Unmanned aerial vehicle base station deployment method of air-ground network based on non-Poisson point process

Abstract

The invention discloses an unmanned aerial vehicle base station deployment method for an air-ground network based on a non-Poisson point process, and the method comprises the following specific steps: 1, constructing an air-ground heterogeneous network composed of an unmanned aerial vehicle base station, a user terminal, and a ground base station; 2, the ground base station collects the state information periodically reported to the ground base station by the unmanned aerial vehicle base station, and after the user terminal transmits the information to the ground base station, the information sent by the unmanned aerial vehicle base station and the user terminal is reported to the cloud network; and a third step, after the cloud network collects information reported by the ground base station, optimal parameter configuration is searched according to a beta-Ginibre point process, and unmanned aerial vehicle base station position deployment is obtained. According to the method, an adjustable parameter beta is introduced into beta-GPP and used for controlling balance between randomness and rejection intensity of node distribution, various distribution characteristics from complete random to strong rejection can be flexibly described by adjusting beta, and the method can better adapt to various different scenes.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a method for deploying unmanned aerial vehicle base stations in an air-to-ground network based on a non-Poisson point process. Background Art

[0002] With the development of communication technology, air-ground networks have gradually become a research hotspot as an important solution to support high-dynamic environments and high-flexibility coverage requirements. The flexible deployment capability of drone base stations makes them valuable in disaster relief, remote area communication coverage, and hot spot area capacity enhancement. However, the efficient deployment of drone base stations still faces the following problems: 1. UAV base station deployment based on Poisson point process: Poisson point process is the most commonly used point process model in random geometry theory, which assumes that base stations and users are randomly distributed in the target area. Its mathematical model is simple, and closed-form expressions can be derived to analyze network performance indicators such as coverage probability and signal-to-interference-noise ratio. Due to its own characteristics, it cannot reflect the repulsive characteristics between nodes and is difficult to adapt to the characteristics of base station distribution in actual scenarios.

[0003] 2. Base station deployment based on hard core point process: The hard core point process adds a minimum distance constraint to the Poisson point process to force base stations to maintain a certain physical distance to reflect the node exclusion characteristics. Due to the rigidity of the constraint conditions and the lack of flexibility, it is difficult to describe the exclusion requirements in different scenarios. Summary of the invention

[0004] The present invention provides a method for deploying UAV base stations in an air-to-ground network based on a non-Poisson point process, so as to solve the problem that the existing point process model has limitations in modeling flexibility and is difficult to effectively adapt to the needs of various scenarios as mentioned in the above background technology.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: A method for deploying UAV base stations in an air-to-ground network based on a non-Poisson point process comprises the following specific steps: The first step is to build an air-ground heterogeneous network consisting of drone base stations, user terminals, and ground base stations; In the second step, the ground base station collects the status information that the drone base station periodically reports to the ground base station, and after the user terminal transmits information to the ground base station, it reports the information sent by the drone base station and the user terminal to the cloud network; In the third step, after the cloud network collects the information reported by the ground base station, it searches for the optimal parameter configuration according to the β-Ginibre point process to obtain the location deployment of the drone base station; The fourth step is to send the optimal parameter configuration and the UAV base station location deployment parameters to the ground base station, and the ground base station then sends the optimal parameter configuration and the UAV base station location deployment parameters to the UAV base station; In the fifth step, the drone base station is deployed according to the parameters. Then the cloud network determines the user terminal accessing the drone base station based on the deployment of the drone base station. The ground base station issues a switching or access command. Finally, the user terminal selects the ground base station to access based on the maximum signal access criterion.

[0006] As a further improvement of the present technical solution: the UAV base station is a UAV base station suspended in the air, which provides flexible and on-demand coverage and is suitable for use far away from ground base stations or in emergency scenarios.

[0007] As a further improvement of the present technical solution: the ground base station is a fixed traditional cellular network base station, which mainly covers urban areas or places with complete infrastructure.

[0008] As a further improvement of the present technical solution: the user terminal is a user device randomly distributed in the network, including a mobile device and an Internet of Things terminal; the user is distinguished according to the different network access points of the user, and the user whose service base station is a drone base station is a UUE, and the user whose service base station is a ground base station is a GUE.

[0009] As a further improvement of this technical solution: the distribution of drone base stations follows the β-Ginibre point process, which is used to describe the distribution of drone base stations in space, taking into account repulsion and avoiding excessive density of drone base stations; in the β-Ginibre point process, Used to adjust the spatial repulsion characteristics between nodes.

[0010] 6. The method for deploying UAV base stations in an air-to-ground network based on a non-Poisson point process according to claim 1 is characterized in that when When , the base station distribution tends to the Ginibre point process; when When , the base station distribution degenerates into a completely independent Poisson point process.

[0011] As a further improvement of the technical solution: the generation process of the β-Ginibre point process is as follows: first, a standard Ginibre point process is generated, whose nodes follow an exclusive spatial distribution, and the correlation between the nodes is determined by the kernel function of the Ginibre point process; Then, for each generated node, independently Decide whether to keep the node. If so, proceed to the next step. If not, delete the node. Finally, for each retained node, the scaling factor The position adjustment is performed to keep the intensity of the entire point process unchanged. The scaling operation ensures that the sparse point set still has the same intensity as the original Ginibre point process.

[0012] Compared with the prior art, the present invention has the following beneficial effects: 1. Improve the flexibility and adaptability of the model: The β-GPP of the present invention introduces an adjustable parameter β to control the balance between the randomness of node distribution and the repulsion strength. When β is close to 0, the distribution of β-GPP approaches the Poisson point process, which is suitable for completely random distribution scenarios; when β is close to 1, the distribution of β-GPP is close to the standard Ginibre point process, which is suitable for dense coverage scenarios that require strong repulsion. By adjusting β, a variety of distribution characteristics from completely random to strongly repulsive can be flexibly described, which can better adapt to a variety of different scenarios, including cities, high-density areas, disaster areas, etc., and avoid the applicability limitations of a single model.

[0013] 2. Accurate performance analysis: Analytical performance evaluation: Performance analysis based on β-GPP can provide closed-form expressions to quantify the randomness and repulsion of base station distribution for key performance indicators such as coverage probability, signal-to-interference-to-noise ratio, and throughput. For example, in areas with sparse users, base stations can be distributed more randomly, while in areas with dense users, the repulsion strength will be automatically increased to avoid interference caused by excessive aggregation of base stations.

[0014] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention in conjunction with the accompanying drawings. The specific implementation of the present invention is given in detail by the following embodiments and their accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 This is a schematic diagram of the air-ground heterogeneous network structure composed of the UAV base station, user terminal, and ground base station proposed in the present invention; Figure 2 Schematic diagram of the workflow of the present invention. DETAILED DESCRIPTION

[0016] The principles and features of the present invention are described below in conjunction with the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention. The present invention is described in more detail by way of example with reference to the accompanying drawings in the following paragraphs. The advantages and features of the present invention will become clearer according to the following description and claims. It should be noted that the drawings are all in a very simplified form and are not in precise proportions, and are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention.

[0017] In an embodiment of the present invention, a method for deploying a drone base station in an air-to-ground network based on a non-Poisson point process includes the following specific steps: The first step is to build an air-ground heterogeneous network consisting of drone base stations, user terminals, and ground base stations; In the second step, the ground base station collects the status information that the drone base station periodically reports to the ground base station, and after the user terminal transmits information to the ground base station, it reports the information sent by the drone base station and the user terminal to the cloud network; In the third step, after the cloud network collects the information reported by the ground base station, it searches for the optimal parameter configuration according to the β-Ginibre point process to obtain the location deployment of the drone base station; The fourth step is to send the optimal parameter configuration and the UAV base station location deployment parameters to the ground base station, and the ground base station then sends the optimal parameter configuration and the UAV base station location deployment parameters to the UAV base station; In the fifth step, the drone base station is deployed according to the parameters. Then the cloud network determines the user terminal accessing the drone base station based on the deployment of the drone base station. The ground base station issues a switching or access command. Finally, the user terminal selects the ground base station to access based on the maximum signal access criterion.

[0018] UAV Base Station (UBS): A UAV base station suspended in the air, providing flexible, on-demand coverage. Suitable for use far away from ground base stations or in emergency scenarios.

[0019] Ground Base Station (GBS): A fixed traditional cellular network base station that mainly covers urban areas or places with complete infrastructure.

[0020] User terminal (UE): User equipment randomly distributed in the network, including mobile devices and IoT terminals. It is distinguished according to the different network access points of users. Users whose service base stations are UBS are UUE, and those whose service base stations are GBS are GUE. (2) Model spatial distribution The distribution of UBS follows the β-Ginibre point process (β-GPP), which describes the distribution of UBS in space and takes repulsion into account (avoiding overcrowding). Used to adjust the spatial repulsion characteristics between nodes. When , the base station distribution tends to the Ginibre point process; when When , the base station distribution degenerates into a completely independent Poisson point process.

[0021] The GPP generation process is as follows: First, a standard GPP is generated, whose nodes follow an exclusive spatial distribution, and the correlation between nodes is determined by the kernel function of the GPP. Then, for each generated node, independently Decide whether to keep the node. If so, proceed to the next step; if not, delete the node. Finally, for each node that is kept, scale it according to the scaling factor The position adjustment is performed to keep the intensity of the entire point process unchanged. The scaling operation ensures that the sparse point set still has the same intensity as the original GPP.

[0022] The distribution of GBS follows the Poisson point process (PPP), which describes the random distribution of ground base stations and is used for irregular layouts.

[0023] User distribution follows PPP modeling, and the distribution of user devices is usually random, which is suitable for most scenarios.

[0024] Among them, the distribution density of ground base stations is PPP , the transmission power is ; The drone base station hovers at the same height h, and the projection distribution on the ground follows the density of , the transmission power is . Users are divided into GUE and UUE according to their access points. The distribution of GUE follows independent PPP distribution. Typical user TUE and its ground service base station The distance between The distribution of is:

[0025] The distribution of UUEs follows an independent PPP distribution, where each UUE is associated with its nearest UBS. The distance between a typical UUE and its serving base station UBS is The distribution of is:

[0026] Channel Model: Considering the air-to-ground link between the drone base station and the user, we adopt a propagation path loss model that combines LOS and NLOS. The probability that the link between the drone base station and the user is LOS is

[0027] Among them, B and C are constants related to the environment, r is the distance between the drone and the user, and h is the height at which the drone hovers. Therefore, the probability of an NLOS link is .

[0028] The path loss function for air-to-ground is:

[0029] in, are the path loss exponents of LOS and NLOS respectively.

[0030] For small-scale fading modeled as Nakagami fading, the link parameters for LOS and NLOS are It follows the gamma distribution, that is , .in, and is the Nakagami fading coefficient of LOS and NLOS links.

[0031] For the link between the ground base station and the user, the path loss coefficient is considered to be The standard path loss model For small-scale fading, standard Rayleigh fading with unity mean is used.

[0032] The beam of each drone is pointed vertically downward, and the antenna gain

[0033] in, is the main lobe gain, is the sidelobe gain, The critical angle at which the transmitted signal deviates from the baseline vertically downward to the ground, is half the half-power beamwidth.

[0034] SIR Model: For typical GUE It can be expressed as

[0035] in, Typical GUE and ground service base station The small-scale fading coefficient of follows an exponential distribution with a mean of 1. is the total interference from UBS, is the total interference from the interfering GBS, and the specific forms are

[0036]

[0037] Typical UUE It can be expressed as

[0038] in, , is the total interference from the interfering UBS, is the total interference from GBS, and the specific forms are

[0039]

[0040] Coverage probability: The coverage probability can be expressed as ,in is the SIR threshold.

[0041] The coverage probability of typical GUE and UUE is shown in Figure 2. and To express.

[0042] Typical GUE coverage probability

[0043] Typical UUE coverage probability

[0044] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Any ordinary technician in the industry can smoothly implement the present invention as shown in the drawings and described above. However, any equivalent changes, modifications and evolutions made by technicians familiar with the profession without departing from the scope of the technical solution of the present invention using the technical content disclosed above are all equivalent embodiments of the present invention. At the same time, any equivalent changes, modifications and evolutions made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the technical solution of the present invention.

Claims

1. A method for deploying drone base stations in an air-to-ground network based on a non-Poisson point process, characterized in that: The specific steps include: The first step is to build an air-ground heterogeneous network consisting of drone base stations, user terminals, and ground base stations; In the second step, the ground base station collects the status information that the drone base station periodically reports to the ground base station, and after the user terminal transmits information to the ground base station, it reports the information sent by the drone base station and the user terminal to the cloud network; In the third step, after the cloud network collects the information reported by the ground base station, it searches for the optimal parameter configuration according to the β-Ginibre point process to obtain the location deployment of the drone base station; The fourth step is to send the optimal parameter configuration and the UAV base station location deployment parameters to the ground base station, and the ground base station then sends the optimal parameter configuration and the UAV base station location deployment parameters to the UAV base station; In the fifth step, the drone base station is deployed according to the parameters. Then the cloud network determines the user terminal accessing the drone base station based on the deployment of the drone base station. The ground base station issues a switching or access command. Finally, the user terminal selects the ground base station to access based on the maximum signal access criterion.

2. According to the method for deploying UAV base stations in an air-to-ground network based on a non-Poisson point process in claim 1, it is characterized in that: The drone base station is a drone base station suspended in the air, providing flexible, on-demand coverage, and is suitable for use far away from ground base stations or in emergency scenarios.

3. According to the method for deploying UAV base stations in an air-to-ground network based on a non-Poisson point process in claim 1, it is characterized in that: The ground base station is a fixed traditional cellular network base station, which mainly covers urban areas or places with complete infrastructure.

4. According to the method for deploying drone base stations in an air-to-ground network based on a non-Poisson point process in claim 1, it is characterized in that: The user terminals are user devices randomly distributed in the network, including mobile devices and Internet of Things terminals; they are distinguished according to the different network access points of the users. The users whose service base stations are drone base stations are UUEs, and those whose service base stations are ground base stations are GUEs.

5. The method for deploying drone base stations in an air-to-ground network based on a non-Poisson point process according to claim 1 is characterized in that: The distribution of drone base stations follows the β-Ginibre point process, which is used to describe the distribution of drone base stations in space, taking into account repulsion and avoiding over-density of drone base stations. In the β-Ginibre point process, Used to adjust the spatial repulsion characteristics between nodes.

6. The method for deploying drone base stations in an air-to-ground network based on a non-Poisson point process according to claim 1 is characterized in that: when When , the base station distribution tends to the Ginibre point process; when When , the base station distribution degenerates into a completely independent Poisson point process.

7. The method for deploying drone base stations in an air-to-ground network based on a non-Poisson point process according to claim 1 is characterized in that: The generation process of the β-Ginibre point process is as follows: first, a standard Ginibre point process is generated, whose nodes follow an exclusive spatial distribution, and the correlation between nodes is determined by the kernel function of the Ginibre point process; Then, for each generated node, independently Decide whether to keep the node. If so, proceed to the next step. If not, delete the node. Finally, for each retained node, the scaling factor The position adjustment is performed to keep the intensity of the entire point process unchanged. The scaling operation ensures that the sparse point set still has the same intensity as the original Ginibre point process.

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