A collaborative uplink communication performance analysis method for drone-assisted MEC networks

Abstract

This invention discloses a method for analyzing the collaborative uplink communication performance of a drone-assisted MEC network. The method comprises the following steps: constructing a spatial distribution model of drones and ground users in a region-centered drone-assisted MEC network; calculating the signal-to-interference ratio (SIR) received by uplink drones from typical users based on the spatial distribution model; and analyzing the communication performance of the collaborative uplink of the drone-assisted MEC network based on the SIR. This invention solves the technical problem of accurately and quickly analyzing the uplink communication link performance in drone-assisted MEC scenarios and outputting the results.

Description

Technical Field

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

[0002] With the rapid development of the Internet of Things and wireless communication technologies, emerging applications and services such as augmented reality and virtual reality are placing higher demands on computing latency and power. To meet mobile users' demands for high-quality computing and services, Mobile Edge Computing (MEC) technology has emerged. By deploying MEC servers at the edge of the network, MEC provides users with efficient, near-end computing services. However, traditional ground-based edge infrastructure faces the risk of insufficient mobility and communication interruption during emergencies, such as natural disasters. Patent application number 202311015465.X discloses a distributed control method for multiple UAVs when the communication network is under attack. This method establishes a dynamic model for multiple UAVs, defines a directed communication topology for the multiple UAVs, and captures the communication status between each UAV when the communication network is under attack. Based on the position information between each UAV and its neighbors, the target position of the multiple UAVs under distributed control is predicted. The combined lift and attitude controller inputs of the UAVs are solved using an error dynamics system and a backstepping method combined with a filter. The position and attitude of the UAVs are then controlled using a composite controller, achieving convergence of the position and attitude errors to zero, thereby achieving the distributed control objective. Drone technology, due to its low cost and high mobility, has enormous potential for application in mobile edge computing networks. Drones can serve as base stations for drones equipped with MEC servers, providing flexible computing and communication services. The use of drones can reduce computing latency and compensate for the shortcomings of ground-based MEC in emergency scenarios. Unlike ground-based user equipment, drones fly at higher altitudes and are less affected by obstructions from buildings. Typically, drones can adjust their position to maintain line-of-sight communication links. However, as drones fly at increasing altitudes, path loss increases, weakening the advantages of line-of-sight transmission and leading to degraded uplink performance. Therefore, a method for analyzing the collaborative uplink communication performance of drone-assisted MEC networks is urgently needed to address the technical challenge of accurately and rapidly analyzing and outputting the uplink performance results in drone-assisted MEC scenarios. Summary of the Invention

[0003] The main purpose of this invention is to propose a collaborative uplink communication performance analysis method for drone-assisted MEC networks, aiming to solve the technical problem of how to accurately and quickly analyze the uplink communication link performance of drone-assisted MEC scenarios and output the results.

[0004] To achieve the above object, the present invention provides a method for analyzing the cooperative uplink communication performance of a drone-assisted MEC network, wherein the method for analyzing the cooperative uplink communication performance of a drone-assisted MEC network comprises the following steps:

[0005] S1. Build a spatial distribution model of drones and ground users based on a region-centric drone-assisted MEC network;

[0006] S2, calculate the signal-to-interference ratio received by the uplink UAV from a typical user based on the spatial distribution model;

[0007] S3. Analyze the communication performance of the collaborative uplink of the drone-assisted MEC network based on the signal-to-interference ratio.

[0008] In one preferred solution, step S1 constructs a spatial distribution model of drones and ground users based on a region-centered drone-assisted MEC network, specifically:

[0009] Each drone is equipped with a MEC server and K antennas, and hovers at the same altitude;

[0010] Each ground user is configured with a single antenna, where the ground user at the origin is a typical user;

[0011] The plane formed by the drones is divided into a number of non-intersecting regular polygonal areas, and the regular polygonal areas are collaborative areas where the drones located in the areas work together.

[0012] In one of the preferred solutions, the location distributions of the UAV and ground users both obey a homogeneous Poisson point process.

[0013] In one preferred solution, the distance distribution probability density function of the spatial distribution model of drones and ground users based on the region-centered drone-assisted MEC network is:

[0014]

[0015] in, is the distance distribution probability density function, r io is the horizontal distance between the i-th UAV and the typical user, R is the radius of the approximate circle of the area of ​​the cooperation area, and r is the horizontal distance between the ground user and the UAV.

[0016] In one preferred solution, the signal-to-interference ratio (SIR) received by the uplink UAV from a typical user is:

[0017]

[0018] j∈Φ G \{o}

[0019] Where SIR is the signal-to-interference ratio received by the uplink UAV from a typical user, g io is the channel gain received by the i-th UAV from the typical user, j∈Φ G \{o} is the set of interfering users, Φ G is the set of ground users, o is a typical user, ξ(r) is the channel path loss, ξ(r io ) is the channel path loss from the i-th UAV to the typical user, ξ(r ij ) is the channel path loss from the i-th UAV to the j-th interfering UAV, h ij is the channel gain of the jth interfering user, S is the useful signal power, and I is the total interference power.

[0020] In one preferred solution, step S3 analyzes the communication performance of the cooperative uplink of the drone-assisted MEC network based on the signal-to-interference ratio, specifically:

[0021] According to the calculated signal-to-interference ratio received by each UAV connected to a typical user during uplink transmission, the uplink with the largest signal-to-interference ratio, i.e., the uplink in the best state, is selected;

[0022] A signal-to-interference ratio threshold is set, and the probability that the signal-to-interference ratio of the uplink in the optimal state is greater than the signal-to-interference ratio threshold is calculated. The probability is the uplink communication success probability, which can reflect the uplink communication performance of the drone-assisted MEC network.

[0023] In one preferred solution, the uplink communication success probability is:

[0024]

[0025] Among them, P sup is the uplink communication success probability, λ A is the UAV density, K is the number of UAV antennas, R is the radius of the approximate circle with the area of ​​the cooperation area, r is the horizontal distance between the ground user and the UAV, ξ(r) is the channel path loss, γ is the signal-to-interference ratio threshold, I is the total interference power, is the Laplace transform of the total interference power L I The nth derivative of (s), where

[0026] In one preferred solution, the Laplace transform of the total interference power is:

[0027]

[0028] Among them, L I (s) is the Laplace transform of the total interference power, λG is the ground user density, h A is the altitude of the UAV, α is the path loss coefficient, and 2F1{a,b;c;z} is the Gaussian hypergeometric function.

[0029] One of the preferred solutions, the Laplace transform L of the total interference power I The nth derivative of (s) is:

[0030]

[0031] in, is the Laplace transform of the total interference power L I The n-1th derivative of (s).

[0032] One of the preferred solutions, after step S3, further includes:

[0033] A simulation test is conducted to compare the collaborative uplink communication performance analysis results of the UAV-assisted MEC network with the simulation results to verify the accuracy of the communication performance analysis.

[0034] In the technical solution described above, the method for analyzing the collaborative uplink communication performance of a drone-assisted MEC network includes the following steps: constructing a spatial distribution model of drones and ground users in a region-centered drone-assisted MEC network; calculating the signal-to-interference ratio (SIR) received by uplink drones from typical users based on the spatial distribution model; and analyzing the communication performance of the collaborative uplink of the drone-assisted MEC network based on the SIR. This invention solves the technical problem of accurately and quickly analyzing the uplink communication link performance in drone-assisted MEC scenarios and outputting the results.

[0035] In this invention, a region-centered drone-assisted MEC network is proposed. The spatial position distribution of MEC-equipped drones and ground users in the network is constructed using random geometry theory, and collaborative areas are defined. Drones in the same collaborative area jointly serve users in other ground projections. The distribution of drones and ground users, as well as the interference distribution between the signals received by drones from typical users and other users are comprehensively considered, so as to accurately calculate the performance indicators of the network uplink communication, that is, the success probability of uplink communication, and realize the communication performance analysis of the collaborative uplink of the drone-assisted MEC network. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

[0037] Figure 1 Schematic diagram of a method for analyzing collaborative uplink communication performance of a drone-assisted MEC network according to an embodiment of the present invention;

[0038] Figure 2 The spatial distribution model of drones and ground users based on a region-centric drone-assisted MEC network in an embodiment of the present invention;

[0039] Figure 3 This is a comparison chart of the calculated results and simulation results of the success probability of uplink communication for typical users corresponding to drones with different numbers of antennas configured as the threshold value changes according to an embodiment of the present invention;

[0040] Figure 4 This is a comparison chart of the success probability of uplink communication for typical users at different flight altitudes of the UAV according to an embodiment of the present invention;

[0041] Figure 5 FIG. 1 is a graph showing the relationship between the average achievable data rate and the path loss index under different numbers of antennas according to an embodiment of the present invention.

[0042] The realization of the objectives, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0044] In addition, the terms "first," "second," and so on, used in this disclosure are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, features specified as "first" or "second" may explicitly or implicitly include at least one of these features.

[0045] Moreover, the technical solutions between the various embodiments of the present invention may be combined with each other, but this must be based on the fact that ordinary technicians in this field can implement them. When the combination of technical solutions is mutually contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0046] See also Figure 1-Figure 2 According to one aspect of the present invention, a method for analyzing the cooperative uplink communication performance of a drone-assisted MEC network is provided, wherein the method for analyzing the cooperative uplink communication performance of a drone-assisted MEC network comprises the following steps:

[0047] S1. Build a spatial distribution model of drones and ground users based on a region-centric drone-assisted MEC network;

[0048] S2, calculate the signal-to-interference ratio received by the uplink UAV from a typical user based on the spatial distribution model;

[0049] S3. Analyze the communication performance of the collaborative uplink of the drone-assisted MEC network based on the signal-to-interference ratio.

[0050] Specifically, in this embodiment, step S1 constructs a spatial distribution model of drones and ground users based on a region-centered drone-assisted MEC network, specifically:

[0051] Each drone is equipped with a MEC server to provide task offloading services for ground users. At the same time, all drones are equipped with K antennas and hover at the same height. The distribution of all drones obeys the homogeneous Poisson point process, which is expressed as Φ A ={1,2,...,n}, with density λ A ;

[0052] Each ground user is equipped with a single antenna and obeys a homogeneous parking point process, which is expressed as Φ G ={1,2,...,m}, with density λ G , where the typical user is located at the ground origin, i.e. O(0,0,0);

[0053] The plane formed by each drone is divided into several non-intersecting regular polygonal areas, and the regular polygonal area is a collaborative area where the drones located in the area work together; in the present invention, the entire drone plane is divided into several non-intersecting regular hexagonal areas with a side length of 1 km and the entire regular hexagonal area is defined as a collaborative area. The drones can work together in the same collaborative area to provide services to ground users in the area. For the convenience of calculation, each regular hexagonal area can be equivalent to a circle with an equal area and a radius of R.

[0054] Specifically, in this embodiment, the distance distribution probability density function of the spatial distribution model of drones and ground users based on the region-centered drone-assisted MEC network is:

[0055]

[0056] in, is the distance distribution probability density function, r io is the horizontal distance between the i-th UAV and the typical user, R is the radius of the approximate circle of the area of ​​the cooperation area, and r is the horizontal distance between the ground user and the UAV.

[0057] Specifically, in this embodiment, it is considered that each UAV is deployed with a maximum ratio combining receiver, and the communication links between the UAV and the ground user are independent of each other and are affected by Rayleigh fading. Based on the path loss and the spatial distribution model of the UAV and the ground user, the signal-to-interference ratio received by the UAV connected to the typical user during the uplink transmission process is calculated; the signal-to-interference ratio received by the uplink UAV from the typical user is:

[0058]

[0059] j∈Φ G \{o}

[0060] Among them, SIR is the signal-to-interference ratio received by the uplink UAV from the typical user. During the uplink transmission of the typical user, the remaining users will be regarded as interfering users, expressed as: j∈Φ G \{o} is the set of interfering users; g io is the channel gain received by the i-th UAV from the typical user, which obeys the gamma distribution with shape parameter K and scale parameter 1, that is, g io ~Γ(K,1);Φ G is the set of all terrestrial users, o is a typical user, ξ(r) is the channel path loss, ξ(r io ) is the channel path loss from the i-th UAV to the typical user, ξ(r ij ) is the channel path loss from the i-th UAV to the j-th interfering UAV, h ij is the channel gain of the jth interfering user, which obeys the exponential distribution with parameter 1, that is, h ij ~exp(1), S is the useful signal power, and I is the total interference power.

[0061] Specifically, in this embodiment, the channel path loss is:

[0062]

[0063] Where r is the horizontal distance between the ground user and the UAV, h A is the altitude of the UAV, and α is the path loss coefficient.

[0064] Specifically, in this embodiment, the present invention adopts random geometry theory to establish a spatial distribution model of drones and ground users in a region-centered drone-assisted MEC network, comprehensively considering the distribution of drones and ground users, as well as the interference distribution between the signals received by drones from typical users and other users, so as to accurately calculate the performance indicators of the network uplink communication.

[0065] Specifically, in this embodiment, it is assumed that drones in the same collaborative area can share the offloaded data received from ground users. The user selects the drone with the best uplink status for task offloading, calculates the signal-to-interference ratio received by each drone connected to the typical user during the uplink transmission process, and selects the link with the largest signal-to-interference ratio among all uplinks, that is, the link with the best uplink status. The probability that the maximum signal-to-interference ratio of the link is greater than the preset signal-to-interference ratio threshold is calculated. This probability is the probability of successful uplink communication, which reflects the uplink communication performance of the drone-assisted MEC network.

[0066] Specifically, in this embodiment, step S3 analyzes the communication performance of the cooperative uplink of the drone-assisted MEC network according to the signal-to-interference ratio, specifically:

[0067] According to the calculated signal-to-interference ratio received by each UAV connected to a typical user during uplink transmission, the uplink with the largest signal-to-interference ratio, i.e., the uplink in the best state, is selected;

[0068] A signal-to-interference ratio threshold is set, and the probability that the signal-to-interference ratio of the uplink in the optimal state is greater than the signal-to-interference ratio threshold is calculated. The probability is the uplink communication success probability, which can reflect the uplink communication performance of the drone-assisted MEC network.

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

[0070]

[0071] Among them, P sup is the uplink communication success probability, λ A is the UAV density, K is the number of UAV antennas, R is the radius of the approximate circle with the area of ​​the cooperation area, r is the horizontal distance between the ground user and the UAV, ξ(r) is the channel path loss, γ is the signal-to-interference ratio threshold, I is the total interference power, is the Laplace transform of the total interference power L IThe nth derivative of (s), where

[0072] Specifically, in this embodiment, the Laplace transform of the total interference power is:

[0073]

[0074] Among them, L I (s) is the Laplace transform of the total interference power, λ G is the ground user density, h A is the altitude of the UAV, α is the path loss coefficient, and 2F1{a,b;c;z} is the Gaussian hypergeometric function.

[0075] One of the preferred solutions, the Laplace transform L of the total interference power I The nth derivative of (s) is:

[0076]

[0077] in, is the Laplace transform of the total interference power L I The n-1th derivative of (s).

[0078] Specifically, in this embodiment, after step S3, the following steps are further included:

[0079] A simulation test is carried out, and the collaborative uplink communication performance analysis results of the UAV-assisted MEC network are compared with the simulation results to verify the accuracy of the communication performance analysis. In the present invention, the uplink communication performance analysis method of the UAV-assisted MEC network based on the region-centered method is simulated and analyzed in Matlab using the Monte Carlo method to obtain the simulation results of the uplink communication success probability of the model. For the accurate calculation of the uplink communication success probability, the uplink communication success probability is calculated based on the geometric theory to obtain the theoretical value of the uplink communication success probability. The accuracy of the communication performance analysis is verified by comparing the simulation results with the theoretical value results. In addition, the influence of relevant system parameters on the uplink communication performance of the UAV-assisted MEC network can also be analyzed.

[0080] Specifically, in this embodiment, see Figure 3This is a comparison chart of the simulated values ​​and calculated values ​​of the uplink communication success probability of a typical user as the threshold value changes based on a region-centered drone-assisted network with the number of antennas being K. The Monte Carlo analysis method is used to statistically analyze the calculation results of the samples to obtain the simulation value results. Through comparative analysis, it can be found that regardless of the different antenna configurations of the drone, the simulation data and the theoretical calculation data are consistent, thereby verifying the correctness of the present invention. It can also be found that as the number of drone antennas increases, the uplink communication success probability gradually increases.

[0081] Specifically, in this embodiment, see Figure 4 This is a comparison chart of the success probability of uplink communication for typical users under different flight altitudes of drones based on a region-centered drone-assisted network. It can be found that the simulation results for various different flight altitudes are basically the same; since the coverage radius of the drone network is much larger than the altitude of the drone, the impact of slight changes in the drone altitude on the success probability of uplink communication for typical users can be ignored.

[0082] Specifically, in this embodiment, see Figure 5 This is a graph showing the relationship between the average achievable data rate and the path loss index for different numbers of antennas based on a region-centric drone-assisted network. It can be seen that the achievable data transmission rate increases with the increase of the path loss coefficient. As the path loss coefficient increases, the useful signal power and total interference power also decrease accordingly; however, the total interference power is the sum of the interference powers of all users except the target user, and the decrease in total interference power is more significant than the decrease in useful signal power; in addition, as the number of antennas increases, the signal power also increases, which further significantly improves the achievable data rate.

[0083] Specifically, in this embodiment, the present invention is based on a region-centric drone-assisted MEC network, in which drones collaborate to jointly serve ground users, and multiple drones jointly handle user offloading. By adopting random geometry theory, an analytical model of the probability of successful uplink communication is established, which helps to optimize the performance of the drone-assisted MEC network in emergency scenarios and improve the reliability of the network in emergency situations such as natural disasters.

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

Claims

1. A method for analyzing the collaborative uplink communication performance of a drone-assisted MEC network, characterized in that: The following steps are involved: S1. Build a spatial distribution model of drones and ground users based on a region-centric drone-assisted MEC network; S2, calculate the signal-to-interference ratio received by the uplink UAV from a typical user based on the spatial distribution model; S3. Analyze the communication performance of the cooperative uplink of the UAV-assisted MEC network based on the signal-to-interference ratio; specifically: According to the calculated signal-to-interference ratio received by each UAV connected to a typical user during uplink transmission, the uplink with the largest signal-to-interference ratio, i.e., the uplink in the best state, is selected; Setting a signal-to-interference ratio threshold value, and calculating the probability that the signal-to-interference ratio of the uplink in the optimal state is greater than the signal-to-interference ratio threshold value, wherein the probability is the uplink communication success probability, and the uplink communication performance of the drone-assisted MEC network can be reflected by the uplink communication success probability; The uplink communication success probability is: Among them, P sup is the uplink communication success probability, λ A is the UAV density, K is the number of UAV antennas, R is the radius of the approximate circle with the area of ​​the cooperation area, r is the horizontal distance between the ground user and the UAV, ξ(r) is the channel path loss, γ is the signal-to-interference ratio threshold, I is the total interference power, is the Laplace transform of the total interference power L I The nth derivative of (s), where 2. The method for analyzing the cooperative uplink communication performance of a drone-assisted MEC network according to claim 1 is characterized in that: The step S1 constructs a spatial distribution model of drones and ground users based on a region-centered drone-assisted MEC network, specifically: Each drone is equipped with a MEC server and K antennas, and hovers at the same altitude; Each ground user is configured with a single antenna, where the ground user at the origin is a typical user; The plane formed by the drones is divided into a number of non-intersecting regular polygonal areas, and the regular polygonal areas are collaborative areas where the drones located in the areas work together.

3. The method for analyzing the collaborative uplink communication performance of a drone-assisted MEC network according to claim 2 is characterized in that: The location distributions of the UAV and ground users all obey the homogeneous Poisson point process.

4. The method for analyzing the cooperative uplink communication performance of a drone-assisted MEC network according to claim 3 is characterized in that: The distance distribution probability density function of the spatial distribution model of drones and ground users based on the region-centered drone-assisted MEC network is: in, is the distance distribution probability density function, r io is the horizontal distance between the i-th UAV and the typical user, R is the radius of the approximate circle with the area of ​​the cooperation area, and r is the horizontal distance between the ground user and the UAV.

5. The method for analyzing cooperative uplink communication performance of a UAV-assisted MEC network according to any one of claims 1 to 4, characterized in that: The signal-to-interference ratio (SIR) received by the uplink drone from a typical user is: Where SIR is the signal-to-interference ratio received by the uplink UAV from a typical user, g io is the channel gain received by the i-th UAV from the typical user, j∈Φ G \{o} is the set of interfering users, Φ G is the set of ground users, o is a typical user, ξ(r) is the channel path loss, ξ(r io ) is the channel path loss from the i-th UAV to the typical user, ξ(r ij ) is the channel path loss from the i-th UAV to the j-th interfering user, h ij is the channel gain from the i-th UAV to the j-th interfering user, S is the useful signal power, and I is the total interference power.

6. The method for analyzing cooperative uplink communication performance of a drone-assisted MEC network according to any one of claims 1 to 4, characterized in that: The Laplace transform of the total interference power is: Among them, L I (s) is the Laplace transform of the total interference power, λ G is the ground user density, h A is the altitude of the UAV, α is the path loss coefficient, and 2F1{a,b;c;z} is the Gaussian hypergeometric function.

7. The method for analyzing cooperative uplink communication performance of a drone-assisted MEC network according to claim 6, characterized in that: The Laplace transform of the total interference power L I The nth derivative of (s) is: in, is the Laplace transform of the total interference power L I The nth derivative of (s).

8. The method for analyzing cooperative uplink communication performance of a drone-assisted MEC network according to any one of claims 1 to 4, characterized in that: After step S3, the method further includes: A simulation test is conducted to compare the collaborative uplink communication performance analysis results of the UAV-assisted MEC network with the simulation results to verify the accuracy of the communication performance analysis.

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