A link performance analysis method for UAV-assisted communication cooperative networks

Abstract

This invention discloses a link performance analysis method for a UAV-assisted communication cooperative network, comprising the following steps: constructing a region-centered UAV-assisted communication cooperative network model and deriving the signal-to-interference-and-noise ratio (SINR) of typical ground users; calculating the conditional success probability (CSP) of typical users on the network downlink based on the SIN; calculating the B-order moment of the CSP based on the CSP of typical users on the network downlink; and deriving the meta-distribution of the UAV-assisted communication cooperative network based on the B-order moment of the CSP of typical users on the network downlink. This invention addresses the technical issues that existing methods cannot comprehensively evaluate link-level performance distribution in a network and that the evaluation is not sufficiently refined.

Description

Technical Field

[0001] The present invention relates to the field of mobile communication technologies, and in particular to a link performance analysis method for a UAV-assisted communication cooperative network. Background Art

[0002] Unmanned aerial vehicles (UAVs) offer significant potential for deployment flexibility and high mobility in both civilian and military applications. These applications include public safety communications, data collection in IoT applications, disasters, accidents, and other emergencies, as well as temporary events requiring significant network resources in the short term. In these situations, UAVs can rapidly provide wireless connectivity. However, UAVs typically fly at altitudes higher than ground users and are less obstructed by buildings. This means that the air-to-ground channel is typically a line-of-sight link. This results in interference from strong signals from other cells or UAV base stations in the downlink, significantly degrading the channel transmission performance. Therefore, the introduction of multi-point coordinated transmission (CoP) technology to coordinate base stations is considered to mitigate interference and enhance UAV downlink transmission performance. Furthermore, to facilitate the design of base station coordination strategies, the impact of key network parameters on downlink transmission performance needs to be studied using analytical models. This makes the modeling and analysis of base station coordination performance a key area of focus. In recent years, random geometry has been widely used as an effective mathematical tool for modeling and analyzing cellular networks. This approach uses random point processes to simulate the distribution of network devices and, based on the relevant theories and properties of random point processes, derives average network performance parameters, providing a theoretical basis for system planning and deployment. Currently, existing performance metrics for air-ground converged networks primarily include spectrum efficiency and coverage probability. However, coverage probability only provides an average value for link performance and cannot provide information about the performance of individual links. Therefore, to obtain more granular information about individual links, a link performance analysis method for UAV-assisted communication cooperative networks is urgently needed to address the technical issues of existing methods, such as their inability to comprehensively assess link-level performance distribution within the network and their lack of refinement. Summary of the Invention

[0003] The main purpose of this invention is to propose a link performance analysis method for UAV-assisted communication cooperative networks, aiming to solve the technical problems that existing methods cannot comprehensively evaluate the link-level performance distribution in the network and the evaluation is not refined enough.

[0004] To achieve the above objectives, the present invention provides a link performance analysis method for a UAV-assisted communication cooperative network, wherein the link performance analysis method for a UAV-assisted communication cooperative network comprises the following steps:

[0005] S1. Construct a regional-centric UAV-assisted communication cooperative network model and derive the signal-to-interference-and-noise ratio of typical ground users;

[0006] S2. Calculate the conditional success probability of a typical user on the network downlink based on the signal-to-interference-and-noise ratio;

[0007] S3. Calculate the b-order moment of the conditional success probability based on the conditional success probability of a typical user in the network downlink;

[0008] S4. The Meta distribution of the UAV-assisted communication cooperative network is derived based on the b-order moment of the conditional success probability of typical users in the network downlink.

[0009] In one preferred solution, step S1 is specifically as follows:

[0010] Based on the base station distribution characteristics of the UAV-assisted communication cooperative network model, the horizontal distance distribution characteristics from the UAV base station to the typical ground user and the joint probability density function of the UAV base station distribution are obtained;

[0011] The signal-to-interference-and-noise ratio of typical ground users is derived based on the UAV-assisted communication cooperative network model.

[0012] In one preferred solution, the signal-to-interference-and-noise ratio of the typical ground user is:

[0013]

[0014] Among them, SIR is the signal-to-interference-and-noise ratio of a typical ground user, P aBS is the transmission power of the UAV base station, h i is the channel fading gain of the i-th cooperative base station, g j is the channel fading gain of the j-th interfering base station, χ(r) is the channel path loss of the ith cooperative base station or the j-th interfering UAV, and C is the set of cooperative base stations.

[0015] In one preferred solution, the channel path loss is:

[0016]

[0017] Where w is the link distance between the UAV base station and the typical ground user, α is the path loss index, r is the horizontal distance between the UAV base station and the typical ground user, h aBS is the fixed height of the drone base station.

[0018] In one preferred solution, the conditional success probability of a typical user of the network downlink is:

[0019]

[0020] in, is the conditional success probability of a typical user in the network downlink, t is the number of cooperative base stations, θ is the threshold value, K is the shape parameter, n is the initial value of the tight upper bound using the gamma distribution, Φ b is the set of all drone base stations in the area, χ(r j ) is the channel path loss of the jth interfering UAV, m is the channel fading parameter, θ is the scale parameter,

[0021] In one preferred solution, the shape parameter K is:

[0022]

[0023] Among them, E() is the mean calculation.

[0024] In one preferred solution, the scale parameter θ is:

[0025]

[0026] In one preferred solution, the b-order moment of the conditional success probability in step S3 is:

[0027]

[0028] Among them, M b is the b-order moment of the conditional success probability, b=b1+b2...+b K , E[] is the mean calculation, R is the radius of the circle with the same area as the collaborative area, λ b is the density of drone base stations, is the joint probability density function of the cooperative UAV distribution.

[0029] In one preferred solution, step S4 is specifically as follows:

[0030]

[0031] in, Meta distribution of UAV-assisted communication cooperation network, I x (a, b) are normalized incomplete Beta functions, M1 and M2 are the first-order moment and second-order moment of the conditional success probability given the transmitter position.

[0032] In one preferred solution, the normalized incomplete Beta function is:

[0033]

[0034] Where u is the independent variable parameter of the normalized incomplete Beta function, B(a,b) is the complete Beta function, a>0, b>0.

[0035] In the technical solution described above, the link performance analysis method for a UAV-assisted communication cooperative network includes the following steps: constructing a region-centered UAV-assisted communication cooperative network model and deriving the signal-to-interference-and-noise ratio (SINR) of typical terrestrial users; calculating the conditional success probability (CSP) of a typical downlink user based on the SIN; calculating the b-order moment of the CSP based on the CSP of the typical downlink user; and deriving the meta distribution of the UAV-assisted communication cooperative network based on the B-order moment of the CSP of the typical downlink user. This method addresses the technical issues of existing methods that are unable to comprehensively assess link-level performance distribution in a network and the lack of refinement in the assessment.

[0036] In the present invention, a cooperative network model based on regional-centered drone-assisted communication is constructed according to random geometry theory, that is, a spatial distribution scenario of drone base stations in a cooperative network based on regional-centered drone-assisted communication is constructed, so as to obtain the signal-to-interference-and-noise ratio of typical ground users, and based on this, the conditional success probability of typical ground users is derived in detail, and then the expression of the b-order moment of the conditional success probability is obtained, and further a simple and easy-to-process Beta distribution is used to approximate and derive a refined measurement indicator that can comprehensively evaluate the link-level performance distribution in the network, namely the Meta distribution.

[0037] In the present invention, for the signal part calculation, the Cauchy inequality is used for approximation, and the second-order moment matching method is further used to approximate an equivalent gamma calculation, and accurate calculation expressions are given for the shape parameter and scale parameter respectively. The channel fading coefficient that obeys the gamma distribution is approximated using the tight upper bound of the gamma distribution. This approximation method can be derived based on the lower bound of Alzer's inequality. Finally, on this basis, an accurate expression of the conditional success probability of a typical terrestrial user is derived, and the Meta distribution can be expressed as the distribution of the conditional success probability. By calculating the b-order moment of the success probability and then approximating it using the Beta distribution, the Meta distribution of the SIR can be obtained.

[0038] In this paper, the Meta distribution of SIR is analyzed in detail, and a quickly obtainable system performance indicator is provided for more refined performance evaluation. Compared with the standard success probability, the Meta distribution of SIR provides more refined information by characterizing the distribution of the performance of each link in the network, significantly reducing the complexity of the calculation. In addition, the Beta approximation is accurate and can achieve a near-perfect approximation effect with the Meta distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0040] Figure 1 Schematic diagram of a link performance analysis method for a UAV-assisted communication cooperative network according to an embodiment of the present invention;

[0041] Figure 2 A comparison chart of the calculated and simulated values of Meta distribution according to an embodiment of the invention as the link reliability threshold changes;

[0042] Figure 3 This is a comparison chart of the Meta distribution of the region-centric and user-centric drone-assisted communication cooperation network models according to an embodiment of the present invention as the link reliability threshold value changes.

[0043] The realization of the objectives, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0045] In addition, the terms "first," "second," and so on, used in this disclosure are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, features specified as "first" or "second" may explicitly or implicitly include at least one of these features.

[0046] Moreover, the technical solutions between the various embodiments of the present invention may be combined with each other, but this must be based on the fact that ordinary technicians in this field can implement them. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0047] See also Figure 1 According to one aspect of the present invention, the present invention provides a link performance analysis method for a UAV-assisted communication cooperative network, wherein the link performance analysis method for a UAV-assisted communication cooperative network comprises the following steps:

[0048] S1. Construct a regional-centric UAV-assisted communication cooperative network model and derive the signal-to-interference-and-noise ratio of typical ground users;

[0049] S2. Calculate the conditional success probability of a typical user on the network downlink based on the signal-to-interference-and-noise ratio;

[0050] S3. Calculate the b-order moment of the conditional success probability based on the conditional success probability of a typical user in the network downlink;

[0051] S4. The Meta distribution of the UAV-assisted communication cooperative network is derived based on the b-order moment of the conditional success probability of typical users in the network downlink.

[0052] Specifically, in this embodiment, step S1 is as follows:

[0053] Based on the base station distribution characteristics of the UAV-assisted communication cooperative network model, the horizontal distance distribution characteristics from the UAV base station to the typical ground user and the joint probability density function of the UAV base station distribution are obtained. In the air-ground communication scenario, both the UAV and the ground users are distributed according to the homogeneous Poisson point process. The location of the typical ground user is set as the coordinate origin, represented as (0,0,0). The UAV is used as an aerial base station with a fixed height of h. aBS The entire high-altitude plane where drones are distributed is divided into several non-intersecting regular hexagonal areas with a side length of d. It is assumed that the regular hexagonal area where the projection of a typical ground user into the air is located is the cooperative area of the drone base station. All drones distributed in the cooperative area are cooperative base stations, and drones distributed outside the cooperative area are interference base stations. The probability density function of the horizontal distance from the drone base station to the user is:

[0054]

[0055] Among them, f Ri (ri) is the probability density function of the horizontal distance from the drone base station to the user, Ri is the i-th drone base station, r i is the i-th drone base station R i The horizontal distance to the typical ground user, R is the radius of the cooperation area. Since the cooperation area of the drone base station is set as a regular hexagonal area with a side length of d, for the convenience of calculation, it is approximated as a circle of equal area to simplify the calculation, that is, assuming that the drone base station cooperation area S hex is a regular hexagon with side length d. If d = 1, then Approximate the regular hexagon to a circle of equal area, that is, S hex =π*R 2 ,but

[0056] Assuming that there are t drone base stations evenly distributed in a regular hexagonal area, according to the base station distribution characteristics, their distribution obeys independent and identical distribution. Therefore, the joint probability density function of the distribution of t cooperative base stations can be further obtained. The joint probability density function is:

[0057]

[0058] in, is the joint probability density function of the distribution of t cooperative base stations;

[0059] The signal-to-noise ratio (SINR) of a typical ground user is derived based on the UAV-assisted communication cooperative network model. For channel fading between the UAV base station and the ground user, small-scale fading is primarily considered. For this purpose, the Nakagami-m fading model, which is more suitable for air-ground collaborative scenarios, is selected. This fading model is a generalized model applicable to various fading environments and is therefore more suitable for practical applications. The UAV base station applies multi-point joint transmission technology, employing coherent joint transmission and ignoring the influence of thermal noise, to obtain the SINR of a typical user in the network downlink. The SINR of the typical ground user is:

[0060]

[0061] Among them, SIR is the signal-to-interference-and-noise ratio of a typical ground user, P aBS is the transmission power of the UAV base station, h i is the channel fading gain of the i-th cooperative base station, g j is the channel fading gain of the jth interfering base station, χ(r) is the channel path loss of the i-th cooperative base station or the j-th interfering drone; SIR is the signal-to-interference-noise ratio of the target user, ignoring the influence of thermal noise, where the numerator represents the sum of the received useful signal power and the denominator represents the sum of the power of the interfering base stations. Since the drone base station applies multi-point joint transmission technology and adopts coherent joint transmission, the useful signal represented by the numerator is in the form of the square of the energy. For a typical ground user, it is assumed that it receives information sent by drones distributed in the cooperative base station set C. The base stations distributed outside the cooperative base station set are interfering base stations for the typical user, which is expressed as Φ b It is the collection of all drone base stations in the area.

[0062] Specifically, in this embodiment, the channel path loss is:

[0063]

[0064] Where w is the link distance between the UAV base station and the typical ground user, α is the path loss index, r is the horizontal distance between the UAV base station and the typical ground user, h aBSis the fixed height of the drone base station.

[0065] Specifically, in this embodiment, the present invention utilizes random geometry theory to construct a region-centered UAV-assisted communication cooperative network model and obtains the signal-to-interference-and-noise ratio (SINR) of typical ground users. This is used to derive the conditional success probability given the location of the transmitter point process. The conditional success probability reveals the success probability of each link in a specific sample of the transmitter point process, capturing the SIR performance of a single link in the network. Combined with random geometry theory, the conditional success probability is derived. The conditional success probability for a typical downlink user in the network is:

[0066]

[0067] in, is the conditional success probability of a typical user in the network downlink, t is the number of cooperative base stations, θ is the threshold value, K is the shape parameter, n is the initial value of the tight upper bound using the gamma distribution, Φ b is the set of all UAV base stations in the area, C is the set of cooperative base stations, χ(r j ) is the channel path loss of the jth interfering UAV, m is the parameter of the channel Nakagami fading, θ is the scale parameter, For the Meta distribution, the distribution of link reliability under a given threshold can be obtained. For the calculation of the useful signal portion, the Cauchy inequality is used for approximation, and the second-order moment matching method is further used to approximate an equivalent gamma distribution, whose shape parameter and scale parameter are K and θ, respectively. For the channel fading coefficient that follows the gamma distribution, the close upper bound of the gamma distribution is used to derive its approximate expression. The close upper bound of the gamma distribution can be derived based on the lower bound of Alzer's inequality. The shape parameter K is:

[0068]

[0069] Among them, E() is the mean calculation;

[0070] The scale parameter θ is:

[0071]

[0072] Specifically, in this embodiment, the conditional success probability represents the probability that the SIR is greater than the threshold value under a given transmitter location, which characterizes the complementary cumulative distribution function of the SIR for a given network geometry snapshot and can be regarded as a function of Φ bThe random variable of SIR, and the Meta distribution of SIR represents the complementary cumulative distribution function of the conditional probability of a typical user receiver; Meta distribution can reveal the distribution of the reliability of each link in the network when the SIR threshold value is given. Compared with the standard success probability, it can provide more fine-grained information. However, it is extremely difficult to directly calculate Meta distribution. Therefore, it is necessary to consider methods that can indirectly calculate Meta distribution. Since the b-order moment of conditional success probability can reveal the high-order statistical characteristics of conditional success probability, such indirect methods all start from the moment M of conditional success probability. b This also makes the moment of the conditional success probability extremely important for the performance analysis of the basic Meta distribution; the b-order moment of the conditional success probability in step S3 is:

[0073]

[0074] Among them, M b is the b-order moment of the conditional success probability, b=b1+b2...+b K , E[] is the mean calculation, R is the radius of the circle with the same area as the collaborative area, λ b is the density of drone base stations, is the joint probability density function of the cooperative UAV distribution.

[0075] Specifically, in this embodiment, the problem of how to obtain the distribution of a random variable from its moments is collectively referred to as the Hausdorff moment problem. In the present invention, a simple and easy-to-handle Meta distribution calculation method is used, that is, the Beta approximation method of the Beta distribution is used. By matching the first-order moment M1 and the second-order moment M2 of the conditional success probability with the mean and variance of the Beta distribution, the Meta distribution is approximated using the Beta distribution. The step S4 is specifically as follows:

[0076]

[0077] in, Meta distribution of UAV-assisted communication cooperation network, I x(a, b) is a normalized incomplete Beta function, M1 and M2 are the first-order moment and second-order moment of the conditional success probability under a given transmitter position, respectively; Meta distribution can also be defined as the distribution of conditional success probability, but direct calculation is generally more complicated, and indirect calculation methods are usually used for calculation. For this reason, the b-order moment of the conditional success probability can reveal the high-order statistical characteristics of the conditional success probability. The Meta distribution can be obtained by calculating the b-order moment of the conditional success probability, and the Beta distribution is used to obtain an approximate value; M1 and M2 are the first-order moment M1 and second-order moment M2 of the conditional success probability of a typical user under drone base station cooperation, respectively, and are calculated by taking the coefficient b of the b-order moment of the conditional success probability as 1 and 2. Specifically, in this embodiment, the normalized incomplete Beta function is:

[0078]

[0079] Among them, u is the independent variable parameter of the normalized incomplete Beta function, the integral range of u is 0-x, x is the link reliability threshold, B(a,b) is the complete Beta function, a>0, b>0; a and b correspond to the Meta distribution respectively. The complete Beta function B(a,b) is:

[0080]

[0081] Specifically, in this embodiment, a link performance analysis method for a UAV-assisted communication cooperative network proposed in the present invention is simulated and analyzed in Matlab using the Monte Carlo method to obtain a simulation value of the Meta distribution of the SIR of the model. The calculation of the Meta distribution is based on the knowledge of random geometry theory to derive the signal-to-interference ratio expression of the model and approximate the related expression of the conditional success probability. The Meta distribution can be approximated using the Beta distribution, and only the first-order moment and second-order moment of the conditional success probability need to be used. The use of the Beta distribution approximation can significantly reduce the complexity of the calculation, and in most cases the Beta approximation is very accurate. The approximate expression is calculated using the software Mathematica to obtain the theoretical calculated value of the Meta distribution of the SIR of the model; in addition, the Meta distribution of the SIR of the region-centered model and the user-centered model proposed in the present invention are compared, see. Figure 3 .

[0082] Specifically, in this embodiment, see Figure 2A comparative chart comparing the SIR meta-distribution as a function of the link reliability threshold x is presented for the proposed region-centric UAV-assisted communication cooperative network model. To obtain the SIR meta-distribution simulation results, the randomness of base station locations and the randomness of small-scale fading channels are considered in two steps. Therefore, in the simulation, according to steps S1-S4, Monte Carlo analysis was used to analyze the SIR meta-distribution results for 10,000 × 1,000 samples. Specifically, 1,000 channel state information samples were collected for each fixed-point distribution. The SIR obtained for each fixed-point distribution was then compared with the threshold. In this simulation, the threshold was set to 0 dB, and the probability of the SIR exceeding the threshold was calculated to obtain the simulation results of the conditional success probability. A total of 10,000 distribution results were analyzed to obtain the trend of the SIR as a function of the link reliability threshold x. The simulation results were analyzed by setting different UAV base station density values, such as setting the average number of UAV base stations in a regular hexagonal cooperative area to 1, 2, and 3, respectively. Then, using the same experimental parameters as the simulation experiment, we substitute them into the derived Meta distribution formula, set b to 1 and 2 respectively, and get the calculated values of the first-order moment and the second-order moment. Then, we substitute them into the Beta distribution formula to get their calculated values, which correspond to Figure 2 The star-shaped, diamond-shaped and square lines in the figure. From the comparison results, it can be found that when the number of UAV base station collaborations is 1, 2 and 3 respectively, the Meta distribution is slightly different from the calculated value and the simulation value when the link reliability is 0, and the rest of the values are basically the same. This is because Monte Carlo simulation is performed during the simulation, and the number of points scattered each time is random. The number of collaborations is the average of the number of points scattered each time, but there may be a situation where the number of points falling within the set collaboration area during a certain scattering process is 0, resulting in the calculated signal-to-interference-and-noise ratio value being 0, which makes the simulation value smaller. However, this problem does not exist when performing theoretical calculations, so the results are slightly different. In addition, it can be found that within a certain range, as the number of collaborative base stations increases, the Meta distribution gradually increases, further reflecting the effectiveness of the collaborative model proposed in the present invention in improving performance.

[0083] In addition, the link performance analysis method for the UAV-assisted communication cooperative network proposed in this invention is region-centric. That is, the high-altitude plane is divided into several non-intersecting regular hexagonal regions. The regular hexagonal region where the projection of typical ground users into the air is located is selected as the cooperative region of the UAV base station. All UAVs distributed in this region are cooperative UAV base stations and serve typical ground users. To further demonstrate the performance improvement advantage of this invention compared to other models, the invention is compared with a user-centric model. In the simulation, this invention assumes that there are an average of three cooperative base stations serving typical ground users in the cooperative region. Figure 3A comparison of the meta-distribution of the region-centric model and the user-centric model as a function of the link reliability threshold x is presented. The simulation results show that the meta-distribution of both the region-centric collaborative model and the user-centric model decreases as the link reliability threshold increases. Furthermore, it is clear that the region-centric collaborative model proposed in this invention significantly improves performance compared to the non-collaborative user-centric model, further demonstrating the superiority of this solution in terms of performance improvement.

[0084] The above are only preferred embodiments of the present invention and are not intended to limit the patent scope of the present invention. All equivalent structural transformations made using the contents of the present description and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included in the patent protection scope of the present invention.

Claims

1. A link performance analysis method for a UAV-assisted communication cooperative network, characterized in that: The following steps are involved: S1. Construct a regional-centric UAV-assisted communication cooperative network model and derive the signal-to-interference-and-noise ratio of typical ground users; S2. Calculate the conditional success probability of a typical user on the network downlink based on the signal-to-interference-and-noise ratio; S3. Calculate the b-order moment of the conditional success probability based on the conditional success probability of a typical user in the network downlink; the b-order moment of the conditional success probability in step S3 is: in, is the b-order moment of the conditional success probability, , is a random constant, is the mean calculation, is the conditional success probability of a typical user in the network downlink, is the threshold value, is the shape parameter, is the radius of a circle approximately equal in area to the collaboration area, is the density of drone base stations, is the joint probability density function of the cooperative UAV base station distribution, is an initial value using a tight upper bound of the gamma distribution, For the drone base stations, For the drone base stations Horizontal distance to a typical user on the ground; S4. The Meta distribution of the UAV-assisted communication cooperative network is derived based on the b-order moment of the conditional success probability of typical users in the network downlink; specifically: ； in, Meta distribution of UAV-assisted communication cooperation network, is the link reliability threshold, is the normalized incomplete Beta function, are the first and second moments of the conditional success probability given the transmitter position; The normalized incomplete Beta function is: ； in, is the independent variable parameter of the normalized incomplete Beta function, To complete the Beta function, , .

2. The link performance analysis method for a UAV-assisted communication cooperative network according to claim 1, characterized in that: The step S1 is specifically as follows: Based on the base station distribution characteristics of the UAV-assisted communication cooperative network model, the horizontal distance distribution characteristics from the UAV base station to the typical ground user and the joint probability density function of the UAV base station distribution are obtained; The signal-to-interference-and-noise ratio of typical ground users is derived based on the UAV-assisted communication cooperative network model.

3. A link performance analysis method for a UAV-assisted communication cooperative network according to any one of claims 1-2, characterized in that: The signal-to-interference-and-noise ratio of the typical ground user is: ； in, is the signal-to-interference-and-noise ratio of a typical ground user, is the transmission power of the UAV base station, is the channel fading gain of the i-th cooperative UAV base station, is the channel fading gain of the jth interfering UAV base station, is the channel path loss of the i-th cooperative UAV base station or the j-th interfering UAV base station, A collection of collaborative drone base stations.

4. The link performance analysis method for a UAV-assisted communication cooperative network according to claim 3, characterized in that: The channel path loss is: ； in, is the link distance between the UAV base station and the typical ground user, is the path loss exponent, is the horizontal distance from the drone base station to a typical user on the ground, is the fixed height of the drone base station.

5. The link performance analysis method for a UAV-assisted communication cooperative network according to claim 3, characterized in that: The conditional success probability of a typical user in the network downlink is: ； in, is the conditional success probability of a typical user in the network downlink, is the number of collaborative drone base stations, is the threshold value, is the shape parameter, is the collection of all drone base stations in the area, is the channel path loss of the j-th interfering drone base station, is the channel fading parameter, , is the scale parameter, .

6. The link performance analysis method for a UAV-assisted communication cooperative network according to claim 5, characterized in that: The shape parameters for: ； in, Calculate the mean.

7. The link performance analysis method for a UAV-assisted communication cooperative network according to claim 5, characterized in that: The scale parameter for: 。

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