Capacity optimization method and device for communication system of mooring unmanned aerial vehicle and computer equipment

Abstract

The invention relates to a capacity optimization method and device for a communication system of a mooring unmanned aerial vehicle and computer equipment. The method comprises the following steps: establishing a mooring unmanned aerial vehicle communication system model and a mooring unmanned aerial vehicle channel model; generating a tethered unmanned aerial vehicle communication system capacity optimization model taking the beam forming matrix and the fluid antenna position matrix as input and the tethered unmanned aerial vehicle communication system capacity as output; decomposing the mooring unmanned aerial vehicle communication system capacity optimization model into a beam forming matrix optimization model and a fluid antenna position matrix optimization model; and solving the beam forming matrix optimization model and the fluid antenna position matrix optimization model based on a zero-forcing algorithm and a particle swarm algorithm, and inputting a capacity optimization model of the mooring unmanned aerial vehicle communication system to obtain the optimal system capacity of the mooring unmanned aerial vehicle communication system. By adopting the method, the capacity optimization problem of the mooring unmanned aerial vehicle communication system can be solved, and the system capacity is improved.

Description

Technical Field

[0001] The present application relates to the field of wireless communication multi-antenna transmission technology, and in particular to a method, device and computer equipment for optimizing the capacity of a tethered drone communication system. Background Art

[0002] As a high-altitude platform, the tethered drone can hover and operate for a long time through ground power supply and data transmission cables. In traditional communication systems, the position of the antenna is fixed. Compared with traditional antennas with fixed positions, fluid antennas have the unique flexibility to reconfigure their positions within a given space, providing a new degree of freedom (DoF) that can exploit diversity and multiplexing gains.

[0003] Therefore, how to reasonably add fluid antennas to the existing tethered UAV communication system and introduce new degrees of freedom to improve system capacity is an urgent problem to be solved. Summary of the invention

[0004] Based on this, it is necessary to provide a tethered UAV communication system capacity optimization method, device and computer equipment that can use fluid antennas to assist in improving the channel conditions from UAV to users and enhance the system capacity in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for optimizing the capacity of a tethered drone communication system, comprising:

[0006] Step S1, establishing a tethered drone communication system model according to the communication data of the tethered drone and its user, and establishing a tethered drone channel model based on the tethered drone communication system model;

[0007] Step S2, generating a tethered drone communication system capacity optimization model based on the tethered drone communication system model and the tethered drone channel model, with the beamforming matrix and the fluid antenna position matrix as inputs and the tethered drone communication system capacity as output;

[0008] Step S3, decomposing the tethered UAV communication system capacity optimization model into a beamforming matrix optimization model and a fluid antenna position matrix optimization model;

[0009] Step S4, based on the zero forcing algorithm and the particle swarm algorithm, the beamforming matrix optimization model and the fluid antenna position matrix optimization model are solved, and the optimal fluid antenna position matrix and the normalized beamforming matrix obtained by solving the solution are input into the tethered UAV communication system capacity optimization model to obtain the optimal system capacity of the tethered UAV communication system.

[0010] In one embodiment, the tethered UAV communication system model in step S1 includes a signal model received by a ground user k, which is expressed as:

[0011]

[0012] Among them, h k is the channel vector between the tethered UAV and the ground user k, w k is the beamforming vector corresponding to ground user k, x k [n] represents the signal with k-mean value of 0 and normalized power of 1 transmitted to the ground user in the nth time slot. k represents noise; the channel matrix is ​​defined as H = [h1,h2,...,h K ], the beamforming matrix is ​​defined as W = [w1,w2,...,w K ];

[0013] Based on the tethered UAV communication system model, the moving range of the fluid antenna is defined as a rectangular area of ​​a specified size. A two-dimensional Cartesian local coordinate system is introduced, and the expression for the position of the mth fluid antenna in the nth time slot is:

[0014] t m [n] = [x m [n],y m [n]] T

[0015] The tethered drone updates the fluid antenna position according to the ground user position in each time slot. The position of M fluid antennas in the nth time slot can be expressed as the fluid antenna position matrix expression is as follows:

[0016] T=[t1[n]t2[n]...t M [n]]

[0017] where t m [n] is the position of the mth fluid antenna in the nth time slot;

[0018] The tethered UAV channel model in step S1 includes the line-of-sight channel vector h between the tethered UAV and the ground user k k , the expression is as follows:

[0019]

[0020] Where β0 represents the channel power gain at a reference distance of 1 meter, d k represents the distance between the tethered UAV and the ground user k, α k represents the transmission array vector from the tethered UAV to the ground user k.

[0021] In one embodiment, step S2 comprises:

[0022] In the nth time slot, the signal-to-interference-to-noise ratio of the signal received by ground user k is expressed as follows:

[0023]

[0024] where h k is the channel vector between the tethered UAV and the ground user k, w k is the beamforming vector corresponding to ground user k, is the power of Gaussian white noise;

[0025] According to the signal-to-interference-noise ratio of the target user receiving the signal in the specified time slot, a tethered UAV communication system capacity optimization model is formulated with the beamforming matrix and the fluid antenna position matrix as input and the tethered UAV communication system capacity as output. The expression is as follows:

[0026]

[0027] stC1:tr(WW H )≤P max

[0028]

[0029] C3:||t m [n]-t m' [n]||≥D,1≤m≠m'≤M

[0030] Among them, C1 represents the transmission power limit of the tethered drone in each time slot, P max The maximum transmission power of the tethered drone;

[0031] C2 indicates that the movable range of any fluid antenna position is limited to the launch area Inside;

[0032] C3 indicates that the distance between any two fluid antennas is not less than the minimum distance D to avoid coupling effect between antennas, where D is half of the carrier wavelength.

[0033] In one embodiment, step S3 comprises:

[0034] According to the two optimization variables of the tethered UAV communication system capacity optimization model, it is decomposed into a beamforming matrix optimization model and a fluid antenna position matrix optimization model;

[0035] The beamforming matrix and the fluid antenna position matrix are optimized respectively so that the capacity of the tethered UAV communication system is maximized while satisfying the constraints of the tethered UAV communication system capacity optimization model.

[0036] In one embodiment, optimizing the beamforming matrix and the fluid antenna position matrix respectively so that the capacity of the tethered UAV communication system reaches the maximum under the constraint conditions of the tethered UAV communication system capacity optimization model is satisfied includes:

[0037] Given the fluid antenna position matrix, the beamforming matrix that maximizes the capacity of the tethered UAV communication system is solved under the constraint of the tethered UAV's transmission power in each time slot. The expression is as follows:

[0038]

[0039] stC1;

[0040] Under the constraints that the movable range of any fluid antenna position is limited within the launch area and the distance between any two fluid antennas is not less than the target distance, the fluid antenna position matrix that maximizes the capacity of the tethered UAV communication system is solved. The expression is as follows:

[0041]

[0042] stC2,C3.

[0043] In one embodiment, solving the beamforming matrix optimization model and the fluid antenna position matrix optimization model based on the zero forcing algorithm and the particle swarm algorithm includes:

[0044] The beamforming matrix is ​​optimized based on the zero-forcing algorithm to generate the maximum value of the objective function of the beamforming matrix optimization model;

[0045] The fitness value is calculated based on the particle swarm algorithm, and the fluid antenna position matrix is ​​updated and iterated according to the fitness value;

[0046] When the maximum number of iterations is reached, the iteration is stopped, and the optimal fluid antenna position matrix is ​​output. According to the optimal fluid antenna position matrix, a normalized beamforming matrix is ​​obtained based on a zero-forcing algorithm.

[0047] In one embodiment, optimizing the beamforming matrix based on a zero-forcing algorithm and generating a maximum value of an objective function of the beamforming matrix optimization model includes:

[0048] A zero-forcing algorithm is used to generate a zero-forcing beamforming matrix, and the zero-forcing beamforming matrix is ​​normalized;

[0049] Combined with the given fluid antenna position matrix, the maximum value of the objective function of the beamforming matrix optimization model is obtained.

[0050] In one embodiment, calculating the fitness value based on the particle swarm algorithm, and updating the iterative fluid antenna position matrix according to the fitness value includes:

[0051] Initialize multiple particles randomly in the emission area, and set the initial position values ​​and acceleration of the particles based on the constraint that the movable range of any fluid antenna position is limited to the emission area;

[0052] Based on the constraint that the distance between any two fluid antennas is not less than the target distance, a penalty factor is introduced to formulate a fitness function and calculate the fitness value of the particle;

[0053] The particle position and acceleration values ​​are iteratively updated to obtain the individual optimal solution and group optimal solution of the particles.

[0054] In a second aspect, the present application also provides a tethered drone communication system capacity optimization device, comprising:

[0055] A basic model building module is used to build a tethered UAV communication system model based on the communication data of the tethered UAV and its user, and to build a tethered UAV channel model based on the tethered UAV communication system model;

[0056] A system capacity optimization model building module is used to generate a tethered UAV communication system capacity optimization model based on a tethered UAV communication system model and a tethered UAV channel model, with a beamforming matrix and a fluid antenna position matrix as inputs and a tethered UAV communication system capacity as output;

[0057] A system decomposition module is used to decompose the tethered UAV communication system capacity optimization model into a beamforming matrix optimization model and a fluid antenna position matrix optimization model;

[0058] The model solving module is used to solve the beamforming matrix optimization model and the fluid antenna position matrix optimization model based on the zero forcing algorithm and the particle swarm algorithm, and input the solved optimal fluid antenna position matrix and normalized beamforming matrix into the tethered UAV communication system capacity optimization model to obtain the optimal system capacity of the tethered UAV communication system.

[0059] In a third aspect, the present application also provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a tethered drone communication system capacity optimization method when executing the computer program.

[0060] The above-mentioned tethered drone communication system capacity optimization method, device and computer equipment apply fluid antennas to tethered drone auxiliary communication systems, and improve the channel conditions from drones to users by flexibly changing the position of fluid antennas. A tethered drone communication system model is established based on the communication data of the tethered drone and its users, and a tethered drone channel model is established based on the tethered drone communication system model; based on the tethered drone communication system model and the tethered drone channel model, a tethered drone communication system capacity optimization model is generated with the beamforming matrix and the fluid antenna position matrix as input and the tethered drone communication system capacity as output. Since the tethered drone communication system capacity optimization problem model is a non-convex optimization problem, it is extremely challenging to directly use mathematical algorithms to find the optimal solution. Therefore, the tethered drone communication system capacity optimization model is decomposed into a beamforming matrix optimization model and a fluid antenna position matrix optimization model, and the optimization is performed separately to solve the original optimization problem. Based on the zero forcing algorithm and particle swarm algorithm, the beamforming matrix optimization model and the fluid antenna position matrix optimization model are solved, and the optimal fluid antenna position matrix and normalized beamforming matrix obtained by the solution are input into the tethered UAV communication system capacity optimization model to obtain the optimal system capacity of the tethered UAV communication system, thereby achieving the purpose of improving the capacity of the tethered UAV communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A schematic diagram of a flow chart of a method for optimizing the capacity of a tethered drone communication system in one embodiment;

[0062] Figure 2 A schematic diagram of a fluid antenna assisted tethered UAV communication system model in one embodiment;

[0063] Figure 3 A schematic diagram of a process for jointly solving a beamforming matrix and a fluid antenna position matrix in yet another embodiment;

[0064] Figure 4 It is a schematic flow chart of a method for optimizing the capacity of a tethered UAV communication system in another embodiment;

[0065] Figure 5 Schematic diagram of a module of a tethered UAV communication system capacity optimization device in one embodiment. DETAILED DESCRIPTION

[0066] Tethered drones equipped with base stations have shown unique value and wide application potential in a variety of scenarios such as emergency communications, disaster response, and large-scale events. On the other hand, fluid antennas, as the next-generation reconfigurable antenna technology, are one of the hot technologies of the sixth-generation mobile communication (6G, 6-Generation) and are seen as a promising solution for achieving flexible and adaptive wireless communications. Fluid antennas represent any fluid, conductive or dielectric structures that can be controlled by software and are able to dynamically reconfigure their shape, size, position, length, direction, and other radiation characteristics to change basic RF characteristics.

[0067] Current research on drones acting as base stations or relays for communication mainly focuses on optimizing system capacity based on existing fixed-position antennas as transmitting antennas. Existing fixed-position antennas include single fixed-point antennas, uniform linear arrays, etc. Since the positions of these antennas cannot be changed, it is impossible to use the new degrees of freedom in the continuous spatial domain of a given transmission area to improve system capacity.

[0068] In order to optimize the capacity of the tethered drone downlink multi-user system, this application introduces a fluid antenna to assist tethered drone communications, wherein the fluid antenna includes any fluid, dielectric or conductive structure that can be controlled by software, and its specific forms include but are not limited to liquid-based antennas, pixel-based antennas and metasurfaces. Focus on solving new optimization problems brought about by the introduction of new degrees of freedom. Specifically, in the process of multi-user communication using a tethered drone equipped with a fluid antenna as a base station, it is necessary to optimize the system capacity based on the channel from the tethered drone to the user and the user distribution, which leads to the problem of beamforming design and fluid antenna position update. Therefore, this application focuses on how the fluid antenna-assisted tethered drone communication system can solve the problems of beamforming matrix and fluid antenna position optimization in multi-user communication scenarios, and maximize the capacity of the fluid antenna-assisted tethered drone communication system.

[0069] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0070] In an exemplary embodiment, Figure 1 As shown, a method for optimizing the capacity of a tethered UAV communication system is provided, comprising:

[0071] Step S1, establishing a tethered drone communication system model according to the communication data of the tethered drone and its user, and establishing a tethered drone channel model based on the tethered drone communication system model.

[0072] Among them, the tethered UAV communication system model is also called the fluid antenna assisted tethered UAV communication system model; the communication data of the tethered UAV and its users include the fixed height of the tethered UAV, the maximum transmission power of the tethered UAV, the hovering position of the tethered UAV, the number and position of the fluid antennas carried by the tethered UAV, the ground user position of the tethered UAV, etc.

[0073] Step S2, based on the tethered UAV communication system model and the tethered UAV channel model, a tethered UAV communication system capacity optimization model is generated, which has a beamforming matrix and a fluid antenna position matrix as inputs and a tethered UAV communication system capacity as output.

[0074] Specifically, a capacity optimization model of the tethered UAV communication system is formulated based on the signal-to-interference-noise ratio of the received signal of ground user k.

[0075] Step S3, decomposing the tethered UAV communication system capacity optimization model into a beamforming matrix optimization model and a fluid antenna position matrix optimization model.

[0076] Specifically, the capacity optimization problem of tethered UAV communication system is a non-convex problem that is difficult to solve. The two optimization variables of the original optimization problem are decoupled into two sub-problems of optimizing the beamforming matrix and the fluid antenna position matrix.

[0077] Step S4, based on the zero forcing algorithm and the particle swarm algorithm, the beamforming matrix optimization model and the fluid antenna position matrix optimization model are solved, and the optimal fluid antenna position matrix and the normalized beamforming matrix obtained by solving the solution are input into the tethered UAV communication system capacity optimization model to obtain the optimal system capacity of the tethered UAV communication system.

[0078] Specifically, based on the zero-forcing algorithm and particle swarm algorithm, an iterative alternating optimization method is designed to optimize the two sub-problems of beamforming matrix and fluid antenna position matrix. The iteration is stopped when the maximum number of iterations is reached, and the swarm optimal solution is output, which is the optimal fluid antenna position matrix. The normalized beamforming matrix is ​​calculated based on the zero-forcing algorithm according to the swarm optimal solution. The optimal fluid antenna position matrix and the normalized beamforming matrix are substituted into the objective function of the original optimization problem to calculate and obtain the optimal system capacity.

[0079] In the above-mentioned tethered UAV communication system capacity optimization method, a tethered UAV communication system model is established according to the communication data of the tethered UAV and its users, and a tethered UAV channel model is established based on the tethered UAV communication system model; based on the tethered UAV communication system model and the tethered UAV channel model, a tethered UAV communication system capacity optimization model is generated with the beamforming matrix and the fluid antenna position matrix as input and the tethered UAV communication system capacity as output. Through the flexible change of the fluid antenna position, the channel condition from the tethered UAV to the user is improved, thereby improving the capacity of the tethered UAV communication system. Since the original optimization problem is a non-convex optimization problem, the optimization variables beamforming matrix and fluid antenna position matrix in the original optimization problem are decoupled and decomposed into two sub-problems for optimization respectively to solve the original optimization problem, that is, the tethered UAV communication system capacity optimization model is decomposed into a beamforming matrix optimization model and a fluid antenna position matrix optimization model. At the same time, an iterative alternating optimization method is designed, and the zero forcing algorithm and the particle swarm algorithm are used to alternately optimize the beamforming matrix and the fluid antenna position to iteratively improve the system capacity, providing a low-complexity optimization method for the beamforming matrix and the fluid antenna position in the tethered UAV equipped with a fluid antenna auxiliary communication system.

[0080] In an exemplary embodiment, Figure 2 As shown in the figure, the black square represents the fluid antenna, and the black arrow represents the communication link between the tethered drone and the user. In the downlink communication scenario where the fluid antenna assists the tethered drone to act as a base station, the tethered drone equipped with M fluid antennas hovers at a fixed position at a fixed height H0. The tethered drone provides communication services for K single-antenna users within time T, realizes the transmission of related data, and satisfies M ≥ K. Among them, the maximum transmission power of the tethered drone is P max , its hovering position is represented by q = (x, y, H0) T , the kth (1≤k≤K) user position is represented as u k =(a k ,b k ,0) T The communication service time T of the tethered UAV is divided into N equal-length time slots of length τ. In the nth (1≤n≤N) time slot, the signal received by the ground user k can be expressed as Among them, h k is the channel vector between the tethered UAV and the ground user k, w k is the beamforming vector corresponding to ground user k, x k [n] represents the signal with k-mean value of 0 and normalized power of 1 transmitted to the ground user in the nth time slot. k represents noise. Optionally, n k Indicates power Gaussian white noise. The channel matrix is ​​defined as H = [h1,h2,...,h K ], the beamforming matrix is ​​defined as W = [w1,w2,...,w K ].

[0081] Furthermore, a tethered UAV channel model is established based on the tethered UAV communication system model. When the tethered UAV flies to a certain altitude, the air-to-ground channel gain between the tethered UAV and the user is approximated by the free space path loss model. The moving range of the fluid antenna is defined as a rectangular area of ​​size A×A. A two-dimensional Cartesian local coordinate system is introduced for the rectangular area to accurately describe the position of the fluid antenna. The center of the rectangular area is taken as the origin of the local coordinate system. The moving coordinate area of ​​the fluid antenna is [-A / 2, A / 2]×[-A / 2, A / 2]. The position of the mth fluid antenna in the nth time slot can be expressed as t m [n] = [x m [n],y m [n]] T In two adjacent time slots, the position of the ground user may change or remain unchanged, so the tethered drone needs to update the fluid antenna position according to the ground user position in each time slot. The position of the M fluid antennas in the nth time slot can be expressed as the fluid antenna position matrix T = [t1[n]t2[n]...t M [n]].

[0082] Furthermore, on this basis, the line-of-sight channel vector h between the tethered UAV and the ground user k is k It can be expressed as shown in formula (1):

[0083] (1)

[0084] Where β0 represents the channel power gain at a reference distance of 1 meter, d k =||qu k || represents the distance between the tethered UAV and the ground user k, represents the transmission array vector from the tethered UAV to the ground user k, θ k and φ k are the elevation angle and azimuth angle of the line-of-sight path from the tethered UAV to the ground user k, respectively, which can be calculated by formula (2), where formula (2) is as follows:

[0085]

[0086] In an exemplary embodiment, in step S2, generating a tethered drone communication system capacity optimization model based on the tethered drone communication system model and the tethered drone channel model, with the beamforming matrix and the fluid antenna position matrix as input and the tethered drone communication system capacity as output includes:

[0087] Based on the tethered UAV communication system model and the tethered UAV channel model, the signal-to-interference-noise ratio of the target user's received signal in the specified time slot is obtained; according to the signal-to-interference-noise ratio of the target user's received signal in the specified time slot, a tethered UAV communication system capacity optimization model is formulated with the beamforming matrix and the fluid antenna position matrix as input and the tethered UAV communication system capacity as output.

[0088] Specifically, in the nth time slot, the signal to interference noise ratio of the signal received by ground user k is expressed as shown in formula (3):

[0089]

[0090] where h k is the channel vector between the tethered UAV and the ground user k, w k is the beamforming vector corresponding to ground user k, is the power of Gaussian white noise.

[0091] In the nth time slot, the capacity optimization model of the tethered UAV communication system is formulated based on the received signal-to-interference-noise ratio as shown in formula (4):

[0092]

[0093] stC1:tr(WW H )≤P max

[0094]

[0095] C3:||t m [n]-t m' [n]||≥D,1≤m≠m'≤M

[0096] Among them, C1 represents the limit of the transmission power of the tethered drone in each time slot; C2 represents the limit of the movable range of any fluid antenna position within the transmission area C3 indicates that the distance between any two fluid antennas is not less than the minimum distance D (D is half of the carrier wavelength) to avoid coupling effects between antennas.

[0097] In this embodiment, a tethered UAV communication system capacity optimization model is formulated according to the signal-to-interference-noise ratio of the target user receiving the signal in the specified time slot, so as to further decompose it into sub-problems and obtain the maximum capacity of the tethered UAV communication system.

[0098] In an exemplary embodiment, decomposing the tethered drone communication system capacity optimization model into a beamforming matrix optimization model and a fluid antenna position matrix optimization model includes:

[0099] According to the two optimization variables of the tethered UAV communication system capacity optimization model, it is decomposed into a beamforming matrix optimization model and a fluid antenna position matrix optimization model; the beamforming matrix and the fluid antenna position matrix are optimized respectively so that the capacity of the tethered UAV communication system is maximized while satisfying the constraints of the tethered UAV communication system capacity optimization model.

[0100] Exemplarily, the two optimization variables of the tethered UAV communication system capacity optimization problem are decoupled into two sub-problems of optimizing the beamforming matrix and the fluid antenna position matrix; the beamforming matrix and the fluid antenna position matrix are optimized respectively to maximize the capacity of the tethered UAV communication system while satisfying the tethered UAV maximum transmission power and fluid antenna position constraints; when optimizing the beamforming matrix W or the fluid antenna position matrix T, other variables remain unchanged.

[0101] In this embodiment, since the tethered UAV communication system capacity optimization model is established to solve the tethered UAV communication system capacity optimization problem, and the original optimization problem of tethered UAV communication system capacity optimization is a non-convex problem that is difficult to solve, the two optimization variables of the original optimization problem are decoupled into two sub-problems to reduce the difficulty of solving and ensure the accuracy of the model solution results.

[0102] In an exemplary embodiment, respectively optimizing the beamforming matrix and the fluid antenna position matrix so that the capacity of the tethered UAV communication system reaches the maximum under the constraint conditions of the tethered UAV communication system capacity optimization model is satisfied includes:

[0103] Given a fluid antenna position matrix, the beamforming matrix that maximizes the capacity of the tethered UAV communication system is solved under the constraint that the tethered UAV's transmission power is limited in each time slot; under the constraint that the movable range of any fluid antenna position is limited within the transmission area and the distance between any two fluid antennas is not less than the target distance, the fluid antenna position matrix that maximizes the capacity of the tethered UAV communication system is solved.

[0104] Specifically, for a given fluid antenna position matrix T, we only need to consider optimizing the beamforming matrix W to improve the system capacity, and the C2 and C3 constraints will not be considered. The original optimization problem P1 is transformed as shown in formula (5):

[0105]

[0106] stC1

[0107] The optimization problem P2 is to solve the beamforming matrix W that maximizes the optimization objective under the constraint of C1, where C1 represents the transmission power limit of the tethered UAV in each time slot.

[0108] Furthermore, since the optimal beamforming matrix W for any given fluid antenna position matrix T can be calculated, the original optimization problem can be expressed as an optimization problem related only to the fluid antenna position matrix T, as shown in formula (6):

[0109]

[0110] stC2,C3

[0111] The optimization problem P3 is to solve the fluid antenna position matrix T that maximizes the optimization objective only under the constraints of C2 and C3, where C2 indicates that the movable range of any fluid antenna position is limited to the transmitting area. C3 indicates that the distance between any two fluid antennas is not less than the minimum distance d to avoid coupling effect between antennas.

[0112] In this embodiment, the two sub-problems of optimizing the beamforming matrix and the fluid antenna position matrix are further clarified, which is conducive to improving the accuracy of the solution results.

[0113] In an exemplary embodiment, solving the beamforming matrix optimization model and the fluid antenna position matrix optimization model based on the zero forcing algorithm and the particle swarm algorithm includes:

[0114] The beamforming matrix is ​​optimized based on the zero forcing algorithm to generate the maximum value of the objective function of the beamforming matrix optimization model; the fitness value is calculated based on the particle swarm algorithm, and the fluid antenna position matrix is ​​updated and iterated according to the fitness value; the iteration is stopped when the maximum number of iterations is reached, and the optimal fluid antenna position matrix is ​​output. The normalized beamforming matrix is ​​obtained based on the optimal fluid antenna position matrix based on the zero forcing algorithm.

[0115] Exemplarily, based on the zero-forcing algorithm and the particle swarm algorithm, an iterative alternating optimization method is designed to optimize the two sub-problems of the beamforming matrix and the fluid antenna position matrix to obtain the optimal system capacity. First, the fluid antenna position matrix is ​​initialized, the beamforming matrix is ​​optimized based on the zero-forcing algorithm, and problem P2 is calculated, that is, the maximum value of the objective function of the beamforming matrix W that maximizes the optimization objective under the C1 constraint is solved; then the fitness value is calculated based on the particle swarm algorithm, and the fluid antenna position matrix is ​​updated.

[0116] In this embodiment, the beamforming matrix and the fluid antenna position matrix are optimized by alternately iteratively using the zero-forcing algorithm and the particle swarm algorithm, thereby ensuring that the optimal solution to the capacity optimization problem of the tethered UAV communication system can be obtained.

[0117] In an exemplary embodiment, the beamforming matrix is ​​optimized based on a zero-forcing algorithm, and generating a maximum value of an objective function of the beamforming matrix optimization model includes:

[0118] A zero-forcing algorithm is used to generate a zero-forcing beamforming matrix, and the zero-forcing beamforming matrix is ​​normalized. Combined with the given fluid antenna position matrix, the maximum value of the objective function of the beamforming matrix optimization model is obtained.

[0119] Specifically, the zero-forcing beamforming matrix W is first calculated ZF , which is designed to eliminate interference between users and meet The zero-forcing beamforming matrix is ​​shown in formula (7):

[0120] W ZF =(HH H ) -1 H (7)

[0121] Then, the normalized beamforming matrix W ZF The total transmit power before normalization is The normalized beamforming matrix is ​​shown in formula (8):

[0122]

[0123] Given the antenna position matrix T, the maximum value of problem P2 is

[0124] In this embodiment, for a given fluid antenna position matrix T, the channel matrix H is calculated. Problem P2 needs to be further applied to solve the beamforming matrix W that maximizes the optimization objective under the C1 constraint, so a low-complexity algorithm needs to be designed for solution, where C1 represents the limit of the transmission power of the tethered drone in each time slot. The zero-forcing algorithm is used to obtain the closed-form solution of the beamforming matrix for problem P2 that satisfies the C1 constraint, reducing the complexity of the algorithm solution.

[0125] In an exemplary embodiment, calculating the fitness value based on the particle swarm algorithm, and updating the iterative fluid antenna position matrix according to the fitness value includes:

[0126] Multiple particles are randomly initialized in the emission area, and the initial position values ​​and acceleration of the particles are set based on the constraint that the movable range of any fluid antenna position is limited to the emission area; a penalty factor is introduced based on the constraint that the distance between any two fluid antennas is not less than the target distance to formulate a fitness function, and the fitness value of the particles is calculated; the position values ​​and acceleration values ​​of the particles are iteratively updated to obtain the individual optimal solution and the group optimal solution of the particles.

[0127] Specifically, first in the launch area Initialize S particles randomly, and the initial position of the particles Initial acceleration According to the C2 constraint, if the particle moves out of the feasible area The boundary of the particle is projected to the corresponding minimum or maximum value to ensure that the updated position of the particle meets the constraint. That is, when the particle position value is greater than A / 2, it will be set to A / 2, and when the particle position value is less than -A / 2, its value will be set to -A / 2. In order to meet the C3 constraint, the penalty factor μ is introduced, and the fitness function is formulated as shown in formula (9):

[0128] F(T)=R(T)-μQ (9)

[0129] Among them, the value of μ ensures that the inequality R(T)-μ≤0 is true, and Q is a value between 0 and 1, which determines whether the penalty factor acts on the fitness function. The value determination condition of Q is shown in formula (10):

[0130]

[0131] Calculate and initialize the fitness value of each particle, and get the optimal solution of the sth (1≤s≤S)th particle individual: The optimal solution for the group is

[0132] Then, the velocity and position of each particle are updated iteratively. The updating formula of the particle velocity and position for the i-th (1≤i≤I) time is shown in formula (11):

[0133]

[0134] Among them, ω is the inertia factor, c1 and c2 are individual cognitive factors and group cognitive factors, and rand(0,1) represents a random number in the interval (0,1). Make the updated particle position satisfy constraint C2, [A] ii,，jj represents the item in the iith row and jjth column of matrix A. A linear change strategy is adopted. As the number of iterations increases, the inertia factor ω decreases continuously, so that it has a stronger global convergence ability in the early iterations and a stronger local convergence ability in the later stages. The change form of the inertia factor ω with the number of iterations is: where ω max is the maximum inertia factor, ω min is the minimum inertia factor.

[0135] Next, update the optimal solution T of the sth particle individual s,pbest , the optimal solution of the group is T gbest , the update formula is shown in formula (12):

[0136]

[0137] Furthermore, when the maximum number of iterations I is reached, the iteration is stopped and the optimal solution T is output. gbest That is the optimal fluid antenna position matrix. According to T gbest Calculate the normalized beamforming matrix W based on the zero-forcing algorithm norm . gbest and W norm Substitute the objective function of the original optimization problem, that is, the capacity optimization model of the tethered UAV communication system, and calculate the optimal system capacity.

[0138] In this embodiment, the particle swarm algorithm is used to optimize the position of the fluid antenna under the constraints of C2 and C3, where C2 means that the movable range of any fluid antenna position is limited to the emission area. C3 indicates that the distance between any two fluid antennas is not less than the minimum distance D to avoid coupling effect between antennas.

[0139] In one embodiment, Figure 3 As shown in the figure, the capacity optimization method of the tethered UAV communication system includes establishing a tethered UAV communication system model and a channel model, then formulating a system capacity optimization problem model, and decomposing the original optimization problem into two sub-problems. Finally, an iterative alternating optimization method based on the zero-forcing algorithm and the particle swarm algorithm is designed to optimize the two sub-problems and obtain the optimal system capacity.

[0140] Furthermore, the process of jointly solving the beamforming matrix and the fluid antenna position matrix is ​​as follows: Figure 4 As shown, multiple particles are randomly initialized, the particle swarm size, initial position and speed are set, the beamforming matrix is ​​optimized based on the zero-forcing algorithm according to the initial position of each particle, the fitness value of each particle is further calculated, the current individual optimal solution of each particle and the optimal solution of the entire particle swarm are found, and the speed and position of each particle are updated; it is determined whether the iteration termination condition is met, if the iteration termination condition is met, the optimal beamforming matrix, the fluid antenna position matrix and the optimal value of the system capacity are output, if the iteration termination condition is not met, the steps of optimizing the beamforming matrix based on the zero-forcing algorithm according to the initial position of each particle, calculating the fitness value of each particle, finding the current individual optimal solution of each particle and the optimal solution of the entire particle swarm, and updating the speed and position of each particle are repeated.

[0141] It should be understood that these steps are not necessarily performed in the order indicated by the arrows. Unless there is a clear explanation in this article, these steps can be performed in other orders. Moreover, at least a portion of the steps in the flow charts involved in each embodiment as described above can include multiple steps or multiple stages, which can be performed at different times and can be performed in turn or alternately with other steps or at least a portion of the steps or stages in other steps.

[0142] Based on the same inventive concept, the embodiment of the present application also provides a tethered drone communication system capacity optimization device for implementing the above-mentioned tethered drone communication system capacity optimization method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more tethered drone communication system capacity optimization device embodiments provided below can refer to the limitations of the tethered drone communication system capacity optimization method above.

[0143] In an exemplary embodiment, Figure 5 As shown, a device for optimizing the capacity of a tethered UAV communication system is provided, wherein:

[0144] A basic model building module 501 is used to build a tethered drone communication system model according to the communication data of the tethered drone and its user, and to build a tethered drone channel model based on the tethered drone communication system model;

[0145] A system capacity optimization model building module 502 is used to generate a tethered drone communication system capacity optimization model based on a tethered drone communication system model and a tethered drone channel model, with a beamforming matrix and a fluid antenna position matrix as inputs and a tethered drone communication system capacity as output;

[0146] The system decomposition module 503 is used to decompose the tethered UAV communication system capacity optimization model into a beamforming matrix optimization model and a fluid antenna position matrix optimization model;

[0147] The model solving module 504 is used to solve the beamforming matrix optimization model and the fluid antenna position matrix optimization model based on the zero forcing algorithm and the particle swarm algorithm, and input the solved optimal fluid antenna position matrix and normalized beamforming matrix into the tethered UAV communication system capacity optimization model to obtain the optimal system capacity of the tethered UAV communication system.

[0148] Each module in the above-mentioned tethered drone communication system capacity optimization device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each of the above modules.

[0149] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0150] The technical features of the above embodiments can be combined arbitrarily, and as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification. For ordinary technicians in this field, without departing from the concept of this application, the several modifications and improvements made belong to the protection scope of this application. Therefore, the protection scope of this application shall be based on the attached claims.

Claims

1. A method for optimizing the capacity of a tethered drone communication system, characterized in that: The method comprises: Step S1, establishing a tethered drone communication system model according to the communication data of the tethered drone and its user, and establishing a tethered drone channel model based on the tethered drone communication system model; Step S2, generating a tethered drone communication system capacity optimization model based on the tethered drone communication system model and the tethered drone channel model, with the beamforming matrix and the fluid antenna position matrix as input and the tethered drone communication system capacity as output; Step S3, decomposing the tethered UAV communication system capacity optimization model into a beamforming matrix optimization model and a fluid antenna position matrix optimization model; Step S4, based on the zero forcing algorithm and the particle swarm algorithm, the beamforming matrix optimization model and the fluid antenna position matrix optimization model are solved, and the optimal fluid antenna position matrix and the normalized beamforming matrix obtained by the solution are input into the tethered UAV communication system capacity optimization model to obtain the optimal system capacity of the tethered UAV communication system.

2. The method according to claim 1, characterized in that The tethered UAV communication system model in step S1 includes a signal model received by a ground user k, which is expressed as: Among them, h k is the channel vector between the tethered UAV and the ground user k, w k is the beamforming vector corresponding to ground user k, x k [n] represents the signal with k-mean value of 0 and normalized power of 1 transmitted to the ground user in the nth time slot. k represents noise; the channel matrix is ​​defined as H = [h1, h2, ..., h K ], the beamforming matrix is ​​defined as W = [w1, w2, ..., w K ]; Based on the tethered UAV communication system model, the moving range of the fluid antenna is defined as a rectangular area of ​​a specified size. A two-dimensional Cartesian local coordinate system is introduced, and the expression for the position of the mth fluid antenna in the nth time slot is: t m [n]=[x m [n],y m [n]] T The tethered drone updates the fluid antenna position according to the ground user position in each time slot. The position of M fluid antennas in the nth time slot can be expressed as the fluid antenna position matrix expression is as follows: T=[t1[n]t2[n]...t M [n]] where t m [n] is the position of the mth fluid antenna in the nth time slot; The tethered UAV channel model described in step S1 includes the line-of-sight channel vector h between the tethered UAV and the ground user k k , the expression is as follows: Where β0 represents the channel power gain at a reference distance of 1 meter, d k represents the distance between the tethered UAV and the ground user k, α k represents the transmission array vector from the tethered UAV to the ground user k.

3. The method according to claim 2, characterized in that The step S2 comprises: In the nth time slot, the signal-to-interference-to-noise ratio of the signal received by ground user k is expressed as follows: where h k is the channel vector between the tethered UAV and the ground user k, w k is the beamforming vector corresponding to ground user k, is the power of Gaussian white noise; According to the signal-to-interference-noise ratio of the target user receiving the signal in the specified time slot, a tethered UAV communication system capacity optimization model is formulated with the beamforming matrix and the fluid antenna position matrix as input and the tethered UAV communication system capacity as output. The expression is as follows: s.t.C1:tr(WW H )≤P max C3:||t m [n]-t m′ [n]||≥D,1≤m≠m′≤M Among them, C1 represents the transmission power limit of the tethered drone in each time slot, P max The maximum transmission power of the tethered drone; C2 indicates that the movable range of any fluid antenna position is limited to the launch area Inside; C3 indicates that the distance between any two fluid antennas is not less than the minimum distance D to avoid coupling effect between antennas, where D is half of the carrier wavelength.

4. The method according to claim 1, characterized in that: The step S3 comprises: Decomposing the two optimization variables of the tethered UAV communication system capacity optimization model into a beamforming matrix optimization model and a fluid antenna position matrix optimization model; The beamforming matrix and the fluid antenna position matrix are optimized respectively so that the capacity of the tethered UAV communication system is maximized while satisfying the constraint conditions of the tethered UAV communication system capacity optimization model.

5. The method according to claim 4, characterized in that The respectively optimizing the beamforming matrix and the fluid antenna position matrix so that the capacity of the tethered UAV communication system reaches the maximum under the condition that the constraint conditions of the tethered UAV communication system capacity optimization model are satisfied include: Given the fluid antenna position matrix, the beamforming matrix that maximizes the capacity of the tethered UAV communication system is solved under the constraint of the tethered UAV's transmission power in each time slot. The expression is as follows: stC1; Under the constraints that the movable range of any fluid antenna position is limited within the launch area and the distance between any two fluid antennas is not less than the target distance, the fluid antenna position matrix that maximizes the capacity of the tethered UAV communication system is solved. The expression is as follows: stC2,C3.

6. The method according to claim 5, characterized in that The solving of the beamforming matrix optimization model and the fluid antenna position matrix optimization model based on the zero forcing algorithm and the particle swarm algorithm includes: The beamforming matrix is ​​optimized based on the zero-forcing algorithm to generate the maximum value of the objective function of the beamforming matrix optimization model; The fitness value is calculated based on the particle swarm algorithm, and the fluid antenna position matrix is ​​updated and iterated according to the fitness value; When the maximum number of iterations is reached, the iteration is stopped, and the optimal fluid antenna position matrix is ​​output. According to the optimal fluid antenna position matrix, a normalized beamforming matrix is ​​obtained based on a zero-forcing algorithm.

7. The method according to claim 6, characterized in that The step of optimizing the beamforming matrix based on the zero-forcing algorithm and generating the maximum value of the objective function of the beamforming matrix optimization model includes: A zero-forcing algorithm is used to generate a zero-forcing beamforming matrix, and the zero-forcing beamforming matrix is ​​normalized; Combined with the given fluid antenna position matrix, the maximum value of the objective function of the beamforming matrix optimization model is obtained.

8. The method according to claim 6, characterized in that The step of calculating the fitness value based on the particle swarm algorithm and updating the iterative fluid antenna position matrix according to the fitness value comprises: Initialize multiple particles randomly in the emission area, and set the initial position values ​​and acceleration of the particles based on the constraint that the movable range of any fluid antenna position is limited to the emission area; Based on the constraint that the distance between any two fluid antennas is not less than the target distance, a penalty factor is introduced to formulate a fitness function and calculate the fitness value of the particle; The position values ​​and acceleration values ​​of the particles are iteratively updated to obtain individual optimal solutions and group optimal solutions of the particles.

9. A device for optimizing the capacity of a tethered drone communication system, characterized in that: The device comprises: A basic model building module is used to build a tethered UAV communication system model based on the communication data of the tethered UAV and its user, and to build a tethered UAV channel model based on the tethered UAV communication system model; A system capacity optimization model establishment module, used to generate a tethered drone communication system capacity optimization model based on the tethered drone communication system model and the tethered drone channel model, with the beamforming matrix and the fluid antenna position matrix as input and the tethered drone communication system capacity as output; A system decomposition module, used for decomposing the tethered UAV communication system capacity optimization model into a beamforming matrix optimization model and a fluid antenna position matrix optimization model; The model solving module is used to solve the beamforming matrix optimization model and the fluid antenna position matrix optimization model based on the zero forcing algorithm and the particle swarm algorithm, and input the solved optimal fluid antenna position matrix and normalized beamforming matrix into the tethered UAV communication system capacity optimization model to obtain the optimal system capacity of the tethered UAV communication system.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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