Wireless resource allocation method and device, electronic equipment and storage medium

Abstract

The invention provides a wireless resource allocation method and device, electronic equipment and a storage medium, and belongs to the technical field of wireless communication. Coordinate variables for a movable antenna in a movable antenna constraint are replaced with auxiliary variables, and a penalty term containing the auxiliary variables and the coordinate variables is added to a resource allocation function; the method comprises the steps of obtaining a target resource allocation model, performing model decomposition on the target resource allocation model to obtain a first sub-model aiming at a wireless resource variable, a second sub-model aiming at a coordinate variable and a third sub-model aiming at an auxiliary variable, and performing model solution on the first sub-model, the second sub-model and the third sub-model to obtain a solution data set, and the solution data set is input into the target resource allocation model to obtain a model output result, the operation is stopped until the model output result obtains a minimum value, wireless resource allocation is performed according to the wireless resource value and the antenna coordinate value in the target solution data set, and the optimization solution difficulty of the wireless resource allocation problem is reduced.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to a wireless resource allocation method, device, electronic device, and storage medium. Background Art

[0002] Compared with wireless communication systems that use fixed-position antennas, movable antennas can flexibly change their positions on the antenna panel to maximize the spatial freedom of the antenna. With the increasing number of wireless applications such as video streaming, virtual reality, and augmented reality, movable antennas are introduced into wireless communication systems to improve the capacity of communication systems. The wireless resource allocation problem belongs to the optimization problem, which maximizes the utilization of wireless resources by minimizing the objective function while satisfying a series of constraints. However, the wireless resource allocation problem of a wireless communication system with a movable antenna will introduce non-convex and coupled constraints on the movable antenna and a non-convex objective function, which makes the optimization solution of the wireless resource allocation problem more difficult. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a wireless resource allocation method, device, electronic device, and storage medium, aiming to reduce the difficulty of optimizing the solution to the wireless resource allocation problem.

[0004] To achieve the above object, a first aspect of an embodiment of the present application proposes a method for allocating wireless resources, the method comprising:

[0005] Acquire an initial resource allocation model of the wireless communication system; wherein the initial resource allocation model includes a resource allocation function and a movable antenna constraint, the resource allocation function is used to establish a mapping relationship between a wireless resource variable of the wireless communication system, a coordinate variable of the movable antenna in the wireless communication system and a function output, and the movable antenna constraint is used to constrain the coordinate variable of the movable antenna;

[0006] Replacing the coordinate variables for the movable antenna in the movable antenna constraint with auxiliary variables, and adding a penalty term including the auxiliary variables and the coordinate variables to the resource allocation function to obtain a target resource allocation model;

[0007] Decomposing the target resource allocation model to obtain a first sub-model for the wireless resource variable, a second sub-model for the coordinate variable, and a third sub-model for the auxiliary variable;

[0008] Solving the first sub-model, the second sub-model and the third sub-model respectively to obtain solution data sets, and inputting the solution data sets into the target resource allocation model to obtain model output results;

[0009] Repeat the steps of respectively solving the first sub-model, the second sub-model and the third sub-model to obtain a solution data set, and inputting the solution data set into the target resource allocation model to obtain a model output result, until the model output result stops when a minimum value is obtained, and determine a target solution data set when the model output result obtains a minimum value;

[0010] Wireless resource allocation is performed according to the wireless resource values ​​and antenna coordinate values ​​in the target solution data set.

[0011] In some embodiments, performing model solving on the first sub-model, the second sub-model, and the third sub-model respectively to obtain a solution data set includes:

[0012] Calling a wireless resource allocation algorithm to solve the first sub-model to obtain a wireless resource value of the wireless resource variable in the first sub-model;

[0013] Calling a gradient projection algorithm to solve the second sub-model to obtain antenna coordinate values ​​of the coordinate variables in the second sub-model;

[0014] Calling a geometric optimization algorithm to solve the third sub-model to obtain auxiliary variable values ​​of the auxiliary variables in the third sub-model;

[0015] A solution data set is obtained based on the wireless resource value, the antenna coordinate value and the auxiliary variable value.

[0016] In some embodiments, the calling of a geometric optimization algorithm to solve the third sub-model to obtain the auxiliary variable value of the auxiliary variable in the third sub-model includes:

[0017] Based on the geometric optimization algorithm, other auxiliary variables except the current auxiliary variable in the third sub-model are fixed, and the current auxiliary variable is solved to obtain the auxiliary variable value of the current auxiliary variable;

[0018] The auxiliary variables are traversed in sequence, and the next auxiliary variable is solved until the auxiliary variable values ​​of all auxiliary variables are obtained.

[0019] In some embodiments, solving the current auxiliary variable to obtain the auxiliary variable value of the current auxiliary variable includes:

[0020] Acquire a set of antenna coordinate points that do not satisfy the movable antenna constraint;

[0021] The current auxiliary variable is solved according to the number of coordinate points of the antenna coordinate point set to obtain the auxiliary variable value of the current auxiliary variable.

[0022] In some embodiments, the obtaining of a set of antenna coordinate points that do not satisfy the movable antenna constraint includes:

[0023] Determine a target circle based on other auxiliary variables except the current auxiliary variable;

[0024] Obtaining the intersection points between the target circles to obtain a first coordinate point set;

[0025] Determine a second coordinate point set according to other auxiliary variables except the current auxiliary variable, coordinate variables corresponding to the current auxiliary variable and the target circle;

[0026] The antenna coordinate point set is determined according to the first coordinate point set and the second coordinate point set.

[0027] In some embodiments, determining the second coordinate point set according to other auxiliary variables except the current auxiliary variable, the coordinate variables corresponding to the current auxiliary variable and the target circle includes:

[0028] Determine a target straight line according to other auxiliary variables except the current auxiliary variable and coordinate variables corresponding to the current auxiliary variable;

[0029] The intersection points of the target straight line and the target circle are obtained to obtain the second coordinate point set.

[0030] In some embodiments, solving the current auxiliary variable according to the number of coordinate points of the antenna coordinate point set to obtain the auxiliary variable value of the current auxiliary variable includes:

[0031] If the number of coordinate points is zero, the preset reference antenna coordinate value is used as the auxiliary variable value;

[0032] If the number of the coordinate points is greater than or equal to one, the antenna coordinate point set is screened to obtain the auxiliary variable value.

[0033] To achieve the above object, a second aspect of an embodiment of the present application provides a wireless resource allocation device, the device comprising:

[0034] An acquisition module, configured to acquire an initial resource allocation model of a wireless communication system; wherein the initial resource allocation model includes a resource allocation function and a movable antenna constraint, wherein the resource allocation function is configured to establish a mapping relationship between a wireless resource variable of a wireless communication system, a coordinate variable of a movable antenna in the wireless communication system, and a function output, and wherein the movable antenna constraint is configured to constrain the coordinate variable of the movable antenna;

[0035] A model building module, used for replacing the coordinate variables for the movable antenna in the movable antenna constraint with auxiliary variables, and adding a penalty term including the auxiliary variables and the coordinate variables to the resource allocation function to obtain a target resource allocation model;

[0036] A model decomposition module, used for performing model decomposition on the target resource allocation model to obtain a first sub-model for the wireless resource variable, a second sub-model for the coordinate variable and a third sub-model for the auxiliary variable;

[0037] A first solving module is used to solve the first sub-model, the second sub-model and the third sub-model respectively to obtain a solution data set, and input the solution data set into the target resource allocation model to obtain a model output result;

[0038] a second solving module, configured to repeatedly perform the steps of solving the first sub-model, the second sub-model and the third sub-model respectively to obtain a solving data set, and input the solving data set into the target resource allocation model to obtain a model output result, until the model output result stops when a minimum value is obtained, and determine a target solving data set when the model output result obtains a minimum value;

[0039] The resource allocation module is used to perform wireless resource allocation according to the wireless resource values ​​and antenna coordinate values ​​in the target solution data set.

[0040] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the wireless resource allocation method of the above-mentioned first aspect when executing the computer program.

[0041] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the wireless resource allocation method of the above-mentioned first aspect is implemented.

[0042] The wireless resource allocation method, wireless resource allocation device, electronic device, and computer-readable storage medium proposed in the present application obtain an initial resource allocation model of the wireless communication system, and the initial resource allocation model includes a resource allocation function and a movable antenna constraint, so as to solve the wireless resource allocation problem of the wireless communication system with a movable antenna according to the initial resource allocation model. The resource allocation function is a non-convex objective function, and the movable antenna constraint is a non-convex and coupled constraint. In order to reduce the difficulty of solving the wireless resource allocation problem, the coordinate variables for the movable antenna in the movable antenna constraint are replaced with auxiliary variables to separate the objective function from the movable antenna constraint, so as to facilitate subsequent optimization and solution. In order to improve the solution accuracy of the auxiliary variables and coordinate variables, a penalty term including auxiliary variables and coordinate variables is added to the resource allocation function, so as to use the penalty term to punish the auxiliary variables that do not meet the movable antenna constraint, and obtain the target resource allocation model. The target resource allocation model involves multiple variables including wireless resource variables, coordinate variables, and auxiliary variables, and solving multiple variables at the same time will increase the complexity of the solution. In order to reduce the complexity of solving the wireless resource allocation problem, the target resource allocation model is decomposed to obtain the first sub-model for wireless resource variables, the second sub-model for coordinate variables and the third sub-model for auxiliary variables. The first sub-model, the second sub-model and the third sub-model are respectively solved to obtain a solution data set. By converting the simultaneous solution of the three variables into the solution of the three variables one by one, the difficulty of solving the target resource allocation model is greatly reduced. In order to achieve the optimal wireless resource allocation, the solution data set is input into the target resource allocation model to obtain the model output result, and the steps of respectively solving the first sub-model, the second sub-model and the third sub-model to obtain the solution data set, and inputting the solution data set into the target resource allocation model to obtain the model output result are repeated until the model output result stops when the minimum value is obtained, and the target solution data set when the model output result obtains the minimum value is determined. The solution data set is continuously optimized through multiple iterations, which greatly improves the utilization rate of wireless resources. Wireless resource allocation is performed according to the wireless resource values ​​and antenna coordinate values ​​in the target solution data set, ensuring the accuracy of wireless resource allocation. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a flow chart of a wireless resource allocation method provided in an embodiment of the present application;

[0044] Figure 2 yes Figure 1 Flow chart of step S140 in FIG.

[0045] Figure 3 yes Figure 2 Flow chart of step S230 in FIG.

[0046] Figure 4 yes Figure 3 Flow chart of step S310 in FIG.

[0047] Figure 5 yes Figure 4 Flow chart of step S410 in FIG.

[0048] Figure 6 yes Figure 5 Flow chart of step S530 in FIG.

[0049] Figure 7 yes Figure 4 Flow chart of step S420 in FIG.

[0050] Figure 8 is a rendering of a wireless resource allocation method provided in an embodiment of the present application;

[0051] Fig. 9 is another effect diagram of the wireless resource allocation method provided in an embodiment of the present application;

[0052] Fig.10 is a structural diagram of a wireless resource allocation device provided in an embodiment of the present application;

[0053] Fig.11 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0054] 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.

[0055] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0057] Compared with wireless communication systems that use fixed-position antennas, movable antennas can flexibly change their positions on the antenna panel to maximize the spatial freedom of the antenna. With the increasing number of wireless applications such as video streaming, virtual reality, and augmented reality, movable antennas are introduced into wireless communication systems to improve the capacity of communication systems. The wireless resource allocation problem belongs to the optimization problem, which maximizes the utilization of wireless resources by minimizing the objective function while satisfying a series of constraints. However, the wireless resource allocation problem of a wireless communication system with a movable antenna will introduce non-convex and coupled constraints on the movable antenna and a non-convex objective function, which makes the optimization solution of the wireless resource allocation problem more difficult.

[0058] Based on this, the embodiments of the present application provide a wireless resource allocation method, a wireless resource allocation device, an electronic device and a computer-readable storage medium, aiming to reduce the difficulty of optimizing the solution to the wireless resource allocation problem.

[0059] The wireless resource allocation method, wireless resource allocation device, electronic device and computer-readable storage medium provided in the embodiments of the present application are specifically illustrated through the following embodiments. First, the wireless resource allocation method in the embodiments of the present application is described.

[0060] The wireless resource allocation method provided in the embodiment of the present application relates to the field of wireless communication technology. The wireless resource allocation method provided in the embodiment of the present application can be applied to a terminal, can also be applied to a server side, and can also be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the wireless resource allocation method, etc., but is not limited to the above forms.

[0061] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0062] Figure 1 is an optional flowchart of a wireless resource allocation method provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S110 to S160.

[0063] Step S110, obtaining an initial resource allocation model of the wireless communication system; wherein the initial resource allocation model includes a resource allocation function and a movable antenna constraint, the resource allocation function is used to establish a mapping relationship between a wireless resource variable of the wireless communication system, a coordinate variable of the movable antenna in the wireless communication system and a function output, and the movable antenna constraint is used to constrain the coordinate variable of the movable antenna;

[0064] Step S120, replacing the coordinate variables for the movable antenna in the movable antenna constraint with auxiliary variables, and adding a penalty term including the auxiliary variables and the coordinate variables to the resource allocation function to obtain a target resource allocation model;

[0065] Step S130, performing model decomposition on the target resource allocation model to obtain a first sub-model for wireless resource variables, a second sub-model for coordinate variables, and a third sub-model for auxiliary variables;

[0066] Step S140, respectively solving the first sub-model, the second sub-model and the third sub-model to obtain a solution data set, and inputting the solution data set into the target resource allocation model to obtain a model output result;

[0067] Step S150, repeatedly performing model solving for the first sub-model, the second sub-model and the third sub-model respectively to obtain a solution data set, and inputting the solution data set into the target resource allocation model to obtain a model output result, until the model output result reaches a minimum value, and then determining a target solution data set when the model output result reaches a minimum value;

[0068] Step S160: performing wireless resource allocation according to the wireless resource values ​​and antenna coordinate values ​​in the target solution data set.

[0069] In step S110 of some embodiments, the initial resource allocation model is a model for solving the wireless resource allocation optimization problem. In a wireless communication system integrated with a movable antenna, the initial resource allocation model includes a resource allocation function and a movable antenna constraint. Assuming that the wireless communication system includes a base station equipped with M movable antennas and K multi-antenna user equipments, each user equipment has N antennas, and the resource allocation function is expressed as:

[0070]

[0071] Where f is the resource allocation function, which represents the objective function of the wireless resource allocation optimization problem; M represents the number of movable antennas in the wireless communication system; r m represents the coordinate variable of the mth movable antenna; X is the wireless resource variable, X∈x, x is the constraint of X, and x represents the value range of X.

[0072] The resource allocation function is used to establish the wireless resource variable X of the wireless communication system and the coordinate variables of all movable antennas in the wireless communication system. The mapping relationship between the output of the resource allocation function, the wireless resource variables and the coordinate variables of all movable antennas are the independent variables of the resource allocation function. The wireless resource variables can be the beamforming matrix, the transmission energy matrix, and the task offloading strategy of edge computing. The coordinate variables are used to indicate the two-dimensional spatial coordinates of the movable antenna in the base station.

[0073] The movable antenna constraint is used to constrain the coordinate variables of the movable antenna. The movable antenna constraint is expressed as:

[0074]

[0075] Wherein, C represents the adjustment range of the movable antenna, and the given antenna adjustment area may be a rectangle; and D represents the minimum distance between each pair of movable antennas.

[0076] In step S120 of some embodiments, the first constraint in the movable antenna constraint limits the adjustment range of the movable antenna, and the second constraint is used to limit the distance between any two antennas to avoid the coupling effect between antennas in a given area C. The wireless resource allocation optimization problem introduces non-convex and coupled constraints on movable antennas and non-convex objective functions, which increases the difficulty of solving the wireless resource allocation problem. The second constraint in the movable antenna constraint is a non-convex constraint. In order to facilitate subsequent optimization solutions, auxiliary variables are introduced for variable splitting to separate the second constraint from the resource allocation function. Each coordinate variable of the movable antenna corresponds to an auxiliary variable, and the auxiliary variable can be regarded as a coordinate variable. The coordinate variable for the movable antenna in the second constraint of the movable antenna constraint is replaced with the auxiliary variable, and the second constraint is updated as:

[0077]

[0078] Among them, z m represents the auxiliary variable of the mth movable antenna; 2 represents the second norm.

[0079] Adding a penalty term based on the bi-norm containing auxiliary variables and coordinate variables to the resource allocation function, the target resource allocation model is obtained as follows:

[0080]

[0081] Among them, ρ is the penalty term coefficient.

[0082] By introducing auxiliary variables, the resource allocation optimization problem can be solved by And auxiliary variables The problem is solved by performing alternating optimization, which reduces the difficulty of solving the wireless resource allocation problem, so that the wireless communication system equipped with a movable antenna can provide a higher resource allocation capability. The resource allocation method can be used to solve the resource allocation optimization problem in various scenarios, various optimization objectives, and various system conditions, thereby improving the generalization of the resource allocation method and the quality of the optimization results.

[0083] If the resource allocation function is an objective function to maximize the capacity of a wireless communication system assisted by a movable antenna, consider that the wireless communication system includes a user equipment with N fixed antennas and a base station equipped with M movable antennas. The signal received by the base station can be expressed as:

[0084]

[0085] Where y represents the signal received by the base station; s represents the signal that the user equipment needs to transmit (send); z represents Gaussian noise; H represents the channel matrix; r mrepresents the coordinates of the mth movable antenna, r m =(x m ,y m ), x m and m represent the abscissa and ordinate of the mth movable antenna respectively.

[0086] According to the multipath channel model, the channel matrix is ​​expressed as:

[0087]

[0088] Wherein, G represents the transmitter response matrix, which is the complex gain matrix of the transmission signal of the transmitter and is used to reverse the transmission direction of the transmission signal; the element of the pth row and nth column in the transmitter response matrix is ​​composed of Given, p represents the pth transmission (sending) path, n represents the nth transmitting antenna, t represents the transmitting end, and denote the horizontal component and the elevation component of the transmission angle of the pth transmission path at the transmitter, respectively; H denotes the conjugate transpose; Σ denotes the response between the transmission path and the receiving path; b(r m ) is the response vector of the mth movable antenna at the receiving end, b(r m ) is expressed as:

[0089]

[0090] T represents the transposition operation; ρ q (r m ) represents the difference vector of the transmission distance between the antenna position of the mth movable antenna and the center of the antenna panel in the qth receiving path, which is determined by the parameters of the receiving path, r represents the receiving end, and They represent the horizontal component and the elevation component of the receiving angle of the qth receiving path at the receiving end respectively; L r Indicates the number of receive paths.

[0091] The optimization problem of maximizing the capacity of the wireless communication system is modeled as an initial resource allocation model, which is expressed as:

[0092]

[0093] Tr(Q)≤P max , Q ≥ 0,

[0094]

[0095] Wherein, Q represents the transmission covariance matrix (radio resource variable), that is, the transmission energy matrix; I Mrepresents the M-order unit matrix; σ represents the noise variance; det represents the determinant; Tr represents the trace of the matrix; P max Indicates the maximum power limit. The total transmission power of the wireless communication system does not exceed the maximum power limit.

[0096] Auxiliary variables are introduced to transform the initial resource allocation model into the target resource allocation model. The target resource allocation model is expressed as:

[0097]

[0098] Tr(Q)≤P max , Q ≥ 0,

[0099]

[0100] The problem of solving the above target resource allocation model can be converted into an alternating optimization solution of three sub-problems about the transmission covariance matrix, antenna position and auxiliary variables. By maximizing the communication capacity, the transmission speed of information can be increased, thereby reducing the delay of information transmission.

[0101] If the resource allocation optimization problem is the regularized zero-forcing precoding problem of movable antennas in a multi-user scenario, consider a multi-user multiple-input single-output communication system, where the base station has M movable antennas and K single-antenna users. The signal received by the kth user equipment is:

[0102]

[0103] in, represents the row vector of the channel matrix, i.e., the channel of the kth user equipment; F represents the precoding matrix (radio resource variable); z k represents the Gaussian noise in the signal received by the kth user; s represents the signal that the base station needs to transmit (send).

[0104] The initial resource allocation model based on the regularized zero-forcing coding problem can be constructed as:

[0105]

[0106] Among them, I K represents the K-order identity matrix; α is a hyperparameter that controls the flexibility of the encoding; J represents the J-norm.

[0107] Auxiliary variables are introduced to transform the initial resource allocation model into the target resource allocation model. The target resource allocation model is expressed as:

[0108]

[0109] The problem of solving the above target resource allocation model can be converted into an alternating optimization solution of three sub-problems about precoding matrix, antenna position and auxiliary variables. Precoding technology can reduce the interference between different data streams in a multi-antenna system and improve the quality of the received signal.

[0110] In step S130 of some embodiments, in order to reduce the difficulty of solving the wireless resource allocation optimization problem, the target resource allocation model is decomposed to divide the complete problem into multiple small problems containing one optimization variable, and a first sub-model for the wireless resource variable, a second sub-model for the coordinate variable, and a third sub-model for the auxiliary variable are obtained. The large-scale resource allocation optimization problem is solved by alternating optimization of the first sub-model, the second sub-model, and the third sub-model, which speeds up the convergence speed of the model and reduces the difficulty of wireless resource allocation. The order of solving the first sub-model, the second sub-model, and the third sub-model can be changed arbitrarily, and the embodiment of the present application does not limit the order of solving.

[0111] See also Figure 2 In some embodiments, step S140 may include but is not limited to steps S210 to S240:

[0112] Step S210, calling a wireless resource allocation algorithm to solve the first sub-model, and obtaining a wireless resource value of a wireless resource variable in the first sub-model;

[0113] Step S220, calling the gradient projection algorithm to solve the second sub-model, and obtaining the antenna coordinate value of the coordinate variable in the second sub-model;

[0114] Step S230, calling a geometric optimization algorithm to solve the third sub-model, and obtaining auxiliary variable values ​​of auxiliary variables in the third sub-model;

[0115] Step S240, obtaining a solution data set based on the wireless resource value, the antenna coordinate value and the auxiliary variable value.

[0116] In step S210 of some embodiments, parameter variables unrelated to the radio resource variable X are not considered, and the first sub-model is expressed as:

[0117]

[0118] X∈x.

[0119] The first sub-model can be reduced to the communication system optimization problem of the traditional fixed-position antenna, which can be solved by the traditional wireless resource allocation algorithm. The first sub-model is solved by the traditional wireless resource allocation algorithm. When the output value of the objective function of the first sub-model takes the minimum value, the wireless resource value of the wireless resource variable in the first sub-model is obtained. The wireless resource value is the optimal value of the wireless resource variable. The wireless resource allocation algorithm can be determined according to the wireless resource variable. For example, if the wireless resource variable X is the transmission energy matrix, the wireless resource allocation algorithm is an algorithm based on water injection energy allocation, which obtains a closed expression of the transmission energy by performing singular value decomposition on the channel matrix.

[0120] It should be noted that if the wireless resource allocation algorithm applied to solve the first sub-model can obtain the stationary point of the sub-problem represented by the first sub-model, then the wireless resource allocation algorithm will converge. The stationary point refers to the point where the first-order derivative (gradient) of the objective function with respect to the wireless resource variable is 0, that is, the possible value of the optimal solution to the optimization problem, which ensures the stability of the performance of the wireless communication system to a certain extent.

[0121] In step S220 of some embodiments, the coordinate variables are not considered. The second submodel is expressed as:

[0122]

[0123] The gradient projection algorithm is called to solve the model of the second sub-model. When the output value of the objective function of the second sub-model takes the minimum value, the antenna coordinate value of the coordinate variable of each movable antenna in the second sub-model is obtained. The gradient projection algorithm solves the gradient (generalized derivative) of the objective function through existing tools such as pytorch, and performs iterative optimization according to the gradient. During the iteration process, the results of the coordinate variables in the second sub-model are subject to constraints. If only the gradient descent algorithm is executed, antenna coordinate values ​​that do not meet the constraints may be obtained, that is, invalid solutions. Therefore, only the gradient descent algorithm cannot be executed. Therefore, after updating the coordinate variables using the gradient descent method, the results of the coordinate variables are mapped to the range that meets the constraints through the projection method.

[0124] In step S230 of some embodiments, the auxiliary variable Irrelevant parameter variables, auxiliary variables only appear in the penalty term of the target resource allocation model. The third sub-model is expressed as:

[0125]

[0126] The geometric optimization algorithm is called to solve the third sub-model, and when the output value of the objective function of the third sub-model takes a minimum value, the auxiliary variable value of the auxiliary variable associated with the movable antenna in the third sub-model is obtained.

[0127] In step S240 of some embodiments, the wireless resource value, antenna coordinate value and auxiliary variable value are included in a set to obtain a solution data set. The solution data set is input into a resource allocation function with a penalty term added to the target resource allocation model for solution, and the output value of the resource allocation function is used as the model output result.

[0128] Through the above steps S210 to S240, the optimal values ​​of the variables in the first sub-model, the second sub-model and the third sub-model can be solved respectively, so as to convert the simultaneous solution of multiple parameter variables of the wireless resource allocation problem into the one-by-one solution of multiple parameter variables, which greatly reduces the difficulty of wireless resource allocation.

[0129] See also Figure 3 In some embodiments, step S230 may include but is not limited to steps S310 to S320:

[0130] Step S310, based on the geometric optimization algorithm, fix the other auxiliary variables in the third sub-model except the current auxiliary variable, solve the current auxiliary variable, and obtain the auxiliary variable value of the current auxiliary variable;

[0131] Step S320, traverse the auxiliary variables in sequence, and solve the next auxiliary variable until the auxiliary variable values ​​of all auxiliary variables are obtained.

[0132] In step S310 of some embodiments, there are constraints in the third sub-model, so that the sub-problem represented by the third sub-model is still non-convex. However, the objective function structure of the third sub-model is simple and fixed, so that the sub-problem can be solved by the geometric optimization algorithm. Specifically, the auxiliary variables are optimized one by one, and the other M-1 auxiliary variables except the current auxiliary variable are fixed unchanged during the optimization process, and the third sub-model is updated as:

[0133]

[0134] Among them, z m Represents the auxiliary variable currently optimized.

[0135] The updated third sub-model is solved, and when the output value of the objective function of the updated third sub-model takes a minimum value, the auxiliary variable value of the current auxiliary variable is obtained.

[0136] In step S320 of some embodiments, the auxiliary variables are traversed in sequence, and the other auxiliary variables except the next auxiliary variable are fixed according to step S310, and the next auxiliary variable is solved until the auxiliary variable values ​​of all auxiliary variables are obtained.

[0137] Through the above steps S310 to S320, the sub-problem of solving multiple auxiliary variables at the same time can be converted into the sub-problem of solving multiple auxiliary variables one by one, which reduces the difficulty of solving the third sub-model, and then reduces the difficulty of solving the entire resource allocation model, and reduces the difficulty of allocating wireless communication resources.

[0138] See also Figure 4 In some embodiments, step S310 may include but is not limited to steps S410 to S420:

[0139] Step S410, obtaining a set of antenna coordinate points that do not satisfy the movable antenna constraint;

[0140] Step S420, solving the current auxiliary variable according to the number of coordinate points of the antenna coordinate point set to obtain the auxiliary variable value of the current auxiliary variable.

[0141] In step S410 of some embodiments, obtaining a signal that does not satisfy the movable antenna constraint Auxiliary variables z of each movable antenna l , and obtain the antenna coordinate point set.

[0142] In step S420 of some embodiments, the number of coordinate points is the number of elements in the antenna coordinate point set. The optimal solution of the current auxiliary variable is calculated according to the number of coordinate points in the antenna coordinate point set to obtain the auxiliary variable value of the current auxiliary variable.

[0143] Through the above steps S410 to S420, the auxiliary variable value of the current auxiliary variable can be obtained to complete the solution of the third sub-model, and then the optimal solution to the resource allocation optimization problem is obtained to perform resource allocation.

[0144] See also Figure 5 In some embodiments, step S410 may include but is not limited to steps S510 to S540:

[0145] Step S510, determining a target circle according to other auxiliary variables except the current auxiliary variable;

[0146] Step S520, obtaining the intersection points between the target circles to obtain a first coordinate point set;

[0147] Step S530, determining a second coordinate point set according to other auxiliary variables except the current auxiliary variable, coordinate variables corresponding to the current auxiliary variable and the target circle;

[0148] Step S540: Determine an antenna coordinate point set according to the first coordinate point set and the second coordinate point set.

[0149] In step S510 of some embodiments, for each auxiliary variable except the current auxiliary variable, obtain the auxiliary variable z l The target circle of the auxiliary variables is obtained by taking a circle with a center of θ and a minimum distance D between each pair of movable antennas as the radius. In the first iteration, the initial values ​​of other auxiliary variables can be the random initialization values ​​of the coordinate variables of the corresponding movable antennas. In the current iteration, the values ​​of other auxiliary variables can be the latest values ​​of the current iteration.

[0150] In step S520 of some embodiments, a set of intersection points between target circles is acquired to obtain a first coordinate point set.

[0151] In step S530 of some embodiments, the target straight line is determined based on other auxiliary variables except the current auxiliary variable and the coordinate variables corresponding to the current auxiliary variable, and the intersection of the target straight line and the target circle is obtained to obtain a second coordinate point set.

[0152] In step S540 of some embodiments, the first coordinate point set and the second coordinate point set are combined to obtain the antenna coordinate point set.

[0153] Through the above steps S510 to S540, the antenna coordinate point set can be obtained, and the third sub-model for the auxiliary variables can be solved based on the geometric optimization method and the antenna coordinate point set to obtain the auxiliary variable values ​​of the auxiliary variables.

[0154] See also Figure 6 In some embodiments, step S530 may include but is not limited to steps S610 to S620:

[0155] Step S610, determining a target straight line according to other auxiliary variables except the current auxiliary variable and coordinate variables corresponding to the current auxiliary variable;

[0156] Step S620, obtaining the intersection of the target straight line and the target circle to obtain a second coordinate point set.

[0157] In step S610 of some embodiments, for each auxiliary variable except the current auxiliary variable, according to the auxiliary variable z l The coordinate variable r corresponding to the current auxiliary variable m Determine a straight line and obtain the target straight line of the auxiliary variable.

[0158] In step S620 of some embodiments, for each auxiliary variable except the current auxiliary variable, the intersection point between the target straight line and the target circle of the auxiliary variable is obtained, and the second coordinate point set is determined according to the intersection points corresponding to other auxiliary variables.

[0159] Through the above steps S610 to S620, the second coordinate point set can be calculated to solve the current auxiliary variable based on the second coordinate point set.

[0160] See also Figure 7 In some embodiments, step S420 may include but is not limited to step S710 or step S720:

[0161] Step S710, if the number of coordinate points is zero, the preset reference antenna coordinate value is used as the auxiliary variable value;

[0162] Step S720: If the number of coordinate points is greater than or equal to one, the antenna coordinate point set is screened to obtain an auxiliary variable value.

[0163] In step S710 of some embodiments, the reference antenna coordinate value is the coordinate variable r corresponding to the current auxiliary variable. m If the number of coordinate points is zero, then r m The value of is used as the auxiliary variable value.

[0164] In step S720 of some embodiments, if the number of coordinate points is greater than or equal to one, the difference data of the coordinate variable corresponding to each element in the antenna coordinate point set and the element is calculated, and the element when the difference data takes the minimum value is obtained as the auxiliary variable value. The difference data is expressed as:

[0165]

[0166] Wherein, w and U represent the first coordinate point set and the second coordinate point set respectively.

[0167] Through the above steps S710 to S720, the auxiliary variables of the third sub-model can be updated based on the geometric optimization method. The geometric optimization method performs optimization according to the closed-form solution of the sub-problem without a large number of iterations. It accelerates the solution process while ensuring the quality of the auxiliary variable solution, and ultimately achieves efficient solution to the wireless resource allocation problem in various scenarios.

[0168] In step S150 of some embodiments, the steps of solving the model for the first sub-model, the second sub-model and the third sub-model respectively to obtain a solution data set, and inputting the solution data set into the target resource allocation model to obtain the model output result are repeated. In each iteration process, the model is solved for the first sub-model, the second sub-model and the third sub-model respectively until the model output result reaches the minimum value, the number of iterations reaches the maximum number of iterations, or the change of the model output result between two adjacent iterations is less than the preset change threshold. The solution data set when the model output result reaches the minimum value is used as the target solution data set. The preset change threshold can be 0.001. One solution process of three sub-models is called a complete iteration process.

[0169] In step S160 of some embodiments, the position of the movable antenna in the wireless communication system is determined according to the antenna coordinate value in the target solution data set, and the wireless communication system allocates wireless resources according to the wireless resource value and the position of the movable antenna.

[0170] The embodiment of the present application provides a unified, high-performance solution paradigm for the resource allocation problem of a wireless communication system assisted by a movable antenna, and additionally points out the solution method for each modular sub-problem. The non-convex constraints on the antenna position are converted into constraints on auxiliary variables, so no approximation is used in the solution process, so that the resource allocation performance of the wireless communication system is relatively less affected by specific problems such as channel capacity maximization and regularized zero-forcing precoding problems, and can be widely used to solve resource allocation problems in various movable antenna scenarios. The development of current communication technology has put forward more and more complex optimization goals and resource allocation constraints for wireless network optimization. The embodiment of the present application adopts a distributed structure. With the assistance of a movable antenna, it ensures the quality of solving the resource allocation optimization problem while being compatible with the traditional wireless resource allocation algorithm. It can be easily connected to the existing optimization module, and its scalability is good, which reduces the difficulty and cost of resource allocation.

[0171] Consider a base station equipped with 6 movable antennas (M=6), and the number of antennas of the user equipment is 6 (N=6). The movable antenna's moving area C is a square with a side length of A. The minimum distance D between antennas is half of the communication wavelength λ, and the normalized area side length is defined as A / λ. The initial value of the penalty coefficient ρ is 5, and it will increase with the increase of the number of iterations. The iteration stopping condition is that the change in the output value of the objective function in the target resource allocation model between two adjacent iterations is less than 0.001.

[0172] Taking the signal capacity of the wireless communication system as the evaluation index, the channel capacity optimization result of the optimization problem of maximizing the capacity of the wireless communication system is as follows: Figure 8 As shown, compared with the antenna selection algorithm and the fixed position antenna algorithm, the wireless resource allocation method of the embodiment of the present application can flexibly and continuously optimize the position of the movable antenna, so as to efficiently utilize the spatial degree of the antenna to enhance the channel capacity. Compared with the continuous convex approximation algorithm, the wireless resource allocation method of the embodiment of the present application does not introduce any approximation link, so it has better optimization performance.

[0173] Taking the total rate of information transmission in wireless communication system as the evaluation index, the precoding optimization results of regularized zero-forcing precoding problem are as follows: Fig. 9As shown, the wireless resource allocation method of the embodiment of the present application splits the non-convex objective function and the non-convex constraint condition by introducing auxiliary variables, avoiding the difficulty of solving the problem when the two appear at the same time. The wireless resource allocation method of the embodiment of the present application is superior to the antenna selection algorithm, the fixed position antenna algorithm and the flexible precoding algorithm. Compared with the optimization method proposed by the flexible precoding algorithm that adopts approximation and additionally introduces non-convex and discontinuous zero norm constraints, the method of the embodiment of the present application has obvious data transmission performance advantages.

[0174] See also Fig.10 The embodiment of the present application further provides a wireless resource allocation device, which can implement the above wireless resource allocation method, and the wireless resource allocation device includes:

[0175] The acquisition module 1010 is used to acquire an initial resource allocation model of the wireless communication system; wherein the initial resource allocation model includes a resource allocation function and a movable antenna constraint, the resource allocation function is used to establish a mapping relationship between a wireless resource variable of the wireless communication system, a coordinate variable of the movable antenna in the wireless communication system and a function output, and the movable antenna constraint is used to constrain the coordinate variable of the movable antenna;

[0176] A model building module 1020 is used to replace the coordinate variables for the movable antenna in the movable antenna constraint with auxiliary variables, and add a penalty term including the auxiliary variables and the coordinate variables to the resource allocation function to obtain a target resource allocation model;

[0177] A model decomposition module 1030, configured to perform model decomposition on the target resource allocation model to obtain a first sub-model for wireless resource variables, a second sub-model for coordinate variables, and a third sub-model for auxiliary variables;

[0178] The first solving module 1040 is used to solve the first sub-model, the second sub-model and the third sub-model respectively to obtain a solution data set, and input the solution data set into the target resource allocation model to obtain a model output result;

[0179] The second solving module 1050 is used to repeatedly solve the first sub-model, the second sub-model and the third sub-model respectively to obtain a solution data set, and input the solution data set into the target resource allocation model to obtain a model output result, until the model output result reaches a minimum value, and then determine a target solution data set when the model output result reaches the minimum value;

[0180] The resource allocation module 1060 is used to allocate wireless resources according to the wireless resource values ​​and antenna coordinate values ​​in the target solution data set.

[0181] The specific implementation of the wireless resource allocation device is basically the same as the specific implementation of the wireless resource allocation method described above, and will not be described in detail here.

[0182] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above wireless resource allocation method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0183] See also Fig.11 , Fig.11 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0184] The processor 1110 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (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 the present application;

[0185] The memory 1120 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1120 can store an operating system and other applications. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1120, and the processor 1110 calls and executes the wireless resource allocation method of the embodiment of the present application;

[0186] Input / output interface 1130, used to implement information input and output;

[0187] The communication interface 1140 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0188] bus 1150 , which transmits information between the various components of the device (e.g., processor 1110 , memory 1120 , input / output interface 1130 , and communication interface 1140 );

[0189] The processor 1110 , the memory 1120 , the input / output interface 1130 , and the communication interface 1140 are connected to each other in communication within the device via the bus 1150 .

[0190] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above-mentioned wireless resource allocation method is implemented.

[0191] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0192] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0193] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0194] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0195] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0196] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0197] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0198] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0199] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0200] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0201] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0202] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A wireless resource allocation method, characterized in that: The method comprises: Acquire an initial resource allocation model of the wireless communication system; wherein the initial resource allocation model includes a resource allocation function and a movable antenna constraint, the resource allocation function is used to establish a mapping relationship between a wireless resource variable of the wireless communication system, a coordinate variable of the movable antenna in the wireless communication system and a function output, and the movable antenna constraint is used to constrain the coordinate variable of the movable antenna; Replacing the coordinate variables for the movable antenna in the movable antenna constraint with auxiliary variables, and adding a penalty term including the auxiliary variables and the coordinate variables to the resource allocation function to obtain a target resource allocation model; Decomposing the target resource allocation model to obtain a first sub-model for the wireless resource variable, a second sub-model for the coordinate variable, and a third sub-model for the auxiliary variable; Solving the first sub-model, the second sub-model and the third sub-model respectively to obtain solution data sets, and inputting the solution data sets into the target resource allocation model to obtain model output results; Repeat the steps of respectively solving the first sub-model, the second sub-model and the third sub-model to obtain a solution data set, and inputting the solution data set into the target resource allocation model to obtain a model output result, until the model output result stops when a minimum value is obtained, and determine a target solution data set when the model output result obtains a minimum value; Wireless resource allocation is performed according to the wireless resource values ​​and antenna coordinate values ​​in the target solution data set.

2. The method according to claim 1, characterized in that The performing model solving on the first sub-model, the second sub-model and the third sub-model respectively to obtain a solution data set includes: Calling a wireless resource allocation algorithm to solve the first sub-model to obtain a wireless resource value of the wireless resource variable in the first sub-model; Calling a gradient projection algorithm to solve the second sub-model to obtain antenna coordinate values ​​of the coordinate variables in the second sub-model; Calling a geometric optimization algorithm to solve the third sub-model to obtain auxiliary variable values ​​of the auxiliary variables in the third sub-model; A solution data set is obtained based on the wireless resource value, the antenna coordinate value and the auxiliary variable value.

3. The method according to claim 2, characterized in that The calling of the geometric optimization algorithm to solve the third sub-model to obtain the auxiliary variable value of the auxiliary variable in the third sub-model includes: Based on the geometric optimization algorithm, other auxiliary variables except the current auxiliary variable in the third sub-model are fixed, and the current auxiliary variable is solved to obtain the auxiliary variable value of the current auxiliary variable; The auxiliary variables are traversed in sequence, and the next auxiliary variable is solved until the auxiliary variable values ​​of all auxiliary variables are obtained.

4. The method according to claim 3, characterized in that The step of solving the current auxiliary variable to obtain the auxiliary variable value of the current auxiliary variable includes: Acquire a set of antenna coordinate points that do not satisfy the movable antenna constraint; The current auxiliary variable is solved according to the number of coordinate points of the antenna coordinate point set to obtain the auxiliary variable value of the current auxiliary variable.

5. The method according to claim 4, characterized in that The obtaining of a set of antenna coordinate points that do not satisfy the movable antenna constraint includes: Determine a target circle based on other auxiliary variables except the current auxiliary variable; Obtaining the intersection points between the target circles to obtain a first coordinate point set; Determine a second coordinate point set according to other auxiliary variables except the current auxiliary variable, coordinate variables corresponding to the current auxiliary variable and the target circle; The antenna coordinate point set is determined according to the first coordinate point set and the second coordinate point set.

6. The method according to claim 5, characterized in that The determining of the second coordinate point set according to other auxiliary variables except the current auxiliary variable, the coordinate variables corresponding to the current auxiliary variable and the target circle includes: Determine a target straight line according to other auxiliary variables except the current auxiliary variable and coordinate variables corresponding to the current auxiliary variable; The intersection points of the target straight line and the target circle are obtained to obtain the second coordinate point set.

7. The method according to claim 4, characterized in that Solving the current auxiliary variable according to the number of coordinate points of the antenna coordinate point set to obtain the auxiliary variable value of the current auxiliary variable includes: If the number of coordinate points is zero, the preset reference antenna coordinate value is used as the auxiliary variable value; If the number of the coordinate points is greater than or equal to one, the antenna coordinate point set is screened to obtain the auxiliary variable value.

8. A wireless resource allocation device, characterized in that: The device comprises: An acquisition module, configured to acquire an initial resource allocation model of a wireless communication system; wherein the initial resource allocation model includes a resource allocation function and a movable antenna constraint, wherein the resource allocation function is configured to establish a mapping relationship between a wireless resource variable of a wireless communication system, a coordinate variable of a movable antenna in the wireless communication system, and a function output, and wherein the movable antenna constraint is configured to constrain the coordinate variable of the movable antenna; A model building module, used for replacing the coordinate variables for the movable antenna in the movable antenna constraint with auxiliary variables, and adding a penalty term including the auxiliary variables and the coordinate variables to the resource allocation function to obtain a target resource allocation model; A model decomposition module, used for performing model decomposition on the target resource allocation model to obtain a first sub-model for the wireless resource variable, a second sub-model for the coordinate variable and a third sub-model for the auxiliary variable; A first solving module is used to solve the first sub-model, the second sub-model and the third sub-model respectively to obtain a solution data set, and input the solution data set into the target resource allocation model to obtain a model output result; a second solving module, configured to repeatedly perform the steps of solving the first sub-model, the second sub-model and the third sub-model respectively to obtain a solving data set, and input the solving data set into the target resource allocation model to obtain a model output result, until the model output result stops when a minimum value is obtained, and determine a target solving data set when the model output result obtains a minimum value; The resource allocation module is used to perform wireless resource allocation according to the wireless resource values ​​and antenna coordinate values ​​in the target solution data set.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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