Space-air-ground integrated network throughput optimization method and device, electronic equipment and storage medium

Abstract

Embodiments of the present application provide a throughput optimization method and device for space-air-ground integrated network, electronic equipment and storage medium, relating to the technical field of wireless communication. The method obtains a video list in the current high-altitude platform, according to the popularity of each video, and according to a preset strategy, caches each video content to each unmanned aerial vehicle to obtain video cache information. The steps of calculating the video transmission path information of the high-altitude platform and each unmanned aerial vehicle to each vehicle terminal for transmitting each video content through the distributed alternating direction multiplier method according to the video cache information, and updating the video cache information through the greedy algorithm according to the video transmission path information are repeatedly executed until the video cache information or / and the video transmission path information meets the preset condition, so that the throughput of the space-air-ground integrated network is optimized. Thus, the global throughput of the space-air-ground integrated network can be optimized through the joint strategy of cache optimization and content distribution optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, in particular to a throughput optimization method and device for space-air-ground integrated network, electronic equipment and storage medium. BACKGROUND

[0002] With the continuous development of Internet of Vehicles technology and the wide application of vehicle multimedia services (such as high-definition video streaming), the traditional ground communication network exposes many bottlenecks in hotspot areas, high dynamic traffic scenarios and emergency communication environments, such as high base station load, frequent link switching, large transmission delay, service interruption and other problems, which seriously affect the service experience quality of vehicle users. In order to alleviate these problems, in recent years, the air platform assisted communication technology based on unmanned aerial vehicles has gradually become a research hotspot.

[0003] Unmanned aerial vehicles have the advantages of flexible deployment, low delay transmission, high probability of line-of-sight communication, etc., and can be used as mobile cache nodes and relay devices to assist high-altitude platforms to provide content services to vehicle users. Especially during off-peak hours, unmanned aerial vehicles can pre-cache popular video content and provide efficient services to vehicle users through close-range transmission during peak hours, thereby reducing the pressure on the core network and improving system transmission efficiency and cache hit rate.

[0004] Related technologies have extended the deployment of caches from fixed base stations to unmanned aerial vehicle platforms. For example, some solutions propose to place unmanned aerial vehicle caches based on user request prediction; some research uses a centralized scheduling mechanism to optimize content distribution paths.

[0005] However, most existing solutions optimize caches and content distribution at the same time scale, ignoring the differences in change frequency and update cost, resulting in low utilization of cache resources, slow distribution response and limited system throughput in high dynamic Internet of Vehicles environments. SUMMARY

[0006] Therefore, the purpose of the embodiments of the present application is to provide a throughput optimization method and device for space-air-ground integrated network, electronic equipment and storage medium to at least partially improve the above problems.

[0007] In order to achieve the above purpose, the technical solutions adopted by the embodiments of the present application are as follows:

[0008] In a first aspect, the embodiments of the present application provide a throughput optimization method for space-air-ground integrated network, the space-air-ground integrated network comprising at least one high-altitude platform, a plurality of unmanned aerial vehicles and a plurality of vehicle terminals, each of the unmanned aerial vehicles being in communication connection with each of the high-altitude platforms and each of the vehicle terminals, and the method comprising:

[0009] obtaining a video list in the high-altitude platform, the video list comprising a plurality of video contents and a popularity of each of the video contents, the popularity representing a probability of the video content being requested;

[0010] According to the popularity, each video content is cached to each unmanned aerial vehicle according to a preset strategy to obtain video cache information;

[0011] According to the video cache information, the video transmission path information of the high-altitude platform and each unmanned aerial vehicle transmitting each video content to each vehicle terminal is calculated by a distributed alternating direction multiplier method;

[0012] According to the video transmission path information, the video cache information is updated by a greedy algorithm;

[0013] The steps of calculating the video transmission path information of the high-altitude platform and each unmanned aerial vehicle transmitting each video content to each vehicle terminal by a distributed alternating direction multiplier method according to the video cache information and the step of updating the video cache information by a greedy algorithm according to the video transmission path information are repeatedly executed until the video cache information or / and the video transmission path information meets a preset condition, so that the throughput of the space-air-ground integrated network is optimized.

[0014] Optionally, the video transmission path information comprises unmanned aerial vehicle direct transmission path information and unmanned aerial vehicle relay path information, the unmanned aerial vehicle direct transmission path information representing whether the video content is directly transmitted to the vehicle terminal by the unmanned aerial vehicle, and the unmanned aerial vehicle relay path information representing whether the video content is transmitted to the vehicle terminal by the high-altitude platform through the unmanned aerial vehicle relay, and the step of calculating the video transmission path information of the high-altitude platform and each unmanned aerial vehicle transmitting each video content to each vehicle terminal by a distributed alternating direction multiplier method according to the video cache information comprises:

[0015] The unmanned aerial vehicle direct transmission path information and the unmanned aerial vehicle relay path information are respectively relaxed into first direct transmission continuous variables and first relay continuous variables;

[0016] The first direct transmission continuous variables and the first relay continuous variables are copied to each unmanned aerial vehicle to obtain each direct transmission sub-continuous variable and each relay sub-continuous variable, and an augmented Lagrangian problem for solving the video transmission path information is constructed;

[0017] The augmented Lagrangian problem is distributedly iteratively optimized to calculate second direct transmission continuous variables and second relay continuous variables after iteration;

[0018] The second direct transmission continuous variable and the second relay continuous variable are modified to integer variables to obtain video transmission path information of the high-altitude platform and each unmanned aerial vehicle transmitting each video content to each vehicle-mounted terminal.

[0019] Optionally, the augmented Lagrangian problem is:

[0020]

[0021] wherein, and is a Lagrange multiplier, represents a penalty parameter, represents a constraint set of the augmented Lagrangian problem, is a penalty function when , otherwise, , the direct transmission sub-continuous variable, the relay sub-continuous variable, the first direct transmission continuous variable, and the first relay continuous variable, respectively, F, J, I, and V are the number of video contents, the number of high-altitude platforms, the number of unmanned aerial vehicles, and the number of vehicle-mounted terminals, respectively, is the total data amount transmitted by the unmanned aerial vehicle i.

[0022] Optionally, the distributed iterative optimization is performed on the augmented Lagrangian problem to calculate the second direct transmission continuous variable and the second relay continuous variable after iteration, and the method comprises the following steps.

[0023] For each iteration optimization, the direct transmission sub-continuous variable and the relay sub-continuous variable corresponding to each unmanned aerial vehicle are updated by a sub-variable update formula;

[0024] Based on a global variable update formula, the first direct transmission continuous variable and the first relay continuous variable are updated according to the direct transmission sub-continuous variable and the relay sub-continuous variable;

[0025] Based on a Lagrange multiplier update formula, the Lagrange multiplier of the augmented Lagrangian problem is updated;

[0026] After a preset number of iterations, the second direct transmission continuous variable and the second relay continuous variable after iteration of the first direct transmission continuous variable and the first relay continuous variable are obtained.

[0027] Optionally, the sub-variable update formula is:

[0028]

[0029] The global variable update formula is:

[0030]

[0031] The Lagrange multiplier update formula is:

[0032]

[0033] Wherein, is the number of updates.

[0034] Optionally, the video cache information is updated according to the video transmission path information by using a greedy algorithm, comprising:

[0035] The video cache information is relaxed into a video cache continuous variable;

[0036] According to the video cache continuous variable, the video cache information is reconstructed into a convex problem;

[0037] The continuous optimal solution of the convex problem is calculated using linear programming;

[0038] The continuous optimal solution is adjusted by using a greedy algorithm to obtain an integer optimal solution;

[0039] The video cache information is updated to the integer optimal solution.

[0040] Optionally, the integer optimal solution is obtained by adjusting the continuous optimal solution by using a greedy algorithm, comprising:

[0041] For each of the UAVs, the storage probability of each of the video contents stored by the UAV is found from the continuous optimal solution;

[0042] According to the order from large to small of each of the storage probabilities, the video contents corresponding to each of the storage probabilities are sequentially stored in the UAV until the number of each of the video contents stored in the UAV is equal to the cache capacity of the UAV, and the integer optimal solution of the UAV is obtained.

[0043] In a second aspect, an embodiment of the present application provides a throughput optimization device of an air-space-ground integrated network, the air-space-ground integrated network comprising at least one high-altitude platform, a plurality of UAVs and a plurality of vehicle terminals, each of the UAVs being in communication connection with each of the high-altitude platforms and each of the vehicle terminals, and the device comprising:

[0044] A video acquisition unit is configured to acquire a video list in the current high-altitude platform, wherein the video list comprises a plurality of video contents and a popularity of each of the video contents, and the popularity represents a probability of the video content being requested.

[0045] a video initial caching unit configured to cache each of the video contents to each of the UAVs according to the preset strategy and the popularity of each of the video contents, to obtain video caching information;

[0046] a video transmission path calculation unit configured to calculate, according to the video caching information, video transmission path information of transmission of each of the video contents from the HAP and each of the UAVs to each of the vehicle terminals by a distributed alternating direction multiplier method;

[0047] a video caching information calculation unit configured to update the video caching information by a greedy algorithm according to the video transmission path information;

[0048] an iteration unit configured to repeatedly execute the steps of calculating, according to the video caching information, the video transmission path information of transmission of each of the video contents from the HAP and each of the UAVs to each of the vehicle terminals by the distributed alternating direction multiplier method and the step of updating the video caching information by the greedy algorithm according to the video transmission path information until the video caching information or / and the video transmission path information meets a preset condition, so as to optimize the throughput of the space-aerial-terrestrial integrated network.

[0049] In a third aspect, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the method of any one of the above aspects when executing the computer program.

[0050] In a fourth aspect, a storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the method of any one of the above aspects.

[0051] The throughput optimization method, device, electronic device and storage medium of the space-aerial-terrestrial integrated network provided by the embodiments of the present application can compensate for the high transmission delay of the HAP by the collaborative communication of the space-aerial-terrestrial three-layer network and the flexible deployment capability of the UAV, effectively solve the communication bottleneck problem of the vehicle users in the high dynamic environment in the space-aerial-terrestrial integrated network through the joint optimization mechanism of caching and transmission path, and significantly improve the network throughput and resource utilization.

[0052] In order to make the above objectives, characteristics and advantages of the present application more apparent and easy to understand, the following preferred embodiments are specifically described below, and the accompanying drawings are referred to for detailed description. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. It should be understood that the following drawings only show some of the embodiments of the present application, and therefore should not be regarded as a limitation to the scope. For those skilled in the art, other related drawings can also be obtained without creative labor.

[0054] Figure 1 A schematic structural block diagram of an electronic device provided by the embodiments of the present application;

[0055] Figure 2 A schematic structural block diagram of a space-air-ground integrated network provided by the embodiments of the present application;

[0056] Figure 3 A flowchart of a throughput optimization method of a space-air-ground integrated network provided by the embodiments of the present application;

[0057] Figure 4 Another flowchart of a throughput optimization method of a space-air-ground integrated network provided by the embodiments of the present application;

[0058] Figure 5 A flowchart of step S233 provided by the embodiments of the present application;

[0059] Figure 6 Another flowchart of a throughput optimization method of a space-air-ground integrated network provided by the embodiments of the present application;

[0060] Figure 7 A schematic structural block diagram of a throughput optimization device of a space-air-ground integrated network provided by the embodiments of the present application.

[0061] Figure: 100-electronic device; 101-memory; 102-communication interface; 103-processor; 104-communication bus; 300-throughput optimization device of a space-air-ground integrated network; 310-video acquisition unit; 320-video initial cache unit; 330-video transmission path calculation unit; 340-video cache information calculation unit; 350-iteration unit. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, not all the embodiments. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations.

[0063] The detailed description of the exemplary embodiments of the present application provided below is not intended to limit the scope of the claimed application, but merely to represent selected embodiments of the application. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments of the present application, without making creative efforts, fall within the scope of the present application.

[0064] It should be noted that similar reference numerals and letters refer to similar items throughout the drawings, and once an item is defined in one drawing, it is not necessary for it to be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", and the like are merely used to distinguish different descriptions, and cannot be understood as indicating or implying relative importance.

[0065] It should be noted that, in the present document, the terms such as first and second are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of other identical elements in the process, method, article or device including the element.

[0066] To implement the flow steps and functions of various examples of the present application, please refer to Figure 1 , Figure 1 A schematic structural block diagram of an electronic device according to an embodiment of the present application is provided. The electronic device 100 includes a memory 101 and a processor 103, which are directly or indirectly electrically connected to each other to realize data transmission or interaction. For example, these elements can be electrically connected to each other through one or more communication buses 104 or signal lines. The memory 101 can be used to store software programs and modules, and the processor 103 executes the software programs and modules stored in the memory 101, thereby performing various functional applications and data processing.

[0067] The electronic device 100 can be, but is not limited to, a personal computer (PC), a server, a computer in a distributed deployment, and the like. It can be understood that the electronic device 100 is not limited to a physical server, but can also be a virtual machine on a physical server, a virtual machine built on a cloud platform, and the like, which can provide a computer having the same function as the server or the virtual machine. The operating system of the electronic device 100 can be, but is not limited to, a Windows system, a Linux system, and the like.

[0068] The memory 101 can be, but is not limited to, a random access memory (RAM), a read only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), and the like.

[0069] The communication connection between the electronic device 100 and an external device is realized through at least one communication interface 102 (which can be wired or wireless).

[0070] The processor 103 can be an integrated circuit chip having a processing capability of a signal. In the implementation process, each step of the embodiments of the present application can be completed by an integrated logic circuit or an instruction in the form of software in the processor 103. The processor 103 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), and the like; can also be a digital signal processor (Network Processor, DSP), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a ready programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0071] It can be understood that the processor 103 can implement the steps of the embodiments of the present application in the form of software instructions. Figure 1 The structure shown is only schematic, and the electronic device 100 can further include more or fewer components than those shown in the figure, or have a different configuration from that shown in the figure. Figure 1 The structure shown is only schematic, and the electronic device 100 can further include more or fewer components than those shown in the figure, or have a different configuration from that shown in the figure. Figure 1 The structure shown is only schematic, and the electronic device 100 can further include more or fewer components than those shown in the figure, or have a different configuration from that shown in the figure. Figure 1The components shown in the figures can be implemented in hardware, software, or a combination thereof.

[0072] To facilitate understanding of the embodiments of the present application, first, the space-air-ground integrated network is described with reference to Figure 2 The space-air-ground integrated network includes high-altitude platforms, a plurality of unmanned aerial vehicles, and a plurality of vehicle terminals. Each unmanned aerial vehicle is in communication connection with each high-altitude platform and each vehicle terminal. The high-altitude platform is regarded as quasi-static deployment, wherein the unmanned aerial vehicle can hover for several hours at the quasi-static position, and the high-altitude platform serves as a stable air base to continuously provide services. The set of high-altitude platforms is denoted as The set of unmanned aerial vehicles is denoted as And the set of vehicle terminals is denoted as .

[0073] The high-altitude platform has wide-area coverage capability and can cover the entire vehicle user area. The high-altitude platform not only has strong storage capacity and computing capability, but also can directly obtain data from the core network, and has a complete video content set , that is, the high-altitude platform includes all the video contents. Since the distance between the high-altitude platform and the vehicle terminal is far, there is a high delay and bandwidth bottleneck in data transmission. Therefore, the network deploys unmanned aerial vehicles with cache and relay capabilities to assist the high-altitude platform to transmit the required video data to the ground vehicle users. On the one hand, when the requested video content has been pre-cached by the unmanned aerial vehicle, the unmanned aerial vehicle can directly provide transmission services to the vehicle user, thereby reducing the delay, improving the transmission rate and reliability; on the other hand, when the cache of the unmanned aerial vehicle cannot meet the request, the high-altitude platform assists to complete the data forwarding through the unmanned aerial vehicle relay. That is, the path for the vehicle terminal to obtain the video content includes two paths, one is directly obtained from the unmanned aerial vehicle, and the other is obtained from the high-altitude platform through the unmanned aerial vehicle.

[0074] Since the cache capacity of the unmanned aerial vehicle is limited, it can only cache a part of the popular video content set. An integer variable is used to represent whether the video content is cached in the unmanned aerial vehicle , and represents the maximum cache capacity of the unmanned aerial vehicle . In order to facilitate calculation, the maximum cache capacity is defined as the number of video contents that can be stored. Therefore, for any unmanned aerial vehicle , the cache variable should follow the following constraints: .

[0075] The popularity of the preset video content obeys Zipf distribution. This popularity distribution represents the probability of the vehicle terminal requesting the corresponding video content, and further affects the cache placement and transmission performance. represents the video content Popularity, i.e., the number of times the in-vehicle terminal requests video content. The probability of:

[0076]

[0077] in, This represents the concentration parameter of popularity, and its value is between [0.1, 1.2].

[0078] Use integer variables Indicate whether it is by drone To direct vehicle terminal Transmit video content When drones To vehicle terminal Transmit video content When the variable is active, it is set to 1; otherwise, it is set to 0. When a drone provides transmission services, its cache must be able to meet user requests; that is, the transmission variable must meet the following cache constraints: .

[0079] In the case of providing transmission services from a high-altitude platform, integer variables are used. Identify whether it is a relay node drone Auxiliary aerial platform To vehicle terminal Transmit video content If the relay node Assisting high-altitude platforms To vehicle users Video content ,but If it is set to 1, then it is set to 0.

[0080] Vehicle terminal Get video content The only way will be and One of them, therefore .

[0081] Furthermore, considering bandwidth limitations, there is a limit to the number of users each high-altitude platform and drone can serve during transmission, and this limit must not exceed their respective transmission capabilities. Therefore, this integrated air-space-ground network also requires the following constraints:

[0082]

[0083]

[0084] The service capacity limit of the high-altitude platform is: One vehicle-mounted terminal, while the service capacity limit of the drone is [missing information]. a vehicle terminal.

[0085] Both the high-altitude platform and the UAV use orthogonal frequency division multiplexing technology to transmit data in the downlink, wherein and respectively represent the transmission power of the UAV and the high-altitude platform. The UAV and the vehicle terminal have a channel gain of:

[0086]

[0087] wherein, represents the distance between the UAV and the vehicle terminal , and represents the reference channel gain when the relative distance is 0, . and respectively represent the ground coordinates of the UAV and the vehicle terminal , and represents the flight height of the UAV. Then the transmission bit rate between the UAV and the vehicle terminal is:

[0088]

[0089] wherein, represents the channel bandwidth between the UAV and the vehicle user, represents the noise variance in the communication process. In the embodiment of the present application, the communication channel between the UAV and each vehicle terminal is divided into mutually non-overlapping orthogonal sub-channels on the physical resource block in advance, avoiding the problem of co-frequency interference.

[0090] For the link of obtaining video content from the high-altitude platform through the UAV, the channel transmission rate between the high-altitude platform and the UAV can be obtained as follows:

[0091]

[0092] wherein is the channel bandwidth between the high-altitude platform and the UAV, is the channel gain of the antenna, is the total bus loss, and is the free space loss. Wherein, is the speed of light, is the distance between the UAV and the high-altitude platform the distance between them, is the center frequency. is the Boltzmann constant, represents the system noise temperature. Since the distance between the UAV and the HAP is far, is considered as the vertical distance between the UAV and the HAP.

[0093] In the indirect link communication between the HAP and the vehicle, the UAV acts as a relay node. In order to maximize the utilization efficiency of transmission resources, the data amount in the two transmission stages should be consistent during the transmission process via the relay UAV. Therefore, the HAP and the vehicle terminal The transmission rate through the relay path can be expressed as:

[0094] .

[0095] Through the above description, when the video content requested by the vehicle terminal is satisfied by the UAV cache, the vehicle terminal transmits data through the direct link between the UAV and the vehicle. At this time, the network throughput received by the direct link communication between the UAV and the vehicle terminal is:

[0096] .

[0097] When the UAV cache cannot satisfy the user request, the HAP transmits data to the vehicle terminal through the UAV relay. In this case, the network throughput obtained by the vehicle terminal through the data transmission of the HAP is:

[0098] .

[0099] Finally, the total data throughput actually obtained by the vehicle terminal in the whole transmission process is the sum of the two paths, that is:

[0100] .

[0101] In order to guarantee the continuous play of video services and the image clarity, while avoiding unnecessary resource waste, the upper and lower limit constraints are set for the data throughput received by the vehicle terminal. That is, the receiving bit rate of the vehicle terminal needs to meet the minimum play demand of the video basic layer, and at the same time does not exceed the bit rate upper limit of the highest quality layer:

[0102]

[0103] wherein, is the bit rate corresponding to the video basic layer,​ is the video highest quality corresponding bit rate.

[0104] The purpose of the embodiment of the present application is to maximize the total data throughput received by all vehicle terminals in the space-air-ground integrated network. Therefore, the overall joint optimization model can be constructed as follows:

[0105]

[0106] wherein, is a matrix representing the buffer variable , and are matrices representing the transmission variable and , respectively.

[0107] As can be seen from the above, the problem P0 is a multi-time scale integer nonlinear programming problem, the constraint condition of which is nonlinear and discrete, and the coupling between the buffer variable and the distribution variable exists in the objective function and the constraint condition. Therefore, the problem P0 is an NP-hard problem and its solution in a large-scale network environment is particularly difficult, and the complexity of the exhaustive search method increases exponentially with the number of decision variables of P0. In addition, the existence of various constraint conditions further increases the complexity of the problem.

[0108] Therefore, the throughput optimization method of the space-air-ground integrated network provided by the embodiment of the present application decomposes the joint optimization problem into two interrelated sub-problems, which are independently optimized and solved in different time scales, and finally realizes the approximate optimal solution of the problem through iterative convergence.

[0109] Therefore, the embodiment of the present application decomposes the joint optimization problem into two interrelated sub-problems, which are as follows: first, the content distribution strategy is optimized under the premise that the content buffer decision is known. The sub-problem aims to dynamically adjust the transmission resource allocation and link selection in a short time scale to improve the instantaneous throughput of vehicle users:

[0110] .

[0111] Second, on the basis of the content distribution optimization strategy obtained by solving the sub-problem P1.1, the long-term average reception throughput of the vehicle terminal is further optimized, and the unmanned aerial vehicle buffer placement scheme is optimized and adjusted based on this, the target of the sub-problem is to optimize the content buffer layout in a long time scale to improve the overall average throughput of the system:

[0112] .

[0113] The throughput optimization method of the space-air-ground integrated network provided by the application is exemplarily described below. Referring to Figure 3 , the method execution subject can be the electronic device 100 shown in Figure 1 , and the method comprises the following steps as shown in Figure 3 :

[0114] S210: Obtain a video list in the current high-altitude platform.

[0115] The video list comprises a plurality of video contents and the popularity of each video content, and the popularity represents the probability of the video content being requested.

[0116] S220: According to the popularity, cache each video content to each unmanned aerial vehicle according to a preset strategy to obtain video cache information.

[0117] S230: According to the video cache information, calculate the video transmission path information of the high-altitude platform and each unmanned aerial vehicle to each vehicle-mounted terminal for each video content by using a distributed alternating direction multiplier method.

[0118] S240: According to the video transmission path information, update the video cache information by using a greedy algorithm.

[0119] S250: Repeat steps S230 and S240 until the video cache information or / and the video transmission path information meets a preset condition, so that the throughput of the space-air-ground integrated network is optimized.

[0120] The high-altitude platform can be a low-orbit satellite, which can directly obtain data from a core network and has a complete set of video contents Therefore, obtaining the video list in the current high-altitude platform means obtaining all the video contents, and each video content has a popularity, which represents the probability of the video content being requested by a vehicle-mounted terminal. .

[0121] After obtaining all the current video contents, each video content is initialized and cached to each unmanned aerial vehicle according to a preset strategy to obtain video cache information , so as to calculate the video transmission path information and update the video cache information subsequently.

[0122] Optionally, the preset strategy can be popularity priority, uniform random caching, etc.

[0123] Popularity priority means storing the most popular video content in each drone. For example, if the first drone has a capacity of 10 videos, the top 10 most popular videos will be cached in the first drone; if the second drone has a capacity of 15 videos, the top 15 most popular videos will be cached in the second drone.

[0124] Uniform random caching involves storing random video content across all drones. For example, if the first drone has a capacity of 10 video contents, then 10 video contents are randomly selected and cached on the first drone; if the second drone has a capacity of 15 video contents, then 15 video contents are randomly selected and cached on the second drone.

[0125] Knowing the video cache information Then, according to Calculating the optimal video transmission path information to maximize the throughput of data transmission to each vehicle-mounted terminal in the integrated air-space-ground network (i.e., solving sub-problem P1.1) reveals the transmission path used by any vehicle-mounted terminal to transmit any video content. To briefly explain, a vehicle-mounted terminal can acquire video content in multiple ways. For example, vehicle-mounted terminal 1 can directly acquire video content 1 from drones 1, 2, and 3, while vehicle-mounted terminal 2 can relay video content 2 from drones 1, 2, and 3. To optimize the overall throughput of the integrated air-space-ground network, each vehicle-mounted terminal will ultimately choose only one method to acquire video content. For instance, vehicle-mounted terminal 1 might directly acquire video content 1 from drone 1, while vehicle-mounted terminal 2 might relay video content 2 from drone 2. Since the number of users a high-altitude platform can serve is limited, the method of relaying video acquisition will also determine which high-altitude platform provides the video content. For example, vehicle-mounted terminal 2 might acquire video content 2 from high-altitude platform 1 via drone 2. Through calculation, the video transmission path information, including the transmission path for each vehicle-mounted terminal to acquire video content, can be obtained.

[0126] After calculating the video transmission path information, the video caching information can be further optimized, and the video content cached by each drone can be adjusted, thus solving the above sub-problem P1.2.

[0127] The steps S230 and S240 are repeatedly performed until the video buffer information or / and the video transmission path information meets a preset condition, for example, when the video buffer information is the same for two consecutive times or the video transmission path information is the same for two consecutive times, the optimization is completed, and the error of the video buffer information for two consecutive times can be less than a threshold value, for example, the video buffer information for two consecutive times is changed only for one unmanned aerial vehicle, and the error of the video transmission path information for two consecutive times is less than a threshold value, for example, the video transmission path information for two consecutive times is changed only for a path for receiving video content by one vehicle terminal.

[0128] The method effectively solves the communication bottleneck problem of the vehicle user in the high dynamic environment in the space-ground integration network through the joint optimization mechanism of the buffer and the transmission path, and significantly improves the network throughput and the resource utilization.

[0129] As described above, the video transmission path information includes the unmanned aerial vehicle direct transmission path information and the unmanned aerial vehicle relay path information, the unmanned aerial vehicle direct transmission path information represents whether the video content is directly transmitted by the unmanned aerial vehicle to the vehicle terminal, and the unmanned aerial vehicle relay path information represents whether the video content is relayed by the high-altitude platform through the unmanned aerial vehicle to the vehicle terminal. For example , it represents that the vehicle terminal directly acquires the video content through the unmanned aerial vehicle . , it represents that the vehicle terminal acquires the video content through the unmanned aerial vehicle relay from the high-altitude platform . Therefore, the step S230 needs to solve and , see Figure 4 , the step S230 can include the following steps:

[0130] S231: Relax the unmanned aerial vehicle direct transmission path information and the unmanned aerial vehicle relay path information into first direct transmission continuous variables and first relay continuous variables, respectively.

[0131] S232: Copy the first direct transmission continuous variables and the first relay continuous variables to each unmanned aerial vehicle to obtain each direct transmission sub-continuous variable and each relay sub-continuous variable, and construct an augmented Lagrange problem for solving the video transmission path information.

[0132] S233: Perform distributed iterative optimization on the augmented Lagrange problem to calculate second direct transmission continuous variables and second relay continuous variables after iteration.

[0133] S234: Modify the second direct transmission continuous variables and the second relay continuous variables into integer variables to obtain the video transmission path information of the high-altitude platform and each unmanned aerial vehicle to each vehicle terminal for transmitting each video content.

[0134] In the space-air-ground integrated network, the video content can be transmitted to the vehicle terminal from two paths: the unmanned aerial vehicle direct transmission path (unmanned aerial vehicle→vehicle terminal) and the high-altitude platform relay path (high-altitude platform→unmanned aerial vehicle→vehicle terminal). Since the path selection is a binary decision (0 or 1), direct solving will cause combinatorial explosion and extremely high computational complexity. Therefore, the embodiment of the present application adopts relaxation+distributed optimization+integer mapping to efficiently solve the optimal transmission path.

[0135] The unmanned aerial vehicle direct transmission path information and the unmanned aerial vehicle relay path information are represented by variable matrices and The values of the elements in the variable matrix are only 1 and 0, so first, the binary variables and are relaxed into continuous variables, i.e., the first direct continuous variable and the first relay continuous variable , which converts the originally difficult to solve integer programming problem into a more easily handled continuous variable optimization problem.

[0136] By copying and into local variables and for each unmanned aerial vehicle, the problem is decomposed into distributed independent solving for each unmanned aerial vehicle, so the sub-problem P1.1 can be re-expressed as:

[0137]

[0138] The problem is further constructed as an augmented Lagrangian problem for solving the video transmission path information, which can be expressed as:

[0139]

[0140] where and are Lagrange multipliers, represents a penalty parameter, represents a constraint set of the augmented Lagrangian problem, is a penalty function, when , , otherwise, , are the direct transmission sub-continuous variable, the relay sub-continuous variable, the first direct continuous variable, and the first relay continuous variable, respectively, F, J, I, and V are the number of video contents, the number of high-altitude platforms, the number of unmanned aerial vehicles, and the number of vehicle terminals, respectively, is the total data amount transmitted by the unmanned aerial vehicle i.

[0141] Then the augmented Lagrangian problem is iteratively optimized in a distributed manner to obtain the second direct continuous variable and the second relay continuous variable. The augmented Lagrangian problem can be decomposed into a number of independent sub-problems, each of which can be solved independently and in parallel by each UAV. Through continuous iteration, the optimal solution is approached. Referring to , the above step S233 can include the following steps: Figure 5

[0142] S2331: For each iteration optimization, the direct sub-continuous variable and the relay sub-continuous variable corresponding to each UAV are updated by the sub-variable update formula.

[0143] In each iteration, for each UAV i, the direct sub-continuous variable and the relay sub-continuous variable corresponding to the UAV are independently updated according to the current global variable and the Lagrange multiplier. The updating process is distributed, i.e., each UAV can perform the update in parallel without global information synchronization.

[0144] The sub-variable update formula can be:

[0145]

[0146] wherein, is the number of updates.

[0147] S2332: Based on the global variable update formula, the first direct continuous variable and the first relay continuous variable are updated according to the direct sub-continuous variable and the relay sub-continuous variable.

[0148] The local decisions of all UAVs are aggregated to update the global path allocation variable, ensuring the coordination and consistency of the transmission paths between different UAVs and avoiding conflicts or repeated occupation of resources.

[0149] The global variable update formula can be:

[0150]

[0151] wherein, is the number of updates.

[0152] S2333: Based on the Lagrange multiplier update formula, the Lagrange multiplier of the augmented Lagrangian problem is updated.

[0153] The Lagrange multiplier is used to punish the deviation between the sub-variable and the global variable. By updating it, the system is pushed to converge in a global consistent direction. Therefore, during the iteration process, the Lagrange multiplier needs to be updated to promote the convergence consistency between the sub-variable and the global variable.

[0154] The Lagrange multiplier update formula can be:​

[0155]

[0156] wherein, is the number of updates.

[0157] S2334: After the preset number of iterations, the second direct continuous variable and the second relay continuous variable are obtained after iteration of the first direct continuous variable and the first relay continuous variable.

[0158] After a preset number of iterations or reaching a convergence condition, the final continuous variable, i.e. the second direct continuous variable (optimized direct path) and the second relay continuous variable (optimized relay path), is output.

[0159] After obtaining the iteratively optimized continuous variables (second direct continuous variable and second relay continuous variable), the continuous variables need to be modified to integer variables (0 or 1) to obtain the final video transmission path information. The modification process can be represented by the following formula:

[0160]

[0161]

[0162] In the direct transmission path, the best unmanned vehicle i is selected for vehicle v to request f transmission, and among all unmanned vehicles, the one with the maximum value is selected. For example, vehicle 1 obtains video content 1, and there are 3 unmanned vehicles that can directly transmit to vehicle 1, then corresponds to 3 values , assuming is the maximum, then is set to 1, is set to 0. The relay path is the same. Under the premise of knowing the video transmission path, the video cache content of the unmanned vehicle is optimized to improve the long-term average throughput of the space-ground integration network. In the space-ground integration network, the unmanned vehicle serves as a mobile cache node and a relay node, and is limited by its cache capacity, so it can only cache part of the popular video. The optimization goal is to maximize the probability of vehicle terminal obtaining video content through unmanned vehicles (i.e. cache hit rate), and minimize the delay and resource consumption caused by obtaining video content from high-altitude platforms. In one possible implementation, see

[0163] , the above step S240 can include the following steps: Figure 6

[0164] S241: Relax the video cache information to a video cache continuous variable.

[0165] ​S242: Reconstruct the video cache information solving problem as a convex problem according to the video cache continuous variable.

[0166] S243: Calculate the continuous optimal solution of the convex problem using linear programming.

[0167] S244: Adjust the continuous optimal solution by a greedy algorithm to obtain an integer optimal solution.

[0168] S245: Update the video cache information to the integer optimal solution.

[0169] The original video cache information is an integer variable (0 or 1): indicating whether a certain UAV caches a certain video. In order to solve the integer programming problem, the relaxation technique is adopted to convert these binary variables into continuous variables , the continuous variables after relaxation can be regarded as the probability of the UAV caching video content . At this time, the sub-problem P1.2 is transformed into a convex optimization problem that can be efficiently solved, suitable for large-scale network scenarios.

[0170] After the problem P1.2 is convex optimization problem, linear programming method can be used for solving, for example, standard optimization solver CPLEX, Gurobi, MATLAB, to obtain the optimal continuous cache variable solution , indicating the best caching probability of each UAV for each video content.

[0171] Since the UAV in the actual system can only cache or not cache a certain video content, the continuous variable must be converted to an integer solution. The greedy algorithm is adopted, that is, for each UAV i, the video content is sorted in descending order of probability, and the top-ranked video content is cached first until the cache capacity is exhausted. Exemplarily, this step S244 can include:

[0172] S2441: For each UAV, find the storage probability of each video content stored by the UAV from the continuous optimal solution.

[0173] S2442: According to the order of the storage probabilities from large to small, the video content corresponding to each storage probability is sequentially stored in the UAV until the number of each video content stored in the UAV is equal to the cache capacity of the UAV, obtaining the integer optimal solution of the UAV.

[0174] For example, there are 100 video contents in total, the storage capacity of the UAV 1 is 10 video contents, the probabilities of the UAV 1 storing the 100 video contents from large to small are arranged, the serial numbers of the first 10 video contents are 33, 11, 6, 67, 88, 2, 14, 26, 5 and 7 respectively, the 10 video contents are stored in the UAV 1, and then the value of the video content is 1, and the values of the remaining video contents are 0. The embodiment of the present application is designed for the UAV capacity based on the data of the video contents, if the size of the video is considered, the storage capacity of the UAV 1 is 1 Gb, the video contents are stored in the UAV 1 until the total storage size of the stored video contents is greater than 1 Gb.

[0175] Finally, the integer solution obtained in the last step is substituted into the original video cache information, and the update of the cache strategy is completed.

[0176] Further, the embodiment of the present application further provides a throughput optimization device of a space-air-ground integrated network, referring to Figure 7 The throughput optimization device 300 of the space-air-ground integrated network comprises:

[0177] A video acquisition unit 310 is configured to acquire a video list in a current high-altitude platform, wherein the video list comprises a plurality of video contents and a popularity of each video content, and the popularity represents a probability of the video content being requested.

[0178] A video initial cache unit 320 is configured to cache each video content to each UAV according to a preset strategy according to the popularity, to obtain video cache information.

[0179] A video transmission path calculation unit 330 is configured to calculate video transmission path information of the high-altitude platform and each UAV to each vehicle-mounted terminal for each video content by a distributed alternating direction multiplier method according to the video cache information.

[0180] A video cache information calculation unit 340 is configured to update the video cache information by a greedy algorithm according to the video transmission path information.

[0181] An iteration unit 350 is configured to repeatedly call the video transmission path calculation unit and the video cache information calculation unit until the video cache information or / and the video transmission path information meets a preset condition, so that the throughput of the space-air-ground integrated network is optimized.

[0182] In conclusion, the embodiment of the present application provides a throughput optimization method and device of space-air-ground integrated network, an electronic device and a storage medium, which realizes the collaborative optimization of content caching and content distribution in the space-air-ground integrated network by jointly optimizing the caching and transmission path, thereby significantly improving the overall network throughput. The caching and relay capabilities of the unmanned aerial vehicle make up for the problems of high transmission delay and limited bandwidth of the high-altitude platform, and improve the efficiency and quality of the vehicle terminal in obtaining video content. Through the distributed optimization algorithm, efficient content distribution path planning is realized, the high complexity and communication overhead of centralized scheduling are avoided, and the system scalability and real-time response capability are improved. The original NP difficult integer nonlinear programming problem is decomposed and relaxed, the caching and transmission path variables are relaxed into continuous variables, and the ADMM distributed solving method is combined to convert the problem into a convex optimization problem that can be efficiently solved, thereby reducing the calculation complexity.

[0183] In the embodiments of the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are only schematic. For example, the flowcharts and block diagrams in the drawings show the possible implementation architectures, functions and operations of the devices, methods and computer program products according to the embodiments of the present application. In this regard, each block in the flowcharts or block diagrams can represent a module, a program segment or a part of code, which contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in different orders from those shown in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, and sometimes in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and the combination of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0184] In addition, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0185] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the part of the prior art that contributes to the present application or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a computer readable storage medium, including a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0186] The above merely describes the preferred embodiments of the present application and is not intended to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0187] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be regarded as exemplary and non-limiting, and the scope of the present application is defined by the appended claims rather than the above description, and all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present application. Any reference signs in the claims should not be regarded as limiting the claims involved.

Claims

1. A method for optimizing throughput in an integrated air-space-ground network, characterized in that, The integrated air-space-ground network includes at least one high-altitude platform, multiple unmanned aerial vehicles (UAVs), and multiple vehicle-mounted terminals. Each UAV is communicatively connected to each of the high-altitude platforms and each of the vehicle-mounted terminals. The method includes: A global joint optimization model is constructed; the global joint optimization model is as follows: in, This refers to the collection of the aforementioned high-altitude platforms. The collection of the aforementioned drones, The set of vehicle-mounted terminals, This is a collection of all video content. To represent video content Is it cached on the drone? superior, Indicates drone Maximum cache capacity Indicate whether it is by drone To direct vehicle terminal Transmit video content, Indicates whether it is a relay node drone Auxiliary aerial platform To vehicle terminal Transmit video content The maximum service capacity of the aerial platform is The service capacity of a single vehicle-mounted terminal and a drone is capped at [number]. One vehicle-mounted terminal It is the bitrate corresponding to the video base layer. It is the bitrate corresponding to the highest video quality. It represents the cache variable matrix. , and These represent the transfer variable matrices respectively. and ; Obtain the video list in the current high-altitude platform; the video list includes multiple video contents and the popularity of each video content, the popularity representing the probability that the video content is requested; Based on the popularity of each video, and according to a preset strategy, the video content is cached to each drone to obtain video cache information. Based on the video cache information, the video transmission path information for the high-altitude platform and each of the UAVs to transmit each of the video contents to each of the vehicle terminals is calculated by the distributed alternating direction multiplier method. Based on the video transmission path information, the video cache information is updated using a greedy algorithm; The steps of calculating the video transmission path information of the high-altitude platform and each of the UAVs to each of the vehicle terminals based on the video cache information using the distributed alternating direction multiplier method, and updating the video cache information based on the video transmission path information using a greedy algorithm, are repeated until the video cache information and / or the video transmission path information meet preset conditions, so as to optimize the throughput of the integrated air-space-ground network.

2. The method according to claim 1, characterized in that, The video transmission path information includes direct UAV transmission path information and UAV relay path information. The direct UAV transmission path information indicates whether the video content is directly transmitted from the UAV to the vehicle-mounted terminal, and the UAV relay path information indicates whether the video content is transmitted from the high-altitude platform to the vehicle-mounted terminal via the UAV relay. The step of calculating the video transmission path information from the high-altitude platform and each UAV to each vehicle-mounted terminal based on the video buffer information using the distributed alternating direction multiplier method includes: The direct transmission path information of the UAV and the relay path information of the UAV are relaxed into a first direct transmission continuous variable and a first relay continuous variable, respectively. The first direct transmission continuous variable and the first relay continuous variable are copied to each of the UAVs to obtain each direct transmission sub-continuous variable and each relay sub-continuous variable, and an augmented Lagrange problem for solving the video transmission path information is constructed. Distributed iterative optimization is performed on the augmented Lagrange problem to calculate the second direct-transmission continuous variable and the second relay continuous variable after iteration; The second direct transmission continuous variable and the second relay continuous variable are corrected to integer variables to obtain the video transmission path information of the high-altitude platform and each of the UAVs transmitting the video content to each of the vehicle terminals.

3. The method according to claim 2, characterized in that, The augmented Lagrange problem is: in, and It is a Lagrange multiplier. Represents the penalty parameter. Let the set of constraints of the augmented Lagrange problem be denoted as . Let be the penalty function, when hour, ,otherwise, , These are the direct transmission sub-continuous variables, the relay sub-continuous variables, the first direct transmission continuous variable, and the first relay continuous variable, respectively. F, J, I, and V represent the number of video contents, the number of high-altitude platforms, the number of drones, and the number of vehicle-mounted terminals, respectively. The total amount of data transmitted by the drone i.

4. The method according to claim 3, characterized in that, The distributed iterative optimization of the augmented Lagrange problem, calculating the second direct-transmission continuous variable and the second relay continuous variable after iteration, includes: For each iteration of optimization, the direct transmission sub-continuous variables and the relay sub-continuous variables corresponding to each UAV are updated using the sub-variable update formula; Based on the global variable update formula, the first direct transmission continuous variable and the first relay continuous variable are updated according to the continuous variables of each direct transmission sub-sub and the continuous variables of each relay sub-sub ... The Lagrange multipliers of the augmented Lagrange problem are updated based on the Lagrange multiplier update formula. After performing a preset number of iterations, the second direct transmission continuous variable and the second relay continuous variable are obtained after iterating over the first direct transmission continuous variable and the first relay continuous variable.

5. The method according to claim 4, characterized in that, The formula for updating the sub-variable is: The formula for updating the global variable is: The Lagrange multiplier update formula is as follows: in, This represents the number of times the update has been performed.

6. The method according to claim 1, characterized in that, The step of updating the video cache information using a greedy algorithm based on the video transmission path information includes: Relax the video cache information into a continuous video cache variable; Based on the continuous variables of the video cache, the problem of solving the video cache information is reconstructed into a convex problem; The continuous optimal solution to the convex problem was calculated using linear programming. By adjusting the continuous optimal solutions using a greedy algorithm, an integer optimal solution is obtained. Update the video cache information to the integer optimal solution.

7. The method according to claim 6, characterized in that, The step of adjusting the continuous optimal solutions using a greedy algorithm to obtain integer optimal solutions includes: For each of the drones, the storage probability of each video content stored by the drone is found from the successive optimal solutions; According to the order of the storage probabilities from largest to smallest, the video content corresponding to each storage probability is stored into the drone in sequence until the number of each video content stored in the drone is equal to the cache capacity of the drone, thus obtaining the integer optimal solution for the drone.

8. A throughput optimization device for an integrated air-space-ground network, characterized in that, The integrated air-space-ground network includes at least one high-altitude platform, multiple unmanned aerial vehicles (UAVs), and multiple vehicle-mounted terminals. Each UAV is communicatively connected to each of the high-altitude platforms and each of the vehicle-mounted terminals. The device includes: The optimization model construction unit is used to construct the overall joint optimization model; the overall joint optimization model is as follows: in, This refers to the collection of high-altitude platforms. The collection of the aforementioned drones, The set of vehicle-mounted terminals, This is a collection of all video content. To represent video content Is it cached on the drone? superior, Indicates drone Maximum cache capacity Indicate whether it is by drone To direct vehicle terminal Transmit video content, Indicates whether it is a relay node drone Auxiliary aerial platform To vehicle terminal Transmit video content The maximum service capacity of the aerial platform is The service capacity of a single vehicle-mounted terminal and a drone is capped at [number]. One vehicle-mounted terminal It is the bitrate corresponding to the video base layer. It is the bitrate corresponding to the highest video quality. It represents the cache variable matrix. , and These represent the transfer variable matrices respectively. and ; The video acquisition unit is used to acquire a list of videos currently in the high-altitude platform; the video list includes multiple video contents and the popularity of each video content, the popularity representing the probability that the video content is requested; The video initial caching unit is used to cache the video content of each drone according to the popularity of each video and a preset strategy, thereby obtaining video caching information. The video transmission path calculation unit is used to calculate the video transmission path information of the high-altitude platform and each of the UAVs to transmit each of the video contents to each of the vehicle terminals based on the video cache information and using the distributed alternating direction multiplier method. The video cache information calculation unit is used to update the video cache information according to the video transmission path information using a greedy algorithm; An iterative unit is used to repeatedly execute the steps of calculating the video transmission path information of the high-altitude platform and each of the UAVs to each of the vehicle terminals based on the video cache information using a distributed alternating direction multiplier method, and the steps of updating the video cache information based on the video transmission path information using a greedy algorithm, until the video cache information and / or the video transmission path information meet preset conditions, so as to optimize the throughput of the integrated air-space-ground network.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the method according to any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements the method described in any one of claims 1 to 7.

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