High-altitude platform station-based antenna array layout and flight trajectory optimization method and device

Abstract

This application provides a method and apparatus for antenna array layout and flight trajectory optimization based on an high-altitude platform station. The method includes: acquiring the current location of a ground user, the current attitude and current flight trajectory location of the high-altitude platform station, and a set of element positions for the antenna array on the high-altitude platform station; calculating the channel state between each element position in the element position set and the ground user based on the ground user's current location, attitude, and flight trajectory location; solving the antenna layout optimization objective function based on the current flight trajectory location, the element position set, the channel state between each element position and the ground user, the number of ground users, and the number of target element positions, to obtain the optimal antenna array layout result; and using a particle swarm optimization algorithm to solve the flight trajectory optimization objective function for the high-altitude platform station, to obtain the optimal flight trajectory result. This application can stably improve the system's channel capacity and communication performance.

Description

Technical Field

[0001] This application relates to the field of communication technology, and in particular to a method and apparatus for antenna array layout and flight trajectory optimization based on a high-altitude platform station. Background Technology

[0002] High Altitude Platform Stations (HAPS) are a communication technology that utilizes a flight platform located in the stratosphere as an aerial communication node to provide wide-area wireless coverage for ground users. They feature large coverage areas, good line-of-sight propagation, and flexible deployment, making them suitable for remote area communication, emergency communication, and integrated air-space-ground network scenarios. However, due to the high altitude and continuous movement of the platform, changes in its flight trajectory and attitude, as well as the non-uniform spatial distribution of ground users, all significantly affect communication performance. Summary of the Invention

[0003] In view of this, the purpose of this application is to propose a method and apparatus for antenna array layout and flight trajectory optimization based on a high-altitude platform station.

[0004] To achieve the above objectives, embodiments of this application provide a method for antenna array layout and flight trajectory optimization based on a high-altitude platform station, including:

[0005] The system acquires the current location of the ground user, the current attitude and current flight trajectory of the high-altitude platform station, and the set of array element positions of the antenna array on the high-altitude platform station. Based on the current location of the ground user, the current attitude of the high-altitude platform station, and the current flight trajectory location, calculate the channel state between each array element location in the array element location set and the ground user; Based on the current flight trajectory position, according to the array element position set, the channel state between each array element position and the ground user, the number of ground users, and the number of target array element positions, the pre-constructed antenna layout optimization objective function is solved to obtain the optimal antenna array layout result for the current round. Based on the optimal antenna array layout, the particle swarm optimization algorithm is used to solve the pre-constructed objective function for optimizing the flight trajectory of the high-altitude platform station, thereby obtaining the optimal flight trajectory result for the current round.

[0006] Optionally, the method further includes: Based on the optimal flight trajectory results, the objective function for antenna layout optimization is solved to obtain the optimal antenna array layout results for the next round. Based on the optimal antenna array layout results for the next round, the particle swarm optimization algorithm is used to solve the objective function for optimizing the flight trajectory of the high-altitude platform station, thereby obtaining the optimal flight trajectory results for the next round.

[0007] Optionally, based on the current location of the ground user, the current attitude of the high-altitude platform station, and the current flight trajectory location, the channel state between each element location in the element location set and the ground user is calculated, including: Determine the rotation matrix based on the current posture; Based on the current flight trajectory position, preset deviation, rotation matrix, and array element position, calculate the actual spatial position of the array element. Based on the actual spatial location and the current location of the ground user, calculate the channel state between the array element location and the ground user.

[0008] Optionally, the antenna layout optimization objective function is constructed under the condition of knowing the flight trajectory of the high-altitude platform station, under multiple sampling time slots, with the goal of maximizing the average total channel capacity of the system, and with the physical characteristics of the antenna array layout as constraints.

[0009] Optionally, the objective function for antenna layout optimization is: (8) in, In order to be in Average total system channel capacity within each sampling time slot The number of array element positions. The number of target array element positions. For the number of ground users, It is an identity matrix, and its size is equal to , The signal-to-noise ratio at the transmitting end. u k For the position of the k-th array element, d min Let A be the minimum physical distance between two array element positions, and let A be the physical area on the high-altitude platform station where the antenna array is installed. s n For the first n The selection vector for each array element position. In the sampling time slot , No. Individual position Channel state vector between the user and the ground user for The conjugate transpose of .

[0010] Optionally, solving the pre-constructed antenna layout optimization objective function to obtain the optimal antenna array layout result for the current round includes: The optimization variables of the antenna layout optimization objective function are convexly relaxed into a continuous weight value vector to obtain the convexly relaxed optimization objective function. Solve the optimization objective function after convex relaxation to obtain the weight values ​​of all array element positions; Sort the weight values ​​of all array element positions in descending order, and select the number of array element positions with the largest weight values ​​from the sorted weight values. The selected array element positions are subjected to a predetermined number of local exchange optimization processes to obtain optimized array element positions, which constitute the optimal antenna array layout result.

[0011] Optionally, the objective function for optimizing the flight trajectory of the high-altitude platform station is constructed using the optimal antenna array layout as a known condition, under multiple sampling time slots, with the goal of maximizing the average total channel capacity of the system, and constrained by the motion characteristics of the high-altitude platform station.

[0012] Optionally, the objective function for optimizing the flight trajectory of the high-altitude platform station is: (16) in, It is an aerial platform station The nominal flight trajectory position within each sampling time slot This represents the optimal antenna array layout. This represents the maximum displacement of the high-altitude platform station between adjacent sampling time slots.

[0013] Optionally, the particle swarm optimization algorithm is used to solve the pre-constructed objective function for optimizing the flight trajectory of the high-altitude platform station, obtaining the optimal flight trajectory result for the current round, including: Construct a particle swarm, where each particle represents a flight trajectory scheme; In each iteration of the particle swarm optimization algorithm, a particle swarm search is performed, and the merits of the flight trajectory scheme corresponding to each particle are evaluated using the constructed fitness function; wherein, the fitness function corresponds to the average total system channel capacity of multiple sampling time slots; Based on the evaluation results of each particle, record the individual optimal trajectory and the group optimal trajectory; The search speed and position of the particle swarm are updated based on the individual optimal trajectory and the swarm optimal trajectory. When the maximum number of iterations of the particle swarm optimization algorithm is reached, the optimal flight trajectory point sequence is obtained.

[0014] This application also provides an antenna array layout and flight trajectory optimization device based on a high-altitude platform station, including: The acquisition module is used to acquire the current location of the ground user, the current attitude and current flight trajectory of the high-altitude platform station, and the set of array element positions of the antenna array on the high-altitude platform station; The calculation module is used to calculate the channel state between the position of each array element in the array element position set and the ground user based on the current position of the ground user, the current attitude of the high-altitude platform station and the current flight trajectory position. The antenna array optimization module is used to solve a pre-constructed antenna layout optimization objective function based on the current flight trajectory position, the array element position set, the channel state between each array element position and the ground user, the number of ground users, and the number of target array element positions, to obtain the optimal antenna array layout result for the current round. The flight trajectory optimization module is used to solve the pre-constructed flight trajectory optimization objective function of the high-altitude platform station based on the optimal antenna array layout result, and obtain the optimal flight trajectory result for the current round.

[0015] As can be seen from the above description, the antenna array layout and flight trajectory optimization method and apparatus based on a high-altitude platform station provided in this application obtains the current position of the ground user, the current attitude and current flight trajectory position of the high-altitude platform station, and the set of array element positions of the antenna array on the high-altitude platform station. Based on the current position of the ground user, the current attitude and current flight trajectory position of the high-altitude platform station, the channel state between each array element position in the array element position set and the ground user is calculated. Based on the current flight trajectory position, the antenna layout optimization objective function is solved according to the array element position set, the channel state between each array element position and the ground user, the number of ground users, and the number of target array element positions, to obtain the optimal antenna array layout result. Based on the optimal antenna array layout result, the particle swarm optimization algorithm is used to solve the flight trajectory optimization objective function of the high-altitude platform station to obtain the optimal flight trajectory result. This application can stably improve the channel capacity and communication performance of the system under the dynamic operation state of the high-altitude platform station, and improve the resource utilization of the communication system. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the method flow of an embodiment of this application; Figure 2 This is a schematic diagram of the system architecture of an embodiment of this application; Figure 3 This is a schematic diagram of the joint optimization framework of an embodiment of this application; Figure 4 This is a block diagram of the device structure according to an embodiment of this application; Figure 5 This is a block diagram of the electronic device structure according to an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this disclosure clearer, the following detailed description is provided in conjunction with specific embodiments and the accompanying drawings.

[0019] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of this application should have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms "first," "second," and similar terms used in the embodiments of this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are only used to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0020] As described in the background section, high-altitude platform stations experience attitude disturbances such as yaw, pitch, and roll during actual flight. Simultaneously, the spatial distribution of ground users exhibits highly non-uniform characteristics, causing the geometric relationship and channel characteristics of the communication link between the platform station and the ground to be in a dynamic process of change. In related technologies, antenna array layout schemes are generally based on the quasi-stationary assumption of the platform or an ideally uniformly distributed ground user scenario. They do not consider the impact of trajectory changes of the high-altitude platform station on channel conditions during actual flight, nor do they systematically optimize for the non-uniform spatial distribution of ground users. During dynamic changes, the antenna array layout and the platform station's motion path cannot be designed in tandem, resulting in problems such as insufficient channel capacity, large fluctuations in multi-user communication performance, and inefficient system resource utilization.

[0021] In view of this, this application provides an antenna array layout and flight trajectory optimization method based on a high-altitude platform station. By coordinating the optimization of the antenna array layout and the flight trajectory of the platform station to address the dynamic operating status of the high-altitude platform station, the channel capacity and multi-user communication performance can be steadily improved, thereby increasing the resource utilization of the communication system.

[0022] The technical solution of this application will be further described in detail below through specific embodiments.

[0023] like Figure 1As shown in the embodiments of this application, an antenna array layout and flight trajectory optimization method based on a high-altitude platform station is provided, including: S101: Obtain the current location of the ground user, the current attitude and current flight trajectory of the high-altitude platform station, and the set of array element positions of the antenna array on the high-altitude platform station; In this embodiment, the location of the ground user and the current flight trajectory location of the high-altitude platform station can be obtained through positioning units, position sensors, etc., and the current attitude of the high-altitude platform station can be obtained using attitude sensors. The attitude of the high-altitude platform station includes yaw angle, pitch angle and roll angle. The antenna array deployed on the high-altitude platform station consists of several array elements arranged in a grid. Each array element has a corresponding array element position. The array element positions of all array elements can be determined during the deployment stage, and the array element position set is composed of all array element positions.

[0024] In some implementations, a ground user distribution model is constructed based on the acquired locations of ground users, and this model is used to characterize the non-uniformity of user spatial distribution. This model includes hotspot areas representing high-density user groups and randomly distributed sparse users. The three-dimensional coordinates of each user and the user distribution density within the coverage area can be obtained from the model. Using the obtained three-dimensional coordinates of the users as input parameters, the antenna array layout and flight trajectory are coordinated and optimized, enabling the system to adaptively adapt to the non-uniform user spatial distribution and maximize the total system capacity.

[0025] During modeling, the distribution of ground users can be modeled as a mixture probability density function, in which the set of three-dimensional coordinate locations of ground users is denoted as . In a statistical sense, the probability density function of user distribution in two-dimensional / three-dimensional space It can be obtained by weighting the hotspot area distribution term and the sparse user distribution term, and is expressed as: (1) in, Used to simulate the Each user hotspot can be represented by a two-dimensional Gaussian distribution, that is: (2) in, hotspot areas The center coordinates, hotspot areas Coverage range or diffusion radius; subscript Indicates a hotspot. K This indicates the total number of hotspot areas within the service area.

[0026] To simulate randomly scattered individual users, a uniform distribution is often used, i.e., within the service area. Inside , It is the total area of ​​the service region G, and the subscript S indicates sparse or scattered. This represents the proportion of users in hotspot areas to the total number of users in the service area. hotspot areas The normalized weights satisfy .

[0027] S102: Based on the current location of the ground user, the current attitude of the high-altitude platform station, and the current flight trajectory location, calculate the channel state between each array element location in the array element location set and the ground user; In this embodiment, a communication model is constructed between the high-altitude platform station and multiple ground users to describe the channel interaction relationship in scenarios where multiple ground users simultaneously access the high-altitude platform station. This provides a theoretical and modeling foundation for system capacity assessment and overall performance optimization. By modeling the flight altitude, trajectory, and attitude changes such as yaw, pitch, and roll of the high-altitude platform station, the communication system can adapt to the dynamic operating conditions of the platform station in a large airspace. Specifically: In downlink line-of-sight multiple-input multiple-output (LoS-MIMO) communication scenarios where high-altitude platform stations serve ground users, the high-altitude platform stations are deployed at an altitude of [missing information]. In the stratosphere, the airborne transmitting antenna array consists of Composed of several target array elements, it can provide ground-based [target array elements]. Each single-antenna user is provided with service. High-altitude platform stations are affected by atmospheric disturbances during flight, resulting in attitude jitter and positional shifts.

[0028] like Figure 2 As shown, to accurately characterize the spatial constraints of the antenna array and the geometric characteristics of the high-altitude platform station, a local coordinate system is established with the aircraft body as the reference. The origin is set at the geometric center of the high-altitude platform station. The axis points along the longitudinal axis of the fuselage in the direction of the flight platform's movement. The axis points along the transverse axis of the fuselage towards the wing. The axis is perpendicular to the plane of the aircraft and points downwards. The local coordinate system is used to describe the layout of the antenna array and the motion of the high-altitude platform station, defining the relative position vectors of the antenna elements within the physical region A of the high-altitude platform station. Establish a global coordinate system (ground reference coordinate system) with a fixed point on the ground as the origin. axis, The axis lies in the horizontal plane. The axis is perpendicular to the horizontal plane and points upwards; the global coordinate system is used to uniformly define the position vector of ground users. The nominal flight path of the high-altitude platform station And the measurement of each physical quantity in absolute space.

[0029] To distinguish the representation of the same physical quantity in different coordinate systems, subscripts can be used for identification. vectors (such as) The symbols () represent descriptions in a local coordinate system. During the dynamic operation of an aerial platform station, the positions of array elements in the local coordinate system typically remain constant. This is achieved using subscripts... vectors (such as) The ) represents the description in the global coordinate system, where the position of the array elements changes dynamically with the displacement and attitude adjustment of the high-altitude platform station.

[0030] Physical area for deploying antenna arrays on high-altitude platform stations Inside, a grid-like deployment is made. There are 1 array elements, each with a corresponding array element position. The set of array element positions is represented as... ,in, For the first The position of each element in the local coordinate system The three-dimensional coordinates in the image.

[0031] In some embodiments, based on the current location of the ground user, the current attitude of the high-altitude platform station, and the current flight trajectory location, the channel state between each element location in the element location set and the ground user is calculated, including: Determine the rotation matrix based on the current orientation; Calculate the actual spatial position of the array element based on the current flight trajectory position, preset deviation, rotation matrix, and array element position; Calculate the channel state between the array element location and the ground user based on the actual spatial location and the current location of the ground user.

[0032] Specifically: in any sampling time slot t The instantaneous attitude of the high-altitude platform station can be determined by the yaw angle. Pitch angle and roll angle Characterization, and the resulting rotation matrix for: (3) High-altitude platform station sampling time slot t The nominal flight path position is Considering that the center of the high-altitude platform station is affected by factors such as stratospheric airflow disturbances and dwell control errors, its position relative to the nominal flight trajectory is... Instantaneous three-dimensional position deviation will occur This deviation can be modeled as a zero-mean Gaussian random process.

[0033] The complete transformation of antenna element positions from the local coordinate system to the global coordinate system involves rotation, translation, and perturbation correction. (During the sampling time slot...) , No. The actual spatial position of each element in the global coordinate system for: (4) in, This is the real-time position vector of the center of the high-altitude platform station in the global coordinate system; This is a rotation matrix used to align local axes with global axes; To model the instantaneous three-dimensional position deviation as a zero-mean Gaussian process, used to compensate for nondeterministic displacements such as airflow disturbances.

[0034] Therefore, the first The position of each element and its three-dimensional coordinate position in the global coordinate system are: The The line-of-sight channel gain between ground users is: (5) in, For the first The position of each element to the first The distance between ground users For carrier wavelength, It is a free space path loss model.

[0035] According to formula (5), the sampling time slot can be obtained , No. Individual element position and The set of line-of-sight channel gains among ground users constitutes a dimension of Channel state vector , .

[0036] S103: Based on the current flight trajectory position, according to the array element position set, the channel state between each array element position and the ground user, the number of ground users, and the number of target array element positions, solve the pre-constructed antenna layout optimization objective function to obtain the optimal antenna array layout result for the current round. S104: Based on the optimal antenna array layout, the particle swarm optimization algorithm is used to solve the pre-constructed objective function for optimizing the flight trajectory of the high-altitude platform station, and the optimal flight trajectory result for the current round is obtained.

[0037] In this embodiment, based on the constructed communication model, a joint optimization objective is established to optimize the antenna array layout and the platform station's flight trajectory, while satisfying the antenna array layout constraints and platform station kinematic constraints. Regarding antenna array layout optimization, the element positions of the transmitting antenna array are optimized. By selecting a subset of target element positions corresponding to the target element from the set of element positions, the spatial correlation between elements is reduced, thereby alleviating the channel matrix rank deficit and improving multi-user communication performance. Regarding flight trajectory optimization, the motion path of the high-altitude platform station within the service area is adaptively optimized, allowing the platform station's flight trajectory to be adjusted according to the distribution characteristics of ground users. This improves the geometric relationship between the platform station and ground users and reduces fluctuations in system capacity performance over time.

[0038] In some embodiments, during the joint optimization process, a subset of the element positions of the target array element is selected. The set of nominal flight trajectory points of the platform station This maximizes the average system channel capacity across multiple time slots. Let... To define the maximum permissible displacement of the high-altitude platform station between adjacent sampling time slots, the joint optimization objective function is defined as follows: (6) in, Indicates that the system is in Average total system channel capacity within each sampling time slot The total number of ground users, It is an identity matrix, and its size is equal to , This indicates the signal-to-noise ratio at the system's transmitting end (i.e., the high-altitude platform station). Indicates in time slot , No. The position points of each candidate array element Channel state vector between the user and the ground user yes The conjugate transpose of the channel state vector The value depends directly on the position of the launching array element. Global location of each user Distance between The objective function is optimized by adjusting the vectors through changes in the antenna array element layout and the flight trajectory of the high-altitude platform station. The structure maximizes the total channel capacity of the system.

[0039] Constraints C1 and C2 define the sparse selection characteristics of the antenna array layout, ensuring that from Precisely select from the positions of individual elements The target element positions are used as transmitting antenna elements. Constraint C3 requires that all selected element positions must fall within the physical area A of the high-altitude platform station. Constraint C4 specifies the minimum physical distance between any two elements (the nth and kth elements). This is used to suppress the mutual coupling effect between antenna array elements and ensure the array's feasibility. Constraint C5 is a kinematic constraint term for the high-altitude platform station, used to limit the spatial displacement of the platform station's flight trajectory points between adjacent time slots (the (t+1)th time slot and the tth time slot) to not exceed the maximum allowable displacement. It meets the actual flight maneuverability limitations of high-altitude platform stations.

[0040] like Figure 3 As shown, in this embodiment, the joint optimization problem is decomposed into an antenna array layout optimization sub-problem A and a platform station flight trajectory optimization sub-problem B. By breaking down the complex non-convex joint optimization problem into multiple sub-problems that can be solved alternately, convex optimization and heuristic intelligent optimization algorithms are used for iterative updates, enabling the system to achieve stable convergence under multiple constraints, thereby improving the overall communication performance and multi-user service quality.

[0041] Specifically, sub-problem A mainly addresses the attitude deviations (including yaw, pitch, and roll) and center position shifts of high-altitude platform stations that may be caused by airflow disturbances during flight. Based on sampling multiple discrete time-slot channel matrices along the platform station's flight trajectory, an optimization algorithm combining convex relaxation and heuristic weight rounding is used to select the optimal target element positions from the set of array element positions under a fixed flight trajectory. This determines the optimal non-uniform transmit antenna array layout under a given antenna number constraint. By optimizing the spatial layout of the antenna array, the transmit antenna array can be matched with the distribution of ground users and the multi-time-slot channel conditions, thereby maximizing the total system capacity within the coverage area.

[0042] Subproblem B mainly addresses the non-uniform spatial distribution characteristics of ground users. Based on the kinematic constraints of the platform station and the determined spatial layout of the antenna array, the particle swarm optimization algorithm is used to adaptively plan and finely adjust the flight path of the platform station, determine the nominal flight trajectory point sequence of the platform station within the service period, and then determine the flight trajectory of the platform station. This enables the high-altitude platform station to achieve more efficient coverage and channel sampling in densely populated user areas, further improving the overall communication performance and system channel capacity.

[0043] For the two sub-problems, the antenna array layout and the platform station's flight trajectory are optimized collaboratively through iterative solutions. In each iteration, the platform station's current flight trajectory point sequence is used as input for sub-problem A. Solving sub-problem A yields the optimal antenna array layout for the current flight trajectory position. Then, the optimal antenna array layout is used as input for sub-problem B. Solving sub-problem B re-plans the platform station's flight trajectory to adapt to the spatial distribution characteristics of ground users. Through a closed-loop iterative solution with mutually input parameters, the systems continuously correct and approximate each other. The antenna array optimization results from the first stage guide the flight trajectory search direction in the second stage, until the system's multi-timeslot average channel capacity gain stabilizes or reaches the preset maximum number of optimization iterations. This joint optimization mechanism effectively mitigates channel fluctuations caused by attitude disturbances during dynamic flight, while fully considering the non-uniform spatial distribution characteristics of ground users, thus effectively adapting to the platform station's dynamic operation.

[0044] In some embodiments, the objective function for antenna layout optimization is constructed under the condition of knowing the flight trajectory of the high-altitude platform station and under multiple sampling time slots, with the goal of maximizing the average total channel capacity of the system and the physical characteristics of the antenna array layout as constraints.

[0045] In this embodiment, for sub-problem A, the optimization objective is to optimize from the following: Select from the set of array element positions for each array element position The positions of the target array elements are used as the transmitting antenna array, so that even in the presence of attitude perturbations... Maximize the average system channel capacity within each sampling time slot. Introduce a binary selection vector. ,in, Indicates that the first one is selected. Each array element position, s n =0 indicates that the first option is not selected. The positions of the array elements. Define the first... The full channel matrix corresponding to all element positions in each sampling time slot is: Then, the instantaneous channel capacity corresponding to the actual deployed transmit antenna array can be expressed as a selection variable. Functions: (7) Where, diag(s) is used to construct a dimension of Choose vectors in binary form A diagonal matrix with elements on the main diagonal.

[0046] Combining the minimum spacing constraint of antenna elements and the size constraint of the antenna array, the objective function for antenna layout optimization is constructed as follows: (8) In some embodiments, a pre-built antenna layout optimization objective function is solved to obtain the optimal antenna array layout result for the current round, including: The optimization variables of the antenna layout optimization objective function are convexly relaxed into a continuous weight value vector, resulting in the convexly relaxed optimization objective function. Solve the optimization objective function after convex relaxation to obtain the weight values ​​of all array element positions; Sort all array element positions in descending order of their weight values, and select the number of array element positions with the largest weight values ​​from the sorted weight values. The selected array element positions are subjected to a predetermined number of local exchange optimization processes to obtain optimized array element positions, which constitute the optimal antenna array layout result.

[0047] In this embodiment, an optimization algorithm combining convex relaxation and heuristic weight rounding is used to solve the antenna array layout optimization problem. Specifically, the original optimization problem with binary constraints is addressed by... Optimization variables Relaxation is a continuous weight distribution vector. ,in Weight Indicates the first The importance of individual element positions in improving the average channel capacity of the system. Utilizing The concavity of a function over a set of positive definite matrices allows us to construct the convexly relaxed objective function into a concave form, thus transforming the overall optimization problem into an efficiently solvable convex optimization problem. The convexly relaxed objective function is: (9) The objective function is a convex optimization model that can be solved efficiently. It is solved using an iterative search mechanism based on the primal-dual interior point method. Its core is to use the second derivative information of the objective function to guide the search path to approach the global optimum.

[0048] To achieve optimal average performance in dynamic environments with attitude perturbations, time slots are defined. The system's effective channel covariance matrix at the location for: (10) in, The dimension is equal to the total number of ground users. The identity matrix; In the global coordinate system, the first The location of each array element and all ground users in the time slot The dimension is The complex channel vector; This represents the conjugate transpose operation of a matrix.

[0049] By introducing a logarithmic barrier function By mapping inequality constraints to the objective function, an augmented Lagrangian function is constructed. ,in, The barrier parameter is gradually decreasing.

[0050] Using the principle of matrix differentiation, derive the optimization objective function for the first... Partial derivatives of each weight value That is, the gradient vector Elements: (11) in, Covariance matrix The inverse matrix. This gradient value reflects the increase of the first... The marginal gain of the weight of each array element on the multi-slot average system channel capacity.

[0051] To accurately characterize the curvature features within the feasible region, the Hessian matrix is ​​further derived. element expression : (12) The absolute value squared term in this formula reflects the... The and the first Spatial channel correlation among the positions of array elements. In each round of Newton's iteration in the original dual interior-point method, the update step size is determined while satisfying the constraint C1 of the total number of antennas. The system needs to construct and solve the following system of linear equations for the KKT system: (13) in, The total Hessian matrix of the augmented Lagrangian function is derived from the channel capacity Hessian matrix described above. The summation of the second derivative matrix of the obstacle term constitutes the local curvature of the objective function. For dimension A column vector of all 1s, representing equality constraints. The gradient direction. To augment the current total gradient of the Lagrange function with respect to the weight vector, its components are derived from the aforementioned analytical gradient. It is determined together with the first derivative of the obstacle term. These are the updated Lagrange multiplier values ​​corresponding to the equality constraints. By solving this matrix equation, the optimal search direction in the tangent space can be obtained simultaneously. .

[0052] In obtaining search direction Then, the optimal step size is determined using backtracking linear search. The process first initializes a large step size. And preset the shrinkage factor and sufficient decrease coefficient In each iteration of the step-size search, the first step is to verify whether the current step size satisfies the feasible region constraint, that is, to ensure that the updated weights... It strictly conforms to the [0, 1] interval restriction required by constraint C2. If feasibility is satisfied, then it is further examined whether it satisfies the Armijo condition (i.e., the sufficient descent criterion), the mathematical determination formula of which is: (14) in, For the augmented Lagrangian function defined above, Let be the total gradient vector at the current point. This condition ensures that the objective function value changes sufficiently with the search direction, thus preventing algorithm oscillations or slow convergence due to improper step size selection. If any of the above conditions are not met, the step size is updated using a shrinkage factor, i.e., let Then, the constraints are re-verified and the Armijo condition is re-evaluated. This iterative process continues until the optimal step size is obtained that satisfies all physical constraints and significantly improves the objective function value. Then according to Complete the update step of the weight vector in the current round, and when the iteration meets the convergence accuracy... When outputting the optimal continuous weight vector .

[0053] Considering that the hardware system can only deploy antennas at discrete locations, after solving for multiple weight values, the continuous solution must be... Mapping back to the binary solution space. To achieve the binarization mapping, a weight-based heuristic rounding method is used, sorting the multiple weight values ​​obtained from the solution in descending order, i.e. Select the top-ranked value from the sorted weight values. The initial antenna array layout set is formed by selecting the array element positions corresponding to the largest weight value. .

[0054] The above-mentioned rounding method ensures physical feasibility but may deviate from the global optimum. To address this issue, a local swap optimization process is introduced to compensate for the performance loss. Let the currently selected set of array element positions be... (In the initial state, = A test location is selected from the set of unselected array element locations using either traversal or random sampling. Replace the selected set of array element positions with the test position. a certain position in For the array element position sets before and after the replacement, calculate the difference in the multi-slot average total system channel capacity between the two sets. , represented as: (15) in, This represents the multi-slot average total system channel capacity obtained from the currently selected set of array element positions before performing local switching operations; This indicates the position points in the original set. Replace with new test location points Then, the average total channel capacity of the multi-slot system corresponding to the updated array element position set. The average total channel capacity can be calculated using formula (8), based on the current array element position set (before or after replacement), and the corresponding binary selection vector. The selected positions are set to 1, and the unselected positions are set to 0, utilizing known values. Substitute the full channel matrix under each sampling time slot into the formula to sum and average.

[0055] Based on the difference calculation results, if This indicates that the replaced set of array element positions can achieve better channel capacity. The replacement operation is performed, and the transmit antenna layout is updated according to the replaced set of array element positions. If... If the element is not replaced, no replacement is performed. Following the local exchange optimization method described above, a predetermined number of replacement and calculation processes are executed to obtain a determined set of array element positions. This set serves as the optimal non-uniform antenna array layout obtained under fixed flight trajectory conditions during this round of optimization iterations. Understandably, the number of local swaps determines the exploration depth of the discretization search process, requiring a trade-off between performance improvement and computational overhead.

[0056] The optimal antenna array layout was determined in this round of optimization iterations. Following this, the flight trajectory of the platform station was further optimized. During the flight trajectory optimization process, the nominal flight trajectory point sequence of the high-altitude platform station in each sampling time slot was optimized. The geometric distribution characteristics of the channel matrix are dynamically adjusted to maximize the total channel capacity of the multi-slot average system while satisfying kinematic constraints.

[0057] In some embodiments, the objective function for optimizing the flight trajectory of the high-altitude platform station is constructed with the optimal antenna array layout as a known condition, under multiple sampling time slots, with the goal of maximizing the average total channel capacity of the system, and with the motion characteristics of the high-altitude platform station as a constraint.

[0058] Specifically, in the process of optimizing the flight trajectory, the antenna array layout is... As a known constant, the optimization variable simplifies to the platform's flight path. The objective function for subproblem B can be expressed as: (16) Wherein, channel vector Nominal flight path position The highly nonlinear function indirectly adjusts the spatial distance of all antenna elements relative to ground users by changing the center coordinates of the platform station. And channel phase, thereby optimizing the channel capacity of the LoS-MIMO channel matrix.

[0059] In some embodiments, the particle swarm optimization algorithm is used to solve the pre-constructed objective function for optimizing the flight trajectory of the high-altitude platform station, obtaining the optimal flight trajectory result for the current round, including: Construct a particle swarm, where each particle represents a flight trajectory scheme; In each iteration of the particle swarm optimization algorithm, a particle swarm search is performed, and the merits of the flight trajectory scheme corresponding to each particle are evaluated using the constructed fitness function; where the fitness function corresponds to the average total system channel capacity of multiple sampling time slots. Based on the evaluation results of each particle, record the individual optimal trajectory and the group optimal trajectory; The search speed and position of the particle swarm are updated based on the individual optimal trajectory and the swarm optimal trajectory. When the maximum number of iterations of the particle swarm optimization algorithm is reached, the optimal flight trajectory point sequence is obtained.

[0060] In this embodiment, because the objective function for optimizing the flight trajectory has strong non-convexity with respect to the flight trajectory variables and contains cross-time slot step-size coupling constraints, a particle swarm optimization algorithm is used for global search. Specifically, particle encoding and population initialization are performed first, maintaining a scale of... A swarm of particles, where each particle represents a potential complete flight trajectory scheme. In the particle swarm optimization algorithm... In each iteration, a velocity vector is defined to control the search step size and direction of the flight trajectory points in three-dimensional space. ,particle Location Defined as T The cascade of nominal flight trajectory positions in each sampling time slot is represented as: (17) A fitness function corresponding to the average total channel capacity of the multi-slot system is constructed. During the particle swarm search process, the fitness function is used to evaluate the merits of the flight trajectory schemes corresponding to each particle. The fitness function is expressed as: (18) Among them, the contribution matrix The core parameter is the instantaneous distance from the determined target element position to the ground user. , represented as: (19) Based on the evaluation results of the flight trajectory schemes corresponding to each particle using the fitness function, the individual optimal trajectory of each particle is recorded. and the swarm optimal trajectory of the entire particle swarm The particle updates its search state based on its individual optimal trajectory and the group's optimal trajectory, and the velocity update equation is defined as follows: (20) in, It is the inertial weight that decays with the number of particle swarm iterations. As a learning factor, The weights are random. For the first i In the next iteration, the particles k During sampling time slots t speed, For the first i In the next iteration, the particles k During sampling time slots t The individual's optimal trajectory. For the first i In the next iteration, during the sampling time slot t The optimal trajectory of the group, For the first i+ In one iteration, the particle k During sampling time slots t speed, For the first i In the next iteration, the particles k During sampling time slots t The current nominal flight trajectory position vector.

[0061] The position update equation is: (twenty one) in, Indicates the first i During the next iteration, the particles k During sampling time slots t The flight trajectory position vector represents the intermediate position of the particle after the initial position jump according to the velocity update equation and before the kinematic constraint correction is performed.

[0062] In some embodiments, updating the search position of the particle swarm includes: When the updated particle trajectory scheme does not meet the constraint condition of maximum displacement, the position of the trajectory point that does not meet the constraint condition is corrected.

[0063] In this embodiment, due to limitations in the performance of the physical propulsion system, the displacement of the platform station between adjacent time slots must not exceed [a certain value]. For flight trajectory points that violate displacement constraints after the update, a first-order spatial projection is introduced for correction. The instantaneous displacement vector of adjacent time slots is defined as... ,if Then the correction is made according to the following mapping: (twenty two) in, This is the corrected update location.

[0064] Following the above process, when the maximum number of iterations of the particle swarm optimization algorithm is reached, the optimal flight trajectory point sequence for this round of optimization iterations is obtained. This allows for a round of optimization iterations to achieve coordinated optimization of the antenna array layout and flight trajectory.

[0065] Subsequently, the optimal flight trajectory point sequence determined in this round of optimization iterations is used as the known quantity of the flight trajectory in the next round of optimization iterations for sub-problem A, and is used for the antenna array layout optimization in the next round. Following the above process, sub-problem A is solved iteratively, and the antenna array layout optimization result of sub-problem A is used as the known condition for sub-problem B. Sub-problem B is solved, and the flight trajectory optimization result of sub-problem B is used as the known condition for the next round of sub-problem A, until the set maximum number of optimization iterations is reached. The coordinated optimization of antenna array layout and flight trajectory is achieved through alternating iterative solutions.

[0066] The antenna array layout and flight trajectory optimization method based on a high-altitude platform station provided in this application is designed for communication scenarios between a high-altitude platform station and multiple ground users. It establishes a joint optimization problem for antenna array layout and flight trajectory, taking into account factors such as platform station attitude disturbances and uneven distribution of multiple ground users. The complex non-convex joint optimization problem is decomposed into antenna array layout optimization sub-problems and flight trajectory optimization sub-problems. Through iterative solving of these two sub-problems, the coordinated optimization of antenna array layout and platform station flight trajectory is achieved. This method can stably improve the system's channel capacity and communication performance under dynamic operating conditions of the high-altitude platform station, enhance the resource utilization of the communication system, and solve the problems of dynamic changes in link geometry, channel matrix rank deficit, and drastic capacity fluctuations caused by platform station attitude disturbances and uneven user distribution altitudes.

[0067] It should be noted that the method in this embodiment can be executed by a single device, such as a computer or server. The method can also be applied in a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method in this embodiment, and the multiple devices will interact with each other to complete the method described.

[0068] It should be noted that the above description describes specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims may be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] like Figure 4 As shown in the figure, this application provides an antenna array layout and flight trajectory optimization device based on a high-altitude platform station, including: The acquisition module is used to acquire the current location of the ground user, the current attitude and current flight trajectory of the high-altitude platform station, and the set of array element positions of the antenna array on the high-altitude platform station; The calculation module is used to calculate the channel state between the location of each array element in the array element location set and the ground user, based on the current location of the ground user, the current attitude of the high-altitude platform station, and the current flight trajectory location. The antenna array optimization module is used to solve the pre-constructed antenna layout optimization objective function based on the current flight trajectory position, the set of array element positions, the channel state between each array element position and the ground user, the number of ground users, and the number of target array element positions, to obtain the optimal antenna array layout result for the current round. The flight trajectory optimization module is used to solve the pre-constructed flight trajectory optimization objective function of the high-altitude platform station based on the optimal antenna array layout results, and obtain the optimal flight trajectory result for the current round.

[0070] For ease of description, the above devices are described in terms of function, divided into various modules. Of course, in implementing the embodiments of this application, the functions of each module can be implemented in one or more software and / or hardware.

[0071] The apparatus described above is used to implement the corresponding methods in the foregoing embodiments and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0072] Figure 5 This embodiment illustrates a more specific hardware structure of an electronic device. The device may include a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, memory 1020, input / output interface 1030, and communication interface 1040 are interconnected internally via the bus 1050.

[0073] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0074] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0075] The input / output interface 1030 is used to connect input / output modules to realize information input and output. Input / output modules can be configured as components within the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Input devices may include keyboards, mice, touchscreens, microphones, various sensors, etc., while output devices may include displays, speakers, vibrators, indicator lights, etc.

[0076] The communication interface 1040 is used to connect a communication module (not shown in the figure) to enable communication between this device and other devices. The communication module can communicate via wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0077] Bus 1050 includes a pathway for transmitting information between various components of the device, such as processor 1010, memory 1020, input / output interface 1030, and communication interface 1040.

[0078] It should be noted that although the above-described device only shows the processor 1010, memory 1020, input / output interface 1030, communication interface 1040, and bus 1050, in specific implementations, the device may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the above-described device may only include the components necessary for implementing the embodiments of this specification, and not necessarily all the components shown in the figures.

[0079] The electronic devices described above are used to implement the corresponding methods in the foregoing embodiments and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0080] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0081] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this disclosure (including the claims) is limited to these examples; within the framework of this disclosure, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0082] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this disclosure, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0083] Although this disclosure has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0084] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this disclosure.

Claims

1. A method for antenna array layout and flight trajectory optimization based on a high-altitude platform station, characterized in that, include: The system acquires the current location of the ground user, the current attitude and current flight trajectory of the high-altitude platform station, and the set of array element positions of the antenna array on the high-altitude platform station. Based on the current location of the ground user, the current attitude of the high-altitude platform station, and the current flight trajectory location, calculate the channel state between each array element location in the array element location set and the ground user; Based on the current flight trajectory position, according to the array element position set, the channel state between each array element position and the ground user, the number of ground users, and the number of target array element positions, the pre-constructed antenna layout optimization objective function is solved to obtain the optimal antenna array layout result for the current round. Based on the optimal antenna array layout, the particle swarm optimization algorithm is used to solve the pre-constructed objective function for optimizing the flight trajectory of the high-altitude platform station, thereby obtaining the optimal flight trajectory result for the current round.

2. The method according to claim 1, characterized in that, Also includes: Based on the optimal flight trajectory results, the objective function for antenna layout optimization is solved to obtain the optimal antenna array layout results for the next round. Based on the optimal antenna array layout results for the next round, the particle swarm optimization algorithm is used to solve the objective function for optimizing the flight trajectory of the high-altitude platform station, thereby obtaining the optimal flight trajectory results for the next round.

3. The method according to claim 1, characterized in that, Based on the current location of the ground user, the current attitude of the high-altitude platform station, and the current flight trajectory location, the channel state between each element location in the array element location set and the ground user is calculated, including: Determine the rotation matrix based on the current posture; Based on the current flight trajectory position, preset deviation, rotation matrix, and array element position, calculate the actual spatial position of the array element. Based on the actual spatial location and the current location of the ground user, calculate the channel state between the array element location and the ground user.

4. The method according to claim 1, characterized in that, The antenna layout optimization objective function is constructed under the condition of knowing the flight trajectory of the high-altitude platform station and under multiple sampling time slots, with the goal of maximizing the average total channel capacity of the system and the physical characteristics of the antenna array layout as constraints.

5. The method according to claim 4, characterized in that, The objective function for antenna layout optimization is: （8） in, In order to be in Average total system channel capacity within each sampling time slot The number of array element positions. The number of target array element positions. For the number of ground users, It is an identity matrix, and its size is equal to , The signal-to-noise ratio at the transmitting end. u k For the position of the k-th array element, d min Let A be the minimum physical distance between the two array elements, and let A be the physical area on the high-altitude platform where the antenna array is installed. s n For the first n The selection vector for each array element position. In the sampling time slot , No. Individual position Channel state vector between the user and the ground user for The conjugate transpose of .

6. The method according to claim 5, characterized in that, Solving the pre-constructed antenna layout optimization objective function yields the optimal antenna array layout result for the current round, including: The optimization variables of the antenna layout optimization objective function are convexly relaxed into a continuous weight value vector to obtain the convexly relaxed optimization objective function. Solve the optimization objective function after convex relaxation to obtain the weight values ​​of all array element positions; Sort the weight values ​​of all array element positions in descending order, and select the number of array element positions with the largest weight values ​​from the sorted weight values. The selected array element positions are subjected to a predetermined number of local exchange optimization processes to obtain optimized array element positions, which constitute the optimal antenna array layout result.

7. The method according to claim 6, characterized in that, The objective function for optimizing the flight trajectory of the high-altitude platform station is constructed using the optimal antenna array layout as a known condition, under multiple sampling time slots, with the goal of maximizing the average total channel capacity of the system, and constrained by the motion characteristics of the high-altitude platform station.

8. The method according to claim 7, characterized in that, The objective function for optimizing the flight trajectory of the high-altitude platform station is: （16） in, It is an aerial platform station The nominal flight trajectory position within each sampling time slot This represents the optimal antenna array layout. This represents the maximum displacement of the high-altitude platform station between adjacent sampling time slots.

9. The method according to claim 8, characterized in that, The particle swarm optimization algorithm is used to solve the pre-constructed objective function for optimizing the flight trajectory of the high-altitude platform station, obtaining the optimal flight trajectory result for the current round, including: Construct a particle swarm, where each particle represents a flight trajectory scheme; In each iteration of the particle swarm optimization algorithm, a particle swarm search is performed, and the merits of the flight trajectory scheme corresponding to each particle are evaluated using the constructed fitness function; wherein, the fitness function corresponds to the average total system channel capacity of multiple sampling time slots; Based on the evaluation results of each particle, record the individual optimal trajectory and the group optimal trajectory; The search speed and position of the particle swarm are updated based on the individual optimal trajectory and the swarm optimal trajectory. When the maximum number of iterations of the particle swarm optimization algorithm is reached, the optimal flight trajectory point sequence is obtained.

10. An antenna array layout and flight trajectory optimization device based on a high-altitude platform station, characterized in that, include: The acquisition module is used to acquire the current location of the ground user, the current attitude and current flight trajectory of the high-altitude platform station, and the set of array element positions of the antenna array on the high-altitude platform station; The calculation module is used to calculate the channel state between the position of each array element in the array element position set and the ground user based on the current position of the ground user, the current attitude of the high-altitude platform station and the current flight trajectory position. The antenna array optimization module is used to solve a pre-constructed antenna layout optimization objective function based on the current flight trajectory position, the array element position set, the channel state between each array element position and the ground user, the number of ground users, and the number of target array element positions, to obtain the optimal antenna array layout result for the current round. The flight trajectory optimization module is used to solve the pre-constructed flight trajectory optimization objective function of the high-altitude platform station based on the optimal antenna array layout result, and obtain the optimal flight trajectory result for the current round.

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