Wireless Channel Modeling Method in High-Dynamic Scenarios

Abstract

The present invention discloses a method for wireless channel modeling in a high-dynamic scenario, mainly solving the problem of inaccurate channel models in the high-dynamic scenario of unmanned aerial vehicles (UAVs) in the prior art. The implementation scheme is as follows: 1) Determine the three-dimensional characteristic parameters between the UAV and the ground user equipment during the flight of the UAV; 2) Determine the elevation angle area where the UAV is located according to the elevation angle, and determine the propagation mode of radio waves and the surface characteristic information affecting the air-ground channel transmission in the current environment; 3) Obtain the current environment information, weather information of the transmitter and the receiver, and the known position information, relevant indicators of the transceiver antennas and relevant characteristics of radio waves; 4) Establish path loss and rain attenuation loss according to the information obtained in 2) and 3), and establish a total propagation loss model and an MS fading distribution model. By using the three-dimensional characteristic parameters, the present invention improves the accuracy of the channel model in the high-dynamic scenario, improves the continuity of air-ground signal coverage, and can be applied to UAV-to-ground communication.

Description

Technical Field

[0001] The present invention belongs to the field of communication technologies, and further relates to a method for modeling a wireless channel, which can be applied to drone-to-ground communication. Background Art

[0002] In recent years, the air-to-ground (A2G) communication technology has received extensive attention due to its potential in applications of high-altitude platforms and unmanned aerial vehicles. This is because the A2G network can be used as a sensor platform to achieve remote location coverage in emergency situations such as network damage, or as a low-cost infrastructure to provide traffic offloading in crowded areas. In the A2G network, the air-to-ground wireless channel plays a crucial role, and it becomes particularly important to accurately describe the air-to-ground wireless channel. In the low elevation angle region, the modeling method of the air-ground channel has been gradually completed. However, in the medium and high elevation angle regions, when the drone is in a high-speed moving state, there is currently no complete wireless channel modeling method. With the rapid development of drones and aerial base stations, it is urgent to further study how to establish an air-ground channel model in the medium and high elevation angle regions under high-dynamic scenarios.

[0003] Nanjing University of Aeronautics and Astronautics disclosed in its patent application document with the application number: 2022105273550 "An air-ground channel modeling method considering the physical structure of a high platform". This method aims at the ground communication scenario of a high-altitude platform, calculates the line-of-sight path and non-line-of-sight path in real time, calculates the time delay, the elevation angle and azimuth angle of the departure angle of the transmitting platform, and the elevation angle and azimuth angle of the arrival angle of the ground receiving end in real time. However, the implementation of this method requires real-time three-dimensional data in the air-ground communication scenario, which is difficult to obtain due to the high-speed movement of the drone in actual communication, affecting the accuracy of the model.

[0004] Beijing Runke General Technology Co., Ltd. disclosed in its patent application document with the application number: 201710210448.X "An air-ground channel modeling method and device". The main steps of this method are: (1) determining the surface terrain feature information affecting the air-ground channel transmission in the current drone communication scenario; (2) obtaining the known information of the transmitting end and the receiving end, the climate information of the current environment, and the confidence information of the channel simulation scenario; (3) extracting the influencing parameters affecting the air-ground modeling according to the surface terrain feature information, the known information of the transmitting end and the receiving end, the climate information of the current environment, and the confidence information of the channel simulation scenario; (4) constructing an air-ground channel model based on the influencing parameters. However, when the drone base station is in the medium and high elevation angle regions, this solution does not give priority to considering the propagation mode of radio waves during modeling, resulting in large errors in the modeled channel parameters and affecting the accuracy of the model.

[0005] Beihang University disclosed a statistical model based on the measured flight data of unmanned aerial vehicles (UAVs) in its published paper "A Statistical Model for the UAV Communication Channel". This model divides flight into four situations: en route, hovering, takeoff, and landing, and establishes a channel model for each situation. Although this model is of great significance for simulating the channel performance of UAVs during data measurement, its accuracy is low because it does not consider the rain attenuation factor and the impact of communication scenarios on small-scale fading.

[0006] Beijing University of Posts and Telecommunications disclosed a composite three-dimensional channel model for the air-to-ground communication scenario in its published paper "Channel Modeling and Simulation of High-Speed Moving Aircraft". It introduced a mobility model that follows a Gaussian-Markov process into the air-to-ground scenario to describe the movement and position changes of aircraft and ground stations, divided the scattering variables into two categories, improved several geometric structures applicable to the air-to-ground scenario, and finally analyzed the channel characteristics. However, this model does not consider the impact of the elevation angle of the three-dimensional variables in the air-to-ground communication scenario on the channel model and cannot be applied to the high-elevation air-to-ground communication area. Summary of the Invention

[0007] The purpose of the present invention is to propose a wireless channel modeling method in a high-dynamic scenario to improve the accuracy of the channel model in a high-dynamic scenario by considering the rain attenuation factor and the impact of communication scenarios on small-scale fading, aiming at the deficiencies of the above-mentioned existing technologies.

[0008] To achieve the above purpose, the technical solution of the present invention includes the following steps:

[0009] (1) In the communication scenario, determine the three-dimensional characteristic parameters between the UAV and the ground user equipment during the UAV flight process;

[0010] Let the predetermined flight altitude of the UAV in the scenario be h, the horizontal distance between the UAV and the ground equipment user be d 2D , the three-dimensional distance between the UAV and the user equipment be d 3D , and the elevation angle between the UAV and the ground equipment be θ = arctan(h / d 2D );

[0011] (2) According to the elevation angle θ, determine the elevation angle area where the UAV is located, and determine the propagation mode of radio waves and the surface characteristic information affecting the air-to-ground channel transmission in the current environment:

[0012] If θ ≥ 40°, it is considered that the UAV is in the high-elevation angle area, the electromagnetic wave propagation characteristic is the free space propagation characteristic, and it is affected by weather and atmospheric environment, and at the same time has an obvious double-ray propagation effect;

[0013] If 2.5° < θ ≤ 40°, it is considered that the UAV is in the medium elevation angle area. The electromagnetic wave propagation characteristics are between those of free space and ground link, and it will be partially blocked by terrain and objects, with partial stray losses and shadow effects, and double-ray propagation effects.

[0014] If 2.5° ≤ θ, it is considered that the UAV is in the low elevation angle area. The electromagnetic wave propagation characteristics are close to those of the ground link propagation, and it will be severely blocked by terrain and objects, with obvious stray losses, shadow fading, and multipath effects.

[0015] (3) Obtain the current environmental information, weather information, and known information of the transmitter and receiver. The known information includes location information, relevant indicators of the transceiver antennas, and relevant characteristics of radio waves.

[0016] (4) Based on the surface feature information, known information of the transmitter and receiver, and climate information of the current environment obtained in (3), establish a wireless channel model:

[0017] 4a) Extract the influencing parameters affecting the air-to-ground channel modeling from the surface feature information, known information of the transmitter and receiver, and climate information of the current environment, and establish the path loss PL, rain attenuation loss PL - and total propagation loss PL * :

[0018] PL = 10αlog(d 3D ) + EP a

[0019]

[0020] PL * = PL + EP b (θ - EP c ) + PL -

[0021] where α is the path loss factor, is the three-dimensional distance from the UAV to the ground user equipment, EP a is an empirical parameter related to the carrier frequency, UAV flight altitude, air-to-ground A2G channel environment, and propagation terrain, EP b is a scaling parameter reflecting the relationship between elevation angle and path loss, EP c is the elevation angle-related parameter that adjusts the influence of elevation angle on path loss, with the unit of radian; c is the rain attenuation factor, γ is the rainfall, γ R is the attenuation rate of rain attenuation;

[0022] 4b) According to the A2G propagation environment information obtained in (3), give different MS fading distributions:

[0023] For the receiving end in an open field without any obstruction, the lognormal distribution is used to describe the MS fading, and its probability density distribution function is: where x is the MS fading value, μ is the MS fading mean, and σ is the MS fading variance;

[0024] For the receiving end in a scene with obstructions such as trees, the normal distribution is used to describe the MS fading, and its probability density distribution function is:

[0025]

[0026] For the receiving end in a suburban environment with buildings such as houses nearby, the gamma distribution is used to describe the MS fading, and its probability density distribution function is: where β is the shape parameter and γ is the scale parameter.

[0027] The present invention has the following advantages compared with the prior art:

[0028] First, since the present invention takes into account the transmission attenuation caused by rain and the atmosphere, that is, based on the weather and climate conditions during testing, the characteristic attenuation under dry air conditions, the characteristic attenuation under different water vapor density conditions, and the rainfall are obtained by consulting the literature, the characteristic attenuation rate and the attenuation rate of rain attenuation are calculated respectively, and the transmission attenuation caused by rain and the atmosphere is calculated according to the characteristic attenuation rate and the attenuation rate of rain attenuation. Therefore, compared with the prior art, the accuracy of the model is improved;

[0029] Second, since the present invention takes into account the influence of different A2G environments on multipath and shadow fading, that is, in different propagation scenarios, the mobile phone terminal and the road test terminal are used to measure different received powers and propagation losses, and the differences between the received power, the total propagation attenuation, and the propagation loss at different scenarios are substituted into the chi-square test respectively, and different probability distribution functions are tested respectively. When the chi-square value of any distribution function is 1 and the chi-square values of the remaining distribution functions are 0, it is considered that the multipath and shadow fading at this scenario conform to this distribution. Therefore, compared with the prior art, the accuracy of the model is improved;

[0030] Third, since the present invention takes into account the characteristics of the large elevation angle of the drone to the ground user, that is, when calculating the total transmission loss, the elevation angle, the elevation angle correction parameter, and the influence of three-dimensional parameters such as the elevation angle in the A2G scenario on the propagation loss are utilized, further improving the accuracy of the model. Description of the Drawings

[0031] Figure 1 is a schematic diagram of the drone air-ground communication scenario of the present invention;

[0032] Figure 2 is the implementation flowchart of the present invention;

[0033] Figure 3It is a schematic diagram of the ground test points in the UAV air-ground communication scenario of the present invention;

[0034] Figure 4 It is the simulation result diagram of the present invention. Specific implementation manners

[0035] The embodiments and effects of the invention are further described below with reference to the accompanying drawings

[0036] Refer to Figure 1 , for the UAV air-ground communication scenario used in the present invention, it includes a UAV and a ground user. The flight altitude of the UAV is h, and it circles around a set center point with a radius of r and a speed of v. d 2D is the horizontal distance between the UAV and the ground user, and d 3D is the three-dimensional distance between the UAV and the ground user, and θ is the elevation angle of the UAV relative to the ground base station.

[0037] Refer to Figure 2 , the implementation steps of the wireless channel modeling based on the above scenario in the present invention are as follows;

[0038] Step 1, obtain the elevation angle θ between the UAV and the ground device in the current communication scenario.

[0039] According to the horizontal distance d 2D between the UAV and the ground user, the three-dimensional distance d 3D between the UAV and the ground user, and the flight altitude h of the UAV, calculate the elevation angle between the UAV and the ground device

[0040] θ = arctan(h / d 2D ).

[0041] Step 2, judge the elevation angle area where the UAV is located according to the elevation angle θ.

[0042] If θ ≥ 40°, it is considered that the UAV is in the high elevation angle area, and the electromagnetic wave propagation characteristic is the free space propagation characteristic, which is affected by weather and atmospheric environment, and at the same time has an obvious double-ray propagation effect;

[0043] If 2.5° < θ ≤ 40°, it is considered that the UAV is in the middle elevation angle area, and the electromagnetic wave propagation characteristic is between free space and ground link, and will be partially blocked by terrain and ground objects, with partial stray loss and shadow effect, and has a double-ray propagation effect;

[0044] If 2.5° ≤ θ, it is considered that the UAV is in the low elevation angle area, and the electromagnetic wave propagation characteristic is close to the ground link propagation characteristic, and will be seriously blocked by terrain and ground objects, with obvious stray loss, shadow fading and multipath effect.

[0045] Step 3: Obtain key information such as the current environmental information and weather information of the transmitter and the receiver.

[0046] The environment where the ground test point is located is divided into three categories: the receiver is in an open field without any obstruction, the receiver is in a scene with obstructions such as trees, and the receiver is in a suburban environment with buildings such as houses nearby;

[0047] Based on the current weather and climate conditions, the characteristic attenuation γ0 under dry air conditions and the characteristic attenuation γ under different water vapor density conditions w and the rainfall R are obtained by referring to the literature, and the characteristic attenuation rate γ and the attenuation rate γ of rain attenuation are calculated respectively R :

[0048] γ = γ0 + γ w

[0049] Υ R = kR ψ

[0050] where k and ψ are two coefficients with different values; the unit of R is mm / h, and the unit of γ0 is dB / km.

[0051] Step 4: Establish a wireless channel model according to the surface characteristic information, the known information of the transmitter and the receiver, and the climate information of the current environment obtained in Step 3.

[0052] 4.1) Extract the influencing parameters that affect the air-to-ground channel modeling from the surface characteristic information, the known information of the transmitter and the receiver, and the climate information of the current environment, including the characteristic attenuation rate γ, the attenuation rate γ of rain attenuation R and the elevation angle θ;

[0053] 4.2) According to the above parameters, establish the path loss PL, the rain attenuation loss PL - and the total propagation loss PL * :

[0054] PL = 10αlog(d 3D ) + EP a

[0055]

[0056] PL * = PL + EP b (θ - EP c ) + PL -

[0057] where α is the path loss factor, is the three-dimensional distance from the UAV to the ground user equipment, EP ais an empirical parameter, EP, related to the carrier frequency, the flight altitude of the UAV, the air-to-ground A2G channel environment, and the propagation terrain b is a scaling parameter, EP, reflecting the relationship between the elevation angle and the path loss c is an elevation angle-related parameter that adjusts the influence of the elevation angle on the path loss, in radians; c is the rain attenuation factor;

[0058] 4.3) According to the A2G propagation environment information obtained in Step 3, different MS fading distributions are given:

[0059] For the receiving end in an open field without any obstruction, the lognormal distribution is used to describe the MS fading, and its probability density distribution function is: where x is the MS fading value, μ is the MS fading mean, and σ is the MS fading variance;

[0060] For the receiving end in a scene with obstructions such as trees, the normal distribution is used to describe the MS fading, and its probability density distribution function is:

[0061]

[0062] For the receiving end in a suburban environment with buildings such as houses nearby, the gamma distribution is used to describe the MS fading, and its probability density distribution function is: where β is the shape parameter and γ is the scale parameter.

[0063] The path loss PL, rain attenuation loss PL - , total propagation loss PL * established above, and the different MS fading distributions given constitute the UAV air-to-ground channel model. Using this model, the UAV air-to-ground wireless channel can be constructed to ensure the continuity of UAV-to-ground coverage communication.

[0064] The effects of the present invention can be further illustrated by the following simulations

[0065] I. Simulation Scenario

[0066] The ground test points for air-to-ground communication in the simulation scenario are as Figure 3 shown. The pentagrams marked in the figure are the hovering centers of the UAVs, and the rectangles marked are the ground test points. There are a total of 8 test points. Among them, test point 2 is an open field without any obstruction, test point 4 is a scene where the receiving end is blocked by trees, etc., test point 6 is a suburban environment where the receiving end is near buildings such as houses, and the remaining test points are rural environments where the receiving end is near low-rise houses.

[0067] II. Simulation Content

[0068] Under the above test scenario, the total propagation loss of each test point is calculated using the theoretical model of the present invention, and the total transmission loss of these 8 test points is measured. The results are as Figure 4 shown, where the solid line is the measured data of the total transmission loss of these 8 test points, and the dashed line is the total propagation loss of each test point calculated using the theoretical model of the present invention.

[0069] Using the following root mean square error formula, calculate the root mean square error S between the measured data and the theoretical model:

[0070]

[0071] where x n is the nth measured data of the total transmission loss of 8 test points, and x' n is the nth total propagation loss of each test point calculated by the theoretical model of the present invention.

[0072] The calculation result is S = 5.101, verifying the accuracy and advancement of the channel modeling method of the present invention.

Claims

1. A wireless channel modeling method in a high-dynamic scenario, characterized in that It includes the following steps: (1) In a communication scenario, determine the three-dimensional characteristic parameters between the drone during flight and the ground user equipment; Let the predetermined flight altitude of the drone in the scenario be h, and the horizontal distance between the drone and the ground device user be d 2D , the three-dimensional distance between the drone and the user device is d 3D , the elevation angle between the drone and the ground device is θ = arctan(h / d 2D ); (2) According to the elevation angle θ, determine the elevation angle area where the drone is located, and determine the propagation mode of radio waves and the surface feature information affecting the air-ground channel transmission in the current environment: If θ≥40°, it is considered that the drone is in the high elevation angle area, the electromagnetic wave propagation characteristic is the free space propagation characteristic, and it is affected by weather and atmospheric environment, and at the same time has an obvious double-ray propagation effect; If 2.5°<θ≤40°, it is considered that the drone is in the middle elevation angle area, the electromagnetic wave propagation characteristic is between free space and ground link, and it will be partially blocked by terrain and objects, with partial stray loss and shadow effect, and has a double-ray propagation effect; If 2.5°≤θ, it is considered that the drone is in the low elevation angle area, the electromagnetic wave propagation characteristic is close to the ground link propagation characteristic, and it will be severely blocked by terrain and objects, with obvious stray loss, shadow fading and multipath effect; (3) Obtain the current environment information, weather information, and known information of the transmitter and receiver. The known information includes position information, relevant indicators of the transceiver antennas, and relevant characteristics of radio waves; (4) According to the surface feature information, known information of the transmitter and receiver, and climate information of the current environment obtained in (3), establish a wireless channel model: 4a) Extract the influence parameters affecting the air-ground channel modeling from the characteristic information of the surface, the known information of the transmitter and receiver, and the climate information of the current environment, and establish the path loss PL, rain attenuation loss PL - and the total propagation loss PK * : PL = 10α log(d 3D ) + EP a PL * = PL + EP b (θ - EP c ) + PL - where α is the path loss factor, is the three-dimensional distance from the UAV to the ground user equipment, and EP a is an empirical parameter related to the carrier frequency, the UAV flight altitude, the air-to-ground A2G channel environment, and the propagation terrain, and EP b is a scaling parameter reflecting the relationship between the elevation angle and the path loss, and EP c is the elevation angle-related parameter that adjusts the influence of the elevation angle on the path loss, with the unit of radian; c is the rain attenuation factor, Υ is the characteristic attenuation rate, and Υ R is the attenuation rate of the rain attenuation; 4b) According to the A2G propagation environment information obtained in (3), give different MS fading distributions: For the receiving end in an open field without any obstruction, the lognormal distribution is used to describe the MS fading, and its probability density distribution function is: where x is the MS fading value, μ is the MS fading mean, and σ is the MS fading variance; For the scenario where the receiving end is blocked by trees or other objects, the Nakagami-m fading is described using a normal distribution, and its probability density function is as follows: For the receiving end located in a suburban environment with buildings such as houses nearby, the gamma distribution is used to describe the MS fading, and its probability density distribution function is as follows: where β is the shape parameter and γ is the scale parameter.

2. The method according to claim 1, characterized in that In step 4a), to establish the path loss PL, first use the mobile phone test terminal to measure the path loss PL as the dependent variable, and then use d in step (1) 3D as the independent variable to fit the path loss factor α and the empirical parameter EP a , and then from α and EP a calculate the path loss: PL = 10αlog(d 3D ) + EP a .

3. The method according to claim 1, characterized in that Establish the rain attenuation loss PL in step 4a) - , which is achieved as follows: Based on the current weather and climate conditions, the characteristic attenuation Υ0 under dry air conditions and the characteristic attenuation Υ under different water vapor density conditions are obtained by referring to the literature w and the rainfall R, and the characteristic attenuation rate Υ and the attenuation rate Υ of rain attenuation are calculated respectively R : Υ = Υ0 + Υ w Υ R = kR ψ Among them, k and ψ are two coefficients with different values, the unit of R is mm / h, and the unit of Υ0 is dB / km; According to the characteristic attenuation rate Υ and the attenuation rate Υ of rain attenuation R Calculate the transmission attenuation caused by rainwater and the atmosphere as follows:

4. The method according to claim 1, characterized in that, Establish the total propagation loss PL in step 4a) * , which is achieved as follows: According to the known angular distribution p(θ,φ) of electromagnetic waves in three-dimensional space, obtain the independent distributions of the elevation angle p(θ) and the azimuth plane angle p(φ): p(θ,φ) = p(θ)p(φ); According to the distribution p(θ) of the elevation angle and the distribution p(φ) of the azimuth plane angle, obtain the elevation angle correlation function a(τ) and the azimuth plane angle c(τ); where k0v is the maximum Doppler shift, is the angle between the movement of the mobile device at a constant speed on the plane and the x-axis. If the ground-end device is stationary, take sinθdθdφ is the representation of the element solid angle in the elevation plane, p(θ) = q(θ)sinθ is the representation of p(θ) in the elevation plane, and q(θ) is an auxiliary function similar to the elevation plane pattern of the antenna; According to the elevation angle related function a(τ) and the azimuth plane angle c(τ), the solid angle power fraction q(θ)p(φ)sinθdθdφ with (θ, φ) as the center and sinθdθdφ as the element is obtained. When θ approaches infinity, the solid angle power fraction is mathematically approximated as θ. According to the path loss PL, the rain attenuation loss PL - , the elevation angle θ, the elevation angle correction parameter EP b and EP c , calculate the total propagation loss: PL * = PL + EP b (θ - EP c ) + PL - 。 5. The method according to claim 1, wherein In step 4b), perform the K-square test for the receiver in an open field without any obstruction as follows: Use a mobile phone terminal and a road test terminal ATU to measure the received power P1 and propagation loss L1 at an open field without any obstruction; Substitute the difference between the received power P1 to be tested and the total propagation loss PL * into the chi-square test with the propagation loss L1: When testing the lognormal distribution, the K value is equal to 1; When testing other distributions, the K value is equal to 0, and it is considered that the MS fading at this point conforms to the lognormal distribution.

6. The method according to claim 1, characterized in that, In step 4b), perform the K-square test for the receiver in a scenario with obstructions such as trees as follows: Use a mobile phone terminal and a road test terminal ATU to measure the received power P2 and propagation loss L2 at a scenario with obstructions such as trees, Substitute the difference between the received power P2 and the total propagation loss PL * and the propagation loss L2 into the chi-square test: When testing the gamma distribution, the K value is equal to 1; When testing other distributions, the K value is equal to 0, and it is considered that the MS fading at this point conforms to the gamma distribution.

7. The method according to claim 1, characterized in that, In step 4b), for the receiver in a suburban environment with buildings such as houses nearby, the implementation is as follows: Use a mobile phone terminal and a road test terminal ATU to measure the received power P3 and propagation loss L3 in a suburban environment with buildings such as houses nearby, Substitute the difference between the received power P3 and the total propagation loss PL * and the propagation loss L3 into the chi-square test: When testing for a normal distribution, the value of K is equal to 1; When testing other distributions, the K value is equal to 0, and it is considered that the MS fading at this point conforms to the normal distribution.

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