A method for evaluating task offloading performance of a UAV-assisted MEC

Abstract

The application discloses a kind of unmanned vehicle auxiliary MEC's task unloading performance evaluation method, including the following steps: constructing the dynamic collaborative unloading model of unmanned vehicle based on Delaunay triangulation network structure;Through the dynamic collaborative unloading model of unmanned vehicle, establish the collaborative switching mechanism based on Delaunay triangulation network structure;According to the dynamic collaborative unloading model of unmanned vehicle, the successful computation probability of user equipment unloading task is calculated, and the successful edge computing probability of typical user equipment is obtained based on the successful uplink communication probability and successful computation probability under the unmanned vehicle switching.The application solves the technical problem of how to establish the service cooperation node of unmanned vehicle cooperative switching mechanism for real-time switching of unmanned vehicle based on the mobile characteristics of unmanned vehicle, and carries out switching performance evaluation.

Description

Technical Field

[0001] This invention relates to the field of mobile communication technology, and in particular to a method for evaluating the performance of unmanned aerial vehicle (UAV) assisted MEC (Multi-access Edge Computing). Background Technology

[0002] Drones play a crucial role in mobile edge computing (MEC) within air-to-ground integrated networks, bringing task processing closer to ground users. However, the challenges of link stability and real-time task processing brought about by the mobility of airborne nodes necessitate a reassessment and optimization of the current drone-assisted MEC architecture to meet the stringent requirements of ultra-reliable low-latency communication in the 5G era and beyond. Furthermore, achieving edge computing capabilities and expanding coverage solely by improving the performance of individual drones or expanding drone-assisted MEC is extremely difficult, as this approach struggles to guarantee system stability and reliability, severely hindering the full realization of the potential of large-scale drone MEC architectures. To address this, leveraging collaborative services among multiple drone nodes can improve network service reliability and effectively optimize complex interference issues. However, this collaborative offloading mode increases mobility management challenges. Compared to traditional single-node networks, the switching modes of multi-node collaborative networks are more complex, primarily due to the irregularity and complexity of the service area boundaries formed by multi-node collaboration, making precise description challenging. When a user device crosses the boundary of the collaborative area, it needs to switch its service collaborative node in real time. Considering the random movement of drones, drone collaborative switching becomes even more unpredictable. Therefore, there is an urgent need to propose a performance evaluation method for unmanned aerial vehicle (UAV) assisted MEC task offloading, and to solve the technical problem of how to establish a UAV collaborative switching mechanism based on the mobile characteristics of UAVs to switch the service collaboration nodes of UAVs in real time and to evaluate the switching performance. Summary of the Invention

[0003] The main objective of this invention is to propose a method for evaluating the performance of unmanned aerial vehicle (UAV) assisted MEC (Multi-access Edge Computing), aiming to solve the technical problem of how to establish a UAV collaborative switching mechanism based on the mobile characteristics of UAVs to switch the service collaboration nodes of UAVs in real time and to evaluate the switching performance.

[0004] To achieve the above objectives, the present invention provides a method for evaluating the task offloading performance of a drone-assisted MEC, wherein the method includes the following steps:

[0005] S1. Construct a dynamic cooperative unloading model for unmanned aerial vehicles based on the Delaunay triangular partitioning network structure;

[0006] S2. Establish a collaborative switching mechanism based on the Delaunay triangular partitioning network structure through the aforementioned UAV dynamic collaborative unloading model.

[0007] S3. Based on the UAV dynamic collaborative unloading model, calculate the success probability of the user equipment unloading task, and obtain the success edge computing probability of a typical user equipment based on the success uplink communication probability and success computing probability under the UAV handover.

[0008] One preferred embodiment is that step S1 constructs a dynamic cooperative unloading model for UAVs based on the Delaunay triangular partitioning network structure, specifically as follows:

[0009] All drones obey a density of λ. U The homogeneous Poisson point process is performed, and the same flight altitude h is used for operation. Each UAV is equipped with M antennas and is interconnected with the central server via a backhaul link.

[0010] All user equipment are distributed according to density λ. UE The homogeneous Poisson point process involves each user equipment (UE) equipped with a single antenna. The UE located at the origin is a typical UE. The data from the typical UE is simultaneously offloaded to three UAVs within a specified offload CoMP set, denoted as U0 = {A0, A2, A3}. U0 is the offload CoMP set, A0, A1, and A2 are the UAVs in the offload CoMP set, the UAVs in the offload CoMP set are the serving UAVs, and the remaining UAVs are the jamming UAVs.

[0011] One preferred embodiment is that step S2 establishes a cooperative switching mechanism based on the Delaunay triangular partitioning network structure, specifically as follows:

[0012] Define a handover event, which is whether the average received power of a typical user equipment changes;

[0013] If the average received power of a typical user equipment changes, that is, a handover occurs from the i-th offloaded CoMP set to the adjacent j-th offloaded CoMP set, then the equivalent model of the ground-to-air wireless network is used to treat the initial cooperative handover area as a Voronoi structure with a nearest neighbor association criterion. The Voronoi structure is then quantitatively characterized using stochastic geometry theory.

[0014] One preferred embodiment is that the switching from the i-th unloaded CoMP set to the adjacent j-th unloaded CoMP set specifically involves:

[0015]

[0016] Among them, E i (t) represents the average received power of the i-th offloaded CoMP set, U i(t)={A i B i C i Let} be the i-th unloaded CoMP set, P(t) be the transmit power of the UAV in the i-th unloaded CoMP set, α be the path loss coefficient, and u k (t) represents the distance between the k-th UAV and the typical user equipment for projection, k∈{A} i B i C i}, E j (s) represents the j-th unloaded CoMP set, U j (t)={A j B j C j} represents the j-th unloaded CoMP set. Let u be the transmit power of the j-th unloaded CoMP-focused UAV. n (s) represents the distance between the nth drone and the typical user equipment for projection, where n∈{A} j B j C j}

[0017] One preferred embodiment is the Voronoi structure with the nearest neighbor association criterion, specifically:

[0018] An exclusion region is introduced based on the nearest neighbor association criterion. The exclusion region is... O is the origin of the coordinate system. Let be the horizontal distance between the drone and a typical user, where the interfering drone follows a non-homogeneous point process; the drone density is divided into two parts: drones within the exclusion zone and interfering drones, wherein the drone density within the exclusion zone is . w i Let be the horizontal distance between the i-th drone and the typical user; the density of the interfering drones is . The total density of drones is λ. U ,but

[0019] In one preferred embodiment, the success probability of the user equipment unloading task is calculated as follows:

[0020] P scp (t')=pP[T c ≤t']+(1-p)P[T m ≤t']

[0021] Among them, P scp (t') represents the probability of successful computation, p represents the probability that the task is executed by the central processing unit, and T c T mThe computation latency at the central processing unit and the MEC are respectively, where t' is the time threshold, and P[T] is the computation latency at the central processing unit and the MEC. c ≤t'] represents the probability of successful computation on the central processing unit given the target computation delay, P[T] m ≤t'] represents the probability that the computation delay of a given target is successfully calculated at the MEC end.

[0022] One preferred embodiment is that the probability of successful computation of the given target on the central processing unit is:

[0023] P[T c ≤t']=1-exp(-u c t'+r c t')

[0024] Among them, u c For the central processing unit's service speed, r c The central server is allocated to a proportion p of the total tasks, and the reachability of tasks on the central server is determined.

[0025] In one preferred embodiment, the reachability of the task on the central server is:

[0026] r c =pλ UE |E|P sup h (t)

[0027] Where |E| represents the entire network region, and λ UE Distribution density of ground user equipment, P sup h (t) represents the probability of successful uplink communication during drone handover.

[0028] One preferred embodiment is the probability that the given target computation latency is successfully computed at the MEC end:

[0029]

[0030] Among them, u m For the MEC server service rate, ρ m This is the ratio of the reachable rate of MEC server tasks to the service rate.

[0031] In one preferred embodiment, the successful edge computing probability of the typical user equipment is:

[0032] P secp ≤P sup h (t)P scp (t')

[0033] Among them, P secp To calculate the probability of success at the edge, P sup h (t) represents the probability of successful uplink communication during drone handover.

[0034] In the above technical solution of the present invention, the method for evaluating the task offloading performance of UAV-assisted MEC includes the following steps: constructing a UAV dynamic cooperative offloading model based on the Delaunay triangular partitioned network structure; establishing a cooperative handover mechanism based on the Delaunay triangular partitioned network structure through the UAV dynamic cooperative offloading model; calculating the success calculation probability of user equipment offloading tasks according to the UAV dynamic cooperative offloading model, and obtaining the success edge calculation probability of typical user equipment based on the success uplink communication probability and success calculation probability under the UAV handover. The present invention constructs a UAV dynamic cooperative offloading model based on the Delaunay triangular partitioned network structure and an efficient cooperative handover mechanism based on the Delaunay triangular partitioned network structure, based on the mobile characteristics of UAVs in the 3GPP protocol. By combining the success uplink communication probability with the established UAV dynamic cooperative offloading model, the success edge calculation probability of typical user equipment is optimized, solving the technical problem of how to establish a UAV cooperative handover mechanism to switch the service cooperation nodes of UAVs in real time based on the mobile characteristics of UAVs and to evaluate the handover performance. Attached Figure Description

[0035] 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.

[0036] Figure 1 This is a schematic diagram of a method for evaluating the task offloading performance of a drone-assisted MEC according to an embodiment of the present invention;

[0037] Figure 2 This is a model of drone movement in an embodiment of the present invention;

[0038] Figure 3 This is an embodiment P of the present invention. sup h When = 0.8, the trend of the success edge calculation probability as a function of (p,t') is shown in the graph.

[0039] Figure 4 In this embodiment of the invention, when t' = 2ms, the probability of successful edge computation is defined as (p, P) sup h Trend graph of the function.

[0040] 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

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] See Figures 1-4 According to one aspect of the present invention, the present invention provides a method for evaluating the task offloading performance of a drone-assisted MEC, wherein the method for evaluating the task offloading performance of a drone-assisted MEC includes the following steps:

[0046] S1. Construct a dynamic cooperative unloading model for unmanned aerial vehicles based on the Delaunay triangular partitioning network structure;

[0047] S2. Establish a collaborative switching mechanism based on the Delaunay triangular partitioning network structure through the aforementioned UAV dynamic collaborative unloading model.

[0048] S3. Based on the UAV dynamic collaborative unloading model, calculate the success probability of the user equipment unloading task, and obtain the success edge computing probability of a typical user equipment based on the success uplink communication probability and success computing probability under the UAV handover.

[0049] Specifically, in this embodiment, step S1, which constructs a UAV dynamic cooperative unloading model based on the Delaunay triangular partitioning network structure, specifically involves:

[0050] This invention supports a MEC-based unmanned aerial vehicle (UAV) assisted communication network, wherein all UAVs obey a density of λ.U Homogeneous Poisson point process Φ U They operate at the same flight altitude h, and each drone is equipped with M antennas and is interconnected with the central server via a backhaul link.

[0051] All user equipment are distributed according to density λ. UE Homogeneous Poisson point process Φ UE Each user equipment (UE) is equipped with a single antenna. The UE located at the origin is a typical UE. Data from the typical UE is simultaneously offloaded to three UAVs within a designated offload CoMP set, denoted as U0 = {A0, A2, A3}, where U0 is the offload CoMP set, A0, A1, and A2 are the UAVs in the offload CoMP set, the UAVs in the offload CoMP set are serving UAVs, and the remaining UAVs are jamming UAVs. Each offload CoMP set contains a circular region with radius r0, and the Euclidean distance from the i-th UAV to the typical UE at point O is 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. The offloading CoMP set is formed by selecting the closest UAVs A0 and A1 among the typical users on the ground as two related UAV base stations, establishing the edge of the cooperative triangle region, and defining a quadrilateral with vertices {A0, A1, A2, A3} in the two adjacent triangle regions that share the same side; 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 that is closest to the typical user; in the UAV dynamic cooperative offloading model, each UAV navigates along a straight line at a random speed and altitude, and its direction follows a uniform independent distribution.

[0052] Specifically, in this embodiment, step S2, establishing a cooperative switching mechanism based on the Delaunay triangular partitioning network structure, specifically involves:

[0053] A handover event is defined as whether the average received power of a typical user equipment changes; wherein the average received power of the typical user equipment is relative to channel fading.

[0054] If the average received power of a typical user equipment changes, that is, a handover occurs from the i-th offloaded CoMP set to the adjacent j-th offloaded CoMP set, then the equivalent model of the ground-to-air wireless network is used to treat the initial cooperative handover area as a Voronoi structure with a nearest neighbor association criterion. The Voronoi structure is then quantitatively characterized using stochastic geometry theory.

[0055] Specifically, in this embodiment, when the average received power of a typical user equipment changes, i.e., a handover occurs from the i-th offloaded CoMP set to the adjacent j-th offloaded CoMP set, due to the movement of the UAV, the UAV cooperative set may not be able to form a triangular Delaunay cell at the next moment. That is, the position of the UAV and the triangular list of the typical UE are both time-varying. Therefore, an equivalent model of a ground-to-air wireless network can be considered. In this case, the coverage area of ​​the triangular region depends only on the density of the corresponding PPP. The challenging initial CoMP handover area can be equivalently processed as a classic Voronoi structure with a nearest neighbor association criterion. The resulting Voronoi cell exhibits convexity and can be quantitatively characterized using stochastic geometry theory. The handover from the i-th offloaded CoMP set to the adjacent j-th offloaded CoMP set specifically refers to:

[0056]

[0057] Among them, E i (t) represents the average received power of the i-th offloaded CoMP set, U i (t)={A i B i C i Let} be the i-th unloaded CoMP set, P(t) be the transmit power of the UAV in the i-th unloaded CoMP set, α be the path loss coefficient, and u k (t) represents the distance between the k-th UAV and the typical user equipment for projection, k∈{A} i B i C i}, E j (s) represents the j-th unloaded CoMP set, U j (t)={A j B j C j} represents the j-th unloaded CoMP set. Let u be the transmit power of the j-th unloaded CoMP-focused UAV. n (s) represents the distance between the nth drone and the typical user equipment for projection, where n∈{A} j B j C j}

[0058] Specifically, in this embodiment, the Voronoi structure with the nearest neighbor association criterion is defined as follows: an exclusion region is introduced based on the nearest neighbor association criterion, and the exclusion region is... O is the origin of the coordinate system. Let be the horizontal distance between the drone and a typical user, where the interfering drone follows a non-homogeneous point process; the drone density is divided into two parts: drones within the exclusion zone and interfering drones, wherein the drone density within the exclusion zone is . w i Let be the horizontal distance between the i-th drone and the typical user; the density of the interfering drones is . The total density of drones is λ. U ,but

[0059] Specifically, in this embodiment, based on the cooperative handover mechanism, the probability of successful uplink communication during UAV handover is calculated, and the probability of successful uplink communication during UAV handover is:

[0060]

[0061] Among them, P sup h (t) represents the probability of successful uplink communication during UAV handover. The probability of a handover failure reflects the system's tolerance for handover events. P[Hoff(t)] represents the handover probability within the UAV's cooperative area. c,ul (t) represents the probability of successful uplink communication before the handover.

[0062] Specifically, in this embodiment, the probability of successful uplink communication before the handover is:

[0063]

[0064] Among them, w k Let M be the horizontal distance from the k-th drone to a typical user, M be the number of antennas equipped on the drone, α be the path loss coefficient, and u be the horizontal distance from the k-th drone to a typical user. io Let L be the link distance between the i-th drone and a typical user, γ be the signal-to-interference ratio (SIR) threshold, and L be the link distance between the i-th drone 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.

[0065] Specifically, in this embodiment, the horizontal distance w i The joint probability density function is:

[0066]

[0067] Where, λ U The total density of drones is given.

[0068] Specifically, in this embodiment, the Laplace transform of the uplink interference power is:

[0069]

[0070] Where, λ UE For the distribution density of ground user equipment, u o The Euclidean distance is the distance between the drone closest to the typical user and the typical user located at the origin. Let be a hypergeometric function, and s be a parameter variable.

[0071] Specifically, in this embodiment, the switching probability of the UAV cooperative area is a lower bound of the switching probability, and the desired switching probability can be obtained by taking the equality sign; the switching probability of the UAV cooperative area is:

[0072]

[0073] in, λ represents the horizontal distance between the drone and a typical user. U Total density of drones Let v be the probability density function. To equate the initial cooperative region to the equivalent service drone's movement speed corresponding to a Voronoi structure with a nearest neighbor criterion, 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. To disrupt the density of drones, The goal is to equate the initial cooperative region to the equivalent service drone's movement angle after converting it into a Voronoi structure with a nearest neighbor criterion.

[0074] Specifically, in this embodiment, the distance between the drone and the typical user at time t is:

[0075]

[0076] Specifically, in this embodiment, the density of the interfering drones is:

[0077]

[0078] Where, λ U Total density of drones Let w be the cumulative distribution function of the UAV's flight speed v. i Let be the distance between the i-th drone and the typical user projecting the image. Let v be the probability density function, and v be the flight speed of the UAV. Let be the horizontal distance between the i-th drone and the typical user.

[0079] Specifically, in this embodiment, the moving speed of the equivalent service drone corresponding to the initial cooperative region being equivalent to a Voronoi structure with a nearest neighbor association criterion is:

[0080]

[0081] Among them, v k To unload the CoMP-focused movement speed of each drone, θ k To unload the CoMP focus, the movement angle of each drone is determined.

[0082] Specifically, in this embodiment, the movement angle of the equivalent service drone corresponding to the initial cooperative region being equivalent to a Voronoi structure with a nearest neighbor association criterion is:

[0083]

[0084] Specifically, in this embodiment, the probability density function is:

[0085]

[0086] Where Γ(a) is the gamma function of parameter a, and a is the variable parameter. Let b be the mean of the squared moving speeds of each drone in the CoMP set, and b be a variable parameter.

[0087] Specifically, in this embodiment, the success probability of the user equipment unloading task is calculated as follows:

[0088] P scp (t')=pP[T c ≤t']+(1-p)P[T m ≤t']

[0089] Among them, P scp (t') represents the probability of successful computation, p represents the probability that the task is executed by the central processing unit, and T c T m The computation latency at the central processing unit and the MEC are respectively, where t' is the time threshold, and P[T] is the computation latency at the central processing unit and the MEC. c ≤t'] represents the probability of successful computation on the central processing unit given the target computation delay, P[T] m ≤t'] represents the probability that the computation of a given target will be successful at the MEC end;

[0090] The probability of successful computation of the given target on the central processing unit:

[0091] P[T c ≤t']=1-exp(-u c t'+rc t')

[0092] Among them, u c For the central processing unit's service speed, r c A central server is allocated to each proportion p of the total tasks, and the reachability of the tasks on the central server is defined as follows:

[0093] r c =pλ UE |E|P sup h (t)

[0094] Where |E| represents the entire network region, P sup h (t) represents the probability of successful uplink communication during UAV handover;

[0095] The rate at which tasks are unloaded to the MEC server is:

[0096]

[0097] Where r0 is the rate at which tasks are unloaded to the MEC server;

[0098]

[0099] Where, ρ m r is the ratio of the reachable rate of MEC server tasks to the service rate. m For the task reachability of the MEC server, u m For MEC server service speed;

[0100] The task reachability of the MEC server is:

[0101]

[0102] The probability that the given target computation latency is successfully computed at the MEC end:

[0103]

[0104] Specifically, in this embodiment, the successful edge computing probability of the typical user equipment is:

[0105] P secp ≤P sup h (t)P scp (t')

[0106] Among them, P secp To calculate the probability of success at the edge, P sup h (t) represents the probability of successful uplink communication during drone handover.

[0107] Specifically, in this embodiment, the task offloading performance evaluation method of UAV-assisted MEC proposed in this invention is used in Matlab to perform Monte Carlo simulation analysis to obtain the simulated value of the success edge calculation probability of the model, and the P values ​​are analyzed respectively. sup h The changing trend of the success edge computing probability as a function of (p,t') when t' takes different values ​​is analyzed and characterized to promote the optimized design of the task unloading mechanism.

[0108] Specifically, in this embodiment, see Figure 2 This invention presents a model of UAV movement under the UAV-assisted MEC task offloading performance evaluation method proposed in this invention. Let Q0 represent the distance of the equivalent serving UAV from the typical user equipment projection point. The initial position. Assume the drone reaches a new position Q1 at time t, so that... speed along Directional movement. Using the law of cosines, the distance between Q2 and O′ can be obtained. Therefore, if another equivalent drone, for example, the drone at Q2, is... If it gets closer to O′, a switch will occur.

[0109] Specifically, in this embodiment, see Figure 3 and Figure 4 Taking into account both communication and computing performance metrics, the overall performance of the successful edge computing probability is evaluated. Based on this, the successful edge computing probability is described as a function of parameter p and computing time threshold t'. Figure 3 Analysis revealed that when parameter p remains constant, the probability of successful edge computing increases with the increase of the computation time threshold t'. This means that, under given conditions, the probability of successful edge computing is primarily constrained by the probability of successful computation, which increases with the increase of the computation time threshold. Conversely, when the computation time t' is constant, the probability of successful edge computing reaches a specific maximum value of p within the time interval between 0 and 1. The probability of successful edge computing changes with p, mirroring the change in the probability of successful computation with respect to p. This alignment is primarily because, under the condition of successful uplink communication probability under consistent handover, the probability of successful edge computing is fundamentally influenced by the probability of successful computation. Furthermore, at lower p values, the probability of successful computation increases with increasing p, while at higher p values, the probability of successful computation decreases with increasing p.

[0110] Figure 4 The plot shows the success edge calculation probability as (p, P) at t' = 2ms. sup hThe trend graph of the function shows that when the p value is relatively low, the probability of successful edge computing increases with the probability of successful uplink communication. This phenomenon can be attributed to the fact that task processing at this stage mainly relies on the MEC server. Specifically, as p increases, the arrival rate of tasks on the MEC server (denoted as rm) decreases, while the probability of successful edge computing increases accordingly. A similar trend is observed when p is high. Although the probability of successful edge computing decreases with the increase of p, the continuous increase in the probability of successful uplink communication eventually leads to an increase in the probability of successful edge computing.

[0111] 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 specification 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 method for evaluating the task offloading performance of UAV-assisted mobile edge computing, characterized in that, Includes the following steps: S1. Construct a dynamic cooperative unloading model for unmanned aerial vehicles based on the Delaunay triangular partitioning network structure; S2. Establish a collaborative switching mechanism based on the Delaunay triangular partitioning network structure through the aforementioned UAV dynamic collaborative unloading model. S3. Based on the UAV dynamic collaborative unloading model, calculate the success probability of the user equipment unloading task, and obtain the success edge computing probability of a typical user equipment based on the success uplink communication probability and success computing probability under the UAV handover; wherein, the typical user equipment is the user equipment located at the origin. The probability of successful user equipment unloading task is calculated as follows: in, To successfully calculate the probability, The probability that the task will be computed by the central processing unit. These represent the computation latency of the central processing unit and the mobile edge computing terminal, respectively. As a time threshold, To calculate the probability of successful computation on the central processing unit for a given target, with a given latency. Calculate the probability of successful calculation of latency at the mobile edge computing terminal for a given target.

2. The method for evaluating the task offloading performance of UAV-assisted mobile edge computing according to claim 1, characterized in that, Step S1, which constructs a dynamic cooperative unloading model for UAVs based on the Delaunay triangular partitioning network structure, specifically involves: All drones obey the density rule. The homogeneous Poisson point process, and at the same flight altitude The operation is carried out by each drone equipped with The antennas are interconnected with the central processing unit via a backhaul link; All user equipment is distributed according to a density function. The homogeneous Poisson point process, where each user equipment is equipped with a single antenna; the data from the typical user equipment is simultaneously offloaded to three UAVs within a specified offload CoMP set, denoted as... , To unload the CoMP set, To unload drones from the CoMP set, drones in the CoMP set are designated as service drones, while the remaining drones are designated as jamming drones.

3. A method for evaluating the task offloading performance of UAV-assisted mobile edge computing according to any one of claims 1-2, characterized in that, Step S2 establishes a cooperative switching mechanism based on the Delaunay triangular partitioning network structure, specifically as follows: Define a handover event, which is whether the average received power of a typical user equipment changes; If the average received power of a typical user equipment changes, that is, a handover occurs from the i-th offloaded CoMP set to the adjacent j-th offloaded CoMP set, then the equivalent model of the ground-to-air wireless network is used to treat the initial cooperative handover area as a Voronoi structure with a nearest neighbor association criterion. The Voronoi structure is then quantitatively characterized using stochastic geometry theory.

4. The method for evaluating the task offloading performance of UAV-assisted mobile edge computing according to claim 3, characterized in that, The switching from the i-th unloaded CoMP set to the adjacent j-th unloaded CoMP set specifically involves: in, Let be the average received power of the i-th unloaded CoMP set at time t. For the i-th unloaded CoMP set, Let i be the transmit power of the i-th unloaded CoMP-focused UAV. This is the path loss coefficient. Let k be the distance between the k-th drone and the typical user equipment for projection. , For the j-th unloaded CoMP set, For the j-th unloaded CoMP set, Let j be the transmit power of the unloaded CoMP-focused UAV. Let be the distance between the nth drone and the typical user equipment for projection. .

5. The method for evaluating the task offloading performance of UAV-assisted mobile edge computing according to claim 3, characterized in that, The Voronoi structure with the nearest neighbor association criterion is specifically as follows: An exclusion region is introduced based on the nearest neighbor association criterion. The exclusion region is... , Let O be the origin of the coordinate system. Let be the horizontal distance between the drone and a typical user device, where the interfering drone follows a non-homogeneous point process; the drone density is divided into two parts: the drone density within the exclusion zone and the interfering drone density, wherein the drone density within the exclusion zone is . , Let be the horizontal distance between the i-th UAV and the typical user equipment; the density of the interfering UAVs is . The total density of drones is ,but = - .

6. A method for evaluating the task offloading performance of UAV-assisted mobile edge computing according to any one of claims 1-2, characterized in that, The probability of successful computation of the given target on the central processing unit: in, For the central processing unit's service speed, The probability of tasks assigned to the central processing unit The corresponding task reachability.

7. The method for evaluating the task offloading performance of UAV-assisted mobile edge computing according to claim 6, characterized in that, The reachability of the task on the central processing unit is: in, The area of ​​the entire network region. For the distribution density of ground user equipment, This represents the probability of successful uplink communication during drone handover.

8. A method for evaluating the task offloading performance of UAV-assisted mobile edge computing according to any one of claims 1-2, characterized in that, The probability that the given target computation latency is successfully calculated at the mobile edge computing terminal: in, To improve the service rate of mobile edge computing, This is the ratio of task reachability to service rate at the mobile edge computing endpoint.

9. A method for evaluating the task offloading performance of UAV-assisted mobile edge computing according to any one of claims 1-2, characterized in that, The success edge computing probability of the typical user equipment is: in, Calculate the probability of success at the edge. This represents the probability of successful uplink communication during drone handover.

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