A method, system, and terminal for unmanned aerial vehicle (UAV) network deployment and collaborative caching.

Abstract

This application provides a method, system, and terminal for UAV network deployment and collaborative caching, including the following steps: acquiring user clusters and UAV location information that meet user quality of service (QoS); determining the total content transmission latency between the user clusters, UAVs, and base stations based on the user clusters and UAV location information, and minimizing the total content transmission latency to optimize collaborative caching between UAVs and base stations. The UAV network deployment and collaborative caching method, system, and terminal of this application achieve joint control of UAV three-dimensional location and cache placement, reducing average content transmission latency; provide a user clustering method that meets actual user QoS requirements, improving resource utilization; optimize UAV location deployment, thereby providing better channel conditions for users; and optimize UAV cache deployment, reducing resource consumption of UAV wireless backhaul link transmission and improving traffic of the UAV-assisted wireless network.

Description

Technical Field

[0001] This application belongs to the field of mobile communication technology, and in particular relates to a method, system and terminal for unmanned aerial vehicle (UAV) network deployment and collaborative caching. Background Technology

[0002] Due to their flexible flight characteristics and line-of-sight wireless connectivity with users, unmanned aerial vehicles (UAVs) have been a hot research topic in recent years. By carrying communication equipment, UAVs can act as airborne base stations, providing communication services in specific scenarios such as emergency communication in disaster areas, enhancing cell edge performance, and unloading traffic in sudden traffic hotspots. Therefore, optimizing UAV deployment has received increasing attention. However, due to the limited communication capabilities of UAVs and the limitations of the Quality of Service (QoS) threshold, the number of users in the user cluster served by UAVs is finite, making it unsuitable to directly use the traditional K-means algorithm to solve the UAV deployment problem.

[0003] With the surge in use of time-sensitive content delivery applications, many popular content items are repeatedly requested by users, consuming significant amounts of data. Deploying caching on base stations can reduce transmission latency for many of these repeatedly requested contents and alleviate backhaul link pressure. Mobile edge caching technology is considered a solution for efficient content delivery using 5G wireless networks. This technology deploys caching at the network's edge nodes, allowing users to directly obtain the content they need from edge nodes such as base stations. This reduces the latency caused by content previously needing to be transmitted from the core network to the user, effectively improving the user experience. However, previous research primarily assumed that ground base stations had no caching and focused only on caching in drones, failing to leverage collaborative caching to improve latency performance. Summary of the Invention

[0004] The purpose of this application is to provide a method, system, and terminal for drone network deployment and collaborative caching, which solves the technical problem in the prior art that drone deployment and drone caching cannot be jointly optimized to reduce the average latency of users.

[0005] In a first aspect, this application provides a method for deploying and collaboratively caching unmanned aerial vehicle (UAV) networks, comprising the following steps:

[0006] Obtain user clusters and drone location information that meet user service quality; determine the total content transmission latency between the user clusters, drones, and base stations based on the user clusters and drone location information, and minimize the total content transmission latency to optimize drone and base station collaborative caching.

[0007] In the present application, the joint control of the three-dimensional position of the UAV and the cache placement in the UAV-assisted wireless network is realized, and the average content transmission delay is reduced.

[0008] In an implementation form of the first aspect, the obtaining of the user cluster satisfying the user quality of service and the UAV position information comprises the following steps:

[0009] determining an initial user cluster;

[0010] minimizing the total content transmission delay of each of the user clusters to optimize the UAV position information;

[0011] calculating the signal-to-noise ratio between the UAV and each user in the user cluster at the optimized UAV position;

[0012] judging whether the signal-to-noise ratio satisfies the user quality of service; if not, re-determining the user cluster until the signal-to-noise ratio between the UAV and each user in the user cluster satisfies the requirement of the user quality of service.

[0013] In the implementation form, the user clustering method satisfying the actual user QoS requirement is provided, and the resource utilization is improved by reasonably allocating the UAV base station.

[0014] In an implementation form of the first aspect, the total content transmission delay of each of the user clusters is minimized to optimize the UAV position information by using the following formula:

[0015]

[0016]

[0017]

[0018]

[0019] wherein the total content transmission delay of the user cluster , , the total number of user clusters;

[0020] the storage space of the UAV;

[0021] the position of the UAV when serving the user cluster ;

[0022] the position limit of the UAV when serving.

[0023] In the implementation, the position deployment of the UAV is optimized, so as to provide better channel conditions for the user.

[0024] In an implementation of the first aspect, the total content transmission delay of the user cluster and the UAV and the base station is minimized by using the following formula to optimize the cooperative caching of the UAV and the base station:

[0025]

[0026]

[0027]

[0028] wherein denotes the total content transmission delay of the user cluster , , denotes the total number of user clusters;

[0029] denotes the storage space of the UAV;

[0030] denotes the position of the UAV when serving the user cluster .

[0031] denotes that the first most popular content is cached by the UAV for the user cluster . denotes that the first most popular content is not cached by the UAV for the user cluster .

[0032] In the implementation, the caching deployment of the UAV is optimized, the resource occupation of the UAV wireless backhaul link transmission is reduced, and the traffic of the UAV-aided wireless network is improved.

[0033] In an implementation of the first aspect, the total content transmission delay of the user cluster is calculated by using the formula ; wherein denotes the total content transmission delay of the user cluster . denotes the total number of users in the user cluster ; denotes the user in the user cluster , denotes the first user in the user cluster , .

[0034] ​​​​In one implementation of the first aspect, the formula is used. Computing user clusters The Middle Total content delivery latency for each user;

[0035] in This indicates the probability and latency of the drone directly sending content to the user when the drone has cached the requested content locally.

[0036] This indicates the probability and latency of a drone obtaining content through the base station-drone link and forwarding it to the user when the drone does not have the requested content but the base station has cached the requested content.

[0037] This indicates the probability and latency of the base station obtaining content from the core network and forwarding it to the drone when neither the drone nor the base station has requested the content, and the drone then forwarding the content to the user.

[0038] In one implementation of the first aspect, the following formula is used for calculation. and :

[0039]

[0040]

[0041]

[0042]

[0043]

[0044]

[0045] in, Indicates base station cache content The probability, Indicates user's opinion on content The probability of a request.

[0046] In one implementation of the first aspect, the following formula is used for calculation. and :

[0047]

[0048]

[0049]

[0050] in, Indicates the size of the content. and Indicating drones and user clusters The Middle The communication rate of individual users, the communication rate between drones and base stations, and the rate at which base stations download content from the core network.

[0051] Secondly, this application provides a drone network deployment and collaborative caching system, comprising:

[0052] The location deployment module is used to obtain user cluster and drone location information that meets the user's quality of service requirements;

[0053] A collaborative caching template is used to determine the total content transmission latency between the user cluster, the base station, and the drone based on the user cluster and the drone's location information, and to minimize the total content transmission latency in order to optimize collaborative caching between the drone and the base station.

[0054] Thirdly, this application provides a UAV network deployment and collaborative caching terminal, including: a processor and a memory;

[0055] The memory is used to store computer programs;

[0056] The processor is used to execute the computer program stored in the memory to cause the UAV network deployment and collaborative caching terminal to perform any of the above-described UAV network deployment and collaborative caching methods.

[0057] As described above, the UAV network deployment and collaborative caching method, system, and terminal described in this application have the following beneficial effects:

[0058] It enables joint control of the three-dimensional position of the drone and the placement of the cache in the drone-assisted wireless network, thereby reducing the average content transmission latency;

[0059] A user clustering method that meets the actual QoS requirements of users is provided, which improves resource utilization by reasonably allocating UAV base stations;

[0060] The drone deployment location has been optimized, thus providing users with better channel conditions;

[0061] The cache deployment of drones has been optimized, reducing the resource consumption of drone wireless backhaul link transmission and increasing the traffic of drone-assisted wireless network. Attached Figure Description

[0062] Figure 1 This is shown as an embodiment of the unmanned aerial vehicle (UAV) assisted cellular wireless communication system described in this application.

[0063] Figure 2The flowchart shown is a description of the drone network deployment and collaborative caching method described in this application embodiment.

[0064] Figure 3 The diagram shown is a flowchart of the drone location deployment method in the drone network deployment and collaborative caching method described in this application embodiment.

[0065] Figure 4 The diagram shown is a schematic representation of the unmanned aerial vehicle (UAV) network deployment and collaborative caching system described in an embodiment of this application.

[0066] Figure 5 The diagram shown is a schematic representation of the UAV network deployment and collaborative caching terminal structure described in this application embodiment.

[0067] Component designation explanation

[0068] 41 Location Deployment Module

[0069] 42 Collaborative Caching Module

[0070] 51 processor

[0071] 52 Memory

[0072] 53 Multimedia Components

[0073] 54 I / O interfaces

[0074] 55 Communication Components

[0075] Steps S1~S2 Detailed Implementation

[0076] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0077] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. Therefore, the drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0078] Furthermore, the use of terms such as "first" and "second" in this application is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but only on the basis of being achievable by those skilled in the art. If the combination of technical solutions is contradictory or impossible to implement, such a combination of technical solutions should be considered non-existent and not within the scope of protection claimed in this application.

[0079] The following embodiments of this application provide a method, system, and terminal for drone network deployment and collaborative caching, which can be applied to, for example... Figure 1 The diagram illustrates a drone-assisted cellular wireless communication system. In this system, both the base station (TBS) and the drone (UAV) have buffering devices. Ground users request content from the base station from different locations. The drone, acting as an aerial base station, hovers over each user cluster, serving different user clusters sequentially and transmitting the requested content to each user within the cluster, thereby reducing user latency and offloading base station data traffic.

[0080] like Figure 2 As shown, this embodiment provides a method for drone network deployment and collaborative caching, including the following steps:

[0081] S1. Obtain user clusters and drone location information that meet the user's quality of service requirements.

[0082] like Figure 3 As shown, in one embodiment, obtaining user clusters and drone location information that meet the user service quality includes the following steps:

[0083] S11. Determine the initial user cluster.

[0084] In clustering problems, the K-means algorithm is widely used due to its simple principle and fast convergence speed. Therefore, this application uses the K-means algorithm to cluster users at different geographical locations to obtain a reasonable number of user clusters. Specifically, when using the K-means algorithm to cluster users, the user clusters are set starting from 1.

[0085] S12. Minimize the total content transmission latency for each user cluster to optimize UAV location information.

[0086] After obtaining the initial user cluster, the centroid of the initial user cluster is set as the initial hovering position of the UAV. The next step is to find the optimal position of the UAV.

[0087] In one embodiment, the total content transmission latency for each user cluster is minimized using the following formula to optimize UAV location information:

[0088]

[0089]

[0090]

[0091]

[0092] in Represents user clusters Total content transmission latency , Indicates the total number of user clusters;

[0093] Indicates the storage space of the drone;

[0094] Indicates that the drone is a user cluster Location during service;

[0095] This indicates the location restrictions for drone services.

[0096] In one embodiment, the formula is used. Calculate the user cluster Total content transmission latency; of which Represents user clusters The Middle Total content delivery latency for each user; Represents user clusters The total number of users in China; Represents user clusters All users in; Represents user clusters The Middle One user, .

[0097] S13. Calculate the signal-to-noise ratio between the drone and each user in the user cluster at the optimized drone location.

[0098] The signal-to-noise ratio (SNR) is the ratio of the power of the signal received by the user to the sum of the power of the interfering signal and the power of the ambient noise. Specifically, the signal-to-noise ratio (SNR) for a UAV can be calculated using the following formula: j With user clusters The Middle Signal-to-noise ratio between individual users :

[0099]

[0100] in Indicates drone u j Assigned to user clusters The Middle Power per user, Indicates drone u j and user clusters The Middle Path loss per user This represents noise power. In UAV-user communication links, line-of-sight links constitute the majority.

[0101] Furthermore, the following formula is used for calculation. :

[0102]

[0103] in Indicates drone u j and user clusters The Middle Distance between users Indicates the communication frequency. Represents the speed of light. This represents the shadow fading loss factor for line-of-sight links.

[0104] Furthermore, the following formula is used for calculation. :

[0105]

[0106] in , Indicates that the drone is a user cluster No. The location of each user service.

[0107] S14. Determine whether the signal-to-noise ratio meets the user service quality requirements; if not, redetermine the user cluster until the signal-to-noise ratio between the drone and each user in the user cluster meets the user service quality requirements.

[0108] Specifically, when redetermining user clusters, the number of user clusters is increased from 1 by a preset step size, while keeping the number of users constant. For example, if the preset step size is set to 1, when it is determined that the signal-to-noise ratio (SNR) between the drone and the user does not meet the user service quality requirements, the number of user clusters is increased by 1, and this is used as the newly determined user cluster. Based on the newly determined user clusters, the total content transmission latency of each user cluster is minimized to obtain the optimized 3D position of the drone. The SNR between the drone and each user within the user cluster is calculated at the optimized position. It is determined whether the SNR between the drone and the user meets the user service quality requirements. If it still does not meet the user service quality requirements, the number of user clusters is increased by 1, and the above drone position optimization, SNR calculation, and comparison process is repeated until the SNR between the drone and the user meets the user service quality requirements. The number of user clusters, cluster status, and drone position information in each user cluster are then output. This allows the user clustering and drone deployment strategy that meets the user service quality requirements to be obtained.

[0109] S2. Based on the user cluster and the drone location information, determine the total content transmission latency between the user cluster, the drone, and the base station, and minimize the total content transmission latency to optimize drone and base station collaborative caching.

[0110] In one embodiment, the total content transmission latency between the user cluster and the drone and base station is minimized using the following formula to optimize drone and base station collaborative caching:

[0111]

[0112]

[0113]

[0114] in Represents user clusters Total content transmission latency , Indicates the total number of user clusters;

[0115] Indicates the storage space of the drone;

[0116] Indicates that the drone is a user cluster Location during service;

[0117] The time represents the drone for user clusters The cached first The most popular content; The time indicates that the drone did not provide user clusters Cache number The most popular content.

[0118] In one embodiment, the formula is used. Calculate the user cluster Total content transmission latency; of which Represents user clusters The Middle Total content delivery latency for each user; Represents user clusters The total number of users in China; Represents user clusters Users in Represents user clusters The Middle One user, .

[0119] In one embodiment, the formula is used. Computing user clusters The Middle Total content delivery latency for each user;

[0120] in This indicates the probability and latency of the drone directly sending content to the user when the drone has cached the requested content locally.

[0121] This indicates the probability and latency of a drone obtaining content through the base station-drone link and forwarding it to the user when the drone does not have the requested content but the base station has cached the requested content.

[0122] This indicates the probability and latency of the base station obtaining content from the core network and forwarding it to the drone when neither the drone nor the base station has requested the content, and the drone then forwarding the content to the user.

[0123] In one embodiment, the following formula is used to calculate... and :

[0124]

[0125]

[0126]

[0127]

[0128]

[0129]

[0130] in, Indicates base station cache content The probability, Indicates user's opinion on content The probability of a request.

[0131] In one embodiment, the following formula is used to calculate... and :

[0132]

[0133]

[0134]

[0135] in, Indicates the size of the content. and Representing drones and user clusters respectively The Middle The communication rate of individual users, the communication rate between drones and base stations, and the rate at which base stations download content from the core network.

[0136] In one embodiment, assuming that content popularity follows a Zipf distribution, the following formula is used to calculate... :

[0137]

[0138] Where K represents the total number of content items, This represents the Zipf distribution parameters that influence the distribution of content popularity.

[0139] It should be noted that, assuming the drone's cache content is fixed, the communication rate between the drone and the base station will vary. and the rate at which base stations download content from the core network The changes are relatively minor, so the main focus is on optimizing the communication rate between the drone and the user. To obtain the deployment location of the drone.

[0140] The cache deployment problem for drones is actually a 0-1 integer programming problem. By analyzing the content transmission process, the following three typical cache deployment schemes are summarized:

[0141] I.When At that time, the most popular content is deployed to both base stations and drones until the cache space of both base stations and drones is full; among them This indicates the base station's storage space;

[0142] II. When At that time, the most popular content is deployed to the base station until the base station's cache space is full. After removing the most popular content, the remaining content is deployed to the drones in order of popularity until the drones' cache space is full.

[0143] III. When At that time, the most popular content is deployed to the base station until the base station's cache space is full. The content and the first , Until the The content is cached on the drone, among which, .

[0144] In UAV-assisted wireless communication networks, this application provides a network deployment and cooperative caching method under the constraints of meeting actual user QoS requirements and limited UAV storage space, and obtains the UAV deployment strategy and caching strategy through the following formulas:

[0145]

[0146]

[0147]

[0148]

[0149]

[0150]

[0151] Since problem (1) is a non-convex integer programming problem, the deployment strategy and collaborative caching method of the UAV can be obtained by decomposing problem (1) into subproblems (2) and (3) respectively.

[0152] Specifically, the drone deployment strategy is obtained by solving sub-problem (2):

[0153]

[0154]

[0155]

[0156] Specifically, the drone deployment strategy is obtained by solving sub-problem (3):

[0157]

[0158]

[0159]

[0160] It should be noted that the scope of protection of the UAV network deployment and collaborative caching method described in this application is not limited to the execution order of the steps listed in this embodiment. Any solution implemented by adding, subtracting, or replacing steps in the prior art based on the principles of this application is included within the scope of protection of this application.

[0161] like Figure 4 As shown, this embodiment provides a drone network deployment and collaborative caching system, including:

[0162] Location deployment module 41 is used to obtain user cluster and drone location information that meets the user's quality of service.

[0163] Collaborative caching template 42 is used to determine the total content transmission latency between the user cluster, the base station, and the drone based on the user cluster and the drone location information, and to minimize the total content transmission latency in order to optimize collaborative caching between the drone and the base station.

[0164] It should be noted that the structure and principle of the location deployment module 41 and the collaborative caching template 42 correspond one-to-one with the steps and embodiments in the above-mentioned UAV network deployment and collaborative caching method, so they will not be repeated here.

[0165] The UAV network deployment and collaborative caching system provided in this application can implement the UAV network deployment and collaborative caching method described in this application. However, the implementation apparatus of the UAV network deployment and collaborative caching method described in this application includes, but is not limited to, the structure of the UAV network deployment and collaborative caching system listed in this embodiment. All structural modifications and substitutions of the prior art made based on the principles of this application are included within the protection scope of this application.

[0166] like Figure 5 As shown, this embodiment provides a UAV network deployment and collaborative caching terminal, including: a processor 51 and a memory 52;

[0167] The memory 52 is used to store computer programs;

[0168] The processor 51 is used to execute the computer program stored in the memory 52 to cause the UAV network deployment and collaborative caching terminal to perform any of the above-described UAV network deployment and collaborative caching methods.

[0169] Preferably, the processor 51 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The memory 52 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0170] This embodiment also includes one or more of a multimedia component 53, an input / output (I / O) interface 54, and a communication component 55.

[0171] The multimedia component 53 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory 52 or transmitted via communication component 55. The audio component also includes at least one speaker for outputting audio signals. I / O interface 54 provides an interface between processor 51 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual or physical buttons. Communication component 55 is used for wired or wireless communication between the timer and other devices. Wireless communication may include Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof; therefore, the corresponding communication component 55 may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0172] In one embodiment, the timer may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described UAV network deployment and cooperative caching method.

[0173] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, or methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules / units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of apparatuses or modules or units may be electrical, mechanical, or other forms.

[0174] The modules / units described as separate components may or may not be physically separate. The components shown as modules / units may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules / units can be selected to achieve the objectives of the embodiments of this application, depending on actual needs. For example, the functional modules / units in the various embodiments of this application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0175] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0176] This application also provides a computer-readable storage medium. Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing a processor. The program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof. The storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state drive (SSD)).

[0177] This application embodiment may also provide a computer program product comprising one or more computer instructions. When the computer instructions are loaded and executed on a computing device, all or part of the processes or functions described in this application embodiment are generated. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0178] When the computer program product is executed by a computer, the computer performs the method described in the foregoing method embodiments. The computer program product can be a software installation package; when the foregoing method is required, the computer program product can be downloaded and executed on the computer.

[0179] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0180] In summary, the UAV network deployment and cooperative caching method, system, and terminal of this application achieve joint control of UAV three-dimensional position and cache placement in UAV-assisted wireless networks, reducing average content transmission latency; provide a user clustering method that meets actual user QoS requirements, improving resource utilization by rationally allocating UAV base stations; optimize UAV location deployment, thereby providing users with better channel conditions; and optimize UAV cache deployment, reducing resource consumption of UAV wireless backhaul link transmission and increasing traffic in UAV-assisted wireless networks.

[0181] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for unmanned aerial vehicle (UAV) network deployment and collaborative caching, characterized in that, Includes the following steps: Obtain the 3D locations of user clusters and drones that meet the user's quality of service requirements; Based on the three-dimensional positions of the user cluster and the UAV, the total content transmission latency between the user cluster, the UAV, and the base station is determined, and the total content transmission latency is minimized to optimize the collaborative caching between the UAV and the base station; Obtaining the 3D locations of user clusters and drones that meet the user's quality of service includes the following steps: Determine the initial user cluster; Minimize the total content transmission latency for each of the user clusters to optimize the UAV's 3D positioning; Calculate the signal-to-noise ratio between the drone and each user in the user cluster at the optimized three-dimensional position of the drone; Determine whether the signal-to-noise ratio meets the user service quality requirements; if not, redetermine the user cluster until the signal-to-noise ratio between the drone and each user in the user cluster meets the user service quality requirements.

2. The UAV network deployment and collaborative caching method according to claim 1, characterized in that, The total content transmission latency for each user cluster is minimized using the following formula to optimize the UAV's 3D positioning: ， ， ， ， in Represents user clusters Total content transmission latency , Indicates the total number of user clusters; This represents the storage space used when the drone serves user cluster j. Indicates that the drone is a user cluster Location during service; This indicates the location restrictions for drone services.

3. The UAV network deployment and collaborative caching method according to claim 1, characterized in that, The total content transmission latency between the user cluster and the drone and base station is minimized using the following formula to optimize drone and base station collaborative caching: ， ， ， in Represents user clusters Total content transmission latency , Indicates the total number of user clusters; Indicates the storage space of the drone; Indicates that the drone is a user cluster Location during service; The time represents the drone for user clusters The cached first One of the most popular contents, , Indicates the total number of items; The time indicates that the drone did not provide user clusters Cache number The most popular content.

4. The UAV network deployment and collaborative caching method according to claim 3, characterized in that, Using formula Calculate the user cluster Total content transmission latency; in Represents user clusters The Middle Total content delivery latency for each user; Represents user clusters The total number of users in China; Represents user clusters Users in Represents user clusters The Middle One user, Represents user clusters The Middle The location of each user.

5. The UAV network deployment and collaborative caching method according to claim 4, characterized in that, Using formula Computing user clusters The Middle Total content delivery latency for each user; in This indicates the probability that the drone will directly send the requested content to user i when the drone has cached the content locally. This indicates the delay time before the drone directly sends the requested content to user i, provided that the drone has cached the content locally. This indicates the probability that when the drone does not have the requested content but the base station has cached the requested content, the drone will obtain the content through the base station-drone link and forward it to user i. This indicates the delay time during which the drone obtains the requested content through the base station-drone link and forwards it to user i when the drone does not have the requested content but the base station has cached the requested content. This represents the probability that when neither the drone nor the base station has requested the content, the base station obtains the content from the core network and forwards it to the drone, and the drone then forwards the content to user i. This indicates the delay time during which the base station obtains content from the core network and forwards it to the drone when neither the drone nor the base station has requested the content, and the drone then forwards the content to user i.

6. The UAV network deployment and collaborative caching method according to claim 5, characterized in that, Calculate using the following formula and : ， ， ， ， ， ， in, Indicates base station cache content The probability, Indicates user's opinion on content The probability of a request. Indicating drones and user clusters The Middle Content transmission latency between users This indicates the latency of content transmission between the drone and the base station. This indicates the latency for the base station to download content from the core network.

7. The UAV network deployment and collaborative caching method according to claim 6, characterized in that, Calculate using the following formula and : ， ， ， in, Indicates the size of the content. and Indicating drones and user clusters The Middle The communication rate of individual users, the communication rate between drones and base stations, and the rate at which base stations download content from the core network.

8. A drone network deployment and collaborative caching system, characterized in that, include: The location deployment module is used to obtain the 3D locations of user clusters and drones that meet the user's quality of service requirements; A collaborative caching template is used to determine the total content transmission latency between the user cluster and the base station and the drone based on the three-dimensional position of the user cluster and the drone, and to minimize the total content transmission latency in order to optimize collaborative caching between the drone and the base station; The location deployment module obtains the 3D locations of user clusters and drones that meet the user's quality of service by including the following steps: Determine the initial user cluster; Minimize the total content transmission latency for each of the user clusters to optimize the UAV's 3D positioning; Calculate the signal-to-noise ratio between the drone and each user in the user cluster at the optimized three-dimensional position of the drone; Determine whether the signal-to-noise ratio meets the user service quality requirements; if not, redetermine the user cluster until the signal-to-noise ratio between the drone and each user in the user cluster meets the user service quality requirements.

9. A UAV network deployment and collaborative caching terminal, characterized in that, include: Processor and memory; The memory is used to store computer programs; The processor is used to execute the computer program stored in the memory to cause the UAV network deployment and collaborative caching terminal to perform the UAV network deployment and collaborative caching method according to any one of claims 1 to 7.

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