Cooperative uplink communication performance analysis method of unmanned aerial vehicle assisted MEC network

Abstract

The invention discloses a cooperative uplink communication performance analysis method for an unmanned aerial vehicle assisted MEC network. The method comprises the following steps: constructing a spatial distribution model of unmanned aerial vehicles and ground users based on the unmanned aerial vehicle assisted MEC network taking a region as a center; calculating a signal-to-interference ratio received by the uplink unmanned aerial vehicle from the typical user according to the spatial distribution model; and analyzing the communication performance of the cooperative uplink of the unmanned aerial vehicle assisted MEC network according to the signal-to-interference ratio. According to the invention, the technical problem of how to accurately and quickly analyze the uplink communication link performance of the UAV-assisted EMC scene and output the result is solved.

Description

Technical Field

[0001] The present invention relates to the field of mobile communication technologies, and in particular to a method for analyzing 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 have put forward higher requirements for computing latency and computing power. In order to meet the needs of mobile users for high-quality computing and services, Mobile Edge Computing (MEC) technology has emerged. By deploying MEC servers at the edge of the network, users are provided with proximal and efficient computing services; however, traditional ground edge infrastructure has the risk of insufficient mobility and communication interruption when responding to emergencies such as natural disasters. Patent document No. 202311015465.X discloses a multi-UAV distributed control method based on the attack on the communication network. By establishing a dynamic model of multiple UAVs, defining the directed communication topology of multiple UAVs, and obtaining the communication status of each UAV when the communication network is attacked; based on the position information between each UAV and the neighboring UAVs, the target position of multiple UAVs under distributed control is predicted; the error dynamics system and the backstepping method combined with the filter method are used to solve the combined lift and attitude controller input of the UAV, and the position and attitude of the UAV are controlled based on the composite controller to achieve the convergence of the position error and attitude error to 0, so as to achieve the distributed control goal. Drone technology has great application potential in mobile edge computing networks due to its low cost and high mobility. It serves as a drone base station equipped with MEC servers to provide flexible computing and communication services. The use of drones can reduce computing latency and make up for the shortcomings of ground MEC in emergency scenarios. Unlike ground user equipment, drones fly at a higher altitude and are less affected by building obstructions. Usually, drones can adjust their positions to maintain line-of-sight communication links. However, as the flight altitude of the drone increases, the path loss will also increase, thereby weakening the advantage of line-of-sight transmission and causing the uplink performance to degrade. Therefore, it is urgent to propose a collaborative uplink communication performance analysis method for drone-assisted MEC networks to solve the technical problem of how to accurately and quickly analyze the uplink communication link performance of drone-assisted EMC scenarios and output the results. Summary of the invention

[0003] The main purpose of the present invention is to propose a method for analyzing the collaborative uplink communication performance of a drone-assisted MEC network, aiming to solve the technical problem of how to accurately and quickly analyze the uplink communication link performance of a drone-assisted EMC scenario 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. Construct 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 according to the signal-to-interference ratio.

[0008] In one of the preferred solutions, the 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, wherein the ground user located 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 of the preferred solutions, 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 ith 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 of the preferred solutions, the signal-to-interference ratio received by the uplink drone from a typical user is:

[0017]

[0018] j∈ΦG{o}

[0019] Where SIR is the signal-to-interference ratio received by the uplink UAV from a typical user, gi o 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(ri o ) is the channel path loss from the i-th UAV to the typical user, ξ(ri j ) is the channel path loss from the i-th UAV to the j-th interfering UAV, hi j 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 of the preferred solutions, the step S3 analyzes the communication performance of the cooperative uplink of the drone-assisted MEC network according to 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, an uplink with the largest signal-to-interference ratio, i.e., an 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 probability of successful uplink communication. The uplink communication performance of the drone-assisted MEC network can be reflected by the uplink communication success probability.

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

[0024]

[0025] Where Psup is the probability of successful uplink communication, λA is the drone density, K is the number of drone antennas, R is the radius of the approximate circle of the area of ​​the cooperation area, r is the horizontal distance between the ground user and the drone, ξ(r) is the channel path loss, γ is the signal-to-interference ratio threshold, I is the total interference power, is the nth order derivative of the Laplace transform LI(s) of the total interference power, where

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

[0027]

[0028] Where LI(s) is the Laplace transform of the total interference power, λG is the ground user density, hA is the UAV height, α is the path loss coefficient, and 2F1{a,b;c;z} is the Gaussian hypergeometric function.

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

[0030]

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

[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 above technical solution of the present invention, the cooperative uplink communication performance analysis method of the drone-assisted MEC network includes the following steps: constructing a spatial distribution model of drones and ground users based on a drone-assisted MEC network centered on a region; calculating the signal-to-interference ratio received by the uplink drone from a typical user according to the spatial distribution model; and analyzing the communication performance of the cooperative uplink of the drone-assisted MEC network according to the signal-to-interference ratio. The present invention solves the technical problem of how to accurately and quickly analyze the uplink communication link performance of the drone-assisted EMC scenario and output the results.

[0035] In the present invention, a region-centered drone-assisted MEC network is proposed. The spatial position distribution of drones and ground users equipped with MEC in the network is constructed using random geometry theory, and a collaborative area is 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 of 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 drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying any creative work.

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

[0038] Figure 2 A 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 A comparison diagram of the calculation results and simulation results of the success probability of uplink communication of a typical user corresponding to drones configured with different numbers of antennas as the threshold value changes in an embodiment of the present invention;

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

[0041] Figure 5 This 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 purpose, functional features and advantages of the present invention will be further explained in conjunction with the implementation methods and with reference to the accompanying drawings. DETAILED DESCRIPTION

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

[0044] In addition, in the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" or "second" may explicitly or implicitly include at least one of the features.

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

[0046] See also Figure 1-Figure 2 According to one aspect of the present invention, 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:

[0047] S1. Construct 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 according to the signal-to-interference ratio.

[0050] Specifically, in this embodiment, the 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 each ground user obeys the homogeneous parking point process, represented by 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 the drones is divided into a number of 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 a number of 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 the drone 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 ith 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, considering 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, the signal-to-interference ratio received by the UAV connected to the typical user during the uplink transmission is calculated 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 uplink UAV from the typical user is:

[0058]

[0059] j∈ΦG{o}

[0060] Among them, SIR is the signal-to-interference ratio received by the uplink drone 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; gi o is the channel gain received by the i-th UAV from a typical user, which obeys the gamma distribution with shape parameter K and scale parameter 1, that is, gi o ~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(ri o ) is the channel path loss from the i-th UAV to the typical user, ξ(ri j ) is the channel path loss from the i-th UAV to the j-th interfering UAV, hi j is the channel gain of the jth interfering user, which obeys an exponential distribution with parameter 1, i.e., hi j ~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 of 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 offloading data received from ground users. The user selects the drone with the best uplink status for task offloading, and calculates the signal-to-interference ratio received by each drone connected to a typical user during the uplink transmission process. Among all uplinks, the link with the largest signal-to-interference ratio is selected, that is, the link with the best uplink status, and the probability that the maximum signal-to-interference ratio of the link is greater than the preset signal-to-interference ratio threshold value 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, the 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, an uplink with the largest signal-to-interference ratio, i.e., an 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 probability of successful uplink communication. The uplink communication performance of the drone-assisted MEC network can be reflected by the uplink communication success probability.

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

[0070]

[0071] Where Psup is the probability of successful uplink communication, λA is the drone density, K is the number of drone antennas, R is the radius of the approximate circle of the area of ​​the cooperation area, r is the horizontal distance between the ground user and the drone, ξ(r) is the channel path loss, γ is the signal-to-interference ratio threshold, I is the total interference power, is the nth order derivative of the Laplace transform LI(s) of the total interference power, where

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

[0073]

[0074] Where LI(s) is the Laplace transform of the total interference power, λG is the ground user density, hA is the UAV height, α is the path loss coefficient, and 2F1{a,b;c;z} is the Gaussian hypergeometric function.

[0075] In one of the preferred solutions, the Laplace transform L of the total interference power isI The nth order derivative of (s) is:

[0076]

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

[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 UAV 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, and the uplink communication success probability is accurately 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 3 A comparison chart of the simulation value and the calculated value of the uplink communication success probability of a typical user changing with the threshold value based on a region-centered UAV-assisted network when the number of antennas is K is provided. 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 UAV, the simulation data and the theoretical calculation data are consistent, thereby verifying the correctness of the present invention. It can also be found that with the increase in the number of UAV antennas, 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 of 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 a slight change in the altitude of the drone on the success probability of uplink communication for typical users can be ignored.

[0082] Specifically, in this embodiment, see Figure 5This is a graph showing the relationship between the average achievable data rate and the path loss index under different numbers of antennas based on a region-centric UAV-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 the 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 the total interference power is more significant than the decrease in the 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 unloading. An analytical model of the probability of successful uplink communication is established using random geometry theory, 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 changes made using the contents of the present invention's specification and drawings, or directly / indirectly applied in other related technical fields, are included in the patent protection scope of the present invention.

Claims

1. A method for analyzing the cooperative uplink communication performance of a drone-assisted MEC network, characterized in that: The following steps are involved: S1. Construct 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 collaborative uplink of the drone-assisted MEC network according to the signal-to-interference ratio.

2. According to claim 1, a method for analyzing cooperative uplink communication performance of a drone-assisted MEC network is characterized in that: The step S1 constructs a spatial distribution model of drones and ground users based on a region-centric 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, wherein the ground user located 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 cooperative 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 ith 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.

5. A 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 signal-to-interference ratio that the uplink drone receives from a typical user is: Where SIR is the signal-to-interference ratio received by the uplink UAV from the typical user, gio is the channel gain received by the i-th UAV from the typical user, j∈ΦG\{o} is the set of interfering users, ΦG is the set of ground users, o is the typical user, ξ(r) is the channel path loss, ξ(rio) is the channel path loss from the i-th UAV to the typical user, ξ(rij) is the channel path loss from the i-th UAV to the j-th interfering UAV, hij is the channel gain of the j-th interfering user, S is the useful signal power, and I is the total interference power.

6. A 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 step S3 analyzes the communication performance of the cooperative uplink of the drone-assisted MEC network according to 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, an uplink with the largest signal-to-interference ratio, i.e., an uplink in the best state, is selected; 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 probability of successful uplink communication. The uplink communication performance of the drone-assisted MEC network can be reflected by the uplink communication success probability.

7. The method for analyzing cooperative uplink communication performance of a drone-assisted MEC network according to claim 6, characterized in that: The uplink communication success probability is: Where Psup is the probability of successful uplink communication, λA is the drone density, K is the number of drone antennas, R is the radius of the approximate circle of the area of ​​the cooperation area, r is the horizontal distance between the ground user and the drone, ξ(r) is the channel path loss, γ is the signal-to-interference ratio threshold, I is the total interference power, is the nth order derivative of the Laplace transform LI(s) of the total interference power, where 8. The method for analyzing cooperative uplink communication performance of a drone-assisted MEC network according to claim 7, characterized in that: The Laplace transform of the total interference power is: Where LI(s) is the Laplace transform of the total interference power, λG is the ground user density, hA is the UAV height, α is the path loss coefficient, and 2F1{a,b;c;z} is the Gaussian hypergeometric function.

9. The method for analyzing cooperative uplink communication performance of a drone-assisted MEC network according to claim 8, characterized in that: The Laplace transform of the total interference power L I The nth order derivative of (s) is: in, is the n-1th order derivative of the Laplace transform LI(s) of the total interference power.

10. A 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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