A method for optimizing space-ground network resources by minimizing the age of data collection information

Abstract

The invention discloses an air-space-ground network resource optimization method for minimizing the age of data collection information, and belongs to the field of wireless communication; the method comprises: S1, constructing an initial model; S2, setting initial parameters for iteration; S3, obtaining the optimal time allocation under the conditions of given flight trajectory, transmission power, rate allocation and segmentation ratio; S4, obtaining the optimal flight trajectory and rate allocation under the conditions of given time allocation, transmission power and segmentation ratio; S5, obtaining the optimal segmentation ratio under the conditions of given time allocation, flight trajectory, rate allocation and transmission power; S6, obtaining the optimal transmission power under the conditions of given time allocation, flight trajectory, rate allocation and segmentation ratio; S7, substituting the optimal result obtained in the front of the BCD method into S3, updating the variable of the number of iterations, repeating S3 to S6, until the initial model meets the convergence condition, the iteration is terminated, and the optimal solution of the initial model is obtained.

Description

Technical Field

[0001] The present invention belongs to the field of wireless communications, and in particular relates to an air-ground-space network resource optimization method for minimizing the age of data collection information. Background Art

[0002] With the rapid development of the Internet of Things (IoT), more and more IoT devices need to collect data. Most of the current data collection schemes adopt a single drone-assisted data collection scheme based on traditional orthogonal multiple access technology. Traditional orthogonal multiple access technologies, such as FDMA, TDMA, and CDMA, face challenges in spectrum efficiency and data transmission efficiency. However, when the data packet size is large, a single drone cannot complete the data collection task independently, resulting in increased system processing delays, decreased freshness of data packet information, and increased information age. Therefore, a space-ground network resource optimization method that minimizes the age of data collection information is proposed. Summary of the invention

[0003] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a method for optimizing air-space-ground network resources by minimizing the age of data collection information, thereby solving the problems in the prior art.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] The method for optimizing space-ground network resources by minimizing the age of data collection information comprises the following steps:

[0006] S1, with the goal of minimizing the age of data collection information, build the initial network model of the air-space-ground data edge computing system;

[0007] S2, set the number of iterations , Iteration termination accuracy , given the initial UAV flight trajectory , initial transmission power , Initial Public Rate Allocation , Initial time allocation and initial packet split ratio ;

[0008] S3, given the flight trajectory of the drone , Transmit power , Public Rate Allocation and packet split ratio , optimize the initial network model of the air-space-ground data edge computing system to obtain the optimal time allocation ;

[0009] S4, given time allocation , Transmit power and packet split ratio , based on the SCA method, the initial network model of the air-ground data edge computing system is optimized to obtain the optimal UAV flight trajectory and Public Rate Allocation ;

[0010] S5, given time allocation 、Drone flight trajectory , Public Rate Allocation and transmit power , optimize the initial network model of the air-space-ground data edge computing system to obtain the optimal data packet segmentation ratio ;

[0011] S6, given time allocation 、Drone flight trajectory , Public Rate Allocation and packet split ratio Under the condition of ;

[0012] S7, optimal time allocation based on BCD method 、Drone flight trajectory , Transmit power , Public Rate Allocation and packet split ratio Substitute into S3 and update , repeat S3 to S6 until the initial model of the air-space-ground data edge computing system network meets the convergence conditions, the iteration is terminated, and the optimal solution is obtained.

[0013] The air-space-ground network resource optimization system that minimizes the age of data collection information includes:

[0014] Model building module: with the goal of minimizing the age of data collection information, build the initial network model of the air-space-ground data edge computing system;

[0015] Initial parameter setting module: set the number of iterations , Iteration termination accuracy , given the initial UAV flight trajectory , initial transmission power , Initial Public Rate Allocation , Initial time allocation and initial packet split ratio ;

[0016] The first optimization module: given the UAV flight trajectory , Transmit power , Public Rate Allocation and packet split ratio , optimize the initial network model of the air-space-ground data edge computing system to obtain the optimal time allocation ;

[0017] Second optimization module: given time allocation , Transmit power and packet split ratio , based on the SCA method, the initial network model of the air-ground data edge computing system is optimized to obtain the optimal UAV flight trajectory and Public Rate Allocation ;

[0018] The third optimization module: given time allocation 、Drone flight trajectory , Public Rate Allocation and transmit power , optimize the initial network model of the air-space-ground data edge computing system to obtain the optimal data packet segmentation ratio ;

[0019] The fourth optimization module: given time allocation 、Drone flight trajectory , Public Rate Allocation and packet split ratio Under the condition of ;

[0020] And, iterative module: optimal time allocation based on BCD method 、Drone flight trajectory , Transmit power , Public Rate Allocation and packet split ratio Substitute into the first optimization module and update , repeat the first optimization module to the fourth optimization module until the initial model of the air-space-ground data edge computing system network meets the convergence conditions, the iteration is terminated, and the optimal solution is obtained.

[0021] A computer storage medium stores a readable program, which can execute the above-mentioned air-space-ground network resource optimization method for minimizing the age of data collection information when the program is running.

[0022] An electronic device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus;

[0023] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned air-space-ground network resource optimization method for minimizing the age of data collection information.

[0024] A computer program product includes computer instructions, wherein the computer instructions instruct a computing device to execute operations corresponding to the above-mentioned method for optimizing air-space-ground network resources by minimizing the age of data acquisition information.

[0025] Beneficial effects of the present invention:

[0026] 1. Under the maximum energy consumption constraints of user equipment and drones, the present invention studies the joint optimization of drone flight trajectory, transmission power, data packet segmentation ratio, public rate allocation and time allocation to minimize the system peak information age, and proposes an iterative algorithm based on block coordinate descent method and successive convex approximation method.

[0027] 2. The present invention proposes a new air-space-ground network resource framework for minimizing the age of data collection information. The previously proposed UAV-assisted data collection system framework did not take into account the situation where ground equipment generates a large amount of data, and a large amount of data will cause a large delay, reducing the freshness of the data packet. The framework proposed by the present invention can adapt to special application scenarios more flexibly and conveniently, and improve the freshness of the data packet and minimize the peak information age; the present invention considers multiple UAVs collecting data from ground equipment based on rate splitting multiple access (RSMA), part of the collected data is calculated locally on the UAV, and the remaining data is forwarded to the satellite for processing by the UAV group using non-orthogonal multiple access (NOMA) relay. Under the maximum energy consumption constraints of user equipment and UAVs, with the goal of minimizing the peak information age of the system, the UAV flight trajectory, transmission power, data packet splitting ratio, public rate allocation and time allocation are jointly optimized.

[0028] 3. The drone performs local computing and forwarding to the satellite for processing through NOMA technology, effectively reducing the peak information age of the system. RSMA is a non-orthogonal multiple access technology that improves system efficiency and reduces interference by allocating public and private information.

[0029] 4. Since the proposed optimization problem is non-convex and difficult to solve, the present invention solves four sub-problems through block coordinate descent (BCD) and continuous convex approximation (SCA); the optimization variables of the four sub-problems correspond to four blocks respectively; while keeping the other block variable unchanged, two sub-problems are solved based on the SCA method, and the optimization variables of the four sub-problems are optimized alternately; and the solution obtained in each iteration will be used as the input of the next iteration.

[0030] 5. The convergence of the proposed algorithm based on SCA and BCD can be guaranteed, and the required complexity is low. The simulation results show that the proposed scheme has great performance advantages over existing schemes, such as the scheme where the data packet is only calculated locally on the drone and the scheme where the data packet is only calculated on the satellite. The proposed algorithm significantly improves the performance. In addition, the algorithm can provide more stable performance to adapt to changes in the operating environment, and its advantages will be more prominent when the drone has a large data packet. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0032] Figure 1 This is a framework diagram of the space-ground network resources of the present invention;

[0033] Figure 2 It is a diagram of the data packet segmentation structure;

[0034] Figure 3 This is a flow chart of the method for optimizing air-space-ground network resources of the present invention;

[0035] Figure 4 The algorithm proposed in this invention is used in different device data packets The curve of the system peak information age and the number of iterations achieved under the value of;

[0036] Figure 5 is a line graph of the flight paths of the three UAVs in the present invention;

[0037] Figure 6 A curve diagram showing the relationship between the system peak information age and the maximum energy consumption of the device under the scheme proposed by the present invention, the scheme where the data packets are only calculated in the drone, and the scheme where the data packets are only calculated in the satellite;

[0038] Figure 7 A curve diagram showing the relationship between the system peak information age and the maximum energy consumption of the UAV under the scheme proposed by the present invention, the scheme where the data packets are calculated only in the UAV, and the scheme where the data packets are calculated only in the satellite;

[0039] Figure 8 A curve diagram showing the relationship between the system peak information age and the maximum flight speed of the UAV under the scheme proposed by the present invention, the scheme where the data packets are calculated only in the UAV, and the scheme where the data packets are calculated only in the satellite;

[0040] Fig. 9 It is a curve graph of the relationship between the system peak information age and the data packet size under the scheme proposed in the present invention, the data packet only calculated by the drone scheme, and the data packet only calculated by the satellite scheme. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] Example 1

[0043] like Figure 1 As shown in Figure 1, the air-ground data edge computing system network includes: M drones with edge servers, a satellite with cloud servers, and K user devices; the bandwidth used by the M drones based on RSMA is A single frequency band collects data from K user devices. Part of the collected data is calculated locally on the drone, and the remaining data is calculated by the drone swarm using the NOMA-based bandwidth. The single frequency band is relayed to the satellite for processing.

[0044] Set Represents a user device collection, collection Represents the set of drones. Three-dimensional coordinates are used to represent the positions of user devices, drones, and satellites. The position of the user device is represented as ,in Assume that the drone is at a fixed altitude From the starting point Departure flight to final destination ,in , The satellite altitude is , the three-dimensional coordinates are ,in Among them, N data packets are generated in K user devices, and the set represents the data packets generated for K user devices, data packets The size is , service time is , and the data packet adopts an instant transmission strategy, that is, after the previous data packet is processed, the next data packet has been generated and started to be transmitted immediately. Data packets are generated in a certain user device in chronological order. In order to conveniently represent the generation order and generation location of data packets, a binary function is defined Indicates user equipment The generated packets, otherwise the packet Generated on other user devices.

[0045] like Figure 2 As shown, in order to improve the processing efficiency of data packets, the data packets are Split into 2M packets to facilitate M drones to process and forward data packets. Service time It is divided into two stages. Specifically:

[0046] The first stage is the data packet collection stage, which lasts for During this phase, the user device Packet The packets are divided into M parts and uploaded to M drones in RSMA mode. Each drone only decodes the large packets that need to be processed. The received packet size is , the RSMA transmission duration is ,and .

[0047] The second stage is the packet processing stage, which lasts for In this stage, each drone will split the received large packet into two small packets again, one of which is processed on the edge server of the drone. The size of the small packet is , calculate the duration as ; Another small packet is forwarded to the satellite cloud server by the drone in NOMA mode for processing. The packet size is , the duration is ,and , .

[0048] Example 2

[0049] The space-ground network resource optimization method that minimizes the age of data collection information, such as Figure 3 As shown, the following steps are included:

[0050] S1, build the initial network model of the air-space-ground data edge computing system;

[0051] The initial network model of the air-space-ground data edge computing system is as follows:

[0052]

[0053]

[0054]

[0055]

[0056]

[0057]

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069] in, is the maximum peak information age of the system, Represents a user device collection, collection Represents a collection of drones, a collection represents N data packets generated for K user devices, and the set Before A collection of data packets, is represented as a set of optimization variables, Assign variables to time, Assign variables to public rates, is the packet split ratio variable, where Indicates drone The number of packets n calculated locally occupies the data packet The proportion of Indicates drone The number of packets occupied by the forwarded small packet n proportion. ,in For user equipment Transmitting data packets The transmission power of the public part, For user equipment Transmitting data packets Private sector to drones The transmission power, For drones Forwarding packets The transmission power; Indicates the SIC decoding tolerance or sensitivity; is the UAV flight trajectory variable, where For drones Receive packets on RSMA The discrete position of For drones Transmitting packets in NOMA Discrete positions at the time is the duration of the first phase, and the RSMA transmission duration is ,and , is the duration of the second phase, and the duration of NOMA forwarding is , the drone calculates the duration ,and ; is the maximum flight speed of the drone, is the minimum distance to ensure that drones avoid collision. It is the starting point of the drone’s flight. It is the flight endpoint of the drone; Assigned to drones From the user device Collect data packets The achievable rate of public data flow is is the minimum rate at which each drone can decode the public data stream. It's a drone From the user device Collect data packets The achievable rate of public data flow is is the communication bandwidth size, Is a user device With drones In transmitting data packets The channel gain at is the noise power at the satellite and the UAV; It's a drone RSMA receives data packets The number of bits, if the binary function Indicates user equipment The generated packets, otherwise the packet Generated on other user devices; It's a drone The number of data bits that can be calculated, It's a drone The computing power, i.e. the total number of CPU cycles, Indicates drone Calculate the total number of CPU cycles required for 1 bit of data; It's a drone NOMA transmission data packet The number of bits, It's a drone Transmitting data packets with satellite The channel gain at ; It's a drone The energy consumed, It is the effective switching capacitance of the CPU at the drone, which is related to the structure of the drone chip. It is the maximum energy consumption of the drone; is the energy consumed by the user device, is the maximum energy consumption of the user device; For data packets The size of For data packets Service time, and ; For drones The size of the received large packet and the size of the small packet calculated locally by the drone are , the size of the small packet forwarded by the drone to the satellite cloud server in NOMA mode is ,in Indicates drone The number of packets n calculated locally occupies the data packet The proportion of Indicates drone The number of packets occupied by the forwarded small packet n proportion.

[0070] Objective Function for:

[0071]

[0072] and,

[0073]

[0074] in, is the maximum peak information age of the system, For the The size of the peak information age, For data packets service time.

[0075] Constraint C1 is to ensure that the data packet All processing completed;

[0076] Constraints C2-C5 indicate that the distance between two adjacent discrete positions of the drone cannot exceed its flight capability;

[0077] Constraint C6 indicates that the distance between drones must be large enough to avoid collision;

[0078] Constraint C7 is to ensure that each drone can decode the common data stream;

[0079] Constraint C8 ensures that the drone successfully implements the SIC decoding operation;

[0080] Constraint C9 ensures that user equipment The big bag All RSMAs are uploaded to the drone;

[0081] Constraint C10 ensures that the drone can complete the calculation of the small packet data;

[0082] Constraint C11 ensures drones Can pack All NOMA forwarding completed;

[0083] Constraints C12-C13 limit the energy consumed by the drone and user equipment to not exceed their maximum values;

[0084] Constraints C14-C16 limit the feasible range of the optimization variables.

[0085] S2, set the number of iterations variable , Iteration termination accuracy , given the initial UAV flight trajectory , initial transmission power , Initial Public Rate Allocation , Initial time allocation and initial packet split ratio ;

[0086] S3, in In the iteration, given the flight trajectory of the UAV , Transmit power , Public Rate Allocation and packet split ratio Under the condition of optimizing the initial network model of the air-space-ground data edge computing system, the optimal time allocation is obtained. ,and As input for the next iteration;

[0087] Get the best time allocation The process is:

[0088] In a given Under the condition of , the optimized initial network model of the air-space-ground data edge computing system is:

[0089]

[0090]

[0091] Apparently This is a standard convex problem, which can be solved efficiently using standard convex optimization solvers (such as CVX) to obtain the optimal time allocation .

[0092] S4, in In the iterations, given time allocation , Transmit power and packet split ratio Under the condition of , the initial model of the air-space-ground data edge computing system network is optimized based on the SCA method to obtain the optimal UAV flight trajectory and Public Rate Allocation ,and and As input for the next iteration;

[0093] Get the best flight trajectory for your drone and Public Rate Allocation The steps are:

[0094] S41, in In the iterations, given time allocation , Transmit power and packet split ratio Under the condition of , the optimized initial network model of the air-space-ground data edge computing system is:

[0095]

[0096]

[0097] S42, Constraint By introducing slack variables and rewriting it into a convex function difference form, it can be converted to a convex form. Specifically, for the constraint Introducing slack variables ,have to

[0098]

[0099]

[0100] in, It is ground equipment To the drone Transmitting Data Packets The achievable rate of private data flow at Rewrite it into the difference form of convex function, and we get:

[0101]

[0102] Apply SCA technology to obtain The lower bound function of In the iterations, given the local point and the achievable rate of private data flows On, will The first-order Taylor expansion gives the lower bound function expression :

[0103]

[0104] in:

[0105]

[0106] so The lower bound of is:

[0107]

[0108] Similarly, in Given a local point in the iteration and the public data flow achievable rate On, get The lower bound of is:

[0109]

[0110] in, for The lower bound function expression of is:

[0111]

[0112] So the constraint Approximate constraints

[0113]

[0114] S43, Constraint and constraints For drone tracks It is still non-convex, and obviously the constraint Equivalent to the constraint , in Given the local point of the UAV trajectory in the iteration On the other hand, SCA technology is used to obtain and The lower bound function and :

[0115]

[0116]

[0117] in:

[0118]

[0119]

[0120]

[0121]

[0122] and for At the point About The first derivative of for At the point The value at for At the point About The first derivative of for At the point So Approximate constraints :

[0123]

[0124] Likewise, constraints Approximate constraints :

[0125]

[0126] S44, Constraint Introducing slack variables After that, constraints Approximately the following constraints:

[0127]

[0128]

[0129] in It's a drone Forwarding data packets to satellite NOMA The rate at which In the iterations, given the local point and NOMA reception rate at the satellite On, get The lower bound of is:

[0130]

[0131] in for The lower bound function expression of is:

[0132]

[0133] So the constraint Approximate constraints :

[0134]

[0135] S45, Constraint For drone tracks It is still non-convex. In order to facilitate the analysis of the problem, Expand and rewrite as:

[0136]

[0137] Written in convex difference form

[0138] in:

[0139]

[0140]

[0141] In the Given the local point of the UAV trajectory in the iteration On the other hand, SCA technology is used to obtain The lower bound function for

[0142]

[0143] in

[0144]

[0145]

[0146] in, for At the point About The first derivative of for At the point So Approximately the following constraints:

[0147]

[0148] but For drone tracks is still non-convex, so in Introducing slack variables , and At a given local point Expand on Taylor and get Approximate constraints :

[0149]

[0150]

[0151] S46, Processing Constraints The non-convexity of Given a local point in the iteration Taylor expansion gives The lower bound function of :

[0152]

[0153] So the constraint Approximately the following constraints :

[0154]

[0155] S47, in In the iterations, the time allocation is given , Transmit power and packet split ratio Under the condition of , the initial network model of the air-space-ground data edge computing system to be optimized is updated as follows:

[0156]

[0157]

[0158] Apparently This is a standard convex problem, which can be solved efficiently using a standard convex optimization solver (such as CVX) to obtain the optimal UAV flight trajectory. and Public Rate Allocation .

[0159] S5, in In the iterations, given time allocation 、Drone flight trajectory , Public Rate Allocation and transmit power Under the condition of ,and As input for the next iteration;

[0160] Get the optimal packet segmentation ratio The steps are:

[0161] S51, in In the iterations, given time allocation 、Drone flight trajectory , Public Rate Allocation and transmit power Under the condition of , the optimized initial network model of the air-space-ground data edge computing system is:

[0162]

[0163]

[0164] Apparently This is a standard convex problem, which can be solved efficiently using a standard convex optimization solver (such as CVX) to obtain the optimal packet split ratio. .

[0165] S6, in In the iterations, given time allocation 、Drone flight trajectory , Public Rate Allocation and packet split ratio Under the condition of ,and As input for the next iteration;

[0166] Get the best transmit power The steps are:

[0167] S61, in In the iterations, given time allocation 、Drone flight trajectory , Public Rate Allocation and packet split ratio Under the condition of , the initial network model of the air-space-ground data edge computing system is optimized based on the SCA method:

[0168]

[0169]

[0170] S62, in Given a local point in the iteration and the achievable rate of private data flows On, constraint By introducing slack variables and then rewriting it into a differential form of a convex function, it can be converted into a convex form and introduce slack variables Get approximate constraints

[0171]

[0172]

[0173] S63, Constraint and constraints For transmit power It is still non-convex, and obviously the constraint Equivalent to the constraint In order to facilitate the analysis of the problem, Expand and rewrite it into the difference form of convex function

[0174]

[0175] in

[0176]

[0177]

[0178] Likewise, Expand and rewrite it into the difference form of convex function

[0179]

[0180] in

[0181]

[0182]

[0183] In the In the iterations, given the local point On the function and The first-order Taylor expansion gives the upper bound function expression and :

[0184]

[0185]

[0186] and,

[0187]

[0188]

[0189]

[0190]

[0191] in, for At the point About The first derivative of for At the point The value at for At the point About The first derivative of for At the point So the constraint Approximately the following constraints

[0192]

[0193] Likewise, constraints Approximately the following constraints

[0194]

[0195] S64, similarly, at a given local point and NOMA reception rate at the satellite On, constraint Introducing slack variables , and then rewrite it into the differential form of convex function to obtain the approximate constraint condition

[0196]

[0197]

[0198] S65, Constraint For transmit power It is still non-convex. To facilitate the analysis of the problem, Expand and rewrite it into the difference form of convex function

[0199]

[0200] in

[0201]

[0202]

[0203] In the In the iterations, given the local point On the function The first-order Taylor expansion gives the upper bound function expression

[0204]

[0205] in

[0206]

[0207]

[0208] in, for At the point About The first derivative of for At the point So Approximate constraints

[0209]

[0210] S66, for constraints ,Will Rewritten into the differential form of a convex function, we have:

[0211]

[0212] Apparently For constraints is non-convex, and the SCA technique will It is approximately a concave function. Specifically, in the In the iterations, given the local point and Go up, get The first-order Taylor expansion of the lower bound function is obtained The expression is

[0213]

[0214] in

[0215]

[0216] so The upper bound of

[0217]

[0218] So the constraint Approximate constraints

[0219]

[0220] S67, similarly, so the constraint Approximate constraints

[0221]

[0222] in for The lower bound function expression of for The lower bound function expression of is:

[0223]

[0224]

[0225] S68, No. In the iterations, at a given time, 、Drone flight trajectory , Public Rate Allocation and packet split ratio Under the condition of , the initial network model of the air-space-ground data edge computing system optimized by the SCA method is:

[0226]

[0227] .

[0228] Apparently This is a standard convex problem, which can be solved efficiently using a standard convex optimization solver (such as CVX) to obtain the optimal transmission power. .

[0229] S7, optimal time allocation based on BCD method 、Drone flight trajectory , Transmit power , Public Rate Allocation and packet split ratio Substitute into S3 and update the iteration count variable , repeat S3 to S6 until the initial network model of the air-space-ground-air data edge computing system meets the convergence conditions, the iteration is terminated, and the optimal solution of the initial network model of the air-space-ground-air data edge computing system is obtained.

[0230] The convergence conditions are:

[0231]

[0232] in, is the objective function value obtained in the iteration, Iteration termination precision.

[0233] Based on similar inventive concepts, an embodiment of the present invention also provides a computer storage medium storing a readable program, which, when running, can execute the above-mentioned air-space-ground network resource optimization method for minimizing the age of data collection information.

[0234] Based on similar inventive concepts, an embodiment of the present invention provides an electronic device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus;

[0235] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned air-space-ground network resource optimization method for minimizing the age of data collection information.

[0236] Based on similar inventive concepts, an embodiment of the present invention also provides a computer program product, including computer instructions, which instruct a computing device to execute operations corresponding to the above-mentioned air-space-ground network resource optimization method for minimizing the age of data collection information.

[0237] Example 3

[0238] In this embodiment, the scheme of the present invention is described in detail in conjunction with a specific embodiment. The specific embodiment is simulated using MATLAB software. The specific parameters are set as shown in Table 1.

[0239] Table 1 Simulation parameters

[0240]

[0241] In addition to the above table, the coordinates of the two ground devices are set as =(-100,200), =(100,-200), and device 1 generates four data packets in chronological order first, and device 2 generates four data packets in chronological order later.

[0242] like Figure 4 As shown in the figure, for different packet sizes, the peak information age decreases with the increase of iteration times and eventually stabilizes. This shows that the proposed optimization algorithm can effectively reduce the peak information age of the network system and the algorithm has good convergence. The larger the packet size, the higher the initial peak information age, but through iterative optimization, it can reach a lower stable state.

[0243] like Figure 5 As shown in the figure, the flight trajectory of the drone is wavy. This is because four data packets are about to be generated in the upper device 1 in chronological order, so the three drones are close to the position of device 1 at the beginning. After collecting all the data packets of device 1, four more data packets are about to be generated in the lower device 2 in chronological order, so the three drones start to approach the position of device 2 to better collect the data packets of device 2. The horizontal position of the satellite will be passed during the flight, but the position of the device has a greater impact on the drone trajectory. This is because the satellite is relatively high and relies more on power to complete data forwarding.

[0244] like Figure 6 As shown in Figure 2, as the maximum energy of the device increases, the peak information age of all schemes decreases, but the proposed scheme has the smallest peak information age. This is because more energy enables the device to transmit data faster, reducing the peak information age, but the proposed scheme uses energy resources more efficiently by optimizing packet segmentation and computing resource allocation.

[0245] like Figure 7 As shown in the figure, as the maximum energy of the drone increases, the peak information age of the proposed scheme and the scheme where the data packets are only calculated in the satellite are both decreasing, while the peak information age of the scheme where the data packets are only calculated in the drone has no significant change and is higher than the other two schemes. This is because the proposed scheme optimizes data packet segmentation and computing resource allocation, so that efficient data processing can be achieved under limited energy, thereby reducing the information age. The drone local computing scheme may be limited by the drone's maximum computing CPU cycle. Without a corresponding boost, the ability to process packets does not increase with the added energy, and therefore the peak information age does not improve.

[0246] like Figure 8 As shown in the figure, as the maximum flight speed of the UAV increases, the peak information age of all schemes decreases, but the peak information age of the proposed scheme is the smallest. This is because the UAV flying at high speed can complete data collection and transmission faster, but the proposed scheme further reduces the information processing delay by optimizing the flight trajectory, data packet segmentation ratio and computing resource allocation.

[0247] like Fig. 9 As shown in the figure, as the packet size increases, the peak information age of all schemes increases, but the proposed scheme grows the slowest. The proposed scheme splits the packet so that large packets can more effectively distribute computing resources between the UAV and the satellite, thereby reducing the delay of information processing and lowering the peak information age.

[0248] Example 4

[0249] In this embodiment, a space-ground network resource optimization system for minimizing the age of data collection information is proposed, which specifically includes:

[0250] Model building module: build the initial network model of the air-space-ground data edge computing system;

[0251] Initial parameter setting module: set the number of iterations , Iteration termination accuracy , given the initial UAV flight trajectory , initial transmission power , Initial Public Rate Allocation , Initial time allocation and initial packet split ratio ;

[0252] The first optimization module: given the UAV flight trajectory , Transmit power , Public Rate Allocation and packet split ratio , optimize the initial network model of the air-space-ground data edge computing system to obtain the optimal time allocation ;

[0253] Second optimization module: given time allocation , Transmit power and packet split ratio , based on the SCA method, the initial network model of the air-ground data edge computing system is optimized to obtain the optimal UAV flight trajectory and Public Rate Allocation ;

[0254] The third optimization module: given time allocation 、Drone flight trajectory , Public Rate Allocation and transmit power , optimize the initial network model of the air-space-ground data edge computing system to obtain the optimal data packet segmentation ratio ;

[0255] The fourth optimization module: given time allocation 、Drone flight trajectory , Public Rate Allocation and packet split ratio Under the condition of ;

[0256] And, iterative module: optimal time allocation based on BCD method 、Drone flight trajectory , Transmit power , Public Rate Allocation and packet split ratio Substitute into the first optimization module and update , repeat the first optimization module to the fourth optimization module until the initial model of the air-space-ground data edge computing system network meets the convergence conditions, the iteration is terminated, and the optimal solution is obtained.

[0257] The method of the present invention may be implemented in hardware, firmware, or as software or computer code that may be stored in a recording medium (such as a CDROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded over a network and will be stored in a local recording medium, so that the method described herein may be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that a computer, processor, microprocessor controller, or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, processor, or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the method shown herein.

[0258] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A method for optimizing space-ground network resources by minimizing the age of data collection information, characterized in that: The following steps are involved: S1, with the goal of minimizing the age of data collection information, build the initial network model of the air-space-ground data edge computing system; S2, set the number of iterations , Iteration termination accuracy , given the initial UAV flight trajectory , initial transmission power , Initial Public Rate Allocation , Initial time allocation and initial packet split ratio ; S3, given the flight trajectory of the drone , Transmit power , Public Rate Allocation and packet split ratio , optimize the initial network model of the air-space-ground data edge computing system to obtain the optimal time allocation ; S4, given time allocation , Transmit power and packet split ratio , based on the SCA method, the initial network model of the air-ground data edge computing system is optimized to obtain the optimal UAV flight trajectory and Public Rate Allocation ; S5, given time allocation 、Drone flight trajectory , Public Rate Allocation and transmit power , optimize the initial network model of the air-space-ground data edge computing system to obtain the optimal data packet segmentation ratio ; S6, given time allocation 、Drone flight trajectory , Public Rate Allocation and packet split ratio Under the condition of ; S7, optimal time allocation based on BCD method 、Drone flight trajectory , Transmit power , Public Rate Allocation and packet split ratio Substitute into S3 and update , repeat S3 to S6 until the initial model of the air-space-ground data edge computing system network meets the convergence conditions, the iteration is terminated, and the optimal solution is obtained.

2. The method for optimizing air-space-ground network resources by minimizing the age of data collection information according to claim 1, characterized in that: The network of the air-ground data edge computing system includes: M drones with edge servers, a satellite with a cloud server, and K user devices. N data packets are generated in the K user devices. The bandwidth used by drones based on RSMA is A single frequency band collects data from K user devices. Part of the collected data is calculated locally on the drone, and the remaining data is calculated using the NOMA-based bandwidth. The single frequency band is relayed to the satellite for processing.

3. The method for optimizing air-space-ground network resources by minimizing the age of data collection information according to claim 2, characterized in that: The data packets adopt an immediate transmission strategy: after the previous data packet is processed, the next data packet is generated and starts to be transmitted immediately.

4. The method for optimizing air-space-ground network resources by minimizing the age of data collection information according to claim 3, characterized in that: Data Pack Divide into 2M packets, and the data packets Service time It is divided into the following two stages: The first stage is the data packet collection stage, which lasts for During this phase, the user device Packet The packets are divided into M parts and uploaded to M drones in RSMA mode. Each drone only decodes the large packets that need to be processed. The received packet size is , the RSMA transmission duration is ,and ; The second stage is the packet processing stage, which lasts for In this stage, each drone will split the received large packet into two small packets again, one of which is processed on the edge server of the drone. The size of the small packet is , calculate the duration as ; Another small packet is forwarded to the satellite cloud server by the drone in NOMA mode for processing. The packet size is , the duration is ,and , .

5. The method for optimizing air-space-ground network resources by minimizing the age of data collection information according to claim 4, characterized in that: The initial network model of the air-space-ground data edge computing system is: in, is the maximum peak information age of the system; Represents a user device collection, collection Represents a collection of drones, a collection represents N data packets generated for K user devices, and the set Before A collection of data packets, is represented as a set of optimization variables, Assign variables to time, Assign variables to public rates, is the packet split ratio variable, where Indicates drone The number of packets n calculated locally occupies the data packet The proportion of Indicates drone The number of packets occupied by the forwarded small packet n proportion; ,in For user equipment Transmitting data packets The transmission power of the public part, For user equipment Transmitting data packets Private sector to drones The transmission power, For drones Forwarding packets The transmission power; Indicates the SIC decoding tolerance or sensitivity; is the UAV flight trajectory variable, where For drones Receive packets on RSMA The discrete position of For drones Transmitting packets in NOMA Discrete positions at the time is the maximum flight speed of the drone, is the minimum distance to ensure that drones avoid collision. It is the starting point of the drone’s flight. It is the flight endpoint of the drone; is the minimum rate at which each drone can decode the public data stream. Assigned to drones From user device Collect data packets The achievable rate of public data flows is Is a user device With drones In transmitting data packets The channel gain at is the noise power at the satellite and the UAV; It's a drone RSMA receives data packets The number of bits; It's a drone The number of data bits that can be calculated; It's a drone NOMA transmission data packet The number of bits; It's a drone The energy consumed, It is the maximum energy consumption of the drone; is the energy consumed by the user device, is the maximum energy consumption of the user device; For data packets size; For drones The size of the received large packet and the size of the small packet calculated locally by the drone are , the size of the small packet forwarded by the drone to the satellite cloud server in NOMA mode is ,in Indicates drone The number of packets n calculated locally occupies the data packet The proportion of Indicates drone The number of packets occupied by the forwarded small packet n proportion.

6. The method for optimizing air-space-ground network resources by minimizing the age of data collection information according to claim 5, characterized in that: Maximum peak information age of the system The calculation formula is: in, For the The size of the peak information age.

7. A space-ground network resource optimization system with minimized data collection information age, characterized in that: include: Model building module: with the goal of minimizing the age of data collection information, build the initial network model of the air-space-ground data edge computing system; Initial parameter setting module: set the number of iterations , Iteration termination accuracy , given the initial UAV flight trajectory , initial transmission power , Initial Public Rate Allocation , Initial time allocation and initial packet split ratio ; The first optimization module: given the UAV flight trajectory , Transmit power , Public Rate Allocation and packet split ratio , optimize the initial network model of the air-space-ground data edge computing system to obtain the optimal time allocation ; Second optimization module: given time allocation , Transmit power and packet split ratio , based on the SCA method, the initial network model of the air-ground data edge computing system is optimized to obtain the optimal UAV flight trajectory and Public Rate Allocation ; The third optimization module: given time allocation 、Drone flight trajectory , Public Rate Allocation and transmit power , optimize the initial network model of the air-space-ground data edge computing system to obtain the optimal data packet segmentation ratio ; The fourth optimization module: given time allocation 、Drone flight trajectory , Public Rate Allocation and packet split ratio Under the condition of ; And, iterative module: optimal time allocation based on BCD method 、Drone flight trajectory , Transmit power , Public Rate Allocation and packet split ratio Substitute into the first optimization module and update , repeat the first optimization module to the fourth optimization module until the initial model of the air-space-ground data edge computing system network meets the convergence conditions, the iteration is terminated, and the optimal solution is obtained.

8. A computer storage medium storing a readable program, characterized in that: When the program is running, it can execute the air-space-ground network resource optimization method for minimizing the age of data collection information as described in any one of claims 1-6.

9. An electronic device, characterized in that: include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the air-space-ground network resource optimization method for minimizing the age of data collection information as described in any one of claims 1-6.

10. A computer program product comprising computer instructions, characterized in that: The computer instructions instruct the computing device to execute operations corresponding to the air-space-ground network resource optimization method for minimizing the age of data collection information as described in any one of claims 1-6.

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