CN117320024B - Low-altitude network coverage optimization method based on digital twinning - Google Patents
Low-altitude network coverage optimization method based on digital twinning Download PDFInfo
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Abstract
The invention discloses a low-altitude network coverage optimization method based on digital twinning, which comprises the following steps: s1, obtaining a base station antenna pattern through simulation, constructing a base station antenna radiation model, and reconstructing a three-dimensional environment model through unmanned aerial vehicle aerial photography; s2, combining a three-dimensional environment model and a base station antenna radiation model to construct a digital twin body covered by a low-altitude network; s3, base station deployment planning, configuration optimization and low-altitude network coverage scheme are carried out based on the digital twin body. The invention introduces the wave beam optimizing technology for network coverage and optimization thereof, and improves the benefit of network coverage; and the network coverage of the low-altitude airspace and the ground is jointly optimized, so that the development requirement of the low-altitude airspace communication network is met.
Description
Technical Field
The invention relates to low-altitude network coverage optimization, in particular to a low-altitude network coverage optimization method based on digital twinning.
Background
The base station has very wide application in a communication system, most of the existing base station coverage modes are coverage by applying side lobes of a ground base station antenna, and no technology of beam optimization is applied, so that the coverage efficiency and effect are deficient; in addition, the existing coverage condition only faces the ground communication network and is not comprehensively optimized aiming at the communication network requirement of a low-altitude airspace.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a low-altitude network coverage optimization method based on digital twinning.
The aim of the invention is realized by the following technical scheme: a low-altitude network coverage optimization method based on digital twinning comprises the following steps:
S1, obtaining a base station antenna pattern through simulation, constructing a base station antenna radiation model, and reconstructing a three-dimensional environment model through unmanned aerial vehicle aerial photography;
S2, combining a three-dimensional environment model and a base station antenna radiation model to construct a digital twin body covered by a low-altitude network;
s3, base station deployment planning, configuration optimization and low-altitude network coverage scheme are carried out based on the digital twin body.
The beneficial effects of the invention are as follows: the invention introduces the wave beam optimizing technology for network coverage and optimization thereof, and improves the benefit of network coverage; and the network coverage of the low-altitude airspace and the ground is jointly optimized, so that the development requirement of the low-altitude airspace communication network is met.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is a schematic diagram of a4 x 8 dual polarized array antenna;
FIG. 3 is a schematic diagram of a constructed Cartesian coordinate system and a three-dimensional polar coordinate system;
FIG. 4 is a schematic diagram of a polar coordinate model centered on a base station antenna;
fig. 5 is a polar model centered on the drone receiving antenna.
Detailed Description
The technical solution of the present invention will be described in further detail with reference to the accompanying drawings, but the scope of the present invention is not limited to the following description.
As shown in fig. 1, a low-altitude network coverage optimization method based on digital twinning is characterized in that: the method comprises the following steps:
S1, obtaining a base station antenna pattern through simulation, constructing a base station antenna radiation model, and reconstructing a three-dimensional environment model through unmanned aerial vehicle aerial photography;
In an embodiment of the present application, we simulate a 4×8 dual polarized array antenna arranged as shown in fig. 2, according to the general design of MIMO antennas. In the array antenna, two half-wave dipole antennas are vertically arranged at each position, namely + -45 DEG polarization.
S101, establishing a Cartesian coordinate system and a three-dimensional polar coordinate system shown in FIG. 3, wherein an included angle between a vector of the three-dimensional polar coordinate system and the positive direction of the Z axis is used as a polar angle theta, and an included angle between the projection of the vector on the XOY plane and the positive direction of the X axis is used as an azimuth angle phi; placing the MIMO antenna panel in the YOZ plane at this timePerforming analog simulation on the antenna at the current angle to obtain the initial gain V (theta ', phi') of the array antenna;
The MIMO antenna adopts an M z×My dual-polarized array antenna, wherein M z represents the number of antenna units in the Z-axis direction, and M y represents the number of antenna units in the Y-axis direction; m y＝8,Mz =4;
according to the arrangement of the MIMO antenna array units, the array steering vector corresponding to the (theta, phi) direction is calculated by the following formula:
Wherein v y (θ, φ) and v z (θ, φ) respectively represent antenna element steering vectors in the Y-axis and Z-axis directions, d y and d z are antenna element pitches in the horizontal and vertical directions, λ is wavelength, and a (θ, φ) is an array steering vector of the antenna array as a whole;
for a dual polarized array antenna, the array steering vector of M z×My antenna elements corresponding to each polarization direction is:
a1(θ,φ)＝|f1(0,φ)|a(θ,φ)
a2(θ,φ)＝|f2(θ,φ)|a(θ,φ)
Wherein, f 1 (theta, phi) is the gain amplitude of the antenna array in the (theta, phi) direction of the first polarization direction, f 2 (theta, phi) is the gain amplitude of the antenna in the (theta, phi) direction of the second polarization direction, and a 1 (theta, phi) and a 2 (theta, phi) are the array guide vectors corresponding to the two polarization directions;
Based on the channel state matrix H (θ, Φ) and the antenna initial gain V (θ ', Φ'), the received signal strength in the (θ, Φ) direction, i.e., the antenna gain S (θ, Φ), is calculated:
S(θ,φ)＝|H(θ,φ)TV(θ',φ')|2
Because each set of angles (θ, φ) corresponds to an antenna gain S (θ, φ), the antenna gain values for all angles (θ, φ) form a picture, which is denoted as an antenna pattern S gain, which is used as a base station antenna radiation model;
in the embodiment of the application, the unmanned aerial vehicle is used for actually measuring and verifying the antenna pattern, and the measurement principle of the antenna pattern ((Friis Transmission Formula) is as follows
Wherein R is the distance between Tx and Rx, P R is the received power, P T is the transmitted power, the antenna gain to be measured S T, and the received antenna gain S R; the distance R between Tx-Rx is measured, the antenna gain is received S R, the received power P R, the transmitted power P T, and the antenna gain S T is calculated by these four quantities, which are also sources of error, specifically including:
firstly, constructing a polar coordinate model taking a base station antenna as a center, wherein a five-pointed star is taken as a base station, a circle is taken as a receiving antenna, a horizontal plane is taken as a direction plane, and the north direction is taken as a 0-degree direction angle as shown in fig. 4; the vertical plane is taken as an inclination angle plane, the direction pointing to the ground is 0 degree inclination angle, and any position in space is according to the direction angle Tilt angle θ 1 and relative distance R
Then, constructing a polar coordinate model taking an unmanned aerial vehicle receiving antenna as a center, wherein a five-pointed star is a base station and a cylinder is a receiving antenna as shown in fig. 5; wherein the radial surface is taken as a direction surface, and the front direction is taken as a direction angle of 0 degrees; the longitudinal plane perpendicular to the radial plane is taken as the inclined plane. Any position in space according to direction angleTilt angle θ 2 and relative distance R; (radial plane, longitudinal plane, 0 degree tilt angle, 0 degree direction angle need to be determined according to the actual antenna.
According to the radio transmission model, the antenna receives the power of the signalDirection angle of receiving antenna relative to base station by unmanned aerial vehicleInclination angle theta 1, direction angle of base station relative to unmanned aerial vehicle receiving antennaThe inclination angle theta 2 and the relative distance R between the inclination angle theta 2 and the inclination angle theta 2 are determined;
The gain unit of the antenna is dB, the power unit is dBm, and P T is the power of a base station transmitting signal; for base station antenna Gain in the direction; Is the path loss; Receiving antenna for unmanned aerial vehicle Gain in the direction; w (θ 1, d) is the noise at the base station tilt angle θ 1, distance R.
To measure the gain S T of the base station antenna in each direction, a pattern S gain of the base station antenna is constructed, and the measurement is maintainedMeasuring the direction angle of the receiving antenna relative to the base station without changeThe received signal power at the inclination angle theta 1 can eliminate errors caused by noise w (theta 1, d) as far as possible, such as the influence of error reduction by carrying out multiple measurements on the unmanned aerial vehicle hovering at the same position or error elimination by carrying out calibration before the unmanned aerial vehicle takes off.
S102, necessary information of a region needing to be built into a twin body is obtained through sensing equipment, a three-dimensional map, namely a three-dimensional model is built, the three-dimensional model is downsampled through data processing, for example, a grid method is adopted, the resolution of the model is reduced through removing part of grid vertexes and patches, the number of vertexes is reduced through combining adjacent vertexes, and finally an environment model with the same building outline as a real three-dimensional environment and without affecting electromagnetic simulation effects is obtained.
S2, combining a three-dimensional environment model and a base station antenna radiation model to construct a digital twin body covered by a low-altitude network;
The step S2 includes:
The base station antenna pattern obtained in the step S101 and the three-dimensional environment model obtained in the step S102 are used for setting base station coordinates and base station parameters thereof according to real data, and simulation is carried out through a ray tracing method, so that shielding or multipath effects caused by buildings, indoor compartments or topography of the base station antenna in a real working environment are simulated, and a low-altitude network coverage digital twin body close to reality is obtained;
And acquiring the effective signal intensity, the environmental noise and the interference of other base stations on the current position signal of each position under the three-dimensional environment model through the simulation of the digital twin body.
In an embodiment of the application, network coverage is measured using an unmanned aerial vehicle
And for a test area with length L and width W, adopting an S-shaped track, testing the coverage of the test area with the accuracy of 10m at the height of 60-120m from the horizontal plane of the base station antenna, calculating and evaluating the coverage performance by using measured data through a coverage performance evaluation function, comparing the measured network coverage condition with the digital twin body established in S201, and checking the accuracy of the digital twin body.
In embodiments of the application, a digital twin visualization interface may also be established
Creating a user-friendly interface for the low-altitude network coverage digital twin body to visualize the digital twin body;
The interface allows a user to monitor, control or analyze the behavior of the digital twin, continuously monitor its performance after the visual interface is created, collect real-time data from real base stations to update and refine the model, while the digital twin provides feedback to help improve relevant performance parameters and decision making processes.
S3, base station deployment planning, configuration optimization and low-altitude network coverage scheme are carried out based on the digital twin body.
S301, using a coverage performance evaluation function, giving a measurement result of the current coverage quality:
The coverage performance of the three-dimensional space is measured by the following formula:
Where S (x) represents the coverage index of the x position, P s represents the effective signal strength, N 0 represents the ambient noise, and P b represents the interference of other base stations to the signal.
S302, combining the digital twin body and the coverage requirement of the user, designing an optimization algorithm to obtain the optimal antenna parameter combination
The coverage requirements are: providing a target track in a three-dimensional space, and enabling the coverage performance on the track to be optimal by adjusting base station parameters, wherein the base station parameters comprise an antenna angle, initial gain of an antenna and transmission power;
the base station parameters are adjusted based on the digital twin body, the specific adjustment method comprises a reinforcement learning algorithm, and each base station parameter is adjusted by acting in each interaction process of reinforcement learning; the state is the current parameters of each base station; rewarding as track The average value of the coverage performance of a plurality of points is increased.
The targets are as follows:
in the reinforcement learning process, two networks, namely a strategy network and a value network, are adopted for learning:
The system comprises a strategy network, a value network, a target function and a data set, wherein the strategy network is used for interacting with an environment, learning strategies under the guidance of the value function, the value network is responsible for learning a value function by using a data set collected by interaction of the strategy network and the environment, helping the strategy network to update the strategy, and a track report exists in the gradient of the target function and is used for updating the strategy;
Defining a loss function of a cost function in a value network, and updating the value network parameters by a gradient rising method; in each round of interaction, sampling the current strategy, calculating the gradient of the bid value function, updating the value network parameters, and updating the parameters of the strategy network under the guidance of the new value function;
after the multi-round interaction is carried out, when the objective function is not increased any more, the learning is stopped, and the setting parameters of each base station at the moment are recorded, so that the optimized low-altitude network coverage effect on the given track is obtained.
While the foregoing description illustrates and describes a preferred embodiment of the present invention, it is to be understood that the invention is not limited to the form disclosed herein, but is not to be construed as limited to other embodiments, but is capable of use in various other combinations, modifications and environments and is capable of changes or modifications within the spirit of the invention described herein, either as a result of the foregoing teachings or as a result of the knowledge or skill of the relevant art. And that modifications and variations which do not depart from the spirit and scope of the invention are intended to be within the scope of the appended claims.
Claims (2)
1. A low-altitude network coverage optimization method based on digital twinning is characterized in that: the method comprises the following steps:
S1, obtaining a base station antenna pattern through simulation, constructing a base station antenna radiation model, and reconstructing a three-dimensional environment model through unmanned aerial vehicle aerial photography;
The step S1 includes:
S101, establishing a Cartesian coordinate system and a three-dimensional polar coordinate system, vector and Z axis of three-dimensional polar coordinate system the positive direction included angle is taken as the polar angle theta, the angle between the projection of the vector on the XOY plane and the positive direction of the X axis is used as the azimuth angle phi; placing the MIMO antenna panel in the YOZ plane at this time Phi ' =0, performing analog simulation on the antenna at the current angle to obtain the initial gain V (theta ', phi ') of the array antenna;
according to the arrangement of the MIMO antenna array units, the array steering vector corresponding to the (theta, phi) direction is calculated by the following formula:
vy(θ,φ)＝f(My,θ,φ,λ,dy)
vz(θ,φ)＝f(Mz,θ,φ,λ,dz)
Wherein v y (θ, φ) and v z (θ, φ) respectively represent the antenna element steering vectors in the Y-axis and Z-axis directions, M y and M z are the number of antennas in the Y-axis and Z-axis directions, d y and d z are the antenna element spacing in the horizontal and vertical directions, λ is the wavelength, and a (θ, φ) is the array steering vector of the antenna array as a whole; f () is a generic steering vector calculation function;
the used MIMO antenna is a dual polarized array antenna, and the array steering vector of M z×My antenna units corresponding to each polarization direction is as follows:
a1(θ,φ)＝|f1(θ,φ)|a(θ,φ)
a2(θ,φ)＝|f2(θ,φ)|a(θ,φ)
wherein f 1 (θ, φ) is the gain amplitude of the antenna array in the (θ, φ) direction of the first polarization direction, f 2 (θ, φ) is the gain amplitude of the antenna array in the (θ, φ) direction of the second polarization direction, a 1 (θ, φ) and a 2 (θ, φ) are array steering vectors corresponding to the two polarization directions;
Based on the channel state matrix H (θ, Φ) and the antenna initial gain V (θ ', Φ'), the received signal strength in the (θ, Φ) direction, i.e., the antenna gain S (θ, Φ), is calculated:
S(θ,φ)＝|H(θ,φ)TV(θ′,φ′)|2
Because each set of angles (θ, φ) corresponds to an antenna gain S (θ, φ), the antenna gain values for all angles (θ, φ) form a picture, which is denoted as an antenna pattern S gain, which is used as a base station antenna radiation model;
S102, acquiring necessary information of an area needing to be built with a twin body through sensing equipment, building a three-dimensional map, namely a three-dimensional model, and finally obtaining an environment model with the same building outline as a real three-dimensional environment and without affecting electromagnetic simulation effect through data processing;
S2, combining a three-dimensional environment model and a base station antenna radiation model to construct a digital twin body covered by a low-altitude network;
S3, base station deployment planning, configuration optimization and low-altitude network coverage scheme are carried out based on the digital twin body;
the step S3 includes:
s301, using a coverage performance evaluation function to give a measurement result of current coverage quality:
The coverage performance of the three-dimensional space is measured by the following formula:
Where S (x) represents a coverage index of the x position, P s represents an effective signal strength, N 0 represents environmental noise, interference including background noise and other environments, and P b represents interference of other base stations to signals;
s302, combining the digital twin body and the coverage requirement of a user, and designing an optimization algorithm to obtain an optimal base station parameter combination;
The coverage requirements are: providing a target track in a three-dimensional space, and enabling the coverage performance on the track to be optimal by adjusting base station parameters, wherein the base station parameters comprise an antenna angle, initial gain of an antenna and transmission power;
the base station parameters are adjusted based on the digital twin body, the specific adjustment method comprises a reinforcement learning algorithm, and each base station parameter is adjusted by acting in each interaction process of reinforcement learning; the state is the current parameters of each base station; rewarding as track The average value increment of the coverage performance of a plurality of points is divided uniformly;
The targets are as follows:
in the reinforcement learning process, two networks, namely a strategy network and a value network, are adopted for learning:
The system comprises a strategy network, a value network, a target function and a data set, wherein the strategy network is used for interacting with an environment, learning strategies under the guidance of the value function, the value network is responsible for learning a value function by using a data set collected by interaction of the strategy network and the environment, helping the strategy network to update the strategy, and a track report exists in the gradient of the target function and is used for updating the strategy;
Defining a loss function of a cost function in a value network, and updating the value network parameters by a gradient rising method; in each round of interaction, sampling the current strategy, calculating the gradient of the bid value function, updating the value network parameters, and updating the parameters of the strategy network under the guidance of the new value function;
after the multi-round interaction is carried out, when the objective function is not increased any more, the learning is stopped, and the setting parameters of each base station at the moment are recorded, so that the optimized low-altitude network coverage effect on the given track is obtained.
2. The low-altitude network coverage optimization method based on digital twinning according to claim 1, wherein the method comprises the following steps: the step S2 includes:
The base station antenna pattern obtained in the step S101 and the three-dimensional environment model obtained in the step S102 are used for setting base station coordinates and base station parameters thereof according to real data, and simulation is carried out through a ray tracing method, so that shielding or multipath effects caused by buildings, indoor compartments or topography of the base station antenna in a real working environment are simulated, and a low-altitude network coverage digital twin body close to reality is obtained;
And acquiring the effective signal intensity, the environmental noise and the interference of other base stations on the current position signal of each position under the three-dimensional environment model through the simulation of the digital twin body.
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| CN113645641A (en) * | 2021-08-11 | 2021-11-12 | 北京邮电大学 | Method for configuring air-ground user coexisting unmanned aerial vehicle network air antenna parameters |
| CN116546521A (en) * | 2023-04-04 | 2023-08-04 | 中国移动紫金(江苏)创新研究院有限公司 | Mobile communication network coverage optimization method based on digital twin technology |
| CN116723527A (en) * | 2023-08-09 | 2023-09-08 | 香港中文大学（深圳） | Base station power consumption optimization method based on digital twin |
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