Flight status information enabled uav communication method

Abstract

The application discloses a kind of flight state information enabled unmanned aerial vehicle communication methods, comprising: determining the flight state information related to unmanned aerial vehicle communication, including the position of unmanned aerial vehicle, linear velocity, attitude, angular velocity and its time evolution;Determine the space representation framework for representing the spatial relationship between unmanned aerial vehicle and other communication entities, the space representation framework includes coordinate system set and the conversion relationship between coordinate system;Under the space representation framework, the unified space representation of the flight state information is constructed, and the flight state information model is established;Based on the flight state information model, the association between flight state information and channel state information is constructed, and the fusion channel model of fusion flight state information is established;The flight state information model and the fusion channel model are applied to unmanned aerial vehicle communication processing.The application can accurately depict the time-varying characteristics of unmanned aerial vehicle communication channel, and improve the reliability and efficiency of communication.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a communication method for unmanned aerial vehicles that enables flight status information. Background Technology

[0002] With the development of the low-altitude economy, unmanned aerial vehicles (UAVs), due to their high maneuverability, flexible deployment, and low cost, have been gradually applied in various fields such as industrial inspection, emergency communication, logistics distribution, and security monitoring. UAV communication is also gradually becoming one of the important application scenarios in 5G and 6G mobile communication enhancement. Unlike traditional ground communication equipment, UAVs are usually in motion during flight, and their position, speed, and attitude change constantly over time, resulting in significant time-varying and non-stationary characteristics in UAV communication channels. At the same time, UAV communication links are susceptible to factors such as attitude changes, changes in line-of-sight conditions, and the Doppler effect, posing significant challenges to maintaining stable communication connections and ensuring high-quality data transmission.

[0003] In existing research, earlier works mostly employed trajectory optimization methods to jointly design UAV flight paths and communication resource allocation schemes to improve channel conditions and enhance communication performance. However, in mission-driven application scenarios, the flight path of an UAV is usually determined by the requirements of a predetermined task (such as inspection, monitoring, or transportation), and the flight path is often difficult to actively adjust for communication performance. Against this backdrop, some studies have turned to improving communication performance under arbitrary locations, arbitrary trajectories, and diverse services through communication-side optimization methods such as spectrum allocation and power optimization without adjusting the flight path.

[0004] However, the aforementioned schemes neglect the significant impact of the UAV's position, velocity, attitude, and their time-varying characteristics on communication performance. Although a few studies have attempted to consider the influence of flight state on UAV communication, they only cover a portion of flight states and lack a unified definition and representation of flight state information oriented towards communication needs, nor have they established a unified model capable of characterizing flight state information and its temporal evolution. Furthermore, the application of flight state information in UAV communication remains insufficient, leaving room for further expansion and deepening. Summary of the Invention

[0005] Purpose of the invention: The present invention aims to provide a communication method for unmanned aerial vehicles (UAVs) enabled by flight status information, in order to solve the problems in existing UAV communication methods, such as inaccurate communication channel modeling and insufficient characterization of time-varying characteristics due to insufficient and systematic utilization of flight status information.

[0006] Technical solution: To achieve the above-mentioned objective, the unmanned aerial vehicle communication method enabling flight status information provided by this invention includes the following steps:

[0007] Determine flight status information related to communication with the unmanned aerial vehicle (UAV), including the UAV's position, linear velocity, attitude, angular velocity, and their time evolution;

[0008] A spatial representation framework is determined to characterize the spatial relationship between unmanned aerial vehicles and other communication entities. The spatial representation framework includes a set of coordinate systems and transformation relationships between coordinate systems.

[0009] Within the aforementioned spatial representation framework, a unified spatial representation of the flight status information is constructed, and a flight status information model is established.

[0010] Based on the flight state information model, the correlation between flight state information and channel state information is constructed, and a fused channel model integrating flight state information is established.

[0011] The flight status information model and the fused channel model are applied to the communication processing of unmanned aerial vehicles.

[0012] Preferably, the spatial representation framework employs a set of coordinate systems to describe the spatial relationships between the unmanned aerial vehicle (UAV) and other communication entities. These other communication entities include one or more of base stations, other UAVs, ground users, and satellites. The set of coordinate systems includes a ground-fixed coordinate system, an UAV navigation coordinate system, an UAV body coordinate system, an UAV antenna array coordinate system, other communication entity navigation coordinate systems, and other communication entity antenna array coordinate systems. Based on flight state information, the coordinate transformation relationships between the coordinate systems in the set are determined. These coordinate transformation relationships include rotation transformations and translation transformations.

[0013] Preferably, within the aforementioned spatial representation framework, the specific steps for constructing a unified spatial representation of the flight state information and establishing a flight state information model are as follows:

[0014] Within the aforementioned spatial representation framework, a reference coordinate system is selected. Based on the transformation relationship between the coordinate systems, the position information, linear velocity information, attitude information, and angular velocity information of the unmanned aerial vehicle (UAV) are consistently mapped from its own coordinate system to the reference coordinate system, forming a unified flight state space representation. The unified flight state space representation includes: the three-dimensional position vector of the UAV reference point in the reference coordinate system, the three-dimensional linear velocity vector, the rotation matrix representing the attitude of the UAV, Euler angles or quaternions, and the three-dimensional angular velocity vector in the reference coordinate system.

[0015] For the antenna array coordinate system of an unmanned aerial vehicle (UAV) or other communication entity, considering the antenna array state information of the UAV body or other communication entity, and combining it with the UAV flight state information, the state information of each antenna element in the antenna array under the spatial representation framework is determined; the antenna array state information includes the tilt angle and rotation angle of the antenna array relative to the UAV body or other communication entity, the geometry of the antenna array, and the radiation characteristics; the state information of the antenna element includes one or more of position information and velocity information.

[0016] Based on the constructed flight state space representation, the temporal evolution of the flight state is modeled to obtain a flight state information model that includes the flight state and its temporal evolution.

[0017] As a preferred approach, the specific steps for modeling the temporal evolution of flight states are as follows:

[0018] Acquire current and historical flight status data of the unmanned aerial vehicle from the flight controller, sensors and / or status monitors, as well as flight control commands to guide flight;

[0019] Based on a unified spatial representation of flight state information, a physical mechanism-based modeling method or a data-driven modeling method is adopted. The physical mechanism-based modeling method includes: determining state variables that have predictive capabilities for flight state data, wherein the state variables include linear acceleration and angular acceleration; further, using a state transition method based on kinematic relationships to determine the time-varying relationship of the unmanned aerial vehicle's flight state and establishing a flight state time evolution model; the data-driven modeling method uses machine learning methods to learn the flight state time evolution model from sample data.

[0020] Preferably, the specific steps for constructing the correlation between flight state information and channel state information are as follows: Based on the flight state information model, using the correlation between each flight state parameter and channel characteristics in the flight state information model, the flight state information space is mapped to the channel state information space, and key channel parameters related to the channel state information are determined. The determination of the key channel parameters includes:

[0021] Using flight status information, other communication entity status information, and antenna array status information, signal propagation path information between the UAV and other communication entities is determined to characterize wireless propagation attenuation / signal propagation delay. The signal propagation path information is characterized by a signal propagation direction vector, which is determined based on the positions of the UAV and other communication entities or the positions of antenna array elements. The signal propagation path information is then substituted into a preset path loss model / propagation delay model to determine the corresponding path loss parameters / propagation delay parameters.

[0022] Using the signal propagation path information, combined with the relevant state information of the unmanned aerial vehicle antenna array and other communication entity antenna arrays, the angle of arrival and transmission angle parameters of the signal are calculated;

[0023] Based on the angle of arrival and angle of transmission parameters, and combined with the power radiation mode that matches the radiation characteristics of the antenna array, the corresponding antenna radiation power gain parameters are calculated.

[0024] Using flight status information, status information of other communication entities, and status information of the antenna array, the relative motion relationship between the unmanned aerial vehicle and the communication entity is determined. Combined with the signal propagation direction vector, the Doppler frequency shift and phase change caused by the relative motion are calculated to obtain the Doppler phase parameters. The Doppler phase parameters include the Doppler phase components caused by the relative translational motion and / or the Doppler phase components caused by the rotational motion.

[0025] The relative position offsets between antenna elements in the antenna array are determined based on the arrival angle parameter and the transmission angle parameter, and the attitude phase parameters are calculated in combination with the antenna array geometry.

[0026] Preferably, the calculation of the signal's angle of arrival and transmission angle parameters includes:

[0027] Using the flight status information of the unmanned aerial vehicle (UAV) and the antenna array status information of the UAV and other communication entities, calculate the unit vectors of the coordinate axes representing the UAV antenna array coordinate system and the antenna array coordinate system of other communication entities in the reference coordinate system.

[0028] The signal propagation direction vector is projected onto the antenna array plane of the unmanned aerial vehicle and the antenna array plane of other communication entities to obtain the signal projection vector of the transmitting end and the signal projection vector of the receiving end.

[0029] Based on the unit vector of the coordinate axes and the signal projection vector, the azimuth transmission angle parameter, elevation transmission angle parameter, azimuth reception angle parameter, and elevation reception angle parameter of the signal are calculated using the inverse cosine function.

[0030] As a preferred option, the specific steps for establishing the fusion channel model are as follows:

[0031] Based on the flight state information model and the obtained key channel parameters, a communication channel model for unmanned aerial vehicles that integrates flight state information is constructed. The channel model includes a line-of-sight component, which is established based on path loss, antenna radiated power gain, propagation delay, Doppler phase, and attitude phase parameters.

[0032] Alternatively, in the presence of scatterers or reflectors, for at least one non-line-of-sight (NLS) propagation path, the spatial relationship of the NLS propagation path is determined based on flight state information, and the corresponding NLS channel components are constructed according to key channel parameters; the NLS channel components and NLS channel components are combined to obtain an unmanned aerial vehicle (UAV) communication channel model that integrates flight state information.

[0033] Preferably, the unmanned aerial vehicle communication processing includes at least one of channel estimation, channel prediction, beamforming, beam and antenna joint design, cooperative transmission, cell handover, and interference suppression.

[0034] Preferably, the method for applying the flight state information model and the fused channel model to unmanned aerial vehicle channel estimation includes the following steps:

[0035] Based on the aforementioned key channel parameters, a deterministic channel parameter determined by flight state information is constructed. The deterministic channel parameter is expressed as the product of the equivalent channel gain consisting of path loss and antenna radiated power gain and a complex exponent, wherein the phase of the complex exponent is composed of Doppler phase, attitude phase, and propagation delay.

[0036] The fusion channel model is constructed as a product of the defined channel parameters and a complex number representing the random channel parameters;

[0037] Using the received pilot signal, the known pilot symbols, and the determined channel parameters, the complex number representing the random channel parameters is estimated using the maximum likelihood estimation method;

[0038] The estimated value of the complex number representing the random channel parameters is multiplied by the determined channel parameters to obtain the final channel estimation result.

[0039] The present invention also provides a computer system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the flight status information enabled unmanned aerial vehicle communication method.

[0040] Beneficial effects: Compared with the prior art, the present invention has at least the following beneficial effects:

[0041] 1. This invention selects key parameters related to communication from the set of flight state parameters of unmanned aerial vehicles to form flight state information. The flight state information includes position, linear velocity, attitude, angular velocity and their temporal evolution, and performs a unified spatial representation of the flight state to establish a flight state information model, providing stable spatiotemporal prior state support for unmanned aerial vehicle communication processing.

[0042] 2. This invention proposes a spatial representation framework for characterizing the spatial relationships between unmanned aerial vehicles (UAVs) and other communication entities, and constructs a spatial mapping relationship for flight state information within this framework. The spatial representation framework includes a set of coordinate systems and their coordinate transformation relationships. In multi-antenna array configuration scenarios, a coordinate system related to the antenna array is further introduced to achieve a unified description of the array / element state within the spatial representation framework.

[0043] 3. This invention constructs the correlation between flight state information and channel state information based on the flight state information model, maps the flight state information to the channel state information space, determines the key channel parameters related to the channel state information (including path loss, propagation delay, angle of arrival / transmission angle, antenna radiated power gain, Doppler phase, attitude phase, etc.), and establishes an unmanned aerial vehicle communication channel model that integrates flight state information. This model can accurately characterize the time-varying characteristics of the channel and overcome the problem of insufficient adaptability of traditional channel models to dynamic scenarios.

[0044] 4. The flight state information model and fusion channel model constructed in this invention can be applied to various communication processing of unmanned aerial vehicles, including channel estimation, channel prediction, beamforming, cooperative transmission, cell handover, interference suppression, etc., thereby improving communication efficiency and reliability without relying on trajectory planning, and enhancing applicability and versatility in mission-driven, restricted trajectory and complex environments. Attached Figure Description

[0045] Figure 1 This is a flowchart illustrating the unmanned aerial vehicle communication method enabling flight status information provided in an embodiment of the present invention.

[0046] Figure 2 This is a schematic diagram of a spatial representation framework provided by an embodiment of the present invention for characterizing the spatial relationship between unmanned aerial vehicles and other communication entities.

[0047] Figure 3 This is a schematic diagram illustrating the correlation between flight status information and channel status information, as well as the determination of key channel parameters, provided in an embodiment of the present invention.

[0048] Figure 4 This is a schematic diagram of the application of unmanned aerial vehicle communication processing based on flight state information model and fusion channel model provided in the embodiments of the present invention.

[0049] Figure 5 These are normalized mean square error curves for different channel estimation methods provided in the embodiments of the present invention.

[0050] Figure 6 These are spectral efficiency curves for different beamforming methods provided in the embodiments of the present invention. Detailed Implementation

[0051] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this specification.

[0052] It should be noted that the steps of the corresponding methods are not necessarily performed in the order shown and described in this specification in other embodiments. In some other embodiments, the methods may include more or fewer steps than described in this specification. Furthermore, a single step described in this specification may be broken down into multiple steps in other embodiments; and multiple steps described in this specification may be combined into a single step in other embodiments.

[0053] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings. The embodiments of the present invention provide a communication method for unmanned aerial vehicles enabled by flight status information. Its core lies in establishing a unified framework to characterize and utilize flight status information to improve communication performance. For example... Figure 1 As shown, the unmanned aerial vehicle communication method enabling flight status information specifically includes:

[0054] Step S1: Determine the flight status information related to communication with the unmanned aerial vehicle (UAV), including the UAV's position, linear velocity, attitude, angular velocity, and their time evolution.

[0055] Step S2: Determine a spatial representation framework for characterizing the spatial relationship between the unmanned aerial vehicle and other communication entities. The spatial representation framework includes a set of coordinate systems and the transformation relationships between the coordinate systems.

[0056] Step S3: Under the spatial representation framework, construct a unified spatial representation of the flight status information and establish a flight status information model;

[0057] Step S4: Based on the flight state information model, construct the correlation between flight state information and channel state information, and establish a fused channel model that integrates flight state information;

[0058] Step S5: Apply the flight status information model and the fused channel model to the unmanned aerial vehicle communication processing.

[0059] In some embodiments of the present invention, step S1, specifically determining the flight status information related to communication with the unmanned aerial vehicle, may include:

[0060] Step S11: Obtain a set of flight state parameters that can reflect the flight characteristics of the unmanned aerial vehicle. The flight state parameters include, but are not limited to, the position parameters, attitude parameters, velocity parameters, acceleration parameters, air pressure parameters, temperature parameters, and fuel balance parameters of the unmanned aerial vehicle.

[0061] Step S12: Classify the flight status parameters into flight status parameters related to UAV communication and flight status parameters unrelated to UAV communication. The flight status parameters unrelated to UAV communication include, but are not limited to, parameters mainly used for the operation of the flight control system.

[0062] Step S13: Select key flight status parameters related to UAV communication from the flight status parameters and determine them as candidate parameters for UAV flight status information.

[0063] Step S14: Based on the key flight state parameters, determine flight state information that can comprehensively cover communication related to the unmanned aerial vehicle. The flight state information includes, but is not limited to, the position, linear velocity, attitude, angular velocity and their time evolution of the unmanned aerial vehicle.

[0064] In one or more embodiments of the present invention, in step S2, the spatial representation framework adopts a set of coordinate systems for describing the spatial relationship between the unmanned aerial vehicle (UAV) and other communication entities. The other communication entities include one or more of base stations, other UAVs, ground users, and satellites. The set of coordinate systems includes a ground-fixed coordinate system, an UAV navigation coordinate system, an UAV body coordinate system, an UAV antenna array coordinate system, other communication entity navigation coordinate systems, and other communication entity antenna array coordinate systems. Based on flight state information, the coordinate transformation relationship between each coordinate system in the set of coordinate systems is determined. The coordinate transformation relationship includes rotation transformation and translation transformation.

[0065] Figure 2 This invention illustrates a spatial representation framework for characterizing the spatial relationship between unmanned aerial vehicles (UAVs) and other communication entities. In this embodiment, the spatial representation framework is implemented using a set of coordinate systems and their coordinate transformation relationships. These other communication entities may include, but are not limited to, base stations, other UAVs, ground user terminals, and satellites. This embodiment uses a base station as an example of an other communication entity for illustration.

[0066] When the spatial representation framework is implemented using a set of coordinate systems and the base station is used as another communication entity, the set of coordinate systems may include, but is not limited to, a ground-fixed coordinate system. Base station navigation coordinate system Unmanned aerial vehicle navigation coordinate system Unmanned aerial vehicle body coordinate system Coordinate system of unmanned aerial vehicle antenna array and the base station antenna array coordinate system The details are as follows:

[0067] (1) Establish the Earth-fixed coordinate system The Earth-fixed coordinate system is used to provide a unified geographical reference. It can optionally take the Earth reference point as the origin or a preset geographical reference point as the origin, and its coordinate axis direction can be set according to geographical coordinate conventions or engineering conventions.

[0068] (2) Establish the base station navigation coordinate system Optionally, a base station reference point (e.g., the base station center) can be used as the origin, and its coordinate axis direction can be aligned with the Earth-fixed coordinate system. Alignment or by Earth-fixed coordinate system Obtained through rotation / translation transformation;

[0069] (3) Establish the navigation coordinate system of the unmanned aerial vehicle Optionally, the origin can be set to the reference point of the unmanned aerial vehicle (e.g., the center of mass or geometric center), and the coordinate axes can be aligned with the Earth-fixed coordinate system. Alignment or by Earth-fixed coordinate system Obtained through rotation / translation transformation;

[0070] (4) Establish the coordinate system of the unmanned aerial vehicle body Optionally, the origin can be taken as the reference point of the unmanned aerial vehicle (UAV), and the direction of its coordinate axes can be defined according to the conventions of the UAV's forward, lateral, and downward coordinate systems, and can be correlated with the UAV's navigation coordinate system. There are attitude-related coordinate transformation relationships between them;

[0071] (5) Establish the coordinate system of the unmanned aerial vehicle antenna array and the base station antenna array coordinate system Optionally, the origin can be taken as the array reference point (e.g., the center of the array panel or the array phase center), and the direction of its coordinate axis can be defined according to the geometric direction of the array panel. There is a coordinate transformation relationship between the coordinate axis and the corresponding body coordinate system / navigation coordinate system, which is determined by the array installation attitude, geometry, etc.

[0072] Based on the above set of coordinate systems, the coordinate transformation relationship between any two coordinate systems can be further determined based on the flight status information to form the spatial mapping relationship described in step S2. The coordinate transformation relationship includes, but is not limited to, rotation transformation and translation transformation.

[0073] In some embodiments of the present invention, step S3, under the spatial representation framework, involves constructing a unified spatial representation of the flight state information. Specific steps for establishing the flight state information model may include:

[0074] Step S31: Under the spatial representation framework, a reference coordinate system is selected. Based on the transformation relationship between the coordinate systems, the position information, linear velocity information, attitude information, and angular velocity information of the unmanned aerial vehicle are uniformly mapped from its own coordinate system to the reference coordinate system to form a unified flight state space representation. The unified flight state space representation includes: the three-dimensional position vector of the unmanned aerial vehicle reference point in the reference coordinate system, the three-dimensional linear velocity vector, the rotation matrix representing the body attitude, Euler angles or quaternions, and the three-dimensional angular velocity vector in the reference coordinate system.

[0075] Step S32: For the antenna array coordinate system of the unmanned aerial vehicle (UAV) or other communication entity, considering the antenna array state information of the UAV body or other communication entity, and combining it with the UAV flight state information, determine the state information of each antenna element in the antenna array under the spatial representation framework; the antenna array state information includes the tilt angle and rotation angle of the antenna array relative to the UAV body or other communication entity, the geometric shape of the antenna array, and the radiation characteristics; the state information of the antenna element includes one or more of position information and velocity information;

[0076] Step S33: Based on the constructed flight state space representation, the time evolution of the flight state is modeled to obtain a flight state information model that includes the flight state and its time evolution.

[0077] For example, through the aforementioned spatial mapping relationship, a unified spatial representation of the flight status information of unmanned aerial vehicles and the status information of other communication entities can be established within the spatial representation framework, as detailed below:

[0078] (1) Unmanned aerial vehicle (UAV) position information: The three-dimensional position of the UAV can be obtained in the Earth-fixed coordinate system. and / or base station navigation coordinate system The Chinese character indicates that they are respectively denoted as and .in can be pass The coordinate transformation is used to obtain the position (e.g., translation followed by rotation), thereby achieving a consistent representation of the position under different coordinate systems.

[0079] (2) Linear velocity information of unmanned aerial vehicles: The three-dimensional translational velocity of unmanned aerial vehicles can be obtained in the Earth-fixed coordinate system. and / or base station navigation coordinate system The Chinese character indicates that they are respectively denoted as and .in can be pass The coordinate transformation is obtained. If the change of the reference frame with time is further considered, the velocity can be uniformized by combining the coordinate system change.

[0080] (3) Unmanned Aerial Vehicle Attitude Information: The attitude of the unmanned aerial vehicle can be obtained from the roll angle. Pitch angle and yaw angle The three Euler angles are represented as follows: A rotation matrix can also be used. The matrix is ​​represented as follows:

[0081]

[0082] Assuming the navigation coordinate system of the unmanned aerial vehicle and the coordinate system of the unmanned aerial vehicle The vectors in are respectively and Then there is a relationship between the two. When necessary, it can also be based on The transformation relationship unifies the attitude to or Please refer to the following description.

[0083] (4) Angular velocity information of unmanned aerial vehicles: The three-dimensional rotational angular velocity of an unmanned aerial vehicle can be expressed as: ,in , and These represent the coordinates of the unmanned aerial vehicle (UAV) around its body. The rotation speeds of the x, y, and z axes can be determined, and consistent representations can be achieved across different coordinate systems through coordinate transformation relationships.

[0084] (5) Time Evolution of Unmanned Aerial Vehicle Flight State: In some embodiments of the present invention, the specific steps for modeling the time evolution of the flight state are as follows: acquiring current and historical flight state data of the unmanned aerial vehicle and flight control commands guiding flight from the flight controller, sensors, and / or state monitor; based on a unified spatial representation of flight state information, adopting a physical mechanism-based modeling method or a data-driven modeling method. The physical mechanism-based modeling method includes: determining state variables that have predictive capabilities for flight state data, wherein the state variables include linear acceleration and angular acceleration; further, using a state transition method based on kinematic relationships to determine the relationship between the flight state of the unmanned aerial vehicle and time, and establishing a flight state time evolution model. The data-driven modeling method uses machine learning methods to learn the flight state time evolution model from sample data. For example, the flight state of the unmanned aerial vehicle from... Time's up The evolution at any given moment can optionally be based on linear acceleration. and angular acceleration Modeling is performed. The linear acceleration and angular acceleration can be... or The following indicates (e.g.) , And, by using coordinate transformation relationships to achieve consistency, the update relationships of position, velocity, attitude, and angular velocity over time can be obtained, for example:

[0085]

[0086]

[0087]

[0088]

[0089] It should be understood that the above is only an exemplary representation method, and the specific evolution model can be implemented by a physical mechanism-based method or a data-driven method. This invention is not limited thereto.

[0090] (6) Position of unmanned aerial vehicle array elements (example): Taking the configuration of a uniform planar array of an unmanned aerial vehicle as an example, first in the coordinate system of the unmanned aerial vehicle antenna array The middle determines the first The relative coordinates of each array element For example, using an antenna panel in shaft and The number of array columns of the axes are respectively and (Total number of antennas) )and and Taking all even numbers as an example, using Indicates the first The position of the root antenna has the following relationship: Then the first The relative coordinates of each array element It can be calculated as: ,in, and The antenna spacing refers to the horizontal and vertical planes, respectively. It should be understood that the above is merely an exemplary representation, and the present invention is not limited thereto. Combined with the array's installation offset relative to the aircraft (e.g., the offset distance of the array center relative to the aircraft reference point and the tilt / rotation angle), the array center in the aircraft coordinate system is obtained. The lower position Thus, the array elements in the machine coordinate system are obtained. The lower position Furthermore, based on attitude transformation and coordinate transformation (or The array elements in the base station navigation coordinate system can be obtained separately. The lower position (or in Earth-fixed coordinate system) The lower position According to rigid body kinematics, if we consider the unmanned aerial vehicle (UAV) and its antenna array as a single rigid body, and the array elements as fixed points on this rigid body, their linear velocities relative to the inertial coordinate system are consistent with the linear velocities of the UAV. Considering both the flight of the UAV and the rotation of the antenna array, we can calculate the linear velocities of the array elements, assuming the antenna array can rotate.

[0091] (7) Base station array element position (example): Taking a uniform planar array configuration of the base station as an example, first in the base station antenna array coordinate system The middle determines the first The relative coordinates of each array element For example, equipped with base stations Uniform array antenna and Taking all even numbers as an example, using Indicates the first The position of the root antenna has the following relationship: Then the first The relative coordinates of each array element It can be calculated as: ,in, and The antenna spacing refers to the horizontal and vertical planes, respectively. It should be understood that the above is merely an exemplary representation, and the present invention is not limited thereto. Furthermore, considering the array's installation offset relative to the base station reference point and the array center in the base station navigation coordinate system... The lower position This allows us to obtain the array elements in the base station navigation coordinate system. The lower position Further processing can be conducted if necessary. The coordinate transformation yields the array elements in the Earth-fixed coordinate system. The lower position In this example, the base station is stationary and the velocity of the array elements is zero. However, if the antenna array can rotate, the velocity of the array elements can be calculated by considering the rotation of the antenna array.

[0092] In some embodiments of the present invention, step S4, specifically the step of constructing the correlation between flight state information and channel state information, may be: based on the flight state information model, utilizing the correlation between each flight state parameter and channel characteristics in the flight state information model, mapping the flight state information space to the channel state information space, and determining the key channel parameters related to the channel state information. The determination of the key channel parameters includes:

[0093] Step S41: Using flight status information, other communication entity status information, and antenna array status information, determine the signal propagation path information between the UAV and other communication entities to characterize wireless propagation attenuation / signal propagation delay. The signal propagation path information is characterized by a signal propagation direction vector, which is determined based on the position of the UAV and other communication entities or the position of antenna array elements. Substitute the signal propagation path information into a preset path loss model / propagation delay model to determine the corresponding path loss parameters / propagation delay parameters.

[0094] Step S42: Using the signal propagation path information and combining it with the relevant state information of the unmanned aerial vehicle antenna array and other communication entity antenna arrays, calculate the signal's angle of arrival and transmission angle parameters;

[0095] Step S43: Based on the angle of arrival and transmission angle parameters, and combined with the power radiation mode that matches the radiation characteristics of the antenna array, calculate the corresponding antenna radiation power gain parameters.

[0096] Step S44: Using flight status information, other communication entity status information and antenna array status information, determine the relative motion relationship between the unmanned aerial vehicle and the communication entity, and combine the signal propagation direction vector to calculate the Doppler frequency shift and phase change caused by the relative motion, and obtain the Doppler phase parameters. The Doppler phase parameters include the Doppler phase components caused by relative translational motion and / or the Doppler phase components caused by rotational motion.

[0097] Step S45: Determine the relative position offset between each antenna element in the antenna array based on the arrival angle parameter and the transmission angle parameter, and calculate the attitude phase parameter in combination with the antenna array geometry.

[0098] Figure 3 This demonstrates the correlation between flight status information and channel status information. Using, but not limited to, flight status information, status information of other communication entities (taking a base station as an example in this embodiment), and antenna array status information, key channel parameters related to the channel status information can be determined. These key channel parameters include, but are not limited to:

[0099] (1) Path loss parameters: from Figure 2 The spatial representation shown illustrates the positions of each antenna element of the unmanned aerial vehicle and each antenna element of the base station in the base station navigation coordinate system. The following indicates and Push the base station number From the root antenna array element to the unmanned aerial vehicle The signal propagation direction vector of the root antenna element over time is expressed as: Furthermore, the path loss model for the communication channel of the unmanned aerial vehicle can be obtained. The following example uses the free space loss model, with the path loss parameters as follows:

[0100]

[0101] in, It is the signal transmission frequency. It is a constant equal to 32.4.

[0102] (2) Propagation delay parameter: Based on the signal propagation direction vector calculated in (1), the signal propagation delay can be calculated. Taking the vacuum case as an example, the signal propagation delay parameter can be calculated as follows: ,in It is the speed of light.

[0103] (3) Angle of Arrival and Transmission Angle Parameters: Based on the signal propagation direction determined by the flight status information, and combined with the relative spatial relationship between the UAV array and other communication entity arrays (taking the base station array as an example), the angle of arrival and transmission angle parameters of the signal are derived. In some embodiments, the calculation of the angle of arrival and transmission angle parameters of the signal includes: using the flight status information of the UAV and the antenna array status information of the UAV and other communication entities, calculating the unit vectors of the coordinate axes representing the coordinate systems of the UAV antenna array and other communication entity antenna arrays in the reference coordinate system; projecting the signal propagation direction vector onto the UAV antenna array plane and the other communication entity antenna array plane respectively to obtain the transmitting end signal projection vector and the receiving end signal projection vector; based on the unit vectors of the coordinate axes and the signal projection vector, calculating the azimuth transmission angle parameter, elevation transmission angle parameter, azimuth receiving angle parameter, and elevation receiving angle parameter of the signal using the inverse cosine function. The following example is a base station transmitting a signal to a UAV. The coordinate systems of the UAV antenna array and the base station antenna array are... The unit vector and normal vector of the axis can be respectively in Figure 2 The coordinate system and coordinate system The Chinese character is represented as:

[0104]

[0105]

[0106] in, and ,in Representing the coordinate system of the base station / UAV shaft and Axial unit vector, These represent the tilt angle and rotation angle of the antenna arrays of the unmanned aerial vehicle and the base station, respectively. Combining this with the signal propagation direction, it can be deduced that... At what moment, the signal azimuth transmission angle parameter and elevation launch angle parameters for:

[0107]

[0108]

[0109] azimuth angle parameter of the signal and pitch receiving angle parameters for:

[0110]

[0111]

[0112] in, and These are the transmitting signal projection vector and the receiving signal projection vector on the antenna panels of the base station and the UAV, respectively, representing the signal propagation direction vector. They can be calculated as follows:

[0113]

[0114]

[0115] (4) Antenna radiated power gain parameters: Based on the radiation characteristics of the antenna array, the antenna radiated power gain parameters are calculated using the angle of arrival / emission angle parameters obtained in (3). The following example uses a half-wavelength dipole antenna, whose power radiation mode is as follows: ,in Substituting the angle of arrival and angle of transmission parameters into the normalization coefficient, the antenna radiated power gain can be obtained as follows:

[0116]

[0117]

[0118] (5) Doppler phase parameters: Based on relative motion relationships (including but not limited to translational motion caused by linear velocity and rotational motion caused by angular velocity) and combined with the signal propagation direction, the Doppler frequency shift and phase change are derived, thus obtaining the Doppler phase parameters. Taking the Doppler phase components caused by relative translational motion and the Doppler phase components caused by rotational motion as examples, based on the signal propagation direction vector... The linear velocity and rotational angular velocity of the unmanned aerial vehicle can be used to obtain the Doppler frequency shift caused by relative translational motion. Doppler frequency shift caused by relative rotational motion for:

[0119]

[0120]

[0121] in, , These are the angular velocity vectors used to represent the three rotational motions: roll, pitch, and yaw. The Doppler phase parameter caused by the relative translational motion is... Doppler frequency shift caused by relative rotational motion for:

[0122]

[0123]

[0124] in, The symbol period.

[0125] (6) Attitude phase parameters: Based on the array geometry, signal propagation direction, and UAV attitude changes and / or array panel mounting attitude changes, the relative phase shift between array elements can be derived and the attitude phase parameters can be obtained. The following example illustrates the attitude phase between array elements at the base station and UAV ends, generated by the flight state. ,and The four attitude phase shift parameters are as follows:

[0126]

[0127]

[0128]

[0129]

[0130] in, These represent the antenna array element numbers of the base station and the unmanned aerial vehicle, respectively. These represent the element spacing of the antenna arrays of the base station and the unmanned aerial vehicle on the horizontal and vertical axes, respectively. The relative position offsets of the base station and each antenna element of the UAV caused by the UAV's flight position and attitude are calculated as follows:

[0131]

[0132]

[0133]

[0134]

[0135] It should be understood that all the examples above are merely illustrative representations, and the present invention is not limited thereto.

[0136] In some embodiments of the present invention, the specific steps for establishing a fused channel model may be: based on the flight state information model and the obtained key channel parameters, constructing an unmanned aerial vehicle communication channel model that integrates flight state information, wherein the channel model includes a line-of-sight component, and the line-of-sight channel component is established based on path loss, antenna radiated power gain, propagation delay, Doppler phase and attitude phase parameters.

[0137] For example, based on the above parameters, the channel model that integrates flight state information can be obtained as follows:

[0138]

[0139] in, , , , and These represent the channel path loss parameter, propagation delay parameter, antenna radiated power gain parameter, Doppler phase parameter, and attitude phase parameter, respectively. These parameters can all be obtained from flight state information using the methods described above. and The two random factors, shadow fading and initial phase, constitute the random channel parameters.

[0140] In some embodiments of the present invention, in the presence of scatterers or reflectors, for at least one non-line-of-sight (NLS) propagation path, the spatial relationship of the NLS propagation path is determined based on flight state information, and corresponding NLS channel components are constructed according to key channel parameters. The NLS channel components and NLS channel components are then combined to obtain an unmanned aerial vehicle (UAV) communication channel model that integrates flight state information. A similar construction and superposition process can be used for NLS paths.

[0141] Figure 4 This paper demonstrates application examples of UAV communication processing based on flight state information models and fused channel models, including but not limited to: channel estimation, channel prediction, beamforming, beam and antenna joint design, cooperative transmission, cell handover, and interference suppression. The communication processing can be implemented in different scenarios where other communication entities are base stations, satellites, or other UAVs, and this invention is not limited thereto. The following example illustrates the application of flight state information-assisted channel estimation.

[0142] In some embodiments of the present invention, the method of applying the flight state information model and the fused channel model to unmanned aerial vehicle channel estimation may include the following steps:

[0143] Step S51: Construct a definite channel parameter determined by the flight state information based on the channel key parameters. The definite channel parameter is expressed as the product of the equivalent channel gain consisting of path loss and antenna radiated power gain and a complex exponent. The phase of the complex exponent is composed of Doppler phase, attitude phase and propagation delay.

[0144] Step S52: Construct the fused channel model as the product of the determined channel parameters and a complex number representing the random channel parameters;

[0145] Step S53: Using the received pilot signal, the known pilot symbols, and the determined channel parameters, the complex number representing the random channel parameters is estimated using the maximum likelihood estimation method.

[0146] Step S54: Multiply the estimated value of the complex number representing the random channel parameters with the determined channel parameters to obtain the final channel estimation result.

[0147] In this embodiment, flight state information and pilot estimation are fused. Real-time flight behavior data, highly correlated with channel dynamics, provided by flight state information, is used to achieve high-precision direct acquisition of key channel parameters. Combined with pilot estimation, all channel state information is obtained, thereby significantly reducing system overhead while maintaining estimation accuracy. Assuming the base station uses OFDM modulation... Pilot signals for channel estimation are transmitted on each subcarrier, and the carrier frequency of these signals is... The subcarrier spacing is Assuming channel information is in The unmanned aerial vehicle remains unchanged within a symbol interval, and the unmanned aerial vehicle is in the... The received signals on each subcarrier are:

[0148]

[0149] in, , , , .and Indicates the base station end The transmitted signal sequence of the root antenna, Indicates the first unmanned aerial vehicle end The received signal sequence of the root antenna, Indicates the length of the signal sequence. , This indicates a mean of 0 and A Gaussian white noise sequence with variance.

[0150] Therefore, the first The root of the unmanned aerial vehicle antenna in the first The carrier received the first The pilot symbols can be derived as follows:

[0151]

[0152] The channel derived from flight status information can be represented as:

[0153]

[0154] in, These represent the equivalent channel gain, Doppler phase, attitude phase, and propagation delay, respectively, which consist of path loss and antenna radiated power gain. Here, the equivalent channel gain includes both path loss and antenna radiated power gain. Figure 3 It is known that these parameters can be obtained from flight status information. If is a complex number representing random channel parameters caused by random factors (such as shadowing fading), then based on flight state information, channel estimation is transformed into estimating random parameters. .make It can be deduced that:

[0155]

[0156] We can obtain, Follow the mean The variance is The Gaussian distribution is then calculated. The joint log-likelihood function is:

[0157]

[0158] in, .

[0159] According to the maximum likelihood criterion The optimal estimate is .make right The derivative is zero, and the optimal estimate is obtained. Therefore, the channel estimation based on flight state information is obtained as follows:

[0160]

[0161] It should be noted that the above application examples are not intended to limit the present invention.

[0162] Figure 5Normalized mean square error curves for different channel estimation methods are presented under an example simulation setting (other communication entities are base stations). As an example, the number of antennas for the base station and the UAV can be optionally set as follows: and The carrier frequency is The carrier spacing is The number of carriers is An OFDM communication system in time-division duplex mode was used. Simulation results show that the flight state information-assisted channel estimation method has better estimation performance than traditional LS channel estimation and MMSE channel estimation. The above parameters are only examples and are not intended to limit the invention.

[0163] In some embodiments of the present invention, the method of applying the flight state information model and the fused channel model to beamforming of an unmanned aerial vehicle may include the following steps:

[0164] Step S61: Based on the estimated channel obtained by using flight state information and pilot estimation in the aforementioned fusion channel model, construct the channel matrix between the UAV and the base station; based on the channel matrix, establish the received signal model of the base station when the UAV sends a signal to the base station. The received signal model is represented as the product of the transmit power, the receive precoding vector, the channel matrix, the transmit precoding vector and the transmit signal, and then superimposed with the noise term after the receive precoding vector is applied.

[0165] Step S62: Determine the signal-to-noise ratio of the received signal based on the aforementioned received signal model, and convert the maximization of spectral efficiency into the maximization of the received signal-to-noise ratio based on the correspondence between spectral efficiency and signal-to-noise ratio.

[0166] Step S63 further transforms maximizing the received signal-to-noise ratio into maximizing the target term composed of the received precoding vector, the channel matrix, and the transmitted precoding vector;

[0167] Step S64: Perform singular value decomposition on the channel matrix, and determine the right singular vector and left singular vector obtained by decomposition as the receive precoding vector and the transmit precoding vector, respectively.

[0168] In this embodiment, it is assumed that the unmanned aerial vehicle sends a signal to the base station. The received signal at the base station can be written as:

[0169]

[0170] in, and These are receiving the precoding vector and sending the precoding vector, respectively. It is a noise vector. This refers to the signal transmission power. The signal-to-noise ratio (SNR) of the received signal can be expressed as:

[0171]

[0172] Since the spectral efficiency equals Therefore, maximizing spectral efficiency is equivalent to maximizing the signal-to-noise ratio of the received signal, that is, equivalent to maximizing Channel using singular value decomposition And order and These are equal to the right singular vector and the left singular vector obtained from the decomposition, respectively.

[0173] Figure 6 It gives the answer to the question of how to deal with Figure 5 The spectral efficiency curves of different beamforming methods are shown under the same example settings. Simulation results demonstrate that the proposed beamforming scheme based on flight state information benefits from more accurate channel estimation. Compared with traditional beamforming methods that only use pilot channel estimation, it improves beam alignment accuracy and achieves higher spectral efficiency, thereby enhancing the efficiency and reliability of UAV communication. The above conclusions are also applicable to scenarios where the communication entity is a satellite or other UAV, and this invention is not limited thereto.

[0174] This invention also provides a computer system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of the method described in any of the foregoing embodiments.

[0175] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0176] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0177] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0178] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of one or more embodiments of this specification. The singular forms “a,” “described,” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0179] It should be understood that although the terms first, second, third, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of one or more embodiments of this specification, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "in response to a determination," or "when," or "in the event of a determination."

[0180] The above description is merely a preferred embodiment of one or more embodiments of this specification and is not intended to limit the scope of one or more embodiments of this specification. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this specification should be included within the protection scope of one or more embodiments of this specification.

Claims

1. A communication method for unmanned aerial vehicles enabled by flight status information, characterized in that, Includes the following steps: Determine flight status information related to communication with the unmanned aerial vehicle (UAV), including the UAV's position, linear velocity, attitude, angular velocity, and their time evolution; A spatial representation framework is determined to characterize the spatial relationship between unmanned aerial vehicles and other communication entities. The spatial representation framework includes a set of coordinate systems and transformation relationships between coordinate systems. Within the aforementioned spatial representation framework, a unified spatial representation of the flight status information is constructed, and a flight status information model is established. Based on the flight state information model, the correlation between flight state information and channel state information is constructed, and a fused channel model integrating flight state information is established. The flight status information model and the fused channel model are applied to the communication processing of unmanned aerial vehicles.

2. The unmanned aerial vehicle communication method enabling flight status information according to claim 1, characterized in that, The spatial representation framework employs a set of coordinate systems to describe the spatial relationships between unmanned aerial vehicles (UAVs) and other communication entities. These other communication entities include one or more of base stations, other UAVs, ground users, and satellites. The set of coordinate systems includes a ground-fixed coordinate system, an UAV navigation coordinate system, an UAV body coordinate system, an UAV antenna array coordinate system, other communication entity navigation coordinate systems, and other communication entity antenna array coordinate systems. Based on flight state information, the coordinate transformation relationships between the coordinate systems in the set are determined. These coordinate transformation relationships include rotation transformations and translation transformations.

3. The unmanned aerial vehicle communication method enabling flight status information according to claim 1, characterized in that, Within the aforementioned spatial representation framework, the specific steps for constructing a unified spatial representation of the flight state information and establishing a flight state information model are as follows: Within the aforementioned spatial representation framework, a reference coordinate system is selected. Based on the transformation relationship between the coordinate systems, the position information, linear velocity information, attitude information, and angular velocity information of the unmanned aerial vehicle (UAV) are consistently mapped from its own coordinate system to the reference coordinate system, forming a unified flight state space representation. The unified flight state space representation includes: the three-dimensional position vector of the UAV reference point in the reference coordinate system, the three-dimensional linear velocity vector, the rotation matrix representing the attitude of the UAV, Euler angles or quaternions, and the three-dimensional angular velocity vector in the reference coordinate system. For the antenna array coordinate system of an unmanned aerial vehicle (UAV) or other communication entity, considering the antenna array state information of the UAV body or other communication entity, and combining it with the UAV flight state information, the state information of each antenna element in the antenna array under the spatial representation framework is determined; the antenna array state information includes the tilt angle and rotation angle of the antenna array relative to the UAV body or other communication entity, the geometry of the antenna array, and the radiation characteristics; the state information of the antenna element includes one or more of position information and velocity information. Based on the constructed flight state space representation, the temporal evolution of the flight state is modeled to obtain a flight state information model that includes the flight state and its temporal evolution.

4. The unmanned aerial vehicle communication method enabling flight status information according to claim 3, characterized in that, The specific steps for modeling the time evolution of flight state are as follows: Acquire current and historical flight status data of the unmanned aerial vehicle from the flight controller, sensors and / or status monitors, as well as flight control commands to guide flight; Based on a unified spatial representation of flight status information, a modeling method based on physical mechanisms or a modeling method based on data-driven approaches is adopted. The physical mechanism-based modeling method includes: determining state variables that have predictive capabilities for flight state data, wherein the state variables include linear acceleration and angular acceleration; further, using a state transition method based on kinematic relationships to determine the relationship between the flight state of the unmanned aerial vehicle and time, and establishing a flight state time evolution model; the data-driven modeling method uses machine learning methods to learn the flight state time evolution model from sample data.

5. The unmanned aerial vehicle communication method enabling flight status information according to claim 1, characterized in that, The specific steps for constructing the correlation between flight state information and channel state information are as follows: Based on the flight state information model, using the correlation between each flight state parameter and channel characteristics in the flight state information model, the flight state information space is mapped to the channel state information space, and key channel parameters related to the channel state information are determined. The determination of the key channel parameters includes: Using flight status information, other communication entity status information, and antenna array status information, signal propagation path information between the UAV and other communication entities is determined to characterize wireless propagation attenuation / signal propagation delay. The signal propagation path information is characterized by a signal propagation direction vector, which is determined based on the positions of the UAV and other communication entities or the positions of antenna array elements. The signal propagation path information is then substituted into a preset path loss model / propagation delay model to determine the corresponding path loss parameters / propagation delay parameters. Using the signal propagation path information, combined with the relevant state information of the unmanned aerial vehicle antenna array and other communication entity antenna arrays, the angle of arrival and transmission angle parameters of the signal are calculated; Based on the angle of arrival and angle of transmission parameters, and combined with the power radiation mode that matches the radiation characteristics of the antenna array, the corresponding antenna radiation power gain parameters are calculated. Using flight status information, status information of other communication entities, and status information of the antenna array, the relative motion relationship between the unmanned aerial vehicle and the communication entity is determined. Combined with the signal propagation direction vector, the Doppler frequency shift and phase change caused by the relative motion are calculated to obtain the Doppler phase parameters. The Doppler phase parameters include the Doppler phase components caused by the relative translational motion and / or the Doppler phase components caused by the rotational motion. The relative position offsets between antenna elements in the antenna array are determined based on the arrival angle parameter and the transmission angle parameter, and the attitude phase parameters are calculated in combination with the antenna array geometry.

6. The unmanned aerial vehicle communication method enabling flight status information according to claim 5, characterized in that, The calculation of the signal's angle of arrival and transmission angle parameters includes: Using the flight status information of the unmanned aerial vehicle (UAV) and the antenna array status information of the UAV and other communication entities, calculate the unit vectors of the coordinate axes representing the UAV antenna array coordinate system and the antenna array coordinate system of other communication entities in the reference coordinate system. The signal propagation direction vector is projected onto the antenna array plane of the unmanned aerial vehicle and the antenna array plane of other communication entities to obtain the signal projection vector of the transmitting end and the signal projection vector of the receiving end. Based on the unit vector of the coordinate axes and the signal projection vector, the azimuth transmission angle parameter, elevation transmission angle parameter, azimuth reception angle parameter, and elevation reception angle parameter of the signal are calculated using the inverse cosine function.

7. The unmanned aerial vehicle communication method enabling flight status information according to claim 5, characterized in that, The specific steps for establishing a fusion channel model are as follows: Based on the flight state information model and the obtained key channel parameters, a communication channel model for unmanned aerial vehicles that integrates flight state information is constructed. The channel model includes a line-of-sight component, which is established based on path loss, antenna radiated power gain, propagation delay, Doppler phase, and attitude phase parameters. Alternatively, in the presence of scatterers or reflectors, for at least one non-line-of-sight (NLS) propagation path, the spatial relationship of the NLS propagation path is determined based on flight state information, and the corresponding NLS channel components are constructed according to key channel parameters; the NLS channel components and NLS channel components are combined to obtain an unmanned aerial vehicle (UAV) communication channel model that integrates flight state information.

8. The unmanned aerial vehicle communication method enabling flight status information according to claim 1, characterized in that, The unmanned aerial vehicle communication processing includes at least one of the following: channel estimation, channel prediction, beamforming, beam and antenna joint design, cooperative transmission, cell handover, and interference suppression.

9. The unmanned aerial vehicle communication method enabling flight status information according to claim 1, characterized in that, The method of applying the flight state information model and the fused channel model to unmanned aerial vehicle channel estimation includes the following steps: Based on the aforementioned key channel parameters, a deterministic channel parameter determined by flight state information is constructed. The deterministic channel parameter is expressed as the product of the equivalent channel gain consisting of path loss and antenna radiated power gain and a complex exponent, wherein the phase of the complex exponent is composed of Doppler phase, attitude phase, and propagation delay. The fusion channel model is constructed as a product of the defined channel parameters and a complex number representing the random channel parameters; Using the received pilot signal, the known pilot symbols, and the determined channel parameters, the complex number representing the random channel parameters is estimated using the maximum likelihood estimation method; The estimated value of the complex number representing the random channel parameters is multiplied by the determined channel parameters to obtain the final channel estimation result.

10. A computer system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is executed by the processor, it implements the steps of the unmanned aerial vehicle communication method enabling flight status information according to any one of claims 1-9.

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