Dynamic cooperation uplink performance analysis method of unmanned aerial vehicle assisted MEC

Abstract

The invention discloses a dynamic cooperative uplink performance analysis method for an unmanned aerial vehicle assisted MEC. The method comprises the following steps: constructing random space distribution characteristics of unmanned aerial vehicles and ground users in an unmanned aerial vehicle assisted cooperative network by adopting a random geometric homogeneous Poisson point process; according to the random distribution characteristics of the unmanned aerial vehicle and the ground users, constructing a movement and cooperative unloading model of the unmanned aerial vehicle by utilizing Delou triangulation; quantifying a successful uplink communication probability under coherent joint transmission according to a mobile and cooperative unloading model; and quantifying a successful uplink communication probability under coherent joint transmission based on a mobile and cooperative unloading model, and calculating a switching probability of an unmanned aerial vehicle cooperative area and a third successful uplink communication probability under switching. According to the invention, the technical problem of how to improve the reliability of network service and optimize complex interference through cooperative service among multiple unmanned aerial vehicle nodes is solved.

Description

Technical Field

[0001] The present invention relates to the field of mobile communication technologies, and in particular to a method for analyzing dynamic collaborative uplink performance of a drone-assisted MEC. Background Art

[0002] Drone-assisted mobile edge computing (MEC) networks represent a new architectural paradigm that synergistically combines drones with MEC technologies, with the primary goal of providing adaptable and scalable computing resources to ground user equipment (UE), thereby transcending the limitations inherent in traditional ground computing services. With the continuous advancement of drone and MEC technologies, and the increasing diversification of applications in various fields, the integrated model has great prospects, especially in improving network coverage and improving network computing efficiency. The key to this potential is the use of drones as MEC nodes. These aerial platforms significantly enhance the computing power of ground infrastructure and effectively handle remote and mobile scenarios, providing convenient and fast computing services to user devices.

[0003] At present, the research on drone-assisted MEC networks mainly focuses on optimizing performance within specific parameters, which includes key issues such as allocating communication and computing resources, offloading computing tasks, and formulating strategies for deploying drones and planning trajectories. With the continuous development of emerging applications such as autonomous driving, digital twins, and virtual fields, the demand for real-time processing and computing capabilities is also growing. It is challenging to rely solely on improving the performance of a single drone or expanding drone-assisted MEC to achieve edge computing capabilities and expand coverage, because this approach is difficult to guarantee the stability and reliability of the system. In addition, it hinders the full utilization of the potential of large-scale drone MEC architecture. The key problem is that it is difficult to optimize the performance of a single drone system to adapt to complex and changing mobile environments. Scaling up will introduce complex interference factors, which may reduce the overall task offloading performance, mainly affecting computing and communication delays. Therefore, this has aroused industry-wide attention to the potential computing bottlenecks in large-scale MEC services. Therefore, it is urgent to propose a dynamic cooperative uplink performance analysis method for drone-assisted MEC to solve the technical problems of how to improve the reliability of network services and optimize complex interference through cooperative services between multiple drone nodes. Summary of the invention

[0004] The main purpose of this invention is to propose a dynamic collaborative uplink performance analysis method for UAV-assisted MEC, aiming to solve the technical problem of how to improve the reliability of network services and optimize complex interference through collaborative services among multiple UAV nodes.

[0005] To achieve the above object, the present invention provides a method for analyzing the dynamic collaborative uplink performance of a drone-assisted MEC, wherein the method for analyzing the dynamic collaborative uplink performance of a drone-assisted MEC comprises the following steps:

[0006] S1, using random geometric homogeneous Poisson point process to construct the random spatial distribution characteristics of drones and ground users in drone-assisted collaborative networks;

[0007] S2. Based on the random distribution characteristics of drones and ground users, the Delaunay triangulation is used to build a drone movement and collaborative unloading model;

[0008] S3, quantify the probability of successful uplink communication under coherent joint transmission based on the mobility and cooperative offloading models;

[0009] S4. Based on the mobile and cooperative offloading model, the probability of successful uplink communication under coherent joint transmission is quantified, and the switching probability of the UAV cooperative area and the third successful uplink communication probability under switching are calculated.

[0010] In one of the preferred solutions, step S1 is specifically:

[0011] The random geometric homogeneous Poisson point process is used to construct the random spatial distribution characteristics of drones and ground users in the drone-assisted collaborative network. The height of each drone is fixed at h and the drone distribution density is λ. u , each drone is equipped with M antennas and connected to a central server through backhaul links; the ground user distribution density is λ e , and each ground user is equipped with a single antenna, among which the ground user at the origin is a typical user.

[0012] In one of the preferred solutions, step S2 is specifically:

[0013] Each drone flies along a straight line at a random speed and altitude, and the direction of each drone follows an independent uniform distribution;

[0014] A typical user among ground users selects the nearest UAVs A0 and A1 as two related UAV base stations, establishes the edge of the collaborative triangle area, and defines a quadrilateral with vertices {A0, A1, A2, A3} in two adjacent triangle areas that share the same edge; the typical user selects the two UAVs with the shortest distance among the four UAVs located at the vertices of the quadrilateral, and the UAV located between the other two UAVs and closest to the typical user, to form an unloading CoMP set U0, wherein the UAVs located in the unloading CoMP set U0 are service UAVs, and the other UAVs are interference UAVs.

[0015] In one of the preferred solutions, step S3 is specifically:

[0016] Each UAV is equipped with a receiver with a maximum ratio combination, and the signal-to-interference ratio (SIR) received by the UAV under the mobile and collaborative offloading model is calculated. The first successful uplink communication probability is obtained according to the SIR, which is the probability that the SIR of the UAV with the maximum average receiving power is greater than the SIR threshold value. If the UAV fails to successfully receive data, the uplink transmission from the ground user equipment may be interrupted. The second successful uplink communication probability in the coherent joint transmission mode is quantified according to the mobile and collaborative offloading model, and the second successful uplink communication probability is the successful uplink communication probability of a typical user.

[0017] In one preferred solution, the drones are combined according to the maximum ratio, and the signal-to-interference ratio received by a typical user at the i-th drone is:

[0018]

[0019] Among them, SIR io,ul is the signal-to-interference ratio received by a typical user at the i-th drone, g io is the total receiving channel gain from the i-th UAV to the typical user, g io ~Γ(M,1), M is the number of antennas equipped on the drone, To establish the independent channel gain of the qth interfering user equipment after the mobile and cooperative offloading model, u iq is the link distance between the i-th UAV and the q-th interfering user equipment, α is the path loss coefficient, Φ e Ground user equipment distribution set, u io is the link distance between the i-th UAV and the typical user.

[0020] In one of the preferred solutions, the first successful uplink communication probability is:

[0021]

[0022] Among them, P c,ul (t) is the first successful uplink communication probability, is the maximum value of the signal-to-interference ratio received by the typical user at the i-th UAV according to the maximum ratio combination, and γ is the signal-to-interference ratio threshold.

[0023] In one of the preferred solutions, the second successful uplink communication probability is:

[0024]

[0025] Among them, P c,ul is the second successful uplink communication probability, w iis the distance between the i-th UAV and the projected typical user, M is the number of antennas equipped on the UAV, and u io is the link distance between the i-th UAV and the typical user, α is the path loss coefficient, γ is the signal-to-interference ratio threshold, L I (s) is the Laplace transform of the uplink interference power, To apply Palm distribution theory, we get the horizontal distance w i The joint probability density function of .

[0026] In one of the preferred solutions, the joint probability density function is:

[0027]

[0028] Among them, λ u Total density of drones.

[0029] In one of the preferred solutions, the Laplace transform of the uplink interference power is:

[0030]

[0031] Among them, λ e is the ground user distribution density, u0 is the Euclidean distance between the UAV closest to the typical user and the typical user at the origin, h is the height of the UAV, is the hypergeometric function, and s is the parameter variable.

[0032] In one of the preferred solutions, the switching probability of the UAV cooperation area is:

[0033]

[0034] Among them, P[Hoff(t)] is the switching probability of the UAV cooperation area, is the horizontal distance between the UAV and the typical user, λ u The total density of drones, for The probability density function of The moving speed of the drone after the mobile and cooperative offloading models are equivalent, is the distance between the drone and the typical user at time t, w is the closest horizontal distance between the drone and the typical user, is the density of drones at time t, Drone movement angles after equivalent mobile and collaborative offloading models.

[0035] In one of the preferred solutions, the third probability of successful uplink communication is:

[0036]

[0037] Among them, P suph is the third successful uplink communication probability, is the switching failure probability, P c,ul (t) is the first successful uplink communication probability.

[0038] In the above technical solution of the present invention, the dynamic collaborative uplink performance analysis method of the drone-assisted MEC includes the following steps: using a random geometric homogeneous Poisson point process to construct the random spatial distribution characteristics of drones and ground users in the drone-assisted collaborative network; based on the random distribution characteristics of drones and ground users, using Delaunay triangulation to construct a mobile and collaborative unloading model of drones; quantifying the probability of successful uplink communication under coherent joint transmission according to the mobile and collaborative unloading model; quantifying the probability of successful uplink communication under coherent joint transmission based on the mobile and collaborative unloading model, calculating the switching probability of the drone collaborative area and the third successful uplink communication probability under switching. The present invention solves the technical problem of how to improve the reliability of network services and optimize complex interference through collaborative services between multiple drone nodes.

[0039] In the present invention, the mobility and collaborative offloading model of dynamic drone swarms is established through random geometry theory, the mobility characteristics of drones in the 3GPP protocol are fully considered, and the efficient collaborative switching mechanism of Delaunay triangulation is adopted to improve the network reliability and communication delay, thereby providing a basic model for the dynamic collaborative uplink performance analysis of drone-assisted MEC.

[0040] In the present invention, considering the complexity of the switching mode of the multi-node cooperative network, an equivalent UAV cooperative area switching mechanism is adopted to improve the switching performance of analyzing dynamic UAV cooperative scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. 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.

[0042] Figure 1 A schematic diagram of a method for analyzing dynamic collaborative uplink performance of a drone-assisted MEC according to an embodiment of the present invention;

[0043] Figure 2 A schematic diagram of a mobile and collaborative offloading model according to an embodiment of the present invention;

[0044] Figure 3It is a simulation diagram of the variation trend of the successful communication probability of the dynamic drone swarm movement and cooperative unloading model according to the embodiment of the present invention with the threshold value;

[0045] Figure 4 This is a performance comparison chart of the successful communication probability of the dynamic drone swarm movement and collaborative offloading model of an embodiment of the present invention and the traditional regular hexagonal model.

[0046] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with the implementation methods and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0048] It should be noted that all directional indications (such as up, down, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0049] In addition, in the present invention, the descriptions such as "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of the features.

[0050] Furthermore, 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 they can be implemented by ordinary technicians in the field. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0051] See also Figure 1-Figure 2 According to one aspect of the present invention, the present invention provides a method for analyzing the dynamic collaborative uplink performance of a drone-assisted MEC, wherein the method for analyzing the dynamic collaborative uplink performance of a drone-assisted MEC comprises the following steps:

[0052] S1, using random geometric homogeneous Poisson point process to construct the random spatial distribution characteristics of drones and ground users in drone-assisted collaborative networks;

[0053] S2. Based on the random distribution characteristics of drones and ground users, the Delaunay triangulation is used to build a drone movement and collaborative unloading model;

[0054] S3. Quantifying the probability of successful uplink communication under coherent joint transmission based on mobility and cooperative offloading models

[0055] S4. Based on the mobile and cooperative offloading model, the probability of successful uplink communication under coherent joint transmission is quantified, and the switching probability of the UAV cooperative area and the third successful uplink communication probability under switching are calculated.

[0056] Specifically, in this embodiment, the step S1 is specifically:

[0057] The random geometric homogeneous Poisson point process is used to construct the random spatial distribution characteristics of drones and ground users in the drone-assisted collaborative network. The height of each drone is fixed at h and the drone distribution density is λ. u , the drone distribution set is Φ u , each drone is equipped with M antennas and connected to a central server through backhaul links; the ground user distribution density is λ e , the ground user distribution set is Φ e , and each ground user is equipped with a single antenna, among which the ground user at the origin is a typical user.

[0058] Specifically, in this embodiment, the step S2 is specifically:

[0059] Each drone flies along a straight line at a random speed and altitude, and the direction of each drone follows an independent uniform distribution;

[0060] A typical user among ground users selects the nearest UAVs A0 and A1 as two related UAV base stations, establishes the edge of the cooperative triangle area, and defines a quadrilateral with vertices {A0, A1, A2, A3} in two adjacent triangle areas that share the same edge; the typical user selects the two UAVs with the shortest distance among the four UAVs located at the vertices of the quadrilateral, and the UAV located between the other two UAVs and closest to the typical user, to form an unloading CoMP set U0 = {A0, A1, A2}, where the UAVs located in the unloading CoMP set U0 are service UAVs, and the other UAVs are interference UAVs; each unloading CoMP set contains a circular area with a radius of r0, and the Euclidean distance from the i-th UAV to the typical user device is recorded as u i , where i∈{1,2,...,∞}; w i is the distance between the i-th drone and the projected typical user (0,0,h), then

[0061] Specifically, in this embodiment, step S3 is as follows:

[0062] Each UAV is equipped with a receiver with a maximum ratio combination, and the signal-to-interference ratio (SIR) received by the UAV under the mobile and collaborative offloading model is calculated. The first successful uplink communication probability is obtained according to the SIR, which is the probability that the SIR of the UAV with the maximum average receiving power is greater than the SIR threshold value. If the UAV fails to successfully receive data, the uplink transmission from the ground user equipment may be interrupted. The second successful uplink communication probability in the coherent joint transmission mode is quantified according to the mobile and collaborative offloading model, and the second successful uplink communication probability is the successful uplink communication probability of a typical user.

[0063] Specifically, in this embodiment, the drones are combined according to the maximum ratio, and the signal-to-interference ratio received by a typical user at the i-th drone is:

[0064]

[0065] Among them, SIR io,ul is the signal-to-interference ratio received by a typical user at the i-th drone, g io is the total receiving channel gain from the i-th UAV to the typical user, g io ~Γ(M,1), M is the number of antennas equipped on the drone, To establish the independent channel gain of the qth interfering user equipment after the mobile and cooperative offloading model, u iq is the link distance between the i-th UAV and the q-th interfering user equipment, α is the path loss coefficient, Φ e Ground user equipment distribution set, u io is the link distance between the i-th UAV and the typical user.

[0066] Specifically, in this embodiment, the first successful uplink communication probability is:

[0067]

[0068] Among them, P c,ul (t) is the first successful uplink communication probability, is the maximum value of the signal-to-interference ratio received by the typical user at the i-th UAV according to the maximum ratio combination, and γ is the signal-to-interference ratio threshold.

[0069] Specifically, in this embodiment, the second successful uplink communication probability is:

[0070]

[0071] Among them, P c,ulis the second successful uplink communication probability, w i is the distance between the i-th drone and the projected typical user, u io is the link distance between the i-th UAV and the typical user, α is the path loss coefficient, γ is the signal-to-interference ratio threshold, L I (s) is the Laplace transform of the uplink interference power, f i (wi) is the joint probability density function of the horizontal distance wi obtained by applying the Palm distribution theory.

[0072] Specifically, in this embodiment, the joint probability density function is:

[0073]

[0074] Among them, λ u Total density of drones.

[0075] Specifically, in this embodiment, the Laplace transform of the uplink interference power is:

[0076]

[0077] Among them, λ e is the ground user distribution density, u0 is the Euclidean distance between the UAV closest to the typical user and the typical user at the origin, h is the height of the UAV, is the hypergeometric function, and s is the parameter variable.

[0078] Specifically, in this embodiment, according to the successful uplink communication probability obtained in step S3, the switching probability of the UAV cooperation area is considered to further calculate the successful uplink communication probability under switching. Considering the mobility of the UAV, the position of the UAV and the triangle list of the typical user are time-varying, and the cooperative unloading area of ​​the typical user will also change accordingly. The execution of switching by the typical user device depends on whether the average received power changes, where the average is relative to the channel fading, and the switching failure probability is Combined with the successful uplink communication probability obtained in step S3, the third successful uplink communication probability under switching can be obtained.

[0079] Specifically, in this embodiment, the switching probability of the drone cooperation area is:

[0080]

[0081] Among them, P[Hoff(t)] is the switching probability of the UAV cooperation area, is the horizontal distance between the UAV and the typical user, λ u The total density of drones, for The probability density function of The moving speed of the drone after the mobile and cooperative offloading models are equivalent, is the distance between the drone and the typical user at time t, w is the closest horizontal distance between the drone and the typical user, is the density of drones at time t, Drone movement angles after equivalent mobile and collaborative offloading models.

[0082] Specifically, in this embodiment, the distance between the drone and the typical user at time t is:

[0083]

[0084] Specifically, in this embodiment, the density of drones at time t is:

[0085]

[0086] Among them, w i is the distance between the i-th drone and the projected typical user, is the cumulative distribution function of the UAV flight speed v, f v (x) is the probability density function of v, v is the flight speed of the drone, is the horizontal distance between the ith UAV and the typical user.

[0087] Specifically, in this embodiment, the moving speed of the drone after the movement and cooperative unloading model is equivalent is:

[0088]

[0089] Among them, v k To offload the moving speed of each UAV in the CoMP set, θ k is the movement angle of each UAV in the unloaded CoMP set.

[0090] Specifically, in this embodiment, the moving angle of the drone after the moving and cooperative unloading models are equivalent is:

[0091]

[0092] Specifically, in this embodiment, the The probability density function of is:

[0093]

[0094] Among them, Γ(a) is the gamma function with variable parameter a, is the mean of the square of the moving speed of each UAV in the unloaded CoMP set, b is a variable parameter,

[0095] Specifically, in this embodiment, the third successful uplink communication probability is:

[0096]

[0097] Among them, P suph is the third successful uplink communication probability, is the switching failure probability, P c,ul (t) is the first successful uplink communication probability.

[0098] Specifically, in this embodiment, the dynamic collaborative uplink performance analysis method of the drone-assisted MEC uses the Monte Carlo analysis method to perform simulation analysis in Matlab to obtain a simulation value of the model's successful uplink communication probability. The Monte Carlo analysis method simulates the possible situations of the problem by generating a large number of random numbers, and then estimates the solution to the problem based on the statistical characteristics of these random numbers. By comparing the dynamic drone swarm mobility and collaborative offloading model proposed in the present invention with the successful uplink communication probability performance of the traditional regular hexagonal model, it can be found from the simulation comparison results that the mobility and collaborative offloading model proposed in the present invention has better performance.

[0099] Specifically, in this embodiment, see Figure 2 , the mobile and cooperative unloading model gives two UAV trajectories with random speeds v0 and v1, which are independent and identically distributed, Q k and S k are their turning points at the instantaneous moment, k∈0,1,2; similarly, the angle between the direction from Q0 to Q1 and the positive x-axis is represented by θ0, and θ0 is uniformly distributed in [0,2π). When the drone reaches Q1, it changes direction at a new angle θ1 and reaches the next turning point Q2, where the distribution of θ1 is independent and the same as θ0.

[0100] Specifically, in this embodiment, see Figure 3This is a simulation diagram of the changing trend of the successful communication probability with the threshold value of the dynamic drone swarm movement and cooperative unloading model of the present invention. It can be seen that in all cases, the probability of successful uplink communication decreases with the increase of the SIR threshold, but increases with the increase of the number of antennas M, which is consistent with the expected result; in addition, the simulated probability of successful uplink communication is very consistent with the theoretical prediction, which confirms the effectiveness of the approximate method adopted in the present invention; further analysis shows that when the ground user equipment density and the number of antennas are fixed, the probability of successful uplink communication increases with the increase of drone density; on the other hand, when the drone density and the number of antennas are fixed, the probability of successful uplink communication decreases with the increase of ground user equipment density. The former is due to the increase in the energy received by the ground user equipment as the drone density increases; the latter is attributed to the increase in interference received by the ground user equipment as the user density increases.

[0101] Specifically, in this embodiment, see Figure 4 It is a performance comparison diagram of the successful communication probability of the dynamic drone group movement and cooperative offloading model of the present invention and the traditional regular hexagonal model; wherein, Delaunay represents the drone movement and cooperative offloading model adopted by the present invention based on Delaunay triangulation, Hexagon represents the traditional regular hexagonal model, Voronoi represents the polygonal model, and the traditional regular hexagonal model adopts three drones to cooperate. It is worth noting that the proposed model is superior to the traditional regular hexagonal model in terms of successful uplink communication probability performance. This advantage is attributed to the Delaunay CoMP mechanism, which ensures that the distance between the unloading CoMP set drones and typical users is closer to the three nearest nodes than the disordered distance in the traditional hexagonal area; on the other hand, it also shows that the transmission performance of the proposed scheme is better than the single base station transmission model, indicating that compared with the single base station association model, the proposed transmission scheme can achieve collaborative power gain.

[0102] 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 changes made using the contents of the present invention's specification and drawings, or directly / indirectly applied in other related technical fields, are included in the patent protection scope of the present invention.

Claims

1. A method for analyzing the dynamic collaborative uplink performance of drone-assisted MEC, characterized in that: The following steps are involved: S1, using random geometric homogeneous Poisson point process to construct the random spatial distribution characteristics of drones and ground users in drone-assisted collaborative networks; S2. Based on the random distribution characteristics of drones and ground users, the Delaunay triangulation is used to build a drone movement and collaborative unloading model; S3, quantify the probability of successful uplink communication under coherent joint transmission based on the mobility and cooperative offloading models; S4. Based on the mobile and cooperative offloading model, the probability of successful uplink communication under coherent joint transmission is quantified, and the switching probability of the UAV cooperative area and the third successful uplink communication probability under switching are calculated.

2. According to claim 1, a method for analyzing the dynamic collaborative uplink performance of a drone-assisted MEC is characterized in that: The step S1 is specifically: The random geometric homogeneous Poisson point process is used to construct the random spatial distribution characteristics of drones and ground users in the drone-assisted collaborative network. The height of each drone is fixed at h and the drone distribution density is λ. u , each drone is equipped with M antennas and connected to a central server through backhaul links; the ground user distribution density is λ e , and each ground user is equipped with a single antenna, among which the ground user at the origin is a typical user.

3. A method for analyzing the dynamic collaborative uplink performance of a drone-assisted MEC according to any one of claims 1-2, characterized in that: The step S2 is specifically: Each drone flies along a straight line at a random speed and altitude, and the direction of each drone follows an independent uniform distribution; A typical user among ground users selects the nearest UAVs A0 and A1 as two related UAV base stations, establishes the edge of the collaborative triangle area, and defines a quadrilateral with vertices {A0, A1, A2, A3} in two adjacent triangle areas that share the same edge; the typical user selects the two UAVs with the shortest distance among the four UAVs located at the vertices of the quadrilateral, and the UAV located between the other two UAVs and closest to the typical user, to form an unloading CoMP set U0, wherein the UAVs located in the unloading CoMP set U0 are service UAVs, and the other UAVs are interference UAVs.

4. A method for analyzing the dynamic collaborative uplink performance of a drone-assisted MEC according to any one of claims 1 to 2, characterized in that: The step S3 is specifically: Each UAV is equipped with a receiver with a maximum ratio combination, and the signal-to-interference ratio (SIR) received by the UAV under the mobile and collaborative offloading model is calculated. The first successful uplink communication probability is obtained according to the SIR, which is the probability that the SIR of the UAV with the maximum average receiving power is greater than the SIR threshold value. If the UAV fails to successfully receive data, the uplink transmission from the ground user equipment may be interrupted. The second successful uplink communication probability in the coherent joint transmission mode is quantified according to the mobile and collaborative offloading model, and the second successful uplink communication probability is the successful uplink communication probability of a typical user.

5. According to claim 4, a method for analyzing the dynamic collaborative uplink performance of a drone-assisted MEC is characterized in that: The drones are combined according to the maximum ratio, and the signal-to-interference ratio received by a typical user at the i-th drone is: Among them, SIR io,ul is the signal-to-interference ratio received by a typical user at the i-th drone, g io is the total receiving channel gain from the i-th UAV to the typical user, g io ~Γ(M,1), M is the number of antennas equipped on the drone, To establish the independent channel gain of the qth interfering user equipment after the mobile and cooperative offloading model, u iq is the link distance between the i-th UAV and the q-th interfering user equipment, α is the path loss coefficient, Φ e Ground user equipment distribution set, u io is the link distance between the i-th UAV and the typical user.

6. The method for analyzing the dynamic collaborative uplink performance of a drone-assisted MEC according to claim 5 is characterized in that: The first successful uplink communication probability is: Among them, P c,ul (t) is the first successful uplink communication probability, is the maximum value of the signal-to-interference ratio received by the typical user at the i-th UAV according to the maximum ratio combination, and γ is the signal-to-interference ratio threshold.

7. The method for analyzing the dynamic collaborative uplink performance of a drone-assisted MEC according to claim 4 is characterized in that: The second successful uplink communication probability is: Among them, P c,ul is the second successful uplink communication probability, w i is the distance between the i-th UAV and the projected typical user, M is the number of antennas equipped on the UAV, and u io is the link distance between the i-th UAV and the typical user, α is the path loss coefficient, γ is the signal-to-interference ratio threshold, L I (s) is the Laplace transform of the uplink interference power, To apply Palm distribution theory, we get the horizontal distance w i The joint probability density function of .

8. The method for analyzing the dynamic collaborative uplink performance of a drone-assisted MEC according to claim 7 is characterized in that: The joint probability density function is: Among them, λ u Total density of drones.

9. The method for analyzing the dynamic collaborative uplink performance of a drone-assisted MEC according to claim 7 is characterized in that: The Laplace transform of the uplink interference power is: Among them, λ e is the ground user distribution density, u0 is the Euclidean distance between the UAV closest to the typical user and the typical user at the origin, h is the height of the UAV, is the hypergeometric function, and s is the parameter variable.

10. The method for analyzing the dynamic collaborative uplink performance of a drone-assisted MEC according to claim 4 is characterized in that: The switching probability of the UAV cooperation area is: Among them, P[Hoff(t)] is the switching probability of the UAV cooperation area, is the horizontal distance between the UAV and the typical user, λ u The total density of drones, for The probability density function of The moving speed of the drone after the mobile and cooperative offloading models are equivalent, is the distance between the drone and the typical user at time t, w is the closest horizontal distance between the drone and the typical user, is the density of drones at time t, Drone movement angles after equivalent mobile and collaborative offloading models.

11. The method for analyzing the dynamic collaborative uplink performance of a drone-assisted MEC according to claim 10 is characterized in that: The third successful uplink communication probability is: Among them, P suph is the third successful uplink communication probability, is the switching failure probability, P c,ul (t) is the first successful uplink communication probability.

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