the plurality of user sides and the edge information hub and optimal bandwidth allocation of the communication between the edge information hub and the satellite; and on the basis of the optimal bandwidth allocation of the communication between each of the plurality of user sides and the edge information hub and the optimal bandwidth allocation of the communication between the edge information hub and the satellite, determining optimal computing power allocation and an optimal scheduling ratio of the edge information hub.

Description

Non-terrestrial network data scheduling and resource orchestration based on edge information hubs Technical Field

[0001] This disclosure relates to the field of wireless communication technology, and in particular to a non-terrestrial network data scheduling and resource orchestration based on an edge information hub. Background Technology

[0002] Natural and man-made disasters can cause casualties and economic losses, making rapid response and rescue crucial. Emergency rescue operations require the low-latency transmission of large amounts of data acquired by sensors and rescue teams in the disaster area to a command center outside the disaster zone to analyze the situation and make decisions regarding rescue actions.

[0003] In reality, ground communication infrastructure in disaster areas is usually severely damaged and cannot meet the needs of uploading sensing data. Therefore, it is necessary to consider using non-terrestrial communication systems, such as satellite networks and drone networks. Summary of the Invention

[0004] This disclosure provides a method for non-terrestrial network data scheduling and resource orchestration based on edge information hubs to solve the above-mentioned technical problems.

[0005] According to a first aspect of this disclosure, a non-terrestrial network data scheduling and resource orchestration method is provided, applied to an edge information hub. The method includes: obtaining a first spectral efficiency when multiple user terminals communicate with the edge information hub, and obtaining a second spectral efficiency when the edge information hub communicates with a satellite; obtaining the optimal bandwidth allocation for communication between the multiple user terminals and the edge information hub, and the optimal bandwidth allocation for communication between the edge information hub and the satellite, based on the first spectral efficiency, the second spectral efficiency, and a preset optimization model; and determining the optimal computing power allocation and optimal scheduling ratio of the edge information hub based on the optimal bandwidth allocation for communication between the multiple user terminals and the edge information hub, and the optimal bandwidth allocation for communication between the edge information hub and the satellite.

[0006] Optionally, obtaining the first spectral efficiency when each of the multiple user terminals communicates with the edge information hub includes: obtaining the distance, elevation angle, large-scale channel parameters, carrier frequency, and speed of light of each of the multiple user terminals and the edge information hub as first input data; obtaining the small-scale channel fading distribution, the transmit power of each of the multiple user terminals, and the system noise variance as second input data; obtaining the large-scale channel fading when the multiple user terminals communicate with the edge information hub based on the first input data; and obtaining the first spectral efficiency when the multiple user terminals communicate with the edge information hub based on the second input data and the large-scale channel fading.

[0007] Optionally, obtaining the first spectral efficiency when the plurality of user terminals communicate with the edge information hub based on the second input data and the large-scale channel fading includes: generating a plurality of independent small-scale channel fadings according to the small-scale channel fading distribution; obtaining spectral efficiency based on the large-scale channel fadings, each of the small-scale channel fadings in the plurality of small-scale channel fadings, and the transmit power and system noise variance of each of the user terminals in the second input data, thereby obtaining a plurality of spectral efficiencies; and obtaining the average value of the plurality of spectral efficiencies to obtain the first spectral efficiency.

[0008] Optionally, obtaining the optimal bandwidth allocation for communication between the user terminal and the edge information hub and the optimal bandwidth allocation for communication between the edge information hub and the satellite based on the first spectral efficiency, the second spectral efficiency, and the preset optimization model includes: obtaining the preset optimization model; inputting the first spectral efficiency and the second spectral efficiency into the preset optimization model to obtain the optimal bandwidth allocation for communication between the plurality of user terminals and the edge information hub and the optimal bandwidth allocation for communication between the edge information hub and the satellite.

[0009] Optionally, the preset optimization model includes an objective function and a constraint model, as shown in the following equation: In the formula, T represents the total latency of communication and computation during data upload; D u B represents the amount of data that the u-th user needs to upload. u The bandwidth allocated to the u-th user terminal when it communicates with the edge information hub; r u B is the first spectral efficiency; total The total bandwidth used to serve U user terminals for the edge information hub; ρ u The computational density of data uploaded by the u-th user terminal; ζ u F represents the ratio of the processed data volume to the original data volume, representing the amount of data to be uploaded by the u-th user terminal. total This represents the total computing capacity of the edge information hub; The bandwidth allocated to the u-th user terminal when the edge information hub communicates with the satellite; The total bandwidth for communication between the edge information hub and the satellite; B = [B1,...,B U [Optimal bandwidth allocation for communication between the U user terminals and the edge information hub;] This refers to the optimal bandwidth allocation for each user terminal when the edge information hub communicates with the satellite.

[0010] Optionally, the optimal computing power allocation and the optimal scheduling ratio are respectively shown in the following formulas: In the formula, F uTo allocate computing power to the u-th user terminal, ρ u Let η be the computational density of the data uploaded by the u-th user terminal. u To determine the optimal scheduling ratio for uploading data to the u-th user terminal, B u The bandwidth allocated to the u-th user terminal; r u ζ is the first spectral efficiency; u The ratio of the processed data volume to the original data volume is the amount of data that the u-th user needs to upload. The bandwidth allocated to the u-th user terminal for the edge information hub and the satellite communication; r S This refers to the second spectral efficiency.

[0011] According to a second aspect of this disclosure, a non-terrestrial network collaborative data scheduling and resource orchestration system is provided. The system includes an edge information hub. The edge information hub is configured to perform: acquiring a first spectral efficiency when multiple user terminals communicate with the edge information hub, and acquiring a second spectral efficiency when the edge information hub communicates with a satellite; acquiring the optimal bandwidth allocation for communication between the multiple user terminals and the edge information hub, and the optimal bandwidth allocation for communication between the edge information hub and the satellite, based on the first spectral efficiency, the second spectral efficiency, and a preset optimization model; and determining the optimal computing power allocation and optimal scheduling ratio of the edge information hub based on the optimal bandwidth allocation for communication between the multiple user terminals and the edge information hub, and the optimal bandwidth allocation for communication between the edge information hub and the satellite.

[0012] Optionally, obtaining the first spectral efficiency when each of the multiple user terminals communicates with the edge information hub includes: obtaining the distance, elevation angle, large-scale channel parameters, carrier frequency, and speed of light of each of the multiple user terminals and the edge information hub as first input data; obtaining the small-scale channel fading distribution, the transmit power of each of the multiple user terminals, and the system noise variance as second input data; obtaining the large-scale channel fading when the multiple user terminals communicate with the edge information hub based on the first input data; and obtaining the first spectral efficiency when the multiple user terminals communicate with the edge information hub based on the second input data and the large-scale channel fading.

[0013] Optionally, obtaining the first spectral efficiency when the plurality of user terminals communicate with the edge information hub based on the second input data and the large-scale channel fading includes: generating a plurality of independent small-scale channel fadings according to the small-scale channel fading distribution; obtaining spectral efficiency based on the large-scale channel fadings, each of the small-scale channel fadings in the plurality of small-scale channel fadings, and the transmit power and system noise variance of each of the user terminals in the second input data, thereby obtaining a plurality of spectral efficiencies; and obtaining the average value of the plurality of spectral efficiencies to obtain the first spectral efficiency.

[0014] Optionally, obtaining the optimal bandwidth allocation for communication between the user terminal and the edge information hub and the optimal bandwidth allocation for communication between the edge information hub and the satellite based on the first spectral efficiency, the second spectral efficiency, and the preset optimization model includes: obtaining the preset optimization model; inputting the first spectral efficiency and the second spectral efficiency into the preset optimization model to obtain the optimal bandwidth allocation for communication between the plurality of user terminals and the edge information hub and the optimal bandwidth allocation for communication between the edge information hub and the satellite.

[0015] Optionally, the preset optimization model includes an objective function and a constraint model, as shown in the following equation: In the formula, T represents the total latency of communication and computation during data upload; D u B represents the amount of data that the u-th user needs to upload. u The bandwidth allocated to the u-th user terminal when it communicates with the edge information hub; r u B is the first spectral efficiency; total The total bandwidth used to serve U user terminals for the edge information hub; ρ u The computational density of data uploaded by the u-th user terminal; ζ u F represents the ratio of the processed data volume to the original data volume, representing the amount of data to be uploaded by the u-th user terminal. total This represents the total computing capacity of the edge information hub; The bandwidth allocated to the u-th user terminal when the edge information hub communicates with the satellite; The total bandwidth for communication between the edge information hub and the satellite; B = [B1,...,B U [Optimal bandwidth allocation for communication between the U user terminals and the edge information hub;] This refers to the optimal bandwidth allocation for each user terminal when the edge information hub communicates with the satellite.

[0016] Optionally, the optimal computing power allocation and the optimal scheduling ratio are respectively shown in the following formulas: In the formula, F uTo allocate computing power to the u-th user terminal, ρ u Let η be the computational density of the data uploaded by the u-th user terminal. u To determine the optimal scheduling ratio for uploading data to the u-th user terminal, B u The bandwidth allocated to the u-th user terminal; r u ζ is the first spectral efficiency; u The ratio of the processed data volume to the original data volume is the amount of data that the u-th user needs to upload. The bandwidth allocated to the u-th user terminal for the edge information hub and the satellite communication; r S This refers to the second spectral efficiency.

[0017] According to a third aspect of this disclosure, a computer-readable storage medium is provided that, when an executable computer program in the storage medium is executed by a processor, enables the implementation of the method as described in any of the first aspects.

[0018] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0019] The resource allocation method provided in this embodiment can obtain the first spectral efficiency during user-edge information hub communication and the second spectral efficiency during edge information hub-satellite communication. Then, based on the first spectral efficiency, the second spectral efficiency, and a preset optimization model, the optimal bandwidth allocation for user-edge information hub communication and the optimal bandwidth allocation for edge information hub-satellite communication are obtained. Subsequently, the optimal computing power allocation and optimal scheduling ratio for the edge information hub are determined based on the optimal bandwidth allocation for user-edge information hub communication and the optimal bandwidth allocation for edge information hub-satellite communication. Thus, this embodiment achieves a synergistic optimal design for data stream scheduling, communication bandwidth allocation, and computing power allocation by comparing and analyzing the uploaded data streams during user-edge information hub, edge information hub, and satellite communication processes. This can improve the overall utilization efficiency of multi-dimensional resources and significantly reduce the total latency of sensing data upload.

[0020] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0021] Figure 1 is a schematic diagram of a machine communication network according to an embodiment of the present disclosure.

[0022] Figure 2 is a flowchart of a non-terrestrial network data scheduling and resource orchestration method based on an edge information hub according to an embodiment of the present disclosure.

[0023] Figure 3 is a flowchart of an embodiment of the present disclosure for obtaining a first spectral efficiency.

[0024] Figure 4 is a simulation comparison diagram of an embodiment of this disclosure. Detailed Implementation

[0025] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses consistent with some aspects of this disclosure as detailed in the appended claims.

[0026] Satellite networks can provide long-term and stable global coverage and are an indispensable part of non-terrestrial networks. However, satellite networks also have inherent limitations such as low transmission rates and long latency. It is difficult to support the business needs of low-latency data uploads by relying solely on satellite communication.

[0027] Unmanned aerial vehicle (UAV) networks refer to UAVs that deploy airborne base stations and satellite terminals, enabling them to access user terminals to obtain sensor data and establish high-speed communication links with satellites to achieve low-latency data transmission.

[0028] However, the aforementioned non-terrestrial communication systems are highly dependent on the performance of the communication links and have obvious marginal effects, meaning that increasing communication resources has a rather limited effect on improving latency performance.

[0029] Therefore, there is a need to provide an edge information hub that integrates information processing, information storage, and information transmission functions to meet the business requirements of low-latency uploading of perceived data.

[0030] Figure 1 is a schematic diagram of a machine communication network according to an embodiment of this disclosure. Referring to Figure 1, the machine communication network includes an edge information hub, at least one user terminal (1, 2, ..., U), and a cloud (not shown in the figure). The edge information hub can be deployed on a drone platform, and it can be implemented by a server, transceiver, or other electronic devices, as long as it can communicate with the user terminal and the satellite, and has computing and storage capabilities. This disclosure does not limit the specific implementation form of the edge information hub. For example, the edge information hub includes a user communication module, a satellite communication module, a computing module, and a storage module. The user communication module and the satellite communication module can be implemented by a server, transceiver, etc., respectively; the computing module can be implemented by a server, etc.; and the storage module can be implemented by a memory, etc. This disclosure does not limit the specific implementation form. The user's perception data can be collected and temporarily stored by the edge information hub. Some data is processed by the computing module of the edge information hub to reduce redundancy before being uploaded to the satellite, which then sends it to the cloud. Edge information hubs can avoid the marginal effects of relying solely on communication networks by scheduling and processing data, effectively reducing the upload latency of sensing data.

[0031] Since edge information hubs are deployed on drone platforms, they are subject to strict limitations in terms of size, weight, and energy consumption. Therefore, during the data upload process, it is an important issue that needs to be addressed to rationally schedule data and orchestrate multi-dimensional resources such as communication, computing, and storage, improve resource utilization efficiency, and enhance system latency performance.

[0032] In implementing the solution disclosed herein, the inventors discovered that collaborative data scheduling and multi-dimensional resource orchestration schemes in related technologies typically involve approximate simplifications in system model assumptions or solution processes, failing to arrive at a globally optimal strategy and thus exhibiting limitations in system performance. Therefore, this disclosure provides a non-terrestrial network data scheduling and resource orchestration method based on an edge information hub. The inventive concept lies in analyzing the data flow through the edge information hub during the entire process of uploading sensing data, exploring the collaborative optimal design of data flow scheduling, communication resource allocation, and computing power allocation, thereby improving the system's resource utilization efficiency and performance.

[0033] Figure 2 is a flowchart of a non-terrestrial network data scheduling and resource orchestration method based on an edge information hub according to an embodiment of this disclosure. Referring to Figure 2, a non-terrestrial network data scheduling and resource orchestration method based on an edge information hub can be executed by the edge information hub, including steps 21 to 23.

[0034] In step 21, the first spectral efficiency of each of the multiple user terminals communicating with the edge information hub is obtained, and the second spectral efficiency of the edge information hub communicating with the satellite is obtained.

[0035] In this embodiment, spectral efficiency refers to the transmission rate per unit bandwidth, i.e., spectral efficiency = rate / bandwidth, in bits / s / Hz. Subsequently, the spectral efficiency when one or more user terminals communicate with the edge information hub will be referred to as the first spectral efficiency, to distinguish it from the second spectral efficiency when the edge information hub communicates with a satellite.

[0036] In one example, the edge information hub can obtain the first spectral efficiency when one or more user terminals communicate with the edge information hub, as shown in Figure 3, including steps 31 to 33.

[0037] In step 31, the distance, pitch angle, large-scale channel parameters, carrier frequency, and speed of light between each user terminal and the UAV (i.e., the edge information hub, which is deployed on the UAV) are obtained as the first input data, and the small-scale channel fading distribution, the transmit power of each user terminal, and the system noise variance are obtained as the second input data.

[0038] In this step, the edge information hub can obtain the distance between the user terminal and the drone, the pitch angle, large-scale channel parameters, carrier frequency, and speed of light. For the sake of describing the scheme, in subsequent embodiments, the distance between the user terminal and the drone, the pitch angle, large-scale channel parameters, carrier frequency, and speed of light will be used as the first input data.

[0039] The number of user terminals is U, and the user terminals are numbered 1, 2, 3, ..., u, ..., U. The distance between the u-th user terminal and the drone is d. u Since the edge information hub is deployed on a drone platform, the distance between the user terminal and the drone is equivalent to the distance from the user terminal to the edge information hub. Understandably, the distance d between the user terminal and the drone can be determined by the signal strength of multiple communication communications between the user terminal and the edge information hub. u and pitch angle θ u That is, the strength of each communication signal can determine a certain pitch angle θ. u The circular region below, the overlapping of multiple calculated circular regions can be used to obtain d. u and θ u .

[0040] Among them, the large-scale channel parameters (η) LoS ,η NLoS (a, b) are known system parameters. Among them, the large-scale channel parameters can be used to represent the law that the average power of the received signal decreases with increasing distance when the distance between the user and the UAV varies over a large range (e.g., hundreds or thousands of meters). Understandably, in scenarios where parameters such as the UAV platform's flight altitude and the edge information hub's transmission power are known, the large-scale channel parameters can be obtained by looking up a table.

[0041] Among them, the carrier frequency f is a known quantity when the user terminal and the UAV communication module are known, and can be directly read and used from the local memory.

[0042] In this step, the edge information hub can obtain the small-scale channel fading s distribution and the transmit power p of user terminal u (i.e., the u-th user terminal). u and system noise variance σ 2 This serves as the second input data, distinct from the first input data.

[0043] Small-scale channel fading refers to the loss caused by multipath propagation (the phenomenon of radio waves traveling from the transmitting antenna through multiple paths to the receiving antenna). It reflects the average variation trend of the received signal level within a small range on the order of tens of wavelengths. The transmit power p of user terminal u (i.e., the u-th user terminal) u This can be obtained through communication between the edge information hub and the user terminal u. The system noise variance σ 2 It can be detected; it is a known quantity.

[0044] It should be noted that this step provides a partial example of obtaining the first and second input data. The appropriate acquisition method can be selected according to the specific scenario, and the corresponding method falls within the protection scope of this disclosure.

[0045] In step 32, the large-scale channel fading of communication between each user terminal and the edge information hub is obtained based on the first input data.

[0046] In this step, the edge information hub can obtain the large-scale channel fading of communication between each user terminal and the edge information hub based on the first input data, as shown in equation (1):

[0047] In equation (1), l u For large-scale channel fading; a and b are large-scale channel environmental parameters, i.e., the environmental parameters of the town or the village, which are known quantities; η LoS For line-of-sight communication fading parameters; η NLoS Here are the fading parameters for non-line-of-sight communication; f is the carrier frequency, and θ is the fading parameter. u The pitch angle.

[0048] In step 33, a first spectral efficiency is obtained when one or more user terminals communicate with the edge information hub based on the second input data and the large-scale channel fading.

[0049] In this step, the edge information hub can obtain a first spectral efficiency when one or more user terminals communicate with the edge information hub based on the second input data and the large-scale channel fading. For example, the Monte Carlo method is used to calculate the first spectral efficiency when user terminal u communicates with a UAV. Alternatively, multiple small-scale channel fadings can be generated independently according to the small-scale channel fading distribution (e.g., random distribution); then, the spectral efficiency is obtained based on each small-scale channel fading and the transmit power and system noise variance of each user terminal in the second input data, resulting in multiple spectral efficiencies; finally, the average value of the multiple spectral efficiencies is obtained to obtain the first spectral efficiency, as shown in equation (2).

[0050] In equation (2), r u For the first spectral efficiency, E s [] indicates that the random variable within the brackets is expected, i.e., its mean, where s is the small-scale channel fading, and p u For the transmit power of user terminal u, l u For large-scale channel fading, σ 2 This represents the system noise variance.

[0051] In this step, the edge information hub can obtain the small-scale channel fading distribution, the transmit power of each user terminal and the system noise variance, and obtain the second spectral efficiency when the edge information hub communicates with the satellite, as shown in Equation (3).

[0052] In equation (3), r S For the second spectral efficiency, p S For the drone's transmission power, G T For the transmitting antenna gain, G R For receiver gain, Let d be the system noise variance. S f represents the distance between the drone and the satellite. S For carrier frequency.

[0053] In step 22, the optimal bandwidth allocation for communication between the plurality of user terminals and the edge information hub, and the optimal bandwidth allocation for communication between the edge information hub and the satellite are obtained based on the first spectral efficiency, the second spectral efficiency, and the preset optimization model.

[0054] In this step, the edge information hub stores a preset optimization model, as shown in equation (4).

[0055] In equation (4), T represents the total latency used for communication and computation during data upload from the user end, and is an intermediate variable; D u B represents the amount of data that the u-th user needs to upload. ur is the bandwidth allocated to user terminal u when the u-th user terminal communicates with the edge information hub; u B represents the first spectral efficiency. total The total bandwidth used to serve U users at the edge information hub; ρ u The computational density of data uploaded by the u-th user terminal; ζ u F represents the ratio of the processed data volume to the original data volume, representing the amount of data to be uploaded by the u-th user terminal. total The total computing capacity of the edge information hub; The bandwidth allocated to the u-th user terminal for edge information hub and satellite communication; Total bandwidth for edge information hubs and satellite communications.

[0056] Continuing with equation (4), each expression represents: Line 1 The total communication and computation delay T during data upload should be minimized; (Line 2) The time taken for all users to complete their tasks does not exceed the total time; Line 3 The sum of the user-device communication bandwidth allocated to each user shall not exceed the total user-device communication bandwidth; Line 4 The sum of the drone-satellite communication bandwidth allocated to each user shall not exceed the total drone-satellite communication bandwidth; Line 5 The sum of computing power allocated to each user shall not exceed the total computing power; Line 6 It is a constraint introduced by shrinking the feasible region.

[0057] Thus, by pre-setting the optimization model as a convex optimization problem in this embodiment, the problem of difficulty in solving optimization problems due to complex piecewise functions can be avoided, thereby reducing the difficulty of solving the problem while ensuring that the optimal solution remains unchanged. In other words, in the context of emergency rescue and disaster relief scenarios, the optimal result can be obtained within a limited time, which can improve the overall utilization efficiency of multi-dimensional resources, significantly reduce the total latency of uploading sensing data, and facilitate the decision-making center to make timely decisions based on the uploaded data.

[0058] In this step, the edge information hub can input the first spectral efficiency and the second spectral efficiency into the aforementioned preset optimization model to obtain the optimal bandwidth allocation B = [B1,...,B1] for communication between the user terminal and the edge information hub. U Optimal bandwidth allocation for edge information hubs and satellite communications In this way, by allocating optimal bandwidth, user-end data can be uploaded to the satellite as quickly as possible, improving data transmission efficiency.

[0059] In step 23, the optimal computing power allocation and optimal scheduling ratio of the edge information hub are determined based on the optimal bandwidth allocation for communication between the multiple user terminals and the edge information hub and the satellite.

[0060] In this step, the edge information hub can determine the optimal computing power allocation F = [F1,...,F2] based on the optimal bandwidth allocation for communication between the user terminal and the edge information hub, and the optimal bandwidth allocation for communication between the edge information hub and the satellite. U As shown in equation (5).

[0061] In equation (5), F u The computing power allocated to the u-th user terminal is calculated by multiplying the difference between the communication rate between the user terminal and the drone and the communication rate between the drone and the satellite by the computing density, and the denominator is the proportion of data saved in computation to the original data volume; ρ u The computational density of data uploaded by the u-th user terminal; B u r is the bandwidth allocated to the u-th user terminal when it communicates with the edge information hub. u The first spectral efficiency; Bandwidth allocated to the u-th user terminal for edge information hub and satellite communication; r S For the second spectral efficiency; ζ u The ratio of the processed data volume to the original data volume is the amount of data that the u-th user needs to upload.

[0062] In this step, the edge information hub can determine the optimal scheduling ratio η = [η1,...,η] for user-uploaded data based on the optimal bandwidth allocation for user-edge information hub communication and the optimal bandwidth allocation for edge information hub-satellite communication. U As shown in equation (6).

[0063] In equation (6), η u Let be the optimal scheduling ratio of user-uploaded data for the u-th user terminal. The numerator is the difference between the communication rate between the user terminal and the drone and the communication rate between the drone and the satellite, and the denominator is the communication rate between the user terminal and the drone multiplied by the proportion of the amount of data saved in processing relative to the original amount of data.

[0064] Thus, the resource allocation method provided in this embodiment can obtain the first spectral efficiency when the user terminal communicates with the edge information hub, and the second spectral efficiency when the edge information hub communicates with the satellite. Then, based on the first spectral efficiency, the second spectral efficiency, and a preset optimization model, the optimal bandwidth allocation for communication between the user terminal and the edge information hub, and the optimal bandwidth allocation for communication between the edge information hub and the satellite are obtained. Subsequently, based on the optimal bandwidth allocation for communication between the user terminal and the edge information hub, and the optimal bandwidth allocation for communication between the edge information hub and the satellite, the optimal computing power allocation and the optimal scheduling ratio of the edge information hub are determined. In this way, by comparing and analyzing the uploaded data streams during the communication process between the user terminal, the edge information hub, and the satellite, this embodiment achieves a coordinated optimal design for data stream scheduling, communication bandwidth allocation, and computing power allocation, which can improve the overall utilization efficiency of multi-dimensional resources and significantly reduce the total latency of sensing data upload.

[0065] The following describes a non-terrestrial network data scheduling and resource orchestration method based on an edge information hub, provided in this embodiment, using an emergency rescue scenario as an example. The method includes: assuming the number of users in the system is U (numbered 1, 2, ..., u, ..., U), and the amount of data that the u-th user needs to upload (i.e., the user-uploaded data) is D. u The computational density of the data (the number of CPU cycles required to compute each bit of data) is ρ. u The ratio of the processed data volume to the original data volume is ζ. u .

[0066] For communication between the edge information hub deployed on the drone and the user terminal, the system uses frequency division multiple access (FDMA) to serve different user terminals, with a total bandwidth of B. total The bandwidth allocated to user terminal u is B. u The distance between the user terminal u and the drone is d. u The pitch angle is θ u The large-scale channel parameters are (η) LoS ,η NLoS Given a, b), a carrier frequency of f, and a speed of light of c, the small-scale channel fading s follows a pattern with a mean of 0 and a variance of . The transmission power of user terminal u is a complex Gaussian distribution. u The system noise has a variance σ. 2 Additive white Gaussian noise.

[0067] For communication between the edge information hub and the satellite, the system also uses frequency division multiple access to serve different users, with a total bandwidth of [missing information]. The bandwidth allocated to user u is The distance between the drone and the satellite is d. S The carrier frequency is f S The transmitter antenna gain is G. TThe receiver antenna gain is G. R The drone's transmit power is p. S The system noise is variance Additive white Gaussian noise.

[0068] For the edge information hub's computation, the total computing capacity (number of CPU cycles per second) is F. total The computing capacity allocated to user terminal u is F. u .

[0069] In the user-uploaded data on the user terminal u, there are η u The proportional data needs to be processed at the edge information hub before being uploaded to the satellite; the remaining 1-η u The proportional data is directly uploaded to the satellite from the edge information hub.

[0070] For user-drone communication, the distance d between the user terminal u and the drone is the primary factor. u Pitch angle θ u Large-scale channel parameters (η) LoS ,η NLoS (a, b) Carrier frequency f, speed of light c, and large-scale channel fading l for each user u communicating with the UAV according to equation (1). u .

[0071] Based on large-scale channel fading u Small-scale channel fading s-distribution, user u's transmit power p u System noise variance σ 2 The Monte Carlo method is used to calculate the first spectral efficiency of the communication between the user terminal u and the UAV, as shown in Equation (2).

[0072] For UAV-satellite communication, based on the distance d between the UAV and the satellite S Carrier frequency f S Transmitter antenna gain G T Receiver antenna gain G R The transmit power p of the drone S System noise variance The second spectral efficiency of UAV-satellite communication is calculated as shown in Equation (3).

[0073] For the user-drone-satellite-cloud system, an intermediate variable T is used as the total communication and computation delay during data upload to establish a preset optimization model, as shown in equation (4). It is understood that this preset optimization model is a convex optimization problem. Then, the CVX tool is used to solve this convex optimization problem to obtain the minimum value of the total communication and computation delay during data upload, which is taken as the optimal solution of the preset optimization model, i.e., the optimal bandwidth allocation B = [B1,...,B1] for communication between the user terminal and the edge information hub. U Optimal bandwidth allocation for edge information hubs and satellite communications

[0074] Finally, based on the optimal bandwidth allocation B = [B1,...,B U and optimal bandwidth allocation Based on equation (5), the optimal computing power allocation F = [F1,...,F2] of the computing units in the edge information hub is calculated. U ], and calculate the optimal scheduling ratio η = [η1,...,η] of user data based on equation (6). U ].

[0075] Based on the specific scenarios described above, the following simulation scenarios are set up, including:

[0076] Assuming the number of user terminals U is 4, in the simulation, the user-uploaded data of each user terminal is uniformly and randomly generated between 1 / 10 of the maximum data volume and the maximum data volume. The data computation density is uniformly and randomly generated within [1000, 5000] CPU cycles. The ratio of the processed data volume to the original data volume is uniformly and randomly generated within the range of [0.01, 0.1]. The total bandwidth of the user terminal-UAV communication is 1 MHz. The UAV position is [0, 0, 1000] meters. The positions of each user terminal are uniformly and randomly distributed within a circle with a height of 0, a center of the origin, and a radius of 1000 meters. The large-scale channel parameters are set to (0.1, 21, 5.0188, 0.3511), the carrier frequency is 5.8 GHz, the speed of light is 3 × 10⁸ meters per second, the first spectral efficiency is obtained by averaging 1000 times using the Monte Carlo method, the user transmit power is 1 watt, and the system noise variance is -114 dBm. The total bandwidth of the UAV-satellite communication is 50 MHz, the distance between the UAV and the satellite is 550 km, the carrier frequency is 28 GHz, the antenna gain at both the transmitting and receiving ends is 45 dB, the UAV's transmit power is 5 watts, and the system noise variance is -114 dBm. The total computing capacity of the UAV's upper-edge information hub is 5 × 10⁹ CPU cycles per second.

[0077] Under the above simulation conditions, simulations were conducted for user data volumes ranging from 0.1 Mbps to 10 Mbps to obtain the total latency for data upload under each maximum data volume. The performance of the disclosed solution was then compared with related technical solutions, as shown in Figure 4. Referring to Figure 4, Comparison Scheme 1 represents a related technical solution; Comparison Schemes 2 and 3 are simplified versions of the disclosed solution and Comparison Scheme 1, respectively; and Comparison Scheme 4 represents the result of deploying only a communication unit on the UAV without a computing unit. Thus, the disclosed solution significantly reduces the total latency of data upload compared to related technical solutions, effectively improving the system's resource utilization efficiency. Furthermore, the solution has low complexity. Given the total communication bandwidth and computing power configured in the edge information hub, as well as the communication channel information, transmission power, and noise, the optimal collaborative data flow scheduling and resource orchestration scheme can be obtained by solving a convex optimization problem, which is beneficial for achieving rapid uploading of disaster area information.

[0078] Based on the non-terrestrial network data scheduling and resource orchestration method provided in this disclosure, this disclosure also provides a non-terrestrial network collaborative data scheduling and resource orchestration system, the system including an edge information hub; the edge information hub is configured to perform: obtaining a first spectral efficiency when multiple user terminals communicate with the edge information hub, and obtaining a second spectral efficiency when the edge information hub communicates with a satellite; obtaining the optimal bandwidth allocation for communication between the multiple user terminals and the edge information hub and the satellite based on the first spectral efficiency, the second spectral efficiency, and a preset optimization model; and determining the optimal computing power allocation and optimal scheduling ratio of the edge information hub based on the optimal bandwidth allocation for communication between the multiple user terminals and the edge information hub and the satellite.

[0079] In one embodiment, obtaining the first spectral efficiency of each of the plurality of user terminals communicating with the edge information hub includes: obtaining the distance, pitch angle, large-scale channel parameters, carrier frequency, and speed of light of each of the plurality of user terminals to the UAV as first input data; obtaining the small-scale channel fading distribution, the transmit power of each of the plurality of user terminals, and the system noise variance as second input data; obtaining the large-scale channel fading of the plurality of user terminals communicating with the edge information hub based on the first input data; and obtaining the first spectral efficiency of the plurality of user terminals communicating with the edge information hub based on the second input data and the large-scale channel fading.

[0080] In one embodiment, obtaining the first spectral efficiency when the plurality of user terminals communicate with the edge information hub based on the second input data and the large-scale channel fading includes: generating a plurality of independent small-scale channel fadings according to the small-scale channel fading distribution; obtaining spectral efficiency based on the large-scale channel fadings, each of the small-scale channel fadings in the plurality of small-scale channel fadings, and the transmit power and system noise variance of each of the user terminals in the second input data, thereby obtaining a plurality of spectral efficiencies; and obtaining the average value of the plurality of spectral efficiencies to obtain the first spectral efficiency.

[0081] In one embodiment, obtaining the optimal bandwidth allocation for communication between the user terminal and the edge information hub and the optimal bandwidth allocation for communication between the edge information hub and the satellite based on the first spectral efficiency, the second spectral efficiency, and the preset optimization model includes: obtaining the preset optimization model; inputting the first spectral efficiency and the second spectral efficiency into the preset optimization model to obtain the optimal bandwidth allocation for communication between the plurality of user terminals and the edge information hub and the optimal bandwidth allocation for communication between the edge information hub and the satellite.

[0082] In one embodiment, the preset optimization model includes an objective function and a constraint model, as shown in the following equation:

[0083] In the formula, T represents the total communication and computation delay during data upload, which is an intermediate variable; D u B represents the amount of data that the u-th user needs to upload. u The bandwidth allocated to the u-th user terminal when it communicates with the edge information hub; r u B represents the first spectral efficiency. total The total bandwidth used to serve U user terminals for the edge information hub; ρ u The computational density of data uploaded by the u-th user terminal; ζ u F represents the ratio of the processed data volume to the original data volume, representing the amount of data to be uploaded by the u-th user terminal. total The total computing capacity of the edge information hub; The bandwidth allocated to the u-th user terminal when the edge information hub communicates with the satellite; The total bandwidth for edge information hubs communicating with satellites; B = [B1,...,B U ] Optimal bandwidth allocation for communication between U user terminals and the edge information hub; Optimal bandwidth allocation for each user terminal when the edge information hub communicates with the satellite.

[0084] In one embodiment, the optimal computing power allocation and the optimal scheduling ratio are respectively shown in the following formulas:

[0085] In the formula, B u Bandwidth allocated to user terminal u; r u The first spectral efficiency; ζ u The ratio of the processed data volume to the original data volume is the amount of data that the u-th user needs to upload. Bandwidth allocated to the u-th user terminal for edge information hub and satellite communication; r S This represents the second spectral efficiency.

[0086] It should be noted that the system embodiment provided in this embodiment corresponds to the method embodiment described above. For details, please refer to the content of each method embodiment described above, which will not be repeated here.

[0087] This disclosure also provides an edge information hub, including a processor and a memory; the memory is used to store a computer program executable by the processor; wherein the processor is used to execute the computer program in the memory to implement the method described above.

[0088] This disclosure also provides a non-transitory computer-readable storage medium that, when an executable computer program in the storage medium is executed by a processor, can implement the method described above.

[0089] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This disclosure is intended to cover any variations, uses, or adaptations that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0090] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A non-terrestrial network data scheduling and resource orchestration method, applied to edge information hubs, characterized in that, The method includes: The first spectral efficiency of multiple user terminals communicating with the edge information hub is obtained, and the second spectral efficiency of the edge information hub communicating with a satellite is obtained. The optimal bandwidth allocation for communication between the multiple user terminals and the edge information hub, and the optimal bandwidth allocation for communication between the edge information hub and the satellite are obtained based on the first spectrum efficiency, the second spectrum efficiency, and the preset optimization model. The optimal computing power allocation and optimal scheduling ratio of the edge information hub are determined based on the optimal bandwidth allocation for communication between the multiple user terminals and the edge information hub, and the optimal bandwidth allocation for communication between the edge information hub and the satellite.

2. The method according to claim 1, characterized in that, The step of obtaining the first spectral efficiency when multiple user terminals communicate with the edge information hub includes: The distance, elevation angle, large-scale channel parameters, carrier frequency, and speed of light of each of the multiple user terminals to the edge information hub are obtained as the first input data. The small-scale channel fading distribution, the transmit power of each of the multiple user terminals, and the system noise variance are obtained as the second input data. Based on the first input data, obtain the large-scale channel fading of the communication between the multiple user terminals and the edge information hub; The first spectral efficiency is obtained when the multiple user terminals communicate with the edge information hub based on the second input data and the large-scale channel fading.

3. The method according to claim 2, characterized in that, The first spectral efficiency is obtained based on the second input data and the large-scale channel fading when the multiple user terminals communicate with the edge information hub, including: Multiple independent small-scale channel fadings are generated according to the small-scale channel fading distribution described above; The spectral efficiency is obtained by considering the large-scale channel fading, each of the multiple small-scale channel fadings, and the transmit power and system noise variance of each user terminal in the second input data, thus obtaining multiple spectral efficiencies. The average value of the multiple spectral efficiencies is obtained to obtain the first spectral efficiency.

4. The method according to claim 1, characterized in that, The optimal bandwidth allocation for communication between the user terminal and the edge information hub, and the optimal bandwidth allocation for communication between the edge information hub and the satellite are obtained based on the first spectral efficiency, the second spectral efficiency, and the preset optimization model, including: Obtain the preset optimization model; The first spectral efficiency and the second spectral efficiency are input into the preset optimization model to obtain the optimal bandwidth allocation for communication between the multiple user terminals and the edge information hub, and the optimal bandwidth allocation for communication between the edge information hub and the satellite.

5. The method according to claim 4, characterized in that, The preset optimization model includes an objective function and a constraint model, as shown in the following equation: In the formula, T represents the total latency of communication and computation during data upload; D u Data amount needed to be uploaded for the u-th user terminal; B u a bandwidth allocated to the u-th user terminal when communicating with the edge information hub; r u for the first spectral efficiency; B total total bandwidth used by U user terminals served by the edge information hub; ρ u ρu is the calculated density of uploaded data for the u-th user terminal; ζ u the ratio of the processed data amount to the original data amount required for the u-th user terminal to upload F total total computational capacity of the edge information hub; The bandwidth allocated to the u-th user terminal when the edge information hub communicates with the satellite; This refers to the total bandwidth when the edge information hub communicates with the satellite; B = [B1,...,B U [Optimal bandwidth allocation for communication between the U user terminals and the edge information hub;] This refers to the optimal bandwidth allocation for each user terminal when the edge information hub communicates with the satellite.

6. The method according to claim 1, characterized in that, The optimal computing power allocation and the optimal scheduling ratio are respectively shown in the following formulas: In the formula, F u The computing power allocated to the u-th user terminal, ρ u The computational density of data uploaded by the u-th user terminal. η u To determine the optimal scheduling ratio for uploading data to the u-th user terminal, B u This refers to the bandwidth allocated to the u-th user terminal. r u The first spectral efficiency; ζ u The ratio of the processed data volume to the original data volume is the amount of data that the u-th user needs to upload. The bandwidth allocated to the u-th user terminal for the communication between the edge information hub and the satellite; r S This refers to the second spectral efficiency.

7. A non-terrestrial network collaborative data scheduling and resource orchestration system, characterized in that, The system includes an edge information hub; the edge information hub is configured to execute: The first spectral efficiency of multiple user terminals communicating with the edge information hub is obtained, and the second spectral efficiency of the edge information hub communicating with a satellite is obtained. The optimal bandwidth allocation for communication between the multiple user terminals and the edge information hub, and the optimal bandwidth allocation for communication between the edge information hub and the satellite are obtained based on the first spectrum efficiency, the second spectrum efficiency, and the preset optimization model. The optimal computing power allocation and optimal scheduling ratio of the edge information hub are determined based on the optimal bandwidth allocation for communication between the multiple user terminals and the edge information hub, and the optimal bandwidth allocation for communication between the edge information hub and the satellite.

8. The system according to claim 7, characterized in that, Obtaining the first spectral efficiency of each of the plurality of user terminals when communicating with the edge information hub includes: The distance, elevation angle, large-scale channel parameters, carrier frequency, and speed of light of each of the multiple user terminals to the edge information hub are obtained as the first input data. The small-scale channel fading distribution, the transmit power of each of the multiple user terminals, and the system noise variance are obtained as the second input data. Based on the first input data, obtain the large-scale channel fading of the communication between the multiple user terminals and the edge information hub; The first spectral efficiency is obtained when the multiple user terminals communicate with the edge information hub based on the second input data and the large-scale channel fading.

9. The system according to claim 8, characterized in that, The first spectral efficiency is obtained based on the second input data and the large-scale channel fading when the multiple user terminals communicate with the edge information hub, including: Multiple independent small-scale channel fadings are generated according to the small-scale channel fading distribution described above; The spectral efficiency is obtained by considering the large-scale channel fading, each of the multiple small-scale channel fadings, and the transmit power and system noise variance of each user terminal in the second input data, thus obtaining multiple spectral efficiencies. The average value of the multiple spectral efficiencies is obtained to obtain the first spectral efficiency.

10. A non-transitory computer-readable storage medium, characterized in that, When the executable computer program in the storage medium is executed by a processor, it can implement the method as described in any one of claims 1 to 6.

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