A communication method and system based on aerial drone-assisted 5G ground base station

Abstract

This invention discloses a communication method and system based on aerial drones assisting 5G ground base stations. It establishes an environment centered on a ground base station, with drones orbiting around it to assist communication. The number of drones that can be introduced within the signal coverage area of ​​the ground base station for assisting communication is set. The maximum number of drones that can be introduced is used as the upper limit for the iteration of the algorithm to determine the location and number of drones assisting the ground base station. In the first iteration, no drones are introduced, and only user devices within the signal coverage area of ​​the ground base station can access the network. After the algorithm iterations, the exact number and location coordinates of drones required to support the ground communication network are obtained, realizing communication based on drones assisting 5G ground base stations. This provides an effective communication solution for scenarios with high-density user device network access.

Description

Technical Field

[0001] This invention belongs to the field of information technology, specifically relating to a communication method and system based on an aerial drone-assisted 5G ground base station. Background Technology

[0002] In areas with a high density of ground-based user equipment awaiting access, ground communication base stations often cannot promptly meet the basic communication needs of all users in the area. Introducing drones as temporary aerial base stations to assist communication can undoubtedly effectively improve the communication coverage of users in the system. However, in practical scenarios, how many drones should be deployed and how they should be deployed remains a problem.

[0003] Existing methods for determining the location and number of drones are mostly fine-tunings and customizations of traditional clustering methods such as K-means clustering and hierarchical clustering. These methods are limited by their generality, focusing more on the statistical and mathematical characteristics of the users to be clustered, and failing to adequately address the complexity and diversity of communication channels and environments in real-world scenarios. Furthermore, in 5G scenarios, we employ NOMA and D2D technologies to maximize the number of access devices. The introduction of these two technologies makes it more complex for user devices to choose how to access the communication network, allowing them to access via either a first-hop cellular communication link or a second-hop D2D communication link. Therefore, determining the drone placement location solely based on the distribution of ground users and determining the number of drones by the number of clusters generated by clustering algorithms is inappropriate.

[0004] In summary, existing research proposals do not consider how to introduce and deploy drones to support communication in 5G communication networks assisted by drones on ground base stations from a communication perspective. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a communication method and system based on an aerial drone assisted by a 5G ground base station, which addresses the shortcomings of the prior art and solves the technical problem of not being able to determine the number and deployment location of drones.

[0006] The present invention adopts the following technical solution:

[0007] A communication method based on an aerial drone-assisted 5G ground base station includes the following steps:

[0008] S1. Determine the parameters in the communication environment of 5G ground base stations assisted by aerial drones;

[0009] S2. Based on the parameters obtained in step S1, set the maximum number of drones that can assist 5G ground base stations in communication.

[0010] S3. Input the number of drones obtained in step S2 into the algorithm for solving the location and number of drone-assisted ground base stations and run it to obtain the number of drones and their deployment locations that need to be introduced to support the ground communication network.

[0011] S4. Based on the number of drones and deployment locations required to support the terrestrial communication network obtained in step S3, deploy the drones around the 5G terrestrial base station in this manner.

[0012] Specifically, step S3 is as follows:

[0013] S301. Within the coverage area of ​​the 5G terrestrial base station signal, calculate the signal strength received from the terrestrial base station for all user equipment, calculate the signal-to-interference-plus-noise ratio (SINR) of the user equipment, and classify user equipment that meets the SINR threshold as candidate equipment to access the network via the first hop cellular communication link.

[0014] S302. Select strong and weak users that can form NOMA clusters from the candidate user equipment that communicates via cellular communication links obtained in step S301. Users that form NOMA clusters access the network via NOMA, and users that cannot form NOMA clusters access the network via OMA.

[0015] S303. Calculate the maximum coverage radius of the base station based on the given threshold of the received signal strength of the user equipment.

[0016] S304. Use the coverage radius obtained in step S303 as the radius of the circular track where the drone is deployed; if a drone for auxiliary communication is introduced in this iteration, place the drone evenly on the circular track; the drone acting as a temporary base station provides communication services to user equipment that cannot be directly connected to the base station within the coverage area of ​​the 5G ground base station.

[0017] S305. User equipment that cannot access the network via the first hop cellular link through ground base stations or drone temporary base stations in steps S303 and S304 is selected as second hop candidate users, and HTC users who have already accessed the network via the first hop cellular link are selected as D2D relays; D2D relays support the nearest user equipment that meets the signal-to-noise ratio threshold to transfer into the communication network via D2D link.

[0018] S306. Statistically analyze the network access status of all user equipment in the communication system and calculate the coverage rate of user equipment. If the user equipment coverage rate reaches 1 or the number of drones introduced into the communication system reaches the upper limit set by the system, the iteration ends and the number of drones to be introduced and their corresponding location coordinates are output. Otherwise, the number of drones introduced into the system is incremented by 1 and a new round of iteration begins.

[0019] Furthermore, in step S301, the signal-to-interference-plus-noise ratio (SINR) is calculated as follows:

[0020]

[0021] Where I represents the sum of signal interference received by ground users from other drone base stations, and σ 2 R is the noise interference power experienced by the user from the communication channel. gu The signal strength received by the user equipment from the ground base station.

[0022] Furthermore, the signal strength R received by the user equipment from the ground base station gu The calculation is as follows:

[0023]

[0024] Among them, P b Let be the transmit power of the ground base station, and h, α, and β be the path loss factors in the ground-to-ground communication channel.

[0025] Furthermore, in step S302, the signal-to-interference-plus-noise ratio (SINR) of strong and weak users in the NOMA cluster is... s SINR w Specifically:

[0026]

[0027]

[0028] Where, ε s ε w R is the power allocation factor for strong and weak users in a NOMA cluster. sgu R wgu σ represents the signal strength received by strong and weak users. 2 It is the noise interference power experienced by the user from the communication channel.

[0029] Furthermore, in step S303, the locations that meet the signal reception strength conditions form a circular track with the ground base station as the center. The circular track consists of those points that are d away from the ground base station. th The points constituted, d th The calculation is as follows:

[0030]

[0031] Among them, P b R is the transmit power of the ground base station. th α is the set signal reception strength threshold, and h, α, and β are the path loss factors in the ground-to-ground communication channel.

[0032] Furthermore, in step S304, the communication between the UAV and the user equipment is an air-to-ground channel. Under the air-to-ground channel, the probabilities P of the LosS line-of-sight link and the NloS non-line-of-sight link are... LoS P NLoS And the signal strength R received by the user equipment from the drone base station u The calculation method is as follows:

[0033]

[0034] P NLos =1-P Los

[0035]

[0036] Where C and B are constants depending on the environment, and θ is the elevation angle between the user and the drone base station. u d′ is the transmit power of the drone base station, d′ is the distance between the ground user and the drone base station, K0, τ1, and τ2 are constants in the air-to-ground channel, and μ is the path loss factor of the signal in the channel.

[0037] Secondly, embodiments of the present invention provide a communication system for an aerial unmanned aerial vehicle (UAV) based on a 5G network assisted by a ground base station, comprising:

[0038] The data processing module determines the parameters in the communication environment of the 5G ground base station assisted by the aerial drone and sets the maximum number of drones that can communicate with the 5G ground base station assisted by the aerial drone.

[0039] The simulation module takes the number of UAVs as input into the algorithm for determining the location and number of UAV-assisted ground base stations and runs it.

[0040] The deployment module obtains the number of drones needed to support the terrestrial communication network and their deployment locations, and then deploys the drones around the 5G terrestrial base stations in this manner.

[0041] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described communication method based on an aerial drone-assisted 5G ground base station.

[0042] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described communication method based on an aerial drone-assisted 5G ground base station.

[0043] Compared with the prior art, the present invention has at least the following beneficial effects:

[0044] This invention is based on a communication method for 5G ground base stations assisted by aerial drones. Under the communication system of drone-assisted ground base stations, the algorithm for determining the location and number of drones can achieve good simulation results under different numbers of users to be accessed and different environments, providing a practical solution for solving the communication coverage problem in high-density user access scenarios.

[0045] Furthermore, step S3 describes in detail the access method of user equipment, the type of communication link, and the deployment method of UAV during the communication process of the entire communication system. The setting of each sub-step is conducive to modular analysis of the system model and algorithm, making the description of the algorithm clearer.

[0046] Furthermore, R gu It is used to make a preliminary judgment on the attenuation of the signal from the base station to the user, and can filter out those devices that are outside the communication range of the base station.

[0047] Furthermore, the signal-to-interference-plus-noise ratio (SINR) refers to the ratio of the strength of the received useful signal to the strength of the received interference / noise signal. Setting an appropriate SINR threshold in a communication system allows user equipment to further filter out users with poor signal quality, ensuring the overall communication service quality for all users within the system, while still meeting the required signal reception strength.

[0048] Furthermore, if a user uses NOMA to access the communication system, the signal strength and interference experienced by two users in a NOMA cluster differ from those under the ordinary access method OMA. Therefore, setting different SINR thresholds for strong and weak users in a NOMA cluster is more in line with reality.

[0049] Furthermore, the drones will be deployed in a circle with a radius of d centered on the ground base station. th A circular track is more suitable for real-world communication scenarios. Firstly, clustering algorithms such as K-means clustering are more statistically oriented, while drones act as temporary base stations and participate in communication; secondly, d th The distance is calculated by working backward from the user's received signal strength threshold, which is more in line with the communication environment.

[0050] Furthermore, the communication services provided to users by ground base stations are carried on ground-to-ground communication channels. The communication link between the UAV and ground users is carried on an air-to-ground communication channel. Considering environmental factors such as obstruction, this channel can be further divided into Los-of-Sight (LoS) links and Non-LoS (Non-Line-of-Sight) links. The probability of information transmission through these two types of links is P. LoS P NLoS Therefore, since the channel model differs from that of a ground-to-ground channel, the signal reception strength will also change accordingly, requiring separate settings for R. u .

[0051] In summary, this invention establishes a communication simulation environment for UAV-assisted 5G ground base stations. Regarding the deployment of UAVs assisting ground base station communication in real-world environments, this invention designs an algorithm to determine the location and number of UAV-assisted ground base stations. By solving this algorithm and deploying UAVs in a real-world environment, this invention provides a practical solution for user communication access in scenarios with high-density users awaiting access.

[0052] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0053] Figure 1 A diagram of the communication environment model;

[0054] Figure 2 This is a diagram showing the locations of the deployed drones;

[0055] Figure 3 Example diagram of a NOMA cluster in the downlink communication power domain for two users;

[0056] Figure 4 This is a comparison chart of the method proposed in this invention with random algorithms and K-means algorithms;

[0057] Figure 5 Figures showing the simulation results of the algorithm under different user densities;

[0058] Figure 6 Figure 1000 shows the simulation results of user equipment under different environments;

[0059] Figure 7 Figure 10000 shows the simulation results of user equipment under different environments;

[0060] Figure 8 A flowchart illustrating the implementation of a 5G ground base station communication method based on aerial drones. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0063] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0064] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this document generally indicates that the preceding and following objects have an "or" relationship.

[0065] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0066] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0067] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0068] Please see Figure 1 This invention establishes a network based on NOMA and D2D technologies, primarily using ground base stations and supplemented by UAVs for communication, to support the downlink communication needs of a high density of users seeking access. User equipment can access the communication network either through a first-hop cellular communication link or through a second-hop D2D communication link.

[0069] Please see Figure 2 Taking four drones as an example, in a drone-assisted ground base station communication system, the drones hover evenly around the ground base station according to the circular track shown in the figure to provide communication services to user equipment.

[0070] Please see Figure 3 In a two-user NOMA cluster in the power domain, the user closer to the ground base station is considered a strong user, and the user farther away is considered a weak user. Weak users have a larger power allocation factor, while strong users have a smaller power allocation factor.

[0071] Please see Figure 8 The present invention discloses a communication method based on an aerial drone-assisted 5G ground base station, comprising the following steps:

[0072] S1. Set the parameters in the air-to-ground UAV-assisted 5G ground base station communication environment, such as the path loss parameters h, α, and β in the ground-to-ground channel; the line-of-sight link probability calculation environment parameters C and B in the air-to-ground channel; and the constant parameters K0, τ1, and τ2 in the channel model. In addition, it also includes the power allocation factor ε for strong and weak users in the NOMA cluster. s ε w Ground base stations send data to strong and weak users in the NOMA cluster according to the power allocation factor.

[0073] S2. Set the maximum number of drones that need to be introduced into the communication area of ​​the ground base station, and use the maximum number of drones as the upper limit of the algorithm for solving the location and number of drone-assisted ground base stations.

[0074] S3. Input the number of UAVs obtained in step S2 into the algorithm for solving the location and number of UAV-assisted ground base stations;

[0075] S301. Calculate the signal strength received from the base station for all user equipment within the coverage area of ​​the 5G terrestrial base station, and further calculate the signal-to-interference-plus-noise ratio (SINR) for these user equipment. User equipment meeting the SINR threshold is classified as candidate equipment for accessing the network via the first-hop cellular communication link.

[0076] The received signal strength R that the user can obtain through the ground-to-ground channel gu The calculation formula is as follows:

[0077]

[0078] Among them, P b The transmit power of the ground base station is represented by h, α, and β, which are the channel path loss parameters in the ground-to-ground communication channel model.

[0079] The calculation method for signal-to-interference-plus-noise ratio (SINR) is described as follows:

[0080]

[0081] Among them, R gu σ represents the signal strength received by the ground user, where I represents the signal interference received by the ground user from other drone base stations. 2 This indicates the noise interference power experienced by the user in the channel.

[0082] S302. Select strong and weak users that can form NOMA clusters from the candidate user equipment that communicates via cellular communication links obtained in step S301. Users that form NOMA clusters access the network via NOMA, and users that cannot form NOMA clusters access the network via OMA.

[0083] Because there are strong and weak users in a NOMA pair, the weak users are affected by interference from the strong user signals. Therefore, the signal-to-interference-plus-noise ratio (SINR) calculation for users in a NOMA pair is different from the SINR calculation described above; the SINR for strong and weak users... s SINR w The calculation is described as follows:

[0084]

[0085]

[0086] Where, ε s ε w Let ε represent the power allocation factors for strong and weak users, respectively. s >ε w , ε s +ε w =1. R sgu R wgu These represent the received signal strength for strong and weak users, respectively.

[0087] S303. Calculate the maximum coverage radius of the base station based on the given threshold of the received signal strength of the user equipment.

[0088] It is easy to see that the set of locations that meet the signal reception strength requirements forms a circular trajectory centered on the ground base station. The circular trajectory extends from the ground base station at a distance d. th The points constituted, d th The calculation formula is expressed as follows:

[0089]

[0090] Among them, P b R represents the transmit power of the ground base station. th The signal reception strength threshold is set during initialization, and h, α, and β are the channel path loss parameters in the ground-to-ground communication channel model.

[0091] S304. Use the coverage radius obtained in step S303 as the radius of the circular track where the deployed drones are located. If drones for auxiliary communication are introduced in this iteration, these drones are placed evenly on the circular track. These drones, acting as temporary base stations, provide communication services to user equipment within the coverage area of ​​the 5G ground base station that cannot directly connect to the base station. The calculation and determination methods for whether user equipment can access the network through the drone base station are basically the same as those for ground base stations. The difference lies in the transmission power of the drone base station and the setting of the signal-to-interference-plus-noise ratio threshold for user equipment under the drone base station.

[0092] S305. User equipment that cannot access the network via the first hop cellular link through ground base stations or drone temporary base stations in steps S303 and S304 is selected as second hop candidate users, and HTC users that have already accessed the network via the first hop cellular link are selected as D2D relays; D2D relays can support the nearest user equipment that meets the signal-to-noise ratio threshold to switch into the communication network via D2D link.

[0093] In the calculation process, unlike the ground-to-ground communication channel between the ground base station and the user, the UAV and the ground user use an air-to-ground communication channel; under the air-to-ground channel, the probabilities P of LosS line-of-sight link and NloS non-line-of-sight link are... LoS P NLoS Calculation and signal reception strength R u The calculation is described as follows:

[0094]

[0095] P NLos =1-P Los

[0096]

[0097] Where C and B are constants that depend on the environment, and θ represents the elevation angle between the user and the drone base station. P u d′ represents the transmit power of the drone base station, d′ represents the distance between the ground user and the drone base station, K0, τ1, and τ2 are constant parameters in the channel model, and μ is the path loss factor of the signal in the channel.

[0098] S306. Statistically analyze the network access status of all user equipment within the communication system and calculate the user equipment coverage rate. If the user equipment coverage rate reaches 1 or the number of drones introduced into the communication system reaches the system's set upper limit, the iteration ends, and the number of drones to be introduced and their corresponding location coordinates are output. Otherwise, the number of drones to be introduced into the system is incremented by 1, and a new round of iteration begins.

[0099] S4. Run the algorithm to determine the location and number of UAV-assisted ground base stations, obtain the number of UAVs and their deployment locations needed to support the ground communication network, and deploy the UAVs around the 5G ground base stations in this manner.

[0100] In another embodiment of the present invention, a multi-rotor UAV for collecting data includes an infrared imaging device, a signal sensing sensor, an image transmission module, and a data transmission module. It flies or hovers around a ground base station to collect signal data, scan the number of user devices within the signal coverage area of ​​the ground base station, and sends the data to the data processing module in the communication system.

[0101] In another embodiment of the present invention, a communication system based on an aerial drone-assisted 5G ground base station is provided. This system can be used to implement the aforementioned communication method based on an aerial drone-assisted 5G ground base station. Specifically, the communication system based on an aerial drone-assisted 5G ground base station includes a data processing module, a communication module, a simulation module, and a deployment module.

[0102] The data processing module processes the data collected from the ground base station communication environment, such as the number of user equipment in the communication system and the transmission power of the ground base station. It sets the transmission power, received signal strength threshold, signal-to-interference-plus-noise ratio threshold, and channel parameters in the communication link for the temporary UAV base station, and then transmits this data to the communication module.

[0103] The communication module receives data sent by the data processing module and forwards this data to the communication simulation module.

[0104] The simulation module sets the parameters in the communication simulation environment based on the data transmitted by the communication module, and runs the algorithm for solving the location and number of UAV-assisted ground base stations.

[0105] The deployment module obtains the number of drones needed to support the terrestrial communication network and their corresponding location coordinates, and deploys drones in the actual environment to support 5G terrestrial base stations in this way.

[0106] In another embodiment of the present invention, a terminal device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or corresponding function. The processor described in this embodiment of the present invention can be used for the operation of a communication method based on an aerial drone-assisted 5G ground base station, including:

[0107] The system sets thresholds for received signal strength, signal-to-interference-plus-noise ratio (SINNR), and channel parameters in the communication link. Based on an aerial UAV-assisted ground base station communication system, it constructs an environment centered on the ground base station with UAVs orbiting around it to assist communication. It sets the maximum number of UAVs that can be introduced within the ground base station's signal coverage area for auxiliary communication. The maximum number of UAVs is used as the upper limit for iterating the algorithm to determine the location and number of UAV-assisted ground base stations. Each iteration increments the required number of UAVs by 1. In the first iteration, no UAVs are introduced; only the ground base station provides communication support to user equipment within the communication area. The algorithm for determining the location and number of UAV-assisted ground base stations yields the required number and coordinates of UAVs to support the ground communication network, thus assisting the 5G ground base station in communication.

[0108] In another embodiment of the present invention, a storage medium is also provided, specifically a computer-readable storage medium (memory). This computer-readable storage medium is a memory device in a terminal device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device.

[0109] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the communication method for 5G ground base stations based on aerial drones in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor in the following steps:

[0110] The system sets thresholds for received signal strength, signal-to-interference-plus-noise ratio (SINNR), and channel parameters in the communication link. Based on an aerial UAV-assisted ground base station communication system, it constructs an environment centered on the ground base station with UAVs orbiting around it to assist communication. It sets the maximum number of UAVs that can be introduced within the ground base station's signal coverage area for auxiliary communication. The maximum number of UAVs is used as the upper limit for iterating the algorithm to determine the location and number of UAV-assisted ground base stations. Each iteration increments the required number of UAVs by 1. In the first iteration, no UAVs are introduced; only the ground base station provides communication support to user equipment within the communication area. The algorithm for determining the location and number of UAV-assisted ground base stations yields the required number and coordinates of UAVs to support the ground communication network, thus assisting the 5G ground base station in communication.

[0111] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0112] Please see Figure 4 The results demonstrate a comparison between the proposed method and randomized algorithms and K-means clustering. With the same number of drones, the proposed method shows a significant advantage over both randomized and K-means clustering algorithms. Furthermore, the proposed method requires fewer drones than K-means clustering to achieve full communication coverage for system users.

[0113] Please see Figure 5 The results shown are the performance of this algorithm in an urban environment with different numbers of user devices. With 1000 user devices, the number of users in the communication system is relatively small; introducing 1-2 drones can significantly increase the user coverage. With 5000 or 10000 user devices, the number of users in the communication system is relatively large. Although introducing only 1-2 drones cannot significantly improve the user coverage, as the number of drones introduced increases, the user coverage will approach 100%.

[0114] Please see Figure 6 The results shown are the execution results of this algorithm under different environments with 1000 user devices. The comparison shows the improvement of system coverage of the UAV-assisted ground base station communication system as the number of UAVs increases in suburban, urban, and dense urban environments. Obviously, this algorithm performs well in all three environments.

[0115] Please see Figure 7 The results shown are the execution results of this algorithm under different environments with 10,000 user devices.

[0116] refer to Figure 5 , Figure 6 and Figure 7 The experimental simulation results show that the algorithm designed in this invention has good simulation results under different user conditions and different environments.

[0117] In summary, the present invention provides a communication method and system based on an aerial drone-assisted 5G ground base station. By applying the algorithm for determining the location and number of drones to the communication system of the drone-assisted ground base station, it can achieve good simulation results under different numbers of users waiting to access and different environments. It has a greater performance advantage than random algorithms and K-means clustering algorithms, and provides a practical solution to the communication access problem under high-density users in real life.

[0118] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0119] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0120] Those skilled in the art will 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, or a combination of computer software and electronic hardware. 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 implementations should not be considered beyond the scope of this invention.

[0121] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components 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 between devices or units may be electrical, mechanical, or other forms.

[0122] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0123] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0124] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0125] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0126] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0127] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0128] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A communication method based on an aerial drone-assisted 5G ground base station, characterized in that, Includes the following steps: S1. Determine the parameters in the communication environment of 5G ground base stations assisted by aerial drones; S2. Based on the parameters obtained in step S1, set the maximum number of drones that can assist 5G ground base stations in communication. S3. Input the number of drones obtained in step S2 into the algorithm for determining the location and number of drone-assisted ground base stations and run it to obtain the number of drones and their deployment locations required to support the ground communication network, specifically: S301. Within the coverage area of ​​the 5G terrestrial base station signal, calculate the signal strength received from the terrestrial base station for all user equipment, calculate the signal-to-interference-plus-noise ratio (SINR) of the user equipment, and classify user equipment that meets the SINR threshold as candidate equipment to access the network via the first hop cellular communication link. S302. Select strong and weak users that can form NOMA clusters from the candidate user equipment that communicates via cellular communication links obtained in step S301. Users that form NOMA clusters access the network via NOMA, and users that cannot form NOMA clusters access the network via OMA. S303. Calculate the maximum coverage radius of the base station based on the given threshold of the received signal strength of the user equipment. S304. Use the coverage radius obtained in step S303 as the radius of the circular track where the drone is deployed; if a drone is introduced as an auxiliary communication device in this iteration, place the drone evenly on the circular track; the drone acting as a temporary base station provides communication services to user equipment that cannot be directly connected to the base station within the coverage area of ​​the 5G ground base station. S305. User equipment that cannot access the network via the first hop cellular link through the ground base station or drone temporary base station in steps S303 and S304 is designated as second hop candidate user, and HTC users who have already accessed the network via the first hop cellular link are designated as D2D relays. D2D relay allows the nearest user equipment that meets the signal-to-noise ratio threshold to connect to the communication network via a D2D link. S306. Statistically analyze the network access status of all user equipment in the communication system and calculate the coverage rate of user equipment; If the user equipment coverage reaches 1 or the number of drones introduced into the communication system reaches the upper limit set by the system, the iteration ends and the number of drones to be introduced and their corresponding location coordinates are output; otherwise, the number of drones to be introduced into the system is incremented by 1 and a new round of iteration begins. S4. Based on the number of drones required to support the terrestrial communication network and their deployment locations obtained in step S3, deploy the drones around the 5G terrestrial base station.

2. The communication method based on an aerial drone-assisted 5G ground base station according to claim 1, characterized in that, In step S301, the signal-to-interference-plus-noise ratio (SINR) The calculation is as follows: in, This is the sum of signal interference received by ground users from other drone base stations. It is the noise interference power experienced by the user from the communication channel. The signal strength received by the user equipment from the ground base station.

3. The communication method based on an aerial drone-assisted 5G ground base station according to claim 2, characterized in that, Signal strength received by user equipment from terrestrial base station The calculation is as follows: in, This refers to the transmission power of the ground base station. It is the path loss factor in ground-to-ground communication channels.

4. The communication method based on an aerial drone-assisted 5G ground base station according to claim 1, characterized in that, In step S302, the signal-to-interference-plus-noise ratio (SIR) of strong and weak users in the NOMA cluster is calculated. Specifically: in, The power allocation factor for strong and weak users in the NOMA cluster. The signal strength received by strong and weak users The signal strength received by the user equipment from the ground base station. It is the noise interference power experienced by the user from the communication channel.

5. The communication method based on an aerial drone-assisted 5G ground base station according to claim 1, characterized in that, In step S303, the locations that meet the signal reception strength conditions form a circular track with the ground base station as the center. The circular track is composed of those points that are at a distance of [missing information]. The points constitute, The calculation is as follows: in, This refers to the transmission power of the ground base station. It is the set signal reception strength threshold. It is the path loss factor in ground-to-ground communication channels.

6. The communication method based on an aerial drone-assisted 5G ground base station according to claim 1, characterized in that, In step S304, the communication between the UAV and the user equipment is an air-to-ground channel. Under the air-to-ground channel, the probabilities of LoS line-of-sight link and NloS non-line-of-sight link are... And the signal strength received by user equipment from the drone base station The calculation method is as follows: in, It depends on environmental constants. The elevation angle between the user and the drone base station. It is the transmission power of the drone base station. It is the distance between the ground user and the drone base station. This is a constant in the air-to-ground channel. It is the path loss factor of the signal in the channel.

7. A communication system for aerial unmanned aerial vehicles (UAVs) based on a 5G network-assisted ground base station, characterized in that, include: The data processing module determines the parameters in the communication environment of the 5G ground base station assisted by the aerial drone and sets the maximum number of drones that can communicate with the 5G ground base station assisted by the aerial drone. The simulation module takes the number of UAVs as input into the algorithm for determining the location and number of UAV-assisted ground base stations and runs it. Specifically: Within the coverage area of ​​the 5G terrestrial base station, the signal strength received from the terrestrial base station is calculated for all user equipment (UEs), and the signal-to-interference-plus-noise ratio (SINR) of the UEs is calculated. UEs that meet the SINR threshold are classified as candidate devices for accessing the network via the first-hop cellular communication link. From the candidate UEs communicating via cellular communication links, strong and weak users that can form NOMA clusters are selected. Users that form NOMA clusters access the network via NOMA, while users that cannot form NOMA clusters access the network via OMA. The maximum coverage radius of the base station is calculated based on the given UE received signal strength threshold. The obtained coverage radius is used as the radius of the circular track where the drone is deployed. If drones are introduced as auxiliary communication devices in this iteration, they are placed evenly on the circular track. Drones acting as temporary base stations provide communication services to UEs within the 5G terrestrial base station coverage area that cannot directly connect to the base station. User equipment that cannot access the network via the first hop cellular link through ground base stations or drone temporary base stations will be selected as second hop users, and HTC users who have already accessed the network via the first hop cellular link will be selected as D2D relays. D2D relay allows the nearest user equipment that meets the signal-to-noise ratio threshold to connect to the communication network via a D2D link; it also provides statistics on the network access status of all user equipment within the communication system and calculates the coverage rate of user equipment. If the user equipment coverage reaches 1 or the number of drones introduced into the communication system reaches the upper limit set by the system, the iteration ends and the number of drones to be introduced and their corresponding location coordinates are output; otherwise, the number of drones to be introduced into the system is incremented by 1 and a new round of iteration begins. The deployment module obtains the number of drones needed to support the terrestrial communication network and their deployment locations, and then deploys the drones around the 5G terrestrial base stations in this manner.

8. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform the method of any one of claims 1 to 6.

9. A computing device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including steps for performing the method of any one of claims 1 to 6.

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