A method for analyzing dynamic cooperative uplink performance of unmanned aerial vehicle assisted MEC

Abstract

The application discloses a kind of dynamic cooperation uplink performance analysis methods of unmanned aerial vehicle assisted MEC, including the following steps: random geometric homogeneous Poisson point process is used to construct the random spatial distribution characteristics of unmanned aerial vehicle and ground user in unmanned aerial vehicle assisted cooperation network;According to the random distribution characteristics of unmanned aerial vehicle and ground user, the mobile and cooperation offloading model of unmanned aerial vehicle is constructed using Delaunay triangulation;According to the mobile and cooperation offloading model, the success of uplink communication probability under coherent joint transmission is quantified;Based on the mobile and cooperation offloading model, the success of uplink communication probability under coherent joint transmission is quantified, the switching probability of unmanned aerial vehicle cooperation area and the third success of uplink communication probability under switching are calculated.The application solves the technical problem of how to improve the reliability of network service and optimize complex interference through the cooperation service between multiple unmanned aerial vehicle nodes.

Description

Technical Field

[0001] This invention relates to the field of mobile communication technology, and in particular to a method for dynamic cooperative uplink performance analysis of unmanned aerial vehicle-assisted MEC. Background Technology

[0002] Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks represent a new architectural paradigm that synergistically combines UAVs and MEC technologies. Their primary goal is to provide adaptive and scalable computing resources for ground-based user equipment (UEs), thereby overcoming the inherent limitations of traditional terrestrial computing services. With the continuous advancement of UAVs and MEC technologies, and their increasingly diversified applications across various fields, the integrated model holds immense promise, particularly in improving network coverage and computing efficiency. The key to this potential lies in utilizing UAVs as MEC nodes. These aerial platforms significantly enhance the computing capabilities of ground infrastructure and effectively handle remote and mobile scenarios, providing convenient and efficient computing services to user equipment.

[0003] Current research on UAV-assisted MEC networks primarily focuses on performance optimization within specific parameters. This includes key issues such as allocating communication and computing resources, offloading computational tasks, and developing strategies for deploying UAVs and planning trajectories. With the continuous development of emerging applications such as autonomous driving, digital twins, and virtual environments, the demand for real-time processing and computing capabilities is also increasing. Simply improving the performance of individual UAVs or expanding UAV-assisted MECs to achieve edge computing capabilities and extend coverage is challenging because this approach struggles to guarantee system stability and reliability. Furthermore, it hinders the full utilization of the potential of large-scale UAV MEC architectures. A key issue is the difficulty in optimizing the performance of individual UAV systems to adapt to complex and variable mobile environments. Scaling up introduces complex interference factors, which can reduce overall task offloading performance, primarily affecting computation and communication latency. Therefore, this has raised industry-wide concerns about potential computational bottlenecks in large-scale MEC services. Thus, there is an urgent need to propose a dynamic cooperative uplink performance analysis method for UAV-assisted MECs to address the technical challenges of improving network service reliability and optimizing complex interference through collaborative services among multiple UAV nodes. Summary of the Invention

[0004] The main objective of this invention is to propose a dynamic cooperative uplink performance analysis method for UAV-assisted MEC, aiming to solve the technical problems of improving network service reliability and optimizing complex interference through cooperative services among multiple UAV nodes.

[0005] To achieve the above objectives, this invention provides a method for dynamic cooperative uplink performance analysis of UAV-assisted MEC, wherein the method includes the following steps:

[0006] S1. The random spatial distribution characteristics of UAVs and ground users in the UAV-assisted cooperative network are constructed using a random geometric homogeneous Poisson point process.

[0007] S2. Based on the random distribution characteristics of UAVs and ground users, construct the UAV mobility and cooperative offloading model using Delaunay triangulation.

[0008] S3. Quantify the probability of successful uplink communication under coherent joint transmission based on the mobility and cooperative offloading model;

[0009] S4. Based on the mobile and cooperative offloading model, quantify the probability of successful uplink communication under coherent joint transmission, calculate the handover probability of the UAV cooperative area and the third successful uplink communication probability under the handover.

[0010] One preferred embodiment, step S1, specifically comprises:

[0011] The random spatial distribution characteristics of UAVs and ground users in the UAV-assisted cooperative network are constructed using a random geometric homogeneous Poisson point process. The altitude of each UAV is fixed at h, and the UAV distribution density is λ. u Each drone is equipped with M antennas, which are interconnected to a central server via a backhaul link; the ground user distribution density is λ. e Each ground user is equipped with a single antenna, with the ground user located at the origin being a typical user.

[0012] One preferred embodiment, step S2, specifically comprises:

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

[0014] A typical user among ground users selects the closest drones A0 and A1 as two related drone base stations, establishes the edge of a cooperative triangle region, and defines a quadrilateral with vertices {A0, A1, A2, A3} in two adjacent triangle regions that share the same side. The typical user selects the two drones with the shortest distance among the four drones located at the vertices of the quadrilateral, as well as the drone located between the other two drones and closest to the typical user, to form an offload CoMP set U0. The drones located in the offload CoMP set U0 are serving drones, and the other drones are interfering drones.

[0015] One preferred embodiment, step S3, specifically includes:

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

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

[0018]

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

[0020] One preferred option is that the probability of the first successful uplink communication is:

[0021]

[0022] Among them, P c,ul (t) represents the probability of the first successful uplink communication. Let γ be the maximum signal-to-interference ratio (SIR) received by a typical user at the i-th drone, based on the maximum ratio combination of drones, and let γ be the SIR threshold.

[0023] One of the preferred options is that the probability of successful uplink communication in the second scenario is:

[0024]

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

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

[0027]

[0028] Where, λ u Total density of drones.

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

[0030]

[0031] Where, λ e Let u0 be the Euclidean distance between the UAV closest to a typical user and the typical user located at the origin, and h be the altitude of the UAV. Let be a hypergeometric function, and s be a parameter variable.

[0032] In one preferred embodiment, the switching probability of the UAV cooperative area is:

[0033]

[0034] Where P[Hoff(t)] is the switching probability of the UAV cooperative area. λ represents the horizontal distance between the drone and a typical user. u Total density of drones for The probability density function, The equivalent drone movement speed after the mobile and collaborative unloading model. Let t be the distance between the drone and the typical user, and w be the shortest horizontal distance between the drone and the typical user. Let be the density of drones at time t. The equivalent drone movement angle after the mobile and collaborative unloading model.

[0035] One preferred embodiment is that the probability of successful third uplink communication is:

[0036]

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

[0038] The above-described technical solution of this invention provides a method for analyzing the dynamic cooperative uplink performance of a UAV-assisted MEC, comprising the following steps: constructing the random spatial distribution characteristics of UAVs and ground users in the UAV-assisted cooperative network using a stochastic geometric homogeneous Poisson point process; constructing a UAV mobility and cooperative offloading model using Delaunay triangulation based on the random distribution characteristics of UAVs and ground users; quantifying the probability of successful uplink communication under coherent joint transmission based on the mobility and cooperative offloading model; calculating the handover probability of the UAV cooperative area and the third successful uplink communication probability under handover based on the quantified probability of successful uplink communication under coherent joint transmission using the mobility and cooperative offloading model. This invention addresses the technical problem of improving network service reliability and optimizing complex interference through cooperative services between multiple UAV nodes.

[0039] In this invention, a dynamic UAV swarm mobility and cooperative offloading model is established using stochastic geometry theory. This model fully considers the mobility characteristics of UAVs in the 3GPP protocol and adopts an efficient cooperative switching mechanism based on Delaunay triangulation, which improves network reliability and communication latency. This provides a basic model for the dynamic cooperative uplink performance analysis of UAV-assisted MEC.

[0040] In this invention, considering the complexity of the switching modes of multi-node collaborative networks, an equivalent UAV collaborative area switching mechanism is adopted, which improves the switching performance of analyzing dynamic UAV collaborative scenarios. Attached Figure Description

[0041] 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.

[0042] Figure 1 This is a schematic diagram of a dynamic cooperative uplink performance analysis method for unmanned aerial vehicle-assisted MEC according to an embodiment of the present invention;

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

[0044] Figure 3This is a simulation diagram showing the trend of successful communication probability as a function of a threshold value in the dynamic unmanned aerial vehicle swarm movement and cooperative unloading model according to an embodiment of the present invention.

[0045] Figure 4 This is a performance comparison chart of the successful communication probability between the dynamic UAV swarm movement and cooperative unloading model and the traditional regular hexagonal model in an embodiment of the present invention.

[0046] 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

[0047] 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.

[0048] It should be noted that all directional indicators (such as up, down, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicator will also change accordingly.

[0049] Furthermore, in this invention, descriptions involving "first," "second," etc., are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature.

[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 1-2 According to one aspect of the present invention, the present invention provides a method for dynamic cooperative uplink performance analysis of unmanned aerial vehicle (UAV) assisted MEC, wherein the method for dynamic cooperative uplink performance analysis of UAV assisted MEC includes the following steps:

[0052] S1. The random spatial distribution characteristics of UAVs and ground users in the UAV-assisted cooperative network are constructed using a random geometric homogeneous Poisson point process.

[0053] S2. Based on the random distribution characteristics of UAVs and ground users, construct the UAV mobility and cooperative offloading model using Delaunay triangulation.

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

[0055] S4. Based on the mobile and cooperative offloading model, quantify the probability of successful uplink communication under coherent joint transmission, calculate the handover probability of the UAV cooperative area and the third successful uplink communication probability under the handover.

[0056] Specifically, in this embodiment, step S1 is as follows:

[0057] The random spatial distribution characteristics of UAVs and ground users in the UAV-assisted cooperative network are constructed using a random geometric homogeneous Poisson point process. The altitude of each UAV is fixed at h, and the UAV distribution density is λ. u The distribution set of drones is Φ u Each drone is equipped with M antennas, which are interconnected to a central server via a backhaul link; the ground user distribution density is λ. e The set of ground user distributions is Φ e Each ground user is equipped with a single antenna, with the ground user located at the origin being a typical user.

[0058] Specifically, in this embodiment, step S2 is as follows:

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

[0060] A typical user among ground users selects the closest drones A0 and A1 as two associated drone base stations, establishing the edge of a cooperative triangle region. A quadrilateral with vertices {A0, A1, A2, A3} is defined within two adjacent triangle regions sharing the same side. The typical user selects the two drones with the shortest distance among the four drones located at the vertices of the quadrilateral, and the drone located between the other two drones and closest to the typical user, forming an offload CoMP set U0 = {A0, A1, A2}. Drones within the offload CoMP set U0 are serving drones, while the others are interfering drones. Each offload CoMP set contains a circular region with radius r0, and the Euclidean distance from the i-th drone to the typical user equipment is denoted as u. i , where i∈{1,2,...,∞}; w i Let be the distance between the i-th drone and the typical user (0,0,h) during projection.

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

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

[0063] Specifically, in this embodiment, based on the maximum ratio combination, the signal-to-interference ratio received by a typical user at the i-th drone is:

[0064]

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

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

[0067]

[0068] Among them, P c,ul (t) represents the probability of the first successful uplink communication. Let γ be the maximum signal-to-interference ratio (SIR) received by a typical user at the i-th drone, based on the maximum ratio combination of drones, and let γ be the SIR threshold.

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

[0070]

[0071] Among them, P c,ulFor the second successful uplink communication probability, w i Let u be the distance between the i-th drone and the typical user projecting the image. io Let L be the link distance between the i-th UAV and a typical user, α be the path loss coefficient, γ be the signal-to-interference ratio threshold, and L be the link distance between the i-th UAV and the typical user. I (s) represents the Laplace transform of the uplink interference power. To apply Palm distribution theory, the horizontal distance w is obtained. i The joint probability density function.

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

[0073]

[0074] Where, λ u Total density of drones.

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

[0076]

[0077] Where, λ e Let u0 be the Euclidean distance between the UAV closest to a typical user and the typical user located at the origin, and h be the altitude of the UAV. Let be a hypergeometric function, and s be a parameter variable.

[0078] Specifically, in this embodiment, based on the successful uplink communication probability obtained in step S3, the handover probability of the UAV cooperative area is considered to further calculate the successful uplink communication probability under handover. Considering the mobility of the UAV, the UAV's position and the triangular list of typical users are time-varying, and the cooperative offloading area of ​​typical users will also change accordingly. The handover of typical user equipment depends on whether the average received power changes, where the average is relative to channel fading. The handover failure probability is... By combining the successful uplink communication probability obtained in step S3, the third successful uplink communication probability under the switch can be obtained.

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

[0080]

[0081] Where P[Hoff(t)] is the switching probability of the UAV cooperative area. λ represents the horizontal distance between the drone and a typical user. u Total density of drones for The probability density function, The equivalent drone movement speed after the mobile and collaborative unloading model. Let t be the distance between the drone and the typical user, and w be the shortest horizontal distance between the drone and the typical user. Let be the density of drones at time t. The equivalent drone movement angle after the mobile and collaborative unloading model.

[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 the UAV at time t is:

[0085]

[0086] Among them, w i Let be the distance between the i-th drone and the typical user projecting the image. Let f be the cumulative distribution function of the UAV's flight speed v. v (x) is the probability density function of v, where v is the flight speed of the drone. Let be the horizontal distance between the i-th drone and the typical user.

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

[0088]

[0089] Among them, v k To unload the movement speed of each drone in the CoMP ensemble, θ k The angle of movement for each drone in the CoMP set.

[0090] Specifically, in this embodiment, the drone movement angle equivalent to the mobile and cooperative unloading model is:

[0091]

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

[0093]

[0094] Where Γ(a) is the gamma function with variable parameter a. Let be the mean squared moving speed of each drone in the CoMP set, and b be a variable parameter.

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

[0096]

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

[0098] Specifically, in this embodiment, the dynamic cooperative uplink performance analysis method of the UAV-assisted MEC uses Monte Carlo analysis in Matlab for simulation analysis to obtain the simulated value of the successful uplink communication probability of the model. 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 of the problem based on the statistical characteristics of these random numbers. By comparing the successful uplink communication probability performance of the dynamic UAV swarm movement and cooperative offloading model proposed in this invention with that of the traditional regular hexagonal model, the simulation comparison results show that the movement and cooperative offloading model proposed in this invention has superior performance.

[0099] Specifically, in this embodiment, see Figure 2 The mobile and cooperative unloading model gives two random velocities v0 and v1 for the UAV, which are independent and identically distributed, Q k and S k Let θ be the turning point at the instant, k∈0,1,2; similarly, the angle between the direction from Q0 to Q1 and the positive x-axis is denoted by θ0, which is uniformly distributed in [0,2π). When the UAV 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 that of θ0.

[0100] Specifically, in this embodiment, see Figure 3The simulation graph shows the trend of successful communication probability as a function of a threshold value in the dynamic UAV swarm movement and cooperative offloading model of this invention. It can be seen that in all cases, the successful uplink communication probability decreases with increasing SIR threshold, but increases with increasing antenna number M, consistent with the expected results. Furthermore, the simulated successful uplink communication probability matches the theoretical prediction very well, confirming the effectiveness of the approximation method used in this invention. Further analysis shows that, with fixed ground user equipment density and antenna number, the successful uplink communication probability increases with increasing UAV density. On the other hand, with fixed UAV density and antenna number, the successful uplink communication probability decreases with increasing ground user equipment density. The former is due to the increased energy received by ground user equipment with increasing UAV density; the latter is attributed to the increased interference received by ground user equipment with increasing user density.

[0101] Specifically, in this embodiment, see Figure 4 This diagram compares the performance of the dynamic UAV swarm mobility and cooperative offloading model of this invention with that of the traditional hexagonal model in terms of successful communication probability. Here, Delaunay represents the UAV mobility and cooperative offloading model constructed based on Delaunay triangulation used in this invention, Hexagon represents the traditional hexagonal model, and Voronoi represents the polygonal model. The traditional hexagonal model uses three UAVs in cooperation. Notably, the proposed model outperforms the traditional hexagonal model in terms of successful uplink communication probability. This advantage is attributed to the DelaunayCoMP mechanism, which ensures that the distance between the offloading CoMP ensemble UAVs and the typical user is closer to the three nearest nodes than the disordered distance within the traditional hexagonal region. Furthermore, it shows that the proposed scheme outperforms the single-base station transmission model in terms of transmission performance, indicating that the proposed transmission scheme can achieve cooperative power gain compared to the single-base station association model.

[0102] 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 method for dynamic cooperative uplink performance analysis of UAV-assisted MEC, characterized in that, Includes the following steps: S1. The random spatial distribution characteristics of UAVs and ground users in the UAV-assisted cooperative network are constructed using a random geometric homogeneous Poisson point process. S2. Based on the random distribution characteristics of UAVs and ground users, construct the UAV mobility and cooperative offloading model using Delaunay triangulation. S3. Quantify the probability of successful uplink communication under coherent joint transmission based on the mobility and cooperative offloading model; specifically: Each UAV is equipped with a receiver with the maximum ratio combination. The signal-to-interference ratio (SIR) received by the UAV under the mobile and cooperative offloading model is calculated. The first successful uplink communication probability is obtained based on the SIR, which is the probability that the SIR of the UAV with the maximum average received power is greater than the SIR threshold. If the UAV fails to receive data, the uplink transmission from the ground user equipment may be interrupted. The second successful uplink communication probability under the coherent joint transmission mode is quantified based on the mobile and cooperative offloading model. The second successful uplink communication probability is the successful uplink communication probability of a typical user. The second successful uplink communication probability is: Among them, P c,ul For the second successful uplink communication probability, w i Let u be the distance between the i-th drone and the typical user of the projection, M be the number of antennas equipped on the drone, and u be the distance between the i-th drone and the typical user of the projection. io Let L be the link distance between the i-th UAV and a typical user, α be the path loss coefficient, γ be the signal-to-interference ratio threshold, and L be the link distance between the i-th UAV and the typical user. I (s) represents the Laplace transform of the uplink interference power. To apply Palm distribution theory, the horizontal distance w is obtained. i The joint probability density function; S4. Based on the mobile and cooperative offloading model, quantify the probability of successful uplink communication under coherent joint transmission, calculate the handover probability of the UAV cooperative area and the third successful uplink communication probability under the handover.

2. The method for dynamic cooperative uplink performance analysis of UAV-assisted MEC according to claim 1, characterized in that, Step S1 specifically includes: The random spatial distribution characteristics of UAVs and ground users in the UAV-assisted cooperative network are constructed using a random geometric homogeneous Poisson point process. The altitude of each UAV is fixed at h, and the UAV distribution density is λ. u Each drone is equipped with M antennas, which are interconnected to a central server via a backhaul link; the ground user distribution density is λ. e Each ground user is equipped with a single antenna, with the ground user located at the origin being a typical user.

3. A method for dynamic cooperative uplink performance analysis of UAV-assisted MEC according to any one of claims 1-2, characterized in that, Step S2 specifically includes: Each drone travels along a straight line at a random speed and altitude, and the direction of each drone follows an independent and uniform distribution. A typical user among ground users selects the closest drones A0 and A1 as two related drone base stations, establishes the edge of a cooperative triangle region, and defines a quadrilateral with vertices {A0, A1, A2, A3} in two adjacent triangle regions that share the same side. The typical user selects the two drones with the shortest distance among the four drones located at the vertices of the quadrilateral, as well as the drone located between the other two drones and closest to the typical user, to form an offload CoMP set U0. The drones located in the offload CoMP set U0 are serving drones, and the other drones are interfering drones.

4. A method for dynamic cooperative uplink performance analysis of UAV-assisted MEC according to any one of claims 1-2, characterized in that, Based on the maximum ratio combination of the drones, the signal-to-interference ratio received by a typical user at the i-th drone is: Among them, SIR io,ul For a typical user, the signal-to-interference ratio (SIR) received at the i-th drone is given by g. io Let g be the total received channel gain from the i-th UAV to the typical user. io ~Γ(M,1), where M is the number of antennas equipped on the UAV. To establish the independent channel gain of the q-th interfering user equipment after the mobile and cooperative offloading model, u iq Let α be the link distance between the i-th UAV and the q-th interfering user equipment, and Φ be the path loss coefficient. e Ground user equipment distribution set, u io Let be the link distance between the i-th drone and a typical user.

5. The method for dynamic cooperative uplink performance analysis of UAV-assisted MEC according to claim 4, characterized in that, The probability of the first successful uplink communication is: Among them, P c,ul (t) represents the probability of the first successful uplink communication. Let γ be the maximum signal-to-interference ratio (SIR) received by a typical user at the i-th drone, based on the maximum ratio combination of drones, and let γ be the SIR threshold.

6. A method for dynamic cooperative uplink performance analysis of UAV-assisted MEC according to any one of claims 1-2, characterized in that, The joint probability density function is: Where, λ u Total density of drones.

7. A method for dynamic cooperative uplink performance analysis of UAV-assisted MEC according to any one of claims 1-2, characterized in that, The Laplace transform of the uplink interference power is: Where, λ e Let u0 be the Euclidean distance between the UAV closest to a typical user and the typical user located at the origin, and h be the altitude of the UAV. Let be a hypergeometric function, and s be a parameter variable.

8. A method for dynamic cooperative uplink performance analysis of UAV-assisted MEC according to any one of claims 1-2, characterized in that, The switching probability of the drone's cooperative area is: Where P[Hoff(t)] is the switching probability of the UAV cooperative area. λ represents the horizontal distance between the drone and a typical user. u Total density of drones for The probability density function, The equivalent drone movement speed after the mobile and collaborative unloading model. Let t be the distance between the drone and the typical user, and w be the shortest horizontal distance between the drone and the typical user. Let be the density of drones at time t. The equivalent drone movement angle after the mobile and collaborative unloading model.

9. The method for dynamic cooperative uplink performance analysis of UAV-assisted MEC according to claim 8, characterized in that, The probability of the third successful uplink communication is: Among them, P suph This represents the probability of a successful third uplink communication. P represents the probability of switching failure. c,ul (t) represents the probability of the first successful uplink communication.

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