Unmanned aerial vehicle network coverage rate analysis method taking area as center

Abstract

The invention discloses a region-centered unmanned aerial vehicle coverage rate analysis method. The method comprises the following steps: constructing a region-centered unmanned aerial vehicle cooperative transmission model; fractional power control is carried out on the service unmanned aerial vehicle and the interference unmanned aerial vehicle; calculating the signal-to-interference ratio of the typical user based on the transmitting power of the service unmanned aerial vehicle and the interference unmanned aerial vehicle; based on the stochastic geometry theory, the coverage probability of typical users is obtained through service distance distribution, signal power distribution and interference distribution in combination with a Cauchy-Schwarz inequality and Gamma distribution approximation; the unmanned aerial vehicle density, the power control coefficient, the path loss index and the Nakagami fading parameter are set, the influence of the number of different service unmanned aerial vehicles, the power control coefficient and the channel condition on the coverage probability is analyzed, and simulation verification is carried out. According to the method, the technical problem of how to suppress the network interference of the unmanned aerial vehicle through power control in a complex interference scene is solved, and the network coverage rate of the unmanned aerial vehicle is calculated.

Description

Technical Field

[0001] This invention relates to the field of mobile communication technology, and in particular to a region-centric method for analyzing drone coverage. Background Technology

[0002] With the deep integration of low-altitude economy and Internet of Things (IoT) technology, drones are demonstrating revolutionary application value in typical IoT scenarios such as emergency communication, intelligent agriculture, and smart detection. Their three-dimensional maneuverability, flexible network connectivity, and line-of-sight transmission advantages play a crucial role. Especially in scenarios involving large-scale IoT device connections, drones can carry lightweight base stations as aerial base stations, effectively solving the shortcomings of traditional ground base stations in terms of coverage blind spots, deployment costs, and network flexibility.

[0003] However, in complex IoT communication environments, drones typically fly at altitudes far higher than ground base stations, and their communication paths are less obstructed by buildings, thus their air-to-ground channels primarily exhibit line-of-sight (Line-of-Sight) links. While this propagation characteristic ensures the signal strength required by airborne base stations, it also exacerbates co-channel interference in downlink transmission. Specifically, interference signals from neighboring cells or other airborne base stations may propagate under similar line-of-sight conditions, potentially leading to a significant decrease in the signal-to-interference ratio (SIR) and severely impacting the communication reliability of IoT terminals. To address this challenge, researchers have begun exploring the integration of Co-MP (Cooperation-Multipoint) technology into drone-assisted communication networks, proposing various CoMP strategies to improve A2G network performance. For example, a user-centric drone communication scheme selects the N nearest ground base stations to the drone user and employs cooperative joint transmission technology to provide cooperative services based on the strongest average received power criterion; another CoMP model primarily analyzes the coverage probability of two base stations cooperating. However, user-centric models typically require base station search to select base stations, which can introduce additional signaling overhead. In contrast, area-centric CoMP models do not require additional search resources while still providing seamless connectivity for user equipment. To address the aforementioned technical problems, this invention proposes a region-centric method for analyzing drone coverage. Summary of the Invention

[0004] The main objective of this invention is to propose a region-centric method for UAV coverage analysis, aiming to solve the technical problem of how to suppress UAV network interference through power control and calculate UAV network coverage in complex interference scenarios.

[0005] To achieve the above objectives, the present invention provides a region-centric drone coverage analysis method, wherein the region-centric drone coverage analysis method includes the following steps:

[0006] S1. Construct a region-centric UAV collaborative transmission model;

[0007] S2. Separate power control is used for service drones and jamming drones respectively;

[0008] S3. Calculate the signal-to-interference ratio for a typical user based on the transmit power of the service drone and the jamming drone;

[0009] S4. Based on stochastic geometry theory, the coverage probability of a typical user is obtained by combining the service distance distribution, signal power distribution, and interference distribution with the Cauchy-Schwarz inequality and the Gamma distribution approximation.

[0010] S5. Set the UAV density, power control coefficient, path loss index, and Nakagami fading parameters, analyze the impact of different service UAV numbers, power control coefficients, and channel conditions on coverage probability, and perform simulation verification.

[0011] One preferred embodiment is that step S1 constructs a region-centric UAV cooperative transmission model, specifically as follows:

[0012] S11. Using drones as aerial base stations according to density... The homogeneous Poisson point process at height is The spatial distribution on the horizontal plane forms a set of base station locations. ;

[0013] S12. Divide the airspace into multiple non-overlapping regular hexagonal cooperative regions of equal area, each with a side length of [missing information]. And the regular hexagonal cooperative region is equivalent to a region with a radius of A circular area;

[0014] S13. Based on coordinated multi-point transmission technology, all UAVs within the same regular hexagonal cooperative area can coordinate to serve user equipment through coherent joint transmission.

[0015] In one preferred embodiment, the radius of the circular region is:

[0016]

[0017] in, The radius of the circular region. Let be the side length of the regular hexagonal cooperative region.

[0018] In one preferred embodiment, step S2 employs fractional power control for both the service drone and the jamming drone, including:

[0019] The service drone employs split power control; the service drone's transmit power is adjusted as follows:

[0020]

[0021] in, For the first The transmit power of a service drone. The baseline transmit power without power control. For the first The Euclidean distance from a service drone to a typical user. This is the path loss index. This is the power control coefficient. .

[0022] In one preferred embodiment, step S2 employs fractional power control for both the service drone and the jamming drone, including:

[0023] The jamming drone is controlled by a split power distribution; the transmit power of the jamming drone is adjusted as follows:

[0024]

[0025] in, For the first The transmission power of the jamming drone. For the first The Euclidean distance from a jamming drone to a typical user.

[0026] One preferred embodiment is that the signal-to-interference ratio for the typical user is:

[0027]

[0028] in, For typical users, the signal-to-interference ratio is... For the first Channel fading coefficient of a service drone To serve drone aggregation, To interfere with the drone swarm, For the first The channel fading coefficient of an interfering drone. For the first The Euclidean distance from a jamming drone to a typical user.

[0029] One preferred embodiment is that the service distance distribution in step S4 is specifically as follows:

[0030] The probability density function for service drones is:

[0031]

[0032] in, To serve the probability density function of the drone, For the first The horizontal distance from a service drone to a typical user The radius of the circular region.

[0033] In one preferred embodiment, the upper bound of the signal power distribution in step S4 is derived using the Cauchy-Schwarz inequality, specifically as follows:

[0034]

[0035] in, This is the upper bound of the signal power distribution. For the first Channel fading coefficient of a service drone , For the first The Euclidean distance from a service drone to a typical user. This is the path loss index. This is the power control coefficient. , The number of drones within the collaborative area. To interfere with the channel fading coefficient of drones, It is an equivalent Gamma distribution.

[0036] One preferred embodiment is that the coverage probability of the typical user is:

[0037]

[0038] in, The coverage probability of a typical user. The number of drones within the collaborative area. For the regular hexagonal cooperative region The probability of collaboration between service drones. The radius of the circular region. The horizontal distance from the drone to a typical user. This represents the probability of success for a typical user. For the first The horizontal distance from a service drone to a typical user.

[0039] The above-described technical solution of this invention includes the following steps in its region-centered UAV coverage analysis method: constructing a region-centered UAV cooperative transmission model; applying fractional power control to both serving and interfering UAVs; calculating the signal-to-interference ratio (SIR) for a typical user based on the transmit power of the serving and interfering UAVs; obtaining the coverage probability of a typical user by combining the Cauchy-Schwarz inequality and Gamma distribution approximation with the service distance distribution, signal power distribution, and interference distribution based on stochastic geometry theory; and setting UAV density, power control coefficient, path loss exponent, and Nakagami fading parameters to analyze the impact of different numbers of serving UAVs, power control coefficients, and channel conditions on the coverage probability, and performing simulation verification. This invention solves the technical problem of how to suppress UAV network interference through power control and calculate UAV network coverage in complex interference scenarios. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0041] Figure 1 This is a schematic diagram of a region-centered drone coverage analysis method according to an embodiment of the present invention;

[0042] Figure 2 This is a schematic diagram of the UAV cooperative transmission model according to an embodiment of the present invention;

[0043] Figure 3 This is a schematic diagram of a regional location center power control strategy according to an embodiment of the present invention;

[0044] Figure 4 This is a schematic diagram illustrating the changing trends of coverage probability and SIR threshold for different numbers of cooperative base stations in an embodiment of the present invention.

[0045] Figure 5 This is a schematic diagram illustrating the changing trends of coverage probability and SIR threshold under different power control coefficients in an embodiment of the present invention.

[0046] Figure 6 For typical users, in both LosS and NLoS scenarios, the coverage probability and SIR threshold are discussed in this embodiment of the invention. Relationship diagram;

[0047] Figure 7This is a schematic diagram illustrating the coverage probability under the region-centered model and the user-centered model in embodiments of the present invention.

[0048] The realization of the objective, functional characteristics and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0049] 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, and 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.

[0050] Furthermore, the technical solutions of the various embodiments of the present invention can be combined with each other, but only if they are based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0051] See Figures 1-7 According to one aspect of the present invention, the present invention provides a region-centric drone coverage analysis method, wherein the region-centric drone coverage analysis method includes the following steps:

[0052] S1. Construct a region-centric UAV collaborative transmission model;

[0053] S2. Separate power control is used for service drones and jamming drones respectively;

[0054] S3. Calculate the signal-to-interference ratio for a typical user based on the transmit power of the service drone and the jamming drone;

[0055] S4. Based on stochastic geometry theory, the coverage probability of a typical user is obtained by combining the service distance distribution, signal power distribution, and interference distribution with the Cauchy-Schwarz inequality and the Gamma distribution approximation.

[0056] S5. Set the UAV density, power control coefficient, path loss index, and Nakagami fading parameters, analyze the impact of different service UAV numbers, power control coefficients, and channel conditions on coverage probability, and perform simulation verification.

[0057] Specifically, in this embodiment, step S1, which constructs a region-centric UAV cooperative transmission model, specifically involves:

[0058] S11. Using drones as aerial base stations according to density... The homogeneous Poisson point process at height is The spatial distribution on the horizontal plane forms a set of base station locations. The set of locations , For the first The coordinates of the drone;

[0059] S12. Divide the airspace into multiple non-overlapping regular hexagonal cooperative regions of equal area, each with a side length of [missing information]. And the regular hexagonal cooperative region is equivalent to a region with a radius of A circular area;

[0060] S13. Based on coordinated multipoint transmission technology, all UAVs within the same regular hexagonal cooperative area coordinate to serve user equipment through coherent joint transmission; according to Slivnyak's theorem, located at the origin... The downlink performance of a typical user can represent the average statistical characteristics of all users. For this typical user, we select a subset of their vertical projection points. The regular hexagonal cooperation region serves as the cooperation area, which contains an average of Service drone ensemble formed by drones Base stations outside the cooperation zone are considered sources of interference, namely, swarms of interference drones. .

[0061] Specifically, in this embodiment, for a side length of The area of ​​the regular hexagonal cooperative region can be expressed as: To simplify subsequent analysis and calculations, we approximate it as a circular region with an equivalent area, i.e. Therefore, the radius of the circular region is:

[0062]

[0063] in, The radius of the circular region. Let be the side length of the regular hexagonal cooperative region; furthermore, it is assumed that all UAVs fully reuse the entire network spectrum, which is divided into multiple orthogonal sub-channels. Therefore, intra-cell interference can be ignored, and only co-channel interference is considered.

[0064] Specifically, in this embodiment, for CoMP transmission between the UAV and a typical user, the focus should be on small-scale fading characteristics, i.e., the Nakagami-m fading model. This model can simulate the propagation characteristics of signals under different channel fading environments by adjusting the shape parameters. The channel fading gain of the serving UAV and the interfering UAV are respectively calculated using... and This indicates that, assuming the channel fading coefficient... It follows Nakagami-m fading, i.e. ~ ,but It belongs to the Gamma distribution, and its probability density function is:

[0065]

[0066] in, Let the probability density function be the channel fading coefficient of the interfering UAV. The shape parameter for Nakagami-m fading.

[0067] Specifically, in this embodiment, in order to effectively mitigate interference and achieve downlink power control in the UAV cooperative transmission model, see [link to relevant documentation]. Figure 3 Considering the location at the origin Typical users, each serving drone, indexed as ,in, Located within the same hexagonal collaborative area as the typical user, the Euclidean distance from the service drone to the typical user is... Conversely, base stations outside the cooperation area can cause interference to typical users. For interfering drones, the Euclidean distance from the drone to the typical user is... , To maintain the horizontal distance between the interfering drone and the typical user, step S2 employs fractional power control for both the service drone and the interfering drone, including:

[0068] The service drone employs split power control; the service drone's transmit power is adjusted as follows:

[0069]

[0070] The jamming drone is controlled by a split power distribution; the transmit power of the jamming drone is adjusted as follows:

[0071]

[0072] in, For the first The transmit power of a service drone. The baseline transmit power without power control. For the first The Euclidean distance from a service drone to a typical user. This is the path loss index. , This is the power control coefficient. , For the first The transmission power of the jamming drone. For the first The Euclidean distance from a jamming drone to a typical user.

[0073] Specifically, in this embodiment, all base stations within the CoMP projection area of ​​a typical user employ joint transmission services; furthermore, assuming negligible intra-cell interference and power control for all base stations, and under the assumption of a limited interference network, the impact of thermal noise is not considered; the signal-to-interference ratio for the typical user is:

[0074]

[0075] in, For typical users, the signal-to-interference ratio is... For the first Channel fading coefficient of a service drone To serve drone aggregation, To disrupt the drone swarm, For the first The channel fading coefficient of an interfering drone. For the first The Euclidean distance from a jamming drone to a typical user.

[0076] Specifically, in this embodiment, considering typical users, the horizontal distance from the drones in the cluster to the typical user is... ,use The service distance distribution in step S4 is specifically as follows:

[0077] The probability density function for service drones is:

[0078]

[0079] in, To serve the probability density function of the drone, For the first The horizontal distance from a service drone to a typical user The radius of the circular region. Due to distance They are independent and identically distributed; therefore, the joint probability density function is:

[0080] .

[0081] Specifically, in this embodiment, before calculating the coverage probability, it is necessary to obtain the signal power distribution, assuming... and The signal-to-interference ratio for a typical user can be simplified as:

[0082]

[0083] in, This represents the total interference signal;

[0084] Since the weighted sum of Nakagami-m random variables does not have a closed-form expression, the upper bound of the signal power is derived using the Cauchy-Schwarz inequality:

[0085]

[0086] The derivation of the upper bound of the signal power distribution in step S4 uses the Cauchy-Schwarz inequality, specifically:

[0087]

[0088] in, This is the upper bound of the signal power distribution. For the first Channel fading coefficient of a service drone , For the first The Euclidean distance from a service drone to a typical user. This is the path loss index. This is the power control coefficient. , The number of drones in the collaborative area. To interfere with the channel fading coefficient of drones, It is the weighted sum of the products of N random variables. The total interference signal, The weighted sum of the products of N random variables is equivalent to ;

[0089] Given ~ By utilizing the scaling properties of the Gamma distribution, we obtain Using the second-order moment matching method, the equivalent Gamma distribution is denoted as... It preserves the first and second moments of the original expression, with the following parameters:

[0090]

[0091]

[0092] in, For shape parameters, For scale parameters, The shape parameters for Nakagami-m fading;

[0093] Assumption With the shape parameters fixed, the subsequent derivation can be further simplified, as follows:

[0094]

[0095] in, For mathematical expectation, and These represent the integer and decimal parts of the shape parameter, respectively.

[0096] Therefore, the derivation of the upper bound of the signal power distribution in step S4 can be further simplified using the Cauchy-Schwarz inequality as follows:

[0097] .

[0098] Specifically, in this embodiment, under the power control of all drones, the probability of conditional success for a typical user is:

[0099]

[0100] Among them, (10b) follows the tight upper bound of the gamma distribution. For SIR threshold, Assuming , The results (10c) and (10d) originate from... (10e) is obtained from the decomposition form of the exponential function and the expected product property of independent random variables, and (10f) follows the moment generating function;

[0101] Through the Find the average value. It can be exported as:

[0102]

[0103] Among them, (11a) refers to the horizontal distance Integrating, in (11b), we ignore the effect of height and approximate the result. Then, after permutation and simplification, we finally obtain (11c). (11d) can be obtained from the probability generating function. ,in, ;

[0104] The coverage probability of the typical user is:

[0105]

[0106] in, The coverage probability of a typical user. The number of drones in the collaborative area.

[0107] For the regular hexagonal cooperative region The probability of collaboration between service drones. The radius of the circular region. The horizontal distance from the drone to a typical user. This represents the probability of success for a typical user. For the first The horizontal distance from a service drone to a typical user.

[0108] Specifically, in this implementation, the simulation parameters are the UAV density range. Nakagami fading parameter m=3, side length of regular hexagon =1 km, the drone's altitude is fixed at... =100m, for the power control strategy, due to the power control coefficient Experimental values ​​were set to =0.3; Figure 4 Describes the given power control coefficient The relationship between the coverage probability (SIR) at a value of 0.3 and the SIR threshold was compared with different UAV deployment densities ranging from 1 to 4 aBS within the CoMP region. Simulation results show that the coverage probability continuously decreases with increasing SIR threshold. This trend indicates that at higher SIR thresholds, the interference power increases faster than the desired signal power, leading to a decline in overall coverage performance. On the other hand, for a fixed SIR threshold, the coverage probability significantly increases with the number of serving UAVs. This improvement is due to the additional UAVs in the cooperative region enhancing the desired signal power, while the interference level remains relatively stable. The consistency between theoretical calculations and simulation results validates the effectiveness of the analytical framework and demonstrates the performance optimization capability of the proposed power-controlled cooperative model. Figure 5 The impact of different power control coefficients on downlink coverage probability was examined, with an average of three drones in the cooperative set; notably, at lower SIR thresholds, the power control coefficients... A moderate increase in SIR significantly improves coverage probability; studies show that appropriate power control strategies can improve network performance when signal quality requirements are not high; conversely, at higher SIR thresholds, increasing... This will lead to a significant decrease in coverage probability; this is because the higher Reducing the transmit power of the aBS results in a weaker expected signal strength. Based on the above analysis, in scenarios with low SIR requirements, it is recommended to set an appropriate SIR to improve network performance and fairness. Value; for scenarios with high SIR requirements, it is recommended to use a lower value. Values ​​should be set to avoid network performance degradation due to power limitations. Figure 6This section explains the impact of LoS and NLoS on the coverage probability of various path loss indices; the LoS probability model is shown below; we use a set of parameters (a, b, c) to describe the transmission environment, which are (1, 0.106, 1); it can be seen that, for different path loss indices... Under these conditions, LoS and NLoS have a relatively small impact on the performance of the proposed model; for the same The simulation results for the two scenarios largely overlap, indicating that considering either LoS or NLoS conditions has no significant impact on the experimental results of the model; in different Among them, the LoS and NLoS conditions have relatively minimal impact on the performance of the proposed model; for the same The simulation results of the two scenarios largely overlap, indicating that whether or not the LoS or NLoS conditions are considered has no significant impact on the system performance. In addition, these findings also show that the model is mainly affected by small-scale fading, further verifying the robustness of the proposed design. Figure 7 The coverage probability of the proposed regional-centric UAV joint transmission model is compared with that of the user-centric model; the average number of UAVs is set to N = 3, and the power control coefficient is... =0.3; Specifically, for the user-centric model, the drone closest to the terminal is selected to provide services, thereby eliminating additional signaling search losses; Observations show that the proposed model has an overall average gain of 175% in user coverage probability compared to the user-centric model, which proves that the region-centric model can effectively improve network performance without increasing additional signaling losses.

[0109] Specifically, in this embodiment, the region-centered UAV coverage analysis method uses the PPP model to model the spatial distribution of base stations and combines partial power control to analyze the coverage probability of typical terminals. The results show that, compared with the user-centered model, the proposed region-centered UAV joint transmission model significantly improves network performance while avoiding additional signaling overhead. It guides network operators to dynamically adjust the power control coefficient according to specific business scenario requirements, achieving the optimal balance between enhanced network performance and terminal fairness.

[0110] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. All equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A region-centric method for analyzing drone coverage, characterized in that, Includes the following steps: S1. Construct a region-centric UAV collaborative transmission model; S2. Separate power control is used for service drones and jamming drones respectively; S3. Calculate the signal-to-interference ratio for a typical user based on the transmit power of the service drone and the jamming drone; S4. Based on stochastic geometry theory, the coverage probability of a typical user is obtained by combining the service distance distribution, signal power distribution, and interference distribution with the Cauchy-Schwarz inequality and the Gamma distribution approximation. S5. Set the UAV density, power control coefficient, path loss index, and Nakagami fading parameters, analyze the impact of different service UAV numbers, power control coefficients, and channel conditions on coverage probability, and perform simulation verification.

2. The method for analyzing UAV coverage centered on a region according to claim 1, characterized in that, Step S1, which constructs a region-centric UAV cooperative transmission model, specifically involves: S11. Using drones as aerial base stations according to density... The homogeneous Poisson point process at height is The spatial distribution on the horizontal plane forms a set of base station locations. ; S12. Divide the airspace into multiple non-overlapping regular hexagonal cooperative regions of equal area, each with a side length of [missing information]. And the regular hexagonal cooperative region is equivalent to a region with a radius of A circular area; S13. Based on coordinated multi-point transmission technology, all UAVs within the same regular hexagonal cooperative area can coordinate to serve user equipment through coherent joint transmission.

3. The method for analyzing UAV coverage centered on a region according to claim 2, characterized in that, The radius of the circular region is: ； in, The radius of the circular region. Let be the side length of the regular hexagonal cooperative region.

4. A region-centered UAV coverage analysis method according to any one of claims 1-3, characterized in that, Step S2 employs fractional power control for both the service drone and the jamming drone, including: The service drone employs split power control; the service drone's transmit power is adjusted as follows: ； in, For the first The transmit power of a service drone. The baseline transmit power without power control. For the first The Euclidean distance from a service drone to a typical user. This is the path loss index. This is the power control coefficient. .

5. The method for analyzing UAV coverage centered on a region according to claim 4, characterized in that, Step S2 employs fractional power control for both the service drone and the jamming drone, including: The jamming drone is controlled by a split power distribution; the transmit power of the jamming drone is adjusted as follows: ； in, For the first The transmission power of the jamming drone. For the first The Euclidean distance from a jamming drone to a typical user.

6. The method for analyzing UAV coverage centered on a region according to claim 5, characterized in that, The signal-to-interference ratio for the typical user is: ； in, For typical users, the signal-to-interference ratio is... For the first Channel fading coefficient of a service drone To serve drone aggregation, To interfere with the drone swarm, For the first The channel fading coefficient of an interfering drone. For the first The Euclidean distance from a jamming drone to a typical user.

7. A method for analyzing UAV coverage centered on a region according to any one of claims 1-3, characterized in that, The service distance distribution in step S4 is specifically as follows: The probability density function for service drones is: ； in, To serve the probability density function of the drone, For the first The horizontal distance from a service drone to a typical user The radius of the circular region.

8. A method for analyzing UAV coverage centered on a region according to any one of claims 1-3, characterized in that, The derivation of the upper bound of the signal power distribution in step S4 uses the Cauchy-Schwarz inequality, specifically: ； in, This is the upper bound of the signal power distribution. For the first Channel fading coefficient of a service drone , For the first The Euclidean distance from a service drone to a typical user. This is the path loss index. This is the power control coefficient. , The number of drones within the collaborative area. To interfere with the channel fading coefficient of drones, It is an equivalent Gamma distribution.

9. A method for analyzing UAV coverage centered on a region according to any one of claims 1-3, characterized in that, The coverage probability of the typical user is: ； in, The coverage probability of a typical user. The number of drones within the collaborative area. For the regular hexagonal cooperative region The probability of collaboration between service drones. The radius of the circular region. The horizontal distance from the drone to a typical user. This represents the probability of success for a typical user. For the first The horizontal distance from a service drone to a typical user.

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