CN117320024A - 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 a 4 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 the embodiments 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 timePhi ' =0, performing 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 M z ×M y Dual polarized array antenna, wherein M z Representing the number of antenna elements in the Z-axis direction, M y Representing the number of antenna elements in the Y-axis direction; m is M y ＝8，M z ＝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 is y (θ, φ) and v z (θ, φ) represents the antenna element steering vectors in the Y-axis and Z-axis directions, d y And d z The space between antenna units in the horizontal and vertical directions is lambda is wavelength, and a (theta, phi) is the array guide vector of the whole antenna array;
for dual polarized array antennas, M corresponding to each polarization direction z ×M y The array steering vectors for the individual antenna elements are:
a 1 (θ,φ)＝|f 1 (θ,φ)|a(θ,φ)
a 2 (θ,φ)＝|f 2 (θ,φ)|a(θ,φ)
wherein f 1 (theta, phi) is the gain amplitude of the antenna element in the (theta, phi) direction of the first polarization direction, f 2 (θ, φ) is the gain magnitude of the antenna in the (θ, φ) direction for the second polarization direction, a 1 (θ, φ) and a 2 (θ, φ) is an array steering vector corresponding to 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) T V(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 referred to as an antenna pattern S gain Taking the antenna pattern 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 ((based on Friis Transmission Formula)) is as follows
Wherein R is the distance between Tx-Rx, P R Is the received power, P T Is the transmitting power, the antenna gain S to be measured T Receiving antenna gain S R The method comprises the steps of carrying out a first treatment on the surface of the Measuring distance R between Tx-Rx, receiving antenna gain S R Received power P R Transmit power P T The antenna gain S is calculated by these four calculations T These four quantities are also sources of error, including in particular:
first, a polar coordinate model centered on the base station antenna is constructed, e.gAs shown in fig. 4, the five-pointed star is a base station, the circle is a receiving antenna, wherein the horizontal plane is a direction plane, and the north direction is a direction angle of 0 degrees; 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 angleInclination angle theta 1 And relative distance R to represent
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 angleInclination angle theta 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 vehicle->Inclination angle theta 1 Direction angle of base station relative to unmanned aerial vehicle receiving antenna +.>Inclination angle theta 2 And the relative distance R between the two;
the gain unit of the antenna is dB, the power unit is dBm, and P is T Transmitting signal power for the base station;for base station antenna->Gain in the direction; />Is the path loss; />Is a unmanned aerial vehicle receiving antenna>Gain in the direction; w (θ) 1 D) is the tilt angle θ at the base station 1 Noise at distance R.
To measure the gain S of the base station antenna in all directions T To form a pattern S of the base station antenna gain Hold during measurementInvariable, measure the direction angle of the receiving antenna relative to the base station +.>Inclination angle theta 1 The received signal power under and as far as possible cancel the noise w (θ 1 And d) reducing the error caused by the unmanned aerial vehicle hovering at the same position for multiple measurement, or calibrating and eliminating the error 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 embodiments of the present application, network coverage is measured using a drone
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 present 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:
wherein S (x) represents an overlay index of the x position, P s Representing the effective signal strength, N 0 Representing ambient noise, P b Indicating interference from 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, a downward inclination angle, beam forming and transmitting 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 trackThe 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 (4)
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;
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.
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 S1 includes:
s101, establishing a Cartesian coordinate system and a three-dimensional polar coordinate system, wherein an included angle between a vector of the three-dimensional polar coordinate system and a positive Z-axis direction is formedAs a 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 an azimuth angle phi; placing the MIMO antenna panel in the YOZ plane at this timePhi ' =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:
v y (θ，φ)＝f(M y ，θ，φ，λ，dy)
v z (θ，φ)＝f(M z ，θ，φ，λ，dy)
wherein v is y (θ, φ) and v z (θ, φ) represents the antenna element steering vectors in the Y-axis and Z-axis directions, M y And M z The number of antennas in the Y-axis and Z-axis directions, d y And d z The space between antenna units in the horizontal and vertical directions is lambda is wavelength, and a (theta, phi) is the array guide vector of the whole antenna array; f () is a generic steering vector calculation function;
if the MIMO antenna is a dual polarized array antenna, M corresponds to each polarization direction z ×M y The array steering vectors for the individual antenna elements are:
a 1 (θ，φ)＝|f 1 (θ，φ)|a(θ，φ)
a 2 (θ，φ)＝|f 2 (θ，φ)|a(θ，φ)
wherein f 1 (theta, phi) is the gain amplitude of the antenna element in the (theta, phi) direction of the first polarization direction, f 2 (theta, phi) is the gain amplitude of the antenna element in the (theta, phi) direction of the second polarization direction, a 1 (θ, φ) and a 2 (θ, φ) is an array steering vector corresponding to 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) T V(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 referred to as an antenna pattern S gain Taking the antenna pattern 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.
3. 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.
4. The low-altitude network coverage optimization method based on digital twinning according to claim 1, wherein the method comprises the following steps: 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:
wherein S (x) represents an overlay index of the x position, P s Representing the effective signal strength, N 0 Representing ambient noise, including background noise and interference from other environments, P b Indicating interference of other base stations to the signal;
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 track to reach the best coverage performance by adjusting base station parameters, wherein the base station parameters comprise an antenna angle, a downward inclination angle, an initial gain of an antenna and a transmitting 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 trackThe 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.
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| CN118260972A (en) * | 2024-05-30 | 2024-06-28 | 南京华成微波技术有限公司 | Phased array antenna performance simulation analysis method and device based on digital twin |
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