Method and device for adjusting position of movable antenna, communication system and program product

Abstract

The embodiment of the invention provides a method and device for adjusting the position of a movable antenna, a communication system and a program product. The method comprises the following steps: based on position coordinates of a base station and a mobile terminal in the communication system, constructing a dual-path channel model of the communication system comprising a leading path and a diffusion path; determining an expression of a traversal reachable rate based on a double-path channel model by taking the position coordinates of each movable antenna as unknown quantities; solving target position coordinates of each movable antenna by taking the minimum value of the maximum traversal reachable rate as a target; and adjusting the movable antennas in the communication system according to the target position coordinates of the movable antennas. By constructing the channel model adaptive to the actual communication scene and solving the position coordinate corresponding to the minimum value of the traversal reachable rate of the maximized communication system, the position optimization of the movable antenna is effectively realized, and the method is more adaptive to the actual scene, so that the overall communication quality of the communication system is improved.

Description

Technical Field

[0001] This application relates to the field of wireless communication technology, and in particular to a method, apparatus, communication system, and program product for adjusting the position of a movable antenna. Background Technology

[0002] With the rapid development of global marine economic activities, the demand for maritime communications has exploded. Fields such as maritime shipping, offshore oil exploration, fisheries resource development, marine tourism, and maritime emergency rescue are all placing higher demands on the reliability, coverage, and data transmission rates of communication systems.

[0003] However, due to the special characteristics of maritime communication, such as long-distance transmission and large transmission fluctuations, traditional communication technologies that rely on fixed-location communication antennas are difficult to adapt to the characteristics of maritime communication, resulting in poor communication quality. Summary of the Invention

[0004] The movable antenna position adjustment method, apparatus, communication system, and program product provided in this application improve the spatial freedom of the communication system by using a movable antenna, enabling the antenna to move within a certain range. Furthermore, by constructing a channel model on the system and determining the position of the movable antenna with the goal of maximizing the minimum traversal reach rate, the overall communication quality of the communication system is improved, making it more suitable for maritime communication.

[0005] In a first aspect, embodiments of this application provide a method for adjusting the position of a movable antenna, applied to a communication system, the communication system including multiple movable antennas, the method comprising:

[0006] Based on the location coordinates of the base station and mobile terminal in the communication system, a two-path channel model of the communication system including the dominant path and the diffusion path is constructed; wherein, the location coordinates of each movable antenna in the two-path channel model are unknowns.

[0007] Using the position coordinates of each movable antenna as unknowns, the expression for the traversal reachable rate is determined based on the two-path channel model;

[0008] With the objective of maximizing the minimum reachable traversal rate, the target position coordinates of each movable antenna are solved.

[0009] Adjust the movable antennas in the communication system according to the target position coordinates of each movable antenna.

[0010] In one possible implementation, the expression for the traversal reachability includes terms corresponding to the beamforming matrix and terms corresponding to the channel vector; the channel vector is the output of the two-path channel model, and the method further includes:

[0011] Based on the zero-forcing beamforming algorithm, the channel vector is used to construct the corresponding terms of the beamforming matrix;

[0012] By combining the terms corresponding to the constructed beamforming matrix with the channel vector, an expression for the traversal reachable rate is obtained.

[0013] In one possible implementation, the target position coordinates of each movable antenna are solved with the objective of minimizing the traversal reachability, including:

[0014] Based on Jensen's inequality, determine the fractional expression for the tight lower bound of the traversal reachability.

[0015] Let the components of the denominator in the tight lower bound expression be surrogate functions, and construct the target surrogate function using the Monte Carlo method; the target surrogate function approximates the surrogate function when the number of instantaneous channel realizations is sufficiently large;

[0016] The target position coordinates of each movable antenna are solved by minimizing the target surrogate function.

[0017] In one possible implementation, the target position coordinates of each movable antenna are solved with the objective of minimizing the target surrogate function, including:

[0018] The objective function is determined by subtracting the product of the penalty function and the iteration coefficients from the objective surrogate function; the iteration coefficients are related to the number of iterations when solving for the target position coordinates.

[0019] The position coordinates of the movable antenna are set as the initial position coordinates, and the gradient descent algorithm is executed on the objective function a preset number of times to obtain the position coordinates of the first iteration;

[0020] Repeat the following steps until the iteration termination condition is met, and determine the position coordinates of the last iteration as the target position coordinates of the movable antenna:

[0021] Based on the number of iterations, reduce the iteration coefficient and determine the objective function for this iteration;

[0022] The position coordinates of the movable antenna are set to the position coordinates of the previous iteration, and the gradient descent algorithm is executed a preset number of times on the objective function of the current iteration to obtain the position coordinates of the current iteration;

[0023] The iteration ends when the distance between the position coordinates of the current iteration and the position coordinates of the previous iteration is less than or equal to a preset distance threshold.

[0024] In one possible implementation, a gradient descent algorithm is performed on the objective function a predetermined number of times to obtain the position coordinates of the first iteration, including:

[0025] Repeat the following steps a preset number of times for the objective function, and determine the position coordinates of the last iteration as the position coordinates of the first iteration:

[0026] Based on the current position coordinates, calculate the normalized gradient of the objective function, and determine the position coordinates to be updated and the current objective function value according to the normalized gradient and the step size; the first position coordinates are the initial position coordinates.

[0027] If the location coordinates to be updated meet the movable range of the movable antenna, and the difference between the previous objective function value and the current objective function value is greater than or equal to a preset threshold, then the next location coordinates will be determined as the location coordinates to be updated.

[0028] If the coordinates of the position to be updated do not meet the movable range of the movable antenna, or the difference between the previous objective function value and the current objective function value is less than a preset threshold, then the step size is updated, and the steps of determining the coordinates of the position to be updated and the second function value based on the normalized gradient and the step size are executed.

[0029] In one possible implementation, based on the location coordinates of the base station and mobile terminal in the communication system, a two-path channel model of the communication system, including the dominant path and the diffusion path, is constructed, including:

[0030] For each movable antenna, based on the spatial geometric relationship between the position coordinates of the movable antenna and the position coordinates of the mobile terminal, the first channel vector when the transmitted signal of the movable antenna propagates to the mobile terminal via the dominant path is determined;

[0031] The first channel vectors of each movable antenna are concatenated to obtain the first channel matrix;

[0032] For each movable antenna, the second channel vector when the transmitted signal of the movable antenna propagates to the mobile end through the diffusion path is determined by multiplying the preset spatial covariance matrix by the Kolesky decomposition.

[0033] The second channel vectors of each movable antenna are concatenated to obtain the second channel matrix;

[0034] Adding the first channel matrix and the second channel matrix yields the two-path channel model.

[0035] In one possible implementation, the method further includes, before constructing a two-path channel model comprising the dominant path and the diffusion path:

[0036] Using the base station in the communication system as the origin, and two straight lines parallel to the ground and pointing in a preset direction and perpendicular to each other as the horizontal and vertical axes, and a straight line perpendicular to the ground as the vertical axis, a Cartesian coordinate system for the communication system is constructed.

[0037] Acquire historical trajectories from multiple mobile devices; the historical trajectories include multiple location points.

[0038] Based on historical trajectories, multiple location coordinates corresponding to mobile devices are generated in a Cartesian coordinate system.

[0039] Secondly, embodiments of this application provide an optimization device for a communication system, applied to a communication system including multiple movable antennas, the device comprising:

[0040] The model construction module is used to construct a two-path channel model of a communication system, including the dominant path and the diffusion path, based on the location coordinates of the base station and the mobile terminal in the communication system; wherein, the location coordinates of each movable antenna in the two-path channel model are unknowns.

[0041] The traversal reachable rate representation module is used to determine the expression for the traversal reachable rate based on the two-path channel model, with the position coordinates of each movable antenna as unknowns.

[0042] The target location solution module is used to solve for the target location coordinates of each movable antenna with the objective of minimizing the traversal reach rate.

[0043] The movable antenna adjustment module is used to adjust the movable antennas in the communication system according to the target position coordinates of each movable antenna.

[0044] Thirdly, embodiments of this application provide a communication system, including: a base station, multiple movable antennas, and a control unit;

[0045] The control unit is used to execute the method provided in the first aspect above.

[0046] Fourthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the method provided in the first aspect above.

[0047] The method, apparatus, communication system, and program product for adjusting the position of movable antennas provided in this application are applied to communication systems with multiple movable antennas. By optimizing the position of the movable antennas, the spatial degrees of freedom of the communication system are fully utilized, thereby improving the communication quality. Specifically, based on the positions of the base station and the mobile terminal in the communication system, a channel model including two communication paths—the dominant path and the diffusion path—is constructed. This model dynamically and effectively adapts to the channel characteristics and dynamic characteristics of the mobile terminal in maritime communication scenarios, providing an accurate theoretical basis adapted to the current scenario for the subsequent optimization problem. Then, based on the constructed channel model, the expression for the ergonomic reachability of the model is derived. The value of this ergonomic reachability represents the communication quality of the communication system. By solving for the ergonomic reachability, the position coordinates of the movable antenna corresponding to maximizing the minimum ergonomic reachability are found. The movable antenna is then moved to these position coordinates. Based on this, the minimum communication quality of the communication system is guaranteed, thereby comprehensively improving the overall communication quality level of the communication system. Attached Figure Description

[0048] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0049] Figure 1 This is a schematic diagram of a communication system provided in an embodiment of this application;

[0050] Figure 2 A flowchart illustrating a method for adjusting the position of a movable antenna provided in an embodiment of this application;

[0051] Figure 3 This application provides a schematic diagram of the structure of a communication system according to an embodiment of the present application.

[0052] Figure 4 A flowchart illustrating another method for adjusting the position of a movable antenna provided in an embodiment of this application;

[0053] Figure 5 A flowchart illustrating an iterative method for solving target position coordinates provided in an embodiment of this application;

[0054] Figure 6 A schematic diagram of a movable antenna position adjustment device provided in an embodiment of this application;

[0055] Figure 7 This is a schematic diagram of the structure of a communication system provided in an embodiment of this application.

[0056] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0057] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0058] First, let me explain the terms used in this application:

[0059] Movable antennas (MA), also known as fluid antennas (FA), can dynamically adjust the position and orientation of the antenna within a spatial region to optimize the matching relationship between the antenna and the channel in real time, thereby making full use of the spatial degrees of freedom of the communication system and improving the communication quality of wireless communication.

[0060] Ergodic Achievable Rate is a key metric used to measure the average performance of a communication system, specifically reflecting the average data transmission rate that communication users can achieve.

[0061] With the booming development of global marine activities, such as maritime shipping, offshore oil exploration, fisheries, and marine tourism, the demand for reliable and high-quality communication services from maritime users is becoming increasingly urgent. How to effectively meet the communication needs of maritime users has become a pressing issue in the field of marine communications.

[0062] With the in-depth research and development of sixth-generation mobile communication technology and the gradual advancement of the concept of an integrated air-space-ground-sea network, Multiple-Input Multiple-Output (MIMO) technology, by deploying multiple antennas in a communication system, allows multiple antennas to transmit and receive signals simultaneously, thereby improving the capacity, speed, and reliability of the communication system. Due to its superior performance, it has been recognized as a key technology for enhancing maritime communication.

[0063] Figure 1 This is a schematic diagram of a communication system provided in an embodiment of this application. Figure 1As shown, this application is applied to a communication system scenario, particularly to a communication system for downlink maritime communication. The communication system includes a base station, antennas, and a mobile terminal. The base station, as the core hub, is equipped with multiple antennas, enabling bidirectional communication, including uplink communication (receiving signals transmitted by the mobile terminal) and downlink communication (transmitting signals to the mobile terminal). The mobile terminal is the communication device used by maritime users and may be located anywhere within the coverage of the communication system. During maritime communication, the antenna accurately transmits signals to the target mobile terminal via a wireless channel. After receiving the signal, the target mobile terminal returns a response signal to the antenna according to the communication protocol, thus completing a bidirectional communication loop.

[0064] Currently, traditional communication technologies rely on fixed-location communication antennas, meaning antennas are deployed discretely in fixed locations. However, maritime communication differs significantly from land-based communication, characterized by long-distance transmission and high transmission volatility. Traditional communication technologies are ill-suited to the characteristics of maritime communication, failing to meet the communication needs of users at sea and resulting in poor communication quality. Furthermore, to meet the communication requirements of maritime users, traditional communication technologies require the deployment of numerous antennas and related equipment, such as radio frequency links and phase shifters, leading to high hardware costs and high power consumption in the communication system. The difficulty in deploying antennas and equipment in the maritime communication environment also increases deployment and maintenance costs.

[0065] The movable antenna position adjustment method provided in this application is applied to communication systems with multiple movable antennas. It fully utilizes the spatial freedom of the communication system, enabling high-quality communication with the same or fewer antennas, improving communication performance such as increasing received signal power, suppressing interference, enhancing beamforming flexibility, and increasing spatial multiplexing rate, while reducing the cost and power consumption of the communication system. Specifically, based on the spatial positions of each device in the communication system, a two-path channel model is constructed to adapt to the actual scenario of maritime communication. The channel model is more accurate, providing a high-quality model foundation for subsequent implementation. Then, for this two-path channel model, an expression for the ergonomic reachable rate is determined. The value of this expression reflects the average performance of the communication system. Furthermore, while maximizing the overall ergonomic reachable rate, the positions of each antenna are determined, i.e., aiming to maximize the minimum ergonomic reachable rate. Multiple movable antennas are then moved to their corresponding positions, thereby ensuring the overall fairness of the communication system coverage and achieving the Pareto optimal boundary for communication quality, thus improving communication quality.

[0066] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0067] Figure 2 This is a flowchart illustrating a method for adjusting the position of a movable antenna according to an embodiment of this application. The method provided in this embodiment is applied to a communication system, which includes multiple movable antennas. Figure 2 As shown, the method includes:

[0068] Step S201: Based on the location coordinates of the base station and mobile terminal in the communication system, construct a two-path channel model of the communication system that includes the dominant path and the diffusion path.

[0069] In the two-path channel model, the position coordinates of each movable antenna are unknowns.

[0070] The communication system includes a base station, multiple movable antennas, and a mobile terminal.

[0071] A base station is a core fixed facility in a communication system responsible for signal transmission, reception, and coverage. In this embodiment, a movable antenna is deployed at the top of the base station. The position of the movable antenna directly determines the propagation characteristics of the communication system.

[0072] Mobile terminals are mobile terminal devices within the coverage area of ​​the communication system, including but not limited to user terminal devices, ship control terminals, etc.

[0073] Multiple movable antennas form an antenna array, deployed at the top of the base station. Through mechanical or electromagnetic actuation, each movable antenna can be moved flexibly within a certain area, thereby adjusting the spatial layout of the antenna array to adapt to channel changes and maximize the spatial freedom of the communication system.

[0074] In some embodiments, the antenna array composed of movable antennas can be a planar array, that is, the movable antennas are arranged in a two-dimensional plane.

[0075] In other embodiments, the antenna array composed of movable antennas can also be a linear array (i.e., each movable antenna is located on a straight line) and a three-dimensional antenna array (i.e., the movable antennas are distributed in three-dimensional space).

[0076] Unlike traditional land-based communications, which involve numerous paths, maritime communication channels exhibit sparse characteristics. Specifically, due to the relatively open marine environment and lack of scatterers, the channel from the base station to each mobile terminal primarily comprises two dominant paths: the line-of-sight propagation path and the sea surface reflection path. Furthermore, because the mobile terminal is at a relatively low altitude, local scatterers still exist around it, resulting in diffusion paths within the channel.

[0077] A channel refers to the path and medium through which a signal travels from the transmitter to the receiver; it is the channel through which signals are transmitted in a communication system. The channel response value is a core parameter describing the degree to which the channel affects the transmitted signal. It quantifies the changes in amplitude attenuation and phase shift of the signal as it travels from the transmitter to the receiver, directly reflecting the channel's transmission characteristics and quality. In MIMO technology, the signal transmission characteristics of a single propagation path can be represented by a channel vector; that is, one dominant path or diffusion path corresponds to one channel vector. The elements in the channel vector are the channel response values ​​of the signals transmitted by each transmitter antenna as they travel through the channel to each receiver.

[0078] The two-path channel model is a channel model obtained by combining the channel vectors corresponding to all dominant and diffusion paths in a communication system. This two-path channel model fully considers the characteristics of line-of-sight and reflection paths in maritime communication systems, as well as the characteristics of diffusion paths, making the two-path channel model more adaptable to real-world scenarios and more accurate. It provides a high-quality theoretical foundation for subsequent adjustments to the position of movable antennas.

[0079] The dominant propagation paths include the line-of-sight (LAS) propagation path and the reflection path. The LAS propagation path refers to the unobstructed, direct signal propagation path between a movable antenna and a mobile device in a communication system. The reflection path refers to the propagation path of a signal emitted by a movable antenna in a communication system after being reflected by an obstacle to reach the mobile device. In maritime communication systems, the reflection path refers to the propagation path of a signal emitted by a movable antenna in a communication system after being reflected by the sea surface to reach the receiving end.

[0080] The diffusion path, also known as the scattering path, refers to the propagation path of a signal emitted by a movable antenna in a communication system. When the signal encounters a rough surface during propagation, it disperses in multiple directions and reaches the mobile terminal.

[0081] In this step, a Cartesian coordinate system is first established for the communication system. The Cartesian coordinate system is a mathematical tool that describes spatial positions using three mutually perpendicular coordinate axes: x, y, and z. Based on the position coordinates of each device in the communication system (including base stations, receivers, and movable antennas) in the Cartesian coordinate system, channel vectors for each dominant path and each diffusion path are constructed. Combining the channel vectors of each dominant path and each diffusion path yields the two-path channel model.

[0082] The location coordinates of the base station are known quantities and can be determined based on the relative position of the base station to the origin of the Cartesian coordinate system. In one example, the origin of the Cartesian coordinate system can be set as the bottom of the base station; therefore, the location coordinates of the top of the base station are [0, 0, H]. T (represented as a column vector of coordinates in a Cartesian coordinate system), where H is the height of the base station.

[0083] The position coordinates of the mobile device are known quantities and can be determined based on the relative position of each mobile device to the origin in the Cartesian coordinate system.

[0084] In one example, the position coordinates of each mobile terminal can be added to a Cartesian coordinate system based on the known positions of the mobile terminals, such as the position of a ship. For example, the position coordinates of K mobile terminals can be set as follows: .

[0085] In another example, since the number and location of mobile terminals in the communication system are constantly changing, the mobile terminals in the communication system can be simulated based on the historical distribution of the mobile terminals or through statistical distribution methods (such as uniform distribution, Poisson distribution, etc.), by adding a preset number of mobile terminal location coordinates to the Cartesian coordinate system.

[0086] The position coordinates of the movable antenna are unknown. This two-path channel model simulates the communication quality of the communication system when the movable antenna is in different positions. By placing the movable antenna in a position with better communication quality, the spatial degrees of freedom of the communication system can be explored, thereby improving the communication quality of the system.

[0087] Since the movable antennas are located at the top of the base station, and the movable area of ​​each antenna is limited, the range of the movable antenna's position coordinates can be determined based on the movable range of the antennas and the position coordinates of the top of the base station. Within this range, the communication quality of the communication system can be maximized.

[0088] For example, Figure 3 This is a schematic diagram of a communication system provided in an embodiment of this application. Figure 3 As shown, the coordinates of the top of the base station are [0,0,H]. T The movable antennas are distributed in a planar array centered on the top of the base station. If the planar array is located on the xOz plane, then the position coordinates of each movable antenna can be represented as follows: Where n = 1, ..., N, and N represents the number of movable antennas; Let x be the target x-coordinate of the nth movable antenna; Let be the target vertical coordinate of the nth movable antenna.

[0089] The horizontal and vertical coordinates of the movable antenna's position coordinates are used to construct a vector, i.e., a vector is defined. sum vector These two vectors are the vectors that need to be solved.

[0090] In some embodiments, a two-path channel model for a communication system that includes a dominant path and a diffusion path can be constructed as follows:

[0091] For each mobile terminal, based on the location coordinates of the mobile terminal and the location coordinates of each movable antenna, the signal amplitude and phase changes of each movable antenna along the dominant path to the mobile terminal are quantified, thereby determining the channel vector of the dominant path of the mobile terminal. .in, This is the channel response value of the nth movable antenna via the dominant path to the mobile terminal.

[0092] Based on scattering characteristics (such as scattering coefficient and path loss), the amplitude and phase of the signal scattered from each mobile antenna to the mobile terminal are quantized to determine the channel vector of the diffusion path of the mobile terminal. .in, This is the channel response value from the nth movable antenna to the mobile terminal via the diffusion path.

[0093] The channel vector of the mobile terminal is obtained by adding the channel vector of the dominant path to the channel vector of the diffusion path element by element. .in, .

[0094] Finally, the channel vectors of all mobile devices are concatenated into a channel matrix. This channel matrix is ​​a two-path channel model of a communication system that includes the dominant path and the diffusion path.

[0095] In one embodiment, based on the location coordinates of the mobile terminal and the location coordinates of each movable antenna, the signal amplitude and phase changes of each movable antenna through the dominant path to the mobile terminal are quantified, thereby determining the channel vector of the dominant path of the mobile terminal. Specifically, based on the location coordinates of the mobile terminal and the location coordinates of each movable antenna, and according to spatial geometric relationships, the channel vector of the line-of-sight path and the channel vector of the reflection path are calculated, and the channel vector of the line-of-sight path and the channel vector of the reflection path are superimposed to obtain the channel vector of the dominant path of the mobile terminal.

[0096] For example, continue to refer to Figure 3 There are three mobile devices: Mobile Device 1, Mobile Device 2, and Mobile Device 3. All three mobile devices can use the line-of-sight path (...). Figure 3 The dashed lines pointing to each mobile device) and the reflection path ( Figure 3(The middle line points to the solid lines of each mobile terminal), receiving signals transmitted by the movable antenna. The channel vectors of the line-of-sight path and reflection path of each mobile terminal are calculated separately, and then superimposed to obtain the channel vector of the dominant path of each mobile terminal.

[0097] After obtaining the two-path channel model, expressions for the signals received by all mobile terminals can be derived from it. Therefore, the two-path channel model can be used to represent the communication quality of a communication system. The expressions for the signals received by all mobile terminals can be expressed as:

[0098]

[0099] Where v represents all signals received by the mobile devices; This is the channel matrix, i.e., the output of the two-path channel model; s is the beamforming vector; s is the transmitted signal vector, and I K Let n be a K-dimensional identity matrix; n is Gaussian noise, following the order of k. Distribution, δ 2 This represents the average noise power.

[0100] In some embodiments, due to the highly volatile nature of maritime communications, the location coordinates of the mobile terminal can be updated periodically, thereby updating the two-path channel model of the communication system, which includes the dominant path and the diffusion path. The method is then executed based on the updated two-path channel model.

[0101] This approach ensures the real-time performance of the constructed dual-path channel model, enabling it to dynamically capture changes in the mobile terminal's position. This provides a precise and timely theoretical basis for optimizing the position of the movable antenna, thereby achieving dynamic adjustment of the movable antenna position and improving the flexibility of the adjustment.

[0102] In some embodiments, the channel model can also be dynamically corrected by combining multi-source sensor data (such as wave height sensor, ship radar, and satellite positioning information).

[0103] For example, wave height data can be used to adjust the phase difference of the reflection path, or the motion state of the mobile device can be detected by ship radar to optimize the statistical characteristics of the local scattering path.

[0104] The introduction of multi-source sensor data enables the channel model to more accurately reflect the physical characteristics of the marine environment (such as sea surface fluctuations and mobile device movement), thereby improving the input accuracy of the optimization algorithm. This enhanced channel modeling can further optimize the beamforming matrix and the positional configuration of movable antennas, improving the communication performance stability of the communication system under complex sea conditions.

[0105] Step S202: Using the position coordinates of each movable antenna as unknowns, determine the expression for the traversal achievable rate based on the two-path channel model.

[0106] In the field of communications, the ergodic achievable rate is the maximum transmission rate that a communication system can achieve on a long-term average basis. Its expression is:

[0107]

[0108] in, γ represents the traversal reachable speed; k The signal-to-interference-plus-noise ratio (SINR) for the k-th mobile terminal is also known as the signal-to-interference-plus-noise ratio; E H [ ] represents the statistical expectation of the channel. The signal-to-interference-plus-noise ratio (SINR) can be expressed as:

[0109]

[0110] The expression for traversing the reachable rate includes a term corresponding to the channel vector of the mobile terminal, and the unknown quantity of the channel vector of the mobile terminal is the position coordinate of the movable antenna.

[0111] Step S203: With the goal of maximizing the minimum traversal reach rate, solve for the target position coordinates of each movable antenna.

[0112] The minimum value of the traversal reachable rate is the boundary value of the traversal reachable rate, which is also the asymptotic reachable rate.

[0113] In this step, the expression for the minimum achievable rate is determined. Then, the position coordinates of each movable antenna corresponding to the maximum value of the expression for the minimum value are determined, and these position coordinates are used as the target position coordinates.

[0114] If the movable antenna is set at the actual location point corresponding to the target location coordinates, the lower limit of the traversal reachable rate of the communication system can be maximized, thus ensuring the overall communication quality.

[0115] Specifically, the objective of minimizing the reachable speed of traversal can be modeled as the following objective problem:

[0116]

[0117] Meanwhile, the movement of the movable antenna is subject to constraints, meaning the objective problem modeled above is also subject to constraints. These constraints include, but are not limited to, area restrictions, minimum spacing restrictions, and transmit power restrictions.

[0118] Specifically, the constraints can be expressed as:

[0119]

[0120]

[0121]

[0122] The movable antenna's movement area is Cλ×Cλ, where C is the number of movable wavelengths and λ is the wavelength. With the top of the base station as the center, the first constraint ensures the movable antenna remains within the movement area. The second constraint restricts the spacing between the movable antennas to be greater than or equal to a spacing threshold d. The third constraint limits the maximum transmit power, i.e., the maximum transmit power is less than or equal to a power threshold P. In a beamforming scenario, the sum of the squares of the moduli of all elements of the beamforming matrix W is the sum of the transmit power of all movable antennas, i.e., the total transmit power.

[0123] In one example, the target problem is optimized using an optimization method for the beamforming matrix W, based on the modeled target problem.

[0124] In one example, the optimization method for the beamforming matrix W can utilize traditional precoding optimization approaches used in fixed antenna systems, such as Zero-Forcing Precoding (ZFP), Minimum Mean Square Error Precoding (MMSE-P), and power-constrained optimization algorithms based on the Lagrange multiplier method. By combining channel state information and constraints, the precoding matrix is ​​solved to maximize system throughput or improve the average overall communication quality. The unknowns in the coding matrix include the position coordinates of the movable antenna.

[0125] Then, the precoding matrix is ​​used to replace the beamforming matrix W in the target problem, and the result of the target problem is solved.

[0126] Specifically, the LGD (Leader Gradient Descent) algorithm can be used to solve the target problem.

[0127] The LGD algorithm is an efficient iterative method for solving nonconvex optimization problems. It accelerates convergence by combining gradient information with the dominant direction.

[0128] For example, the initial position coordinates, initial step size, and convergence threshold of the movable antenna are set. Based on Statistical Channel State Information (CSI), an expression for the minimum traversal rate is calculated, and the gradient of this expression is calculated. Historical best positions are introduced to correct the gradient direction, resulting in new position coordinates. If the distance between the new position coordinates and the current position coordinates is less than or equal to the convergence threshold, the new position coordinates are determined as the target position coordinates.

[0129] If the distance between the new position coordinates and the current position coordinates is greater than the convergence threshold, the new position coordinates are used as the initial position coordinates, and the steps of calculating the expression for the minimum traversal rate based on statistical channel state information (CSI) and calculating the gradient of the expression for the minimum value are re-executed until the distance between the new position coordinates and the current position coordinates is less than or equal to the convergence threshold.

[0130] Step S204: Adjust the movable antennas in the communication system according to the target position coordinates of each movable antenna.

[0131] In this step, the position of the movable antenna in the communication system is adjusted according to the relative positional relationship between the target position coordinates of each movable antenna and the position coordinates corresponding to the current position of the movable antenna in the communication system.

[0132] For example, each movable antenna in the communication system is moved to a position corresponding to the target location coordinates.

[0133] For example, suppose the target position coordinates of the nth movable antenna are... The current position coordinates of the nth movable antenna are Then control the nth movable antenna to move in the positive direction of the horizontal axis. ; Move in the positive direction of the vertical coordinate axis .

[0134] In some embodiments, the distance between the target location coordinates and the current location coordinates of each movable antenna can also be calculated. If the distance is greater than or equal to a preset distance threshold, the position of the movable antenna in the communication system is adjusted to the position corresponding to the target location coordinates.

[0135] This method avoids the problem of frequent and meaningless adjustments to the position of the movable antenna, which leads to a waste of hardware or software resources in the communication system. It can extend the lifespan of the communication system and optimize resource utilization.

[0136] The movable antenna position adjustment method provided in this application is applied to communication systems with multiple movable antennas. By optimizing the position of the movable antennas, the spatial degrees of freedom of the communication system are fully utilized, thereby improving the communication quality. Specifically, based on the positions of the base station and mobile terminal in the communication system, a channel model including two communication paths—the dominant path and the diffusion path—is constructed. This model dynamically and effectively adapts to the channel characteristics and dynamic characteristics of the mobile terminal in maritime communication scenarios, providing an accurate theoretical basis suitable for the current scenario for the subsequent optimization problem. Then, based on the constructed channel model, the expression for the ergodic reachable rate of the model is derived. The value of this ergodic reachable rate represents the communication quality of the communication system. By solving for the ergodic reachable rate, the position coordinates of the movable antenna corresponding to maximizing the minimum ergodic reachable rate are found. The movable antenna is then moved to this position coordinate. Based on this, the minimum communication quality of the communication system is guaranteed, thereby comprehensively improving the overall communication quality level of the communication system.

[0137] In one possible implementation, the method can be executed according to a set period to achieve real-time adjustment of the position of the movable antenna in the communication system, improve the timeliness of the position adjustment of the movable antenna, and avoid the problem of poor communication quality caused by large fluctuations in maritime communication transmission.

[0138] In one example, the location coordinates of the mobile terminal can be updated according to a set period. Based on the location coordinates of the base station and the mobile terminal in the communication system, a two-path channel model of the communication system, including the dominant path and the diffusion path, is constructed; where the location coordinates of each movable antenna in the two-path channel model are unknowns. Using the location coordinates of each movable antenna as unknowns, an expression for the traversal reachable rate is determined based on the two-path channel model. The target location coordinates of each movable antenna are solved with the objective of maximizing the minimum value of the traversal reachable rate. Based on the target location coordinates of each movable antenna, the movable antennas in the communication system are adjusted.

[0139] Figure 4 This is a flowchart illustrating another method for adjusting the position of a movable antenna provided in an embodiment of this application. The method provided in this embodiment is... Figure 2 Based on the illustrated embodiment, steps S201 and S203 are refined, and steps related to constructing a Cartesian coordinate system are added. For example... Figure 4 As shown, the method provided in this embodiment includes:

[0140] Step S401: Using the base station in the communication system as the origin of the coordinate system, two straight lines parallel to the ground and pointing in a preset direction and perpendicular to each other are used as the horizontal and vertical coordinate axes, and a straight line perpendicular to the ground is used as the vertical coordinate axis to construct the Cartesian coordinate system of the communication system.

[0141] The preset direction can be a geographical direction or a direction relative to a reference point. In one example, the preset direction can be east-west or north-south. In another example, the area array composed of movable antennas is perpendicular to the ground, and the preset direction can also be a direction perpendicular to the area array or a direction parallel to both the area array and the ground.

[0142] In some embodiments, the planar array of movable antennas is perpendicular to the ground. A Cartesian coordinate system for the communication system can be constructed, with the top of the base station in the communication system as the origin, the direction perpendicular to the planar array of movable antennas as the horizontal axis, the direction parallel to the planar array of movable antennas and the ground as the vertical axis, and a straight line perpendicular to the ground as the vertical axis. In this embodiment, the following steps are performed using this Cartesian coordinate system as an example.

[0143] Step S402: Obtain the historical trajectories of multiple mobile devices.

[0144] The historical trajectory includes multiple location points. The historical trajectory consists of multiple location points of each mobile terminal recorded by the communication system during communication with each mobile terminal.

[0145] In this step, the historical trajectories of multiple mobile devices are obtained from the data recorded by the communication system.

[0146] Step S403: Based on the historical trajectory, generate the position coordinates of multiple mobile terminals in the Cartesian coordinate system.

[0147] In this step, the position coordinates of each location point can be generated in the Cartesian coordinate system based on the historical trajectory of each mobile device.

[0148] In this step, K location points can be selected from multiple location points based on the historical trajectories of all mobile devices, and the corresponding location coordinates of the K location points can be generated in a Cartesian coordinate system. K is a pre-set configurable parameter, which can be less than or equal to the number of movable antennas.

[0149] In this step, the frequently used location points of each mobile device can also be determined based on their historical trajectories. Specifically, this can be achieved by calculating statistical values ​​such as the average and mode of multiple location coordinates. The location coordinates corresponding to the frequently used location points are then generated in a Cartesian coordinate system.

[0150] In this embodiment, in the constructed Cartesian coordinate system, the coordinates of the bottom of the base station are: The position coordinates of each movable antenna are: The location coordinates of each mobile device are .

[0151] By acquiring historical trajectories from multiple mobile devices and generating corresponding location coordinates for each device in a Cartesian coordinate system, discrete trajectory data is transformed into more intuitive and computable spatial information, providing fundamental support for subsequent analysis and modeling. Simultaneously, the use of actual historical trajectories ensures that the subsequently constructed channel model adapts to real-world scenarios, avoiding the disconnect between theoretical models and practical applications, thereby improving the reliability and effectiveness of subsequent optimizations.

[0152] In some embodiments, location prediction algorithms (such as Kalman filtering or deep learning models) can be used to predict the future location change trend of a mobile device based on its historical trajectory. The prediction results are then combined to generate multiple location coordinates corresponding to the mobile device in a Cartesian coordinate system.

[0153] Then, the movable antenna is adjusted to adapt to the future location changes of the mobile device.

[0154] By predicting the dynamic trajectory of the mobile device, the movable antenna can proactively adapt to changes in location, reducing the negative impact of channel time-varying characteristics caused by mobile device movement on system performance. Simultaneously, dynamically adjusting the antenna position can further improve beamforming efficiency and system stability, with particularly significant effects in maritime mobile scenarios (such as ship navigation).

[0155] Step S404: For each movable antenna, based on the spatial geometric relationship between the position coordinates of the movable antenna and the position coordinates of the mobile terminal, determine the first channel vector when the transmitted signal of the movable antenna propagates to the mobile terminal via the dominant path.

[0156] In this step, for each movable antenna, the channel vector of its dominant path is related to the positions of the movable antenna and each mobile terminal. Based on the spatial geometric relationship between the position coordinates of the movable antenna and the position coordinates of the mobile terminals, the channel response values ​​of the transmitted signal from the movable antenna being relayed to each mobile terminal via the dominant path can be simulated. The channel response values ​​corresponding to each mobile terminal are then combined into a vector, which is the first channel vector.

[0157] The dominant paths include the line-of-sight path and the reflection path. Based on spatial geometry, the elevation angle of the transmitted signal from the movable antenna when it propagates to the k-th mobile terminal via the line-of-sight path can be determined. and azimuth launch angle .in,

[0158]

[0159]

[0160] Based on spatial geometry, the elevation angle of arrival of the transmitted signal from the movable antenna when it propagates through the reflection path to the k-th mobile terminal can be determined. and azimuth arrival angle .in,

[0161]

[0162]

[0163] Since the movable area of ​​the movable antenna is much smaller than the propagation distance, the far-field condition is satisfied. According to the field response channel model, the location t of the nth movable antenna... n The phase difference between the line-of-sight path and the reflection path relative to the reference position t0 can be expressed as:

[0164]

[0165]

[0166] in, The phase difference generated for the line-of-sight path corresponding to the nth movable antenna; The phase difference generated by the reflection path corresponding to the nth movable antenna.

[0167] Based on this, the first channel vector can be represented as:

[0168]

[0169] Among them, G k (x,z) can be defined as a field response matrix, where the nth row represents the phase difference between the line-of-sight path and the reflection path of the kth mobile terminal corresponding to the nth mobile antenna; Φ k It can be defined as a path response vector, which includes the negative gain of the k-th mobile line-of-sight path and the reflection path. and Let represent the complex gain of the line-of-sight path and the reflection path of the k-th mobile terminal, respectively; λ is the wavelength. The magnitude of the negative gain is... and The value is determined by path loss and reflection coefficient, and remains stable over a relatively long period of time.

[0170] Step S405: Concatenate the first channel vectors of each movable antenna to obtain the first channel matrix.

[0171] In this step, the first channel vector of each movable antenna is... By concatenating the matrices, we obtain the first channel matrix, i.e. =[ ].

[0172] Further define the extended field response matrix G(x,z) = [G1(x,z), ..., G... K [x,z] and extended path response matrix Φk =Diag{Φ1,…,Φ K},but .

[0173] Step S406: For each movable antenna, the second channel vector when the transmitted signal of the movable antenna propagates to the mobile terminal through the diffusion path is determined by multiplying the preset spatial covariance matrix by the Kolesky decomposition.

[0174] The diffusion path is formed by countless tiny rays generated by local scatterers around the mobile device. According to the central limit theorem, the second channel vector can be modeled as a spatially correlated Gaussian random process with a mean of 0. N×1 This result can represent the channel vector of the diffusion path relatively accurately.

[0175] Specifically, for each movable antenna, a spatial covariance matrix C(x,z) is generated. This spatial covariance matrix C(x,z) is then decomposed using the Kolesky method and multiplied by a Gaussian random vector u. k This yields the second channel vector as the transmitted signal from the movable antenna propagates through the diffusion path to the mobile terminal. It can be expressed as:

[0176]

[0177] in, The matrix obtained by performing Koleski decomposition on the spatial covariance matrix C(x,z) is used to represent the spatial correlation caused by the diffusion path around the mobile terminal; u k It is a Gaussian random vector that follows a normal distribution, i.e. .

[0178] Step S407: Concatenate the second channel vectors of each movable antenna to obtain the second channel matrix.

[0179] In this step, the second channel vector of each movable antenna is... By concatenating the matrices, we obtain the first channel matrix, i.e. =[ ].

[0180] Step S408: Add the first channel matrix and the second channel matrix to obtain the two-path channel model.

[0181] In this step, the first channel matrix and the second channel matrix are added element by element to obtain the two-path channel model.

[0182] The two-path channel model can be represented as:

[0183]

[0184] By leveraging spatial geometric relationships, the channel vector of the dominant path can be accurately simulated, transforming it from an abstract conjecture to a physically precise representation. This provides high-quality basic data that conforms to physical laws for subsequent problem-solving. Furthermore, this application determines the channel vector of the diffusion path through the central limit theorem and uses statistical laws to model the diffusion path, balancing accuracy and computational cost. This avoids modeling distortion caused by random errors in a single path and makes the channel vector more closely resemble the statistical laws of real multipath propagation.

[0185] Step S409: Using the position coordinates of each movable antenna as unknowns, determine the expression for the traversal achievable rate based on the two-path channel model.

[0186] Optionally, the expression for the traversal reachability includes terms corresponding to the beamforming matrix and terms corresponding to the channel vector; the channel vector is the output of the two-path channel model. The method further includes: constructing terms corresponding to the beamforming matrix using the channel vector based on the zero-forcing beamforming algorithm; and combining the constructed terms corresponding to the beamforming matrix and the channel vector to obtain the expression for the traversal reachability.

[0187] The expression for the reachable speed of traversal can be represented as:

[0188]

[0189] The expression for the traversal reachable rate includes the terms corresponding to the beamforming matrix W and the channel vector. The corresponding item.

[0190] Zero-Forcing Beamforming (ZF-BF) is an algorithm that reduces interference between mobile devices by designing the beam weight vectors of each mobile antenna, thereby reducing the interference signal power of the mobile device to zero and improving the spectral efficiency of the system.

[0191] Specifically, using channel vectors and based on the zero-forcing beamforming algorithm, the beamforming matrix is ​​designed, that is, the beamforming matrix is ​​constructed using channel vectors. The terms corresponding to the constructed beamforming matrix are combined with other terms related to the traversal reachability and the terms corresponding to the channel vectors to obtain a new expression for the traversal reachability.

[0192] The constructed beamforming matrix can be represented as:

[0193]

[0194] The constructed beamforming matrix satisfies Therefore, the transmit power limitation condition is met, i.e., the maximum transmit power is less than or equal to the power threshold P. Furthermore, all mobile terminals have the same SINR, i.e.:

[0195]

[0196] Where A(x,z)= ; tr{} represents the trace function, which is the sum of the elements on the main diagonal of the matrix.

[0197] The expression for the reachable speed of traversal is:

[0198]

[0199] In this embodiment, the traversal reachable rate is a communication system characteristic that remains relatively stable over a long period. This characteristic enables the optimization of the movable antenna's location, providing ample time for optimization. Meanwhile, the beamforming matrix design based on the zero-forcing beamforming algorithm is based on the instantaneous channel vector, representing an instantaneous characteristic. By introducing the instantaneous beamforming matrix into the expression for the traversal reachable rate, dual-time-scale optimization is achieved.

[0200] The scheme in this embodiment uses channel vectors to represent the beamforming matrix, which simplifies the expression for the traversal reachability rate, thereby simplifying the complexity of the subsequent solution and significantly reducing the computational difficulty.

[0201] Step S410: Determine the tight lower bound expression fraction for the traversal reachability rate according to Jensen's inequality.

[0202] In this step, according to Jensen's inequality, the expectation of a convex function is greater than or equal to the expected function value. Therefore, the expected function value is determined as a tightly bounded fraction. This tightly bounded fraction reflects the range of the minimum traversal reachable rate.

[0203] For example, according to Jensen's inequality, the traversal reachability satisfies the following formula:

[0204]

[0205] Among them, the definition Express fractions with tight lower bounds.

[0206] Step S411: Let the components of the denominator in the tight lower bound expression be surrogate functions, and construct the target surrogate function using the Monte Carlo method.

[0207] The target surrogate function approximates the surrogate function when the number of instantaneous channel implementations is sufficiently large.

[0208] In this step, after obtaining the tight lower bound expression fraction, the problem of maximizing the minimum traversal reachability can be transformed into the problem of finding the maximum value of the tight lower bound expression fraction.

[0209] In a tightly bounded lower bound expression, the numerator is a fixed value. If the denominator is minimized, the tightly bounded lower bound expression will reach its maximum value. Therefore, let the components of the denominator in the tightly bounded lower bound expression be surrogate functions. ,Right now .

[0210] Finding the position coordinates of the movable antenna corresponding to the minimum value of this surrogate function is equivalent to finding the target position coordinates of each movable antenna with the objective of maximizing the minimum traversal reach rate. Finding the position coordinates of the movable antenna corresponding to the minimum value of this surrogate function can be modeled as follows:

[0211]

[0212] Meanwhile, since the transmit power limit has been met, the constraints for the movable antenna only include area restrictions and minimum spacing restrictions, meaning the following constraints must be met:

[0213]

[0214]

[0215] The Monte Carlo method is a numerical method for solving complex problems through random sampling and statistical simulation.

[0216] Specifically, based on the Monte Carlo method, the proxy function Based on the expectation of instantaneous channel realization H, when the number M of instantaneous channel realizations is sufficiently large, the surrogate function... It can be approximated as The average value, where m = 1, ..., M. The average value is determined as the target surrogate function. .Right now:

[0217]

[0218] Step S412: With the goal of minimizing the target surrogate function, solve for the target position coordinates of each movable antenna.

[0219] In this step, the target position coordinates of the movable antennas corresponding to the minimum value of the target surrogate function are solved. That is, the target position coordinates of each movable antenna are solved with the goal of minimizing the target surrogate function.

[0220] In one example, the position coordinates of the movable antenna can be iteratively updated along the gradient direction of the objective function using gradient descent, gradually approaching the minimum value of the objective function. After obtaining the minimum value, the unknown quantity corresponding to the minimum value is determined as the target position coordinates.

[0221] In other embodiments, the LGD algorithm can be used to solve for the unknown quantity corresponding to the minimum value of the target surrogate function, thereby determining the target location coordinates.

[0222] Step S413: Adjust the movable antennas in the communication system according to the target position coordinates of each movable antenna.

[0223] In this embodiment, the tightly bounded expression of the traversal reachable rate is determined using Jensen's inequality, transforming the problem of minimizing the expression into finding the maximum value of the tightly bounded expression, significantly reducing the computational difficulty. Simultaneously, this application proposes a target surrogate function based on the Monte Carlo method, solving the problem of difficult channel expectation calculation and further reducing the solution difficulty.

[0224] Optionally, with the goal of minimizing the target surrogate function, the target position coordinates of each movable antenna are solved, including:

[0225] The objective function is determined by subtracting the product of the penalty function and the iteration coefficient from the target surrogate function. The iteration coefficient is related to the number of iterations when solving for the target position coordinates. The position coordinates of the movable antenna are set as the initial position coordinates, and a gradient descent algorithm is performed on the objective function a preset number of times to obtain the position coordinates of the first iteration. The following steps are repeated until the iteration termination condition is met, and the position coordinates of the last iteration are determined as the target position coordinates of the movable antenna: Based on the number of iterations, the iteration coefficient is reduced, and the objective function of this iteration is determined. The position coordinates of the movable antenna are set as the position coordinates of the previous iteration, and a gradient descent algorithm is performed on the objective function of this iteration a preset number of times to obtain the position coordinates of this iteration. The iteration termination condition is that the distance between the position coordinates of this iteration and the position coordinates of the previous iteration is less than or equal to a preset distance threshold.

[0226] The gradient descent algorithm for a preset number of iterations can be specifically described as follows: Calculate the gradient of the current position coordinates. Since the negative gradient direction is the direction of the fastest descent of the function value, update the position coordinates along the negative gradient direction. Repeat the update of the position coordinates a preset number of times to obtain the position coordinates of this iteration.

[0227] When using the gradient descent algorithm to solve minimum problems, the algorithm can be sensitive to the initial position coordinates and is prone to converging to local optima. To address this issue, this application introduces a penalty function and performs a double-loop iteration.

[0228] The penalty function is a logarithmic barrier penalty function. The penalty function restricts the position coordinates of the movable antennas during the iteration process to within the constraints, so as to avoid the obtained position coordinates from exceeding the limits, such as the position coordinates exceeding the movement area of ​​the movable antennas, or the spacing of the movable antennas being less than the spacing threshold.

[0229] Specifically, the penalty function is defined as follows:

[0230]

[0231] in, Let the feasible region be the position coordinates of the movable antenna that satisfy the constraints. If the position coordinates of the movable antenna are within the feasible region, then the penalty function is... If the position coordinates of the movable antenna are outside the feasible region, then the penalty function is negative infinity. Defined as:

[0232]

[0233] The objective function is the objective surrogate function minus the product of the penalty function and the iteration coefficients, i.e., the objective function. The iteration coefficient μ is related to the number of iterations required to solve for the target position coordinates.

[0234] In one example, the iteration coefficient μ = μ0×ρ a-1 , where ρ∈ (0,1) is the iteration coefficient adjustment factor; a is the iteration number; μ0 is the initial iteration coefficient, and μ0 is greater than 0.

[0235] In the objective function, if the position coordinates of the movable antenna are outside the feasible region, the penalty function is negative infinity, and consequently, the objective function value is positive infinity. This is clearly not a solution aimed at minimizing the surrogate function. This method ensures that the obtained position coordinates always lie within the feasible region, satisfying the constraints. Furthermore, when When the value is sufficiently small, the penalty function is negligible. Therefore, solving for the target position coordinates of each movable antenna with the objective of minimizing the target surrogate function can be done by minimizing the objective function. To solve for the target position coordinates of each movable antenna, the problem is modeled as follows:

[0236]

[0237] To solve the problem described above, the position coordinates of the movable antenna are first set as the initial position coordinates. These initial position coordinates can be either the actual position coordinates of the movable antenna or any pre-defined initial position coordinates within the moving area.

[0238] In the first iteration, the objective function The gradient descent algorithm is executed a predetermined number of times; that is, after a predetermined number of iterations, the position coordinates of the first iteration are obtained. Then, it is determined whether the position coordinates of the first iteration meet the iteration termination condition. If they do, the iteration stops, and the position coordinates of the first iteration are determined as the target position coordinates.

[0239] If the conditions are not met, proceed to the second iteration.

[0240] In the second iteration, the iteration coefficient is reduced according to the iteration number, for example, the iteration coefficient is reduced to μ = μ0×ρ. 2-1 The position coordinates in the objective function are set as the position coordinates of the first iteration, and the iteration coefficients are updated to the reduced iteration coefficients to obtain the objective function for this iteration. A preset number of gradient descent algorithms are then executed on the objective function for this iteration to obtain the position coordinates for the second iteration. Next, it is determined whether the position coordinates of the second iteration satisfy the iteration termination condition. If they do, the iteration stops, and the position coordinates of the second iteration are determined as the target position coordinates.

[0241] If the condition is not met, the iteration continues. The subsequent iterations follow the same process as the second iteration. Iteration continues until the position coordinates obtained in a given iteration satisfy the iteration termination condition, at which point the iteration stops. The position coordinates of the last iteration are then determined as the target position coordinates of the movable antenna.

[0242] The iteration ends when the distance between the current iteration's position coordinates and the previous iteration's position coordinates is less than or equal to a preset distance threshold. This can be expressed as:

[0243]

[0244] in, This represents the distance between the position coordinates of the current iteration and the position coordinates of the previous iteration. The distance threshold is a pre-set value, which is a configurable parameter; j is the number of iterations in this iteration.

[0245] Optionally, perform a preset number of gradient descent operations on the objective function to obtain the position coordinates of the first iteration, including:

[0246] Repeat the following steps a preset number of times for the objective function, and determine the position coordinates of the last iteration as the position coordinates of the first iteration:

[0247] Based on the current position coordinates, calculate the normalized gradient of the objective function, and determine the position coordinates to be updated and the current objective function value according to the normalized gradient and the step size; the first position coordinates are the initial position coordinates.

[0248] If the location coordinates to be updated meet the movable range of the movable antenna, and the difference between the previous objective function value and the current objective function value is greater than or equal to a preset threshold, then the next location coordinates will be determined as the location coordinates to be updated.

[0249] If the coordinates of the position to be updated do not meet the movable range of the movable antenna, or the difference between the previous objective function value and the current objective function value is less than a preset threshold, then the step size is updated, and the steps of determining the coordinates of the position to be updated and the second function value based on the normalized gradient and the step size are executed.

[0250] The normalized gradient of the objective function can be calculated by taking the gradient of the objective function based on the current position coordinates of the movable antenna. Then, the gradient of the objective function is substituted into the normalized gradient formula to obtain the normalized gradient.

[0251] Specifically, the gradient of the objective function can be expressed as:

[0252]

[0253] (x) (i) ,z (i) Let x be the coordinates of the movable antenna during the i-th execution of the gradient descent algorithm. Then, the gradient of the target surrogate function with respect to x and z during the i-th execution of the gradient descent algorithm is:

[0254]

[0255]

[0256] Among them, e N×1，n It is an N-bit vector where only the nth element is 1, and all other elements are 0.

[0257] The gradient of the penalty function can be expressed as:

[0258]

[0259] The position coordinates are obtained by combining the gradient of the target surrogate function with respect to x and z and the gradient of the penalty function during the i-th execution of the gradient descent algorithm, i.e., (x, z). (i) ,z (i) The gradient at time ( ). Then, substituting this gradient into the normalized gradient formula, we obtain the normalized gradient. The normalized gradient formula can be expressed as:

[0260]

[0261] The step size is a dynamically changing value. Its initial step size is a pre-set configurable parameter. Each time the gradient descent algorithm is executed, the step size is reduced to ensure that the determined coordinates of the position to be updated and the current objective function value meet preset conditions. These preset conditions are that the coordinates of the position to be updated are within the movable range of the movable antenna, and the difference between the previous objective function value and the current objective function value is greater than or equal to a preset threshold.

[0262] The preset threshold is related to the step size and the normalized gradient. Specifically, the preset threshold can be: ,in, These are pre-set coefficients.

[0263] For example, the preset conditions can be expressed as:

[0264]

[0265] The coordinates to be updated are The objective function value in this case is .

[0266] In this embodiment, a gradient descent algorithm is performed on the objective function a preset number of times to obtain the position coordinates of the first iteration, specifically as follows:

[0267] The first gradient descent algorithm is performed based on the initial position coordinates (x... (1) ,z (1) ), calculate the normalized gradient Based on the normalized gradient and the initial step size, determine the coordinates of the position to be updated. and the objective function value of this time If the coordinates of the position to be updated and the current objective function value do not meet the preset conditions, the step size will be updated to half of the original step size, and the process will return to determine the coordinates of the position to be updated based on the normalized gradient and the initial step size. and the objective function value of this time The steps are as follows. The first gradient descent algorithm ends when the coordinates of the position to be updated and the current objective function value meet the preset conditions.

[0268] To perform the i-th gradient descent algorithm, the position coordinates to be updated obtained from the (i-1)-th gradient descent algorithm are determined as the current position coordinates (x, y). (i) ,z (i) Based on position coordinates (x) (i) ,z (i) ), calculate the normalized gradient Based on the normalized gradient and the initial step size, determine the coordinates of the position to be updated. and the objective function value of this time If the coordinates of the position to be updated and the current objective function value do not meet the preset conditions, the step size will be updated to half of the original step size, and the process will return to determine the coordinates of the position to be updated based on the normalized gradient and the initial step size. and the objective function value of this time The steps are as follows. The gradient descent algorithm continues until the coordinates of the position to be updated and the current objective function value meet the preset conditions, at which point the i-th gradient descent algorithm ends.

[0269] After executing the gradient descent algorithm a preset number of times, the position coordinates of the last iteration will be determined as the position coordinates of the first iteration.

[0270] Similarly, the gradient descent algorithm is executed a predetermined number of times on the objective function of this iteration to obtain the position coordinates of this iteration, including:

[0271] Repeat the following steps a preset number of times for the objective function of this iteration, and determine the position coordinates of the last iteration as the position coordinates of this iteration:

[0272] Based on the current position coordinates, calculate the normalized gradient of the objective function for this iteration, and determine the position coordinates to be updated and the objective function value for this iteration based on the normalized gradient and the step size; the position coordinates of the first iteration are the initial position coordinates.

[0273] If the location coordinates to be updated meet the movable range of the movable antenna, and the difference between the previous objective function value and the current objective function value is greater than or equal to a preset threshold, then the next location coordinates will be determined as the location coordinates to be updated.

[0274] If the coordinates of the position to be updated do not meet the movable range of the movable antenna, or the difference between the previous objective function value and the current objective function value is less than a preset threshold, then the step size is updated, and the steps of determining the coordinates of the position to be updated and the second function value based on the normalized gradient and the step size are executed.

[0275] Figure 5 This is a flowchart illustrating an iterative method for solving target position coordinates provided in an embodiment of this application. This iterative method is used to solve for the target position coordinates of each movable antenna with the objective of minimizing the target surrogate function. Figure 5 As shown, the iterative method includes the following steps:

[0276] Step S501: Subtract the product of the penalty function and the iteration coefficient from the target surrogate function to determine the target function.

[0277] Step S502, set the position coordinates of the movable antenna to the initial position coordinates (x... (1) ,z (1) ), and set the iteration coefficient to μ←μ0, and the number of iterations to i←1.

[0278] Step S503, based on the current position coordinates (x (i) ,z (i) ), calculate the normalized gradient of the objective function.

[0279] In this step, firstly, based on the position coordinates (x) of this application (i) ,z (i) ), calculate the gradient of the objective function Calculate the normalized gradient based on the gradient of the objective function value. .

[0280] Step S504, initialize step size η←η0.

[0281] Step S505: Determine the coordinates of the position to be updated and the objective function value for this step based on the normalized gradient and the step size.

[0282] The coordinates to be updated are The objective function value in this case is .

[0283] Step S506: Determine whether the preset conditions are met. If they are met, proceed to step S508; otherwise, proceed to step S507.

[0284] The preset conditions include: the coordinates of the location to be updated satisfy the movable range of the movable antenna, and the difference between the first function value and the second function value is greater than or equal to a preset threshold, i.e. Greater than or equal to .

[0285] Step S507: Update the step size, η←η / 2. Then return to execute step S505.

[0286] Step S508: Determine the next position coordinates as the position coordinates to be updated. That is... .

[0287] Step S509, Number of updates .

[0288] Step S510: Determine if the number of attempts exceeds the preset number of attempts. If yes, proceed to step S511; otherwise, return to step S503.

[0289] Step S511: Calculate the distance between the position coordinates of the current iteration and the position coordinates of the previous iteration. .

[0290] Step S512: Update the initial position coordinates, iteration coefficients, and number of iterations.

[0291] That is, (x (1) ,z (1) ) ←(x (i) ,z (i) ), , .

[0292] Step S513: Determine whether the distance is less than or equal to a preset distance threshold. If the result is less than or equal to the result, proceed to step S514; if the result is greater than the result, return to step S503.

[0293] Step S514: Determine the initial position coordinates as the target position coordinates.

[0294] In this embodiment, a gradient descent algorithm with a preset number of iterations is used to obtain the target position coordinates. By using a fixed number of iterations, there is no need to judge the convergence condition in real time, avoiding additional computational overhead. The algorithm's time consumption is controllable, and it can quickly output the target position coordinates. Moreover, it updates steadily according to the preset number of iterations, unaffected by factors such as local extrema of the objective function or gradient anomalies, avoiding iteration stagnation or over-iteration problems caused by improper convergence judgment threshold settings. In addition, the gradient descent algorithm may be sensitive to the initial value of the position coordinates, which may lead to convergence to a local optimum. To address this problem, this application uses a dynamic penalty function for outer loop iteration, which can enhance the global search capability and obtain more accurate and effective target position coordinates. Furthermore, existing solution algorithms have difficulty handling the non-convex constraint problem of movable antenna positions. This application effectively solves this problem by constructing a penalty function to convert the non-convex constraint into an expression.

[0295] Figure 6 This is a schematic diagram of a movable antenna position adjustment device provided in an embodiment of this application. Figure 6 As shown, the movable antenna position adjustment device provided in this embodiment is applied to a communication system. The communication system includes multiple movable antennas. The device includes: a model construction module 601, a traversal reachable rate representation module 602, a target position solution module 603, and a movable antenna adjustment module 604.

[0296] The model construction module 601 is used to construct a two-path channel model of the communication system, including the dominant path and the diffusion path, based on the location coordinates of the base station and the mobile terminal in the communication system; wherein, the location coordinates of each movable antenna in the two-path channel model are unknowns; the traversal reachable rate representation module 602 is used to determine the expression of the traversal reachable rate based on the two-path channel model, with the location coordinates of each movable antenna as unknowns; the target position solving module 603 is used to solve for the target position coordinates of each movable antenna with the objective of maximizing the minimum value of the traversal reachable rate; and the movable antenna adjustment module 604 is used to adjust the movable antennas in the communication system according to the target position coordinates of each movable antenna.

[0297] Optionally, the expression for the traversal reachable rate includes terms corresponding to the beamforming matrix and terms corresponding to the channel vector; the channel vector is the output of the two-path channel model; the adjustment device for the movable antenna position also includes a simplification module for:

[0298] Based on the zero-forcing beamforming algorithm, the terms corresponding to the beamforming matrix are constructed using the channel vector; the terms corresponding to the constructed beamforming matrix and the channel vector are combined to obtain the expression for the traversal reachability rate.

[0299] Optionally, the target location solving module 603 includes a target surrogate function determination unit and a solving unit, wherein:

[0300] The target surrogate function determination unit is used to determine the tight lower bound expression of the traversal reachable rate based on Jensen's inequality; let the components of the denominator in the tight lower bound expression be surrogate functions, and construct the surrogate functions as target surrogate functions using the Monte Carlo method; the target surrogate function approximates the surrogate function when the number of instantaneous channel realizations is sufficiently large; the solution unit is used to solve for the target position coordinates of each movable antenna with the goal of minimizing the target surrogate function.

[0301] Optionally, this solver element is specifically used for:

[0302] The objective function is determined by subtracting the product of the penalty function and the iteration coefficient from the target surrogate function. The iteration coefficient is related to the number of iterations when solving for the target position coordinates. The position coordinates of the movable antenna are set as the initial position coordinates, and a gradient descent algorithm is performed on the objective function a preset number of times to obtain the position coordinates of the first iteration. The following steps are repeated until the iteration termination condition is met, and the position coordinates of the last iteration are determined as the target position coordinates of the movable antenna: Based on the number of iterations, the iteration coefficient is reduced, and the objective function of this iteration is determined. The position coordinates of the movable antenna are set as the position coordinates of the previous iteration, and a gradient descent algorithm is performed on the objective function of this iteration a preset number of times to obtain the position coordinates of this iteration. The iteration termination condition is that the distance between the position coordinates of this iteration and the position coordinates of the previous iteration is less than or equal to a preset distance threshold.

[0303] Optionally, the preset number of gradient descent algorithms is as follows:

[0304] The following steps are repeated a preset number of times for the objective function, and the position coordinates of the last iteration are determined as the position coordinates of the first iteration: Based on the current position coordinates, the normalized gradient of the objective function is calculated, and the position coordinates to be updated and the current objective function value are determined according to the normalized gradient and the step size; the first position coordinates are the initial position coordinates; if the position coordinates to be updated meet the movable range of the movable antenna, and the difference between the previous objective function value and the current objective function value is greater than or equal to a preset threshold, then the next position coordinates are determined as the position coordinates to be updated; if the position coordinates to be updated do not meet the movable range of the movable antenna, or the difference between the previous objective function value and the current objective function value is less than a preset threshold, then the step size is updated, and the steps of determining the position coordinates to be updated and the second function value based on the normalized gradient and the step size are executed.

[0305] Optional, model construction module 601, specifically used for:

[0306] For each movable antenna, based on the spatial geometric relationship between the position coordinates of the movable antenna and the position coordinates of the mobile terminal, the first channel vector of the transmitted signal of the movable antenna when propagating to the mobile terminal via the dominant path is determined; the first channel vectors of each movable antenna are concatenated to obtain the first channel matrix; for each movable antenna, the second channel vector of the transmitted signal when propagating to the mobile terminal via the diffusion path is determined by multiplying the preset spatial covariance matrix by a Kolesky decomposition; the second channel vectors of each movable antenna are concatenated to obtain the second channel matrix; the first channel matrix and the second channel matrix are added to obtain the two-path channel model.

[0307] Optionally, the device for adjusting the position of the movable antenna also includes a coordinate system construction module for:

[0308] Before constructing a two-path channel model that includes the dominant path and the diffusion path, a Cartesian coordinate system is constructed with the base station in the communication system as the origin, two straight lines parallel to the ground and pointing in a preset direction and perpendicular to each other as the horizontal and vertical axes, and a straight line perpendicular to the ground as the vertical axis. The historical trajectories of multiple mobile terminals are obtained. The historical trajectories include multiple location points. Based on the historical trajectories, the location coordinates of multiple mobile terminals are generated in the Cartesian coordinate system.

[0309] The movable antenna position adjustment device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0310] Figure 7 This is a schematic diagram of a communication system provided in an embodiment of this application. Figure 7 As shown, the communication system provided in this embodiment includes: a base station 710, multiple movable antennas 720, and a control unit 730.

[0311] The control unit 730 is used to perform the methods provided in the above embodiments.

[0312] In the specific implementation process, the control unit 730 executes computer execution instructions, causing the control unit 730 to perform the above-mentioned method.

[0313] The specific implementation process of the control unit 730 can be found in the above method embodiment, and its implementation principle and technical effect are similar. It will not be repeated here.

[0314] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0315] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0316] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0317] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0318] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0319] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0320] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0321] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0322] If a function is implemented as 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 this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0323] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0324] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for adjusting the position of a movable antenna, characterized in that, Applied to a communication system, the communication system including multiple movable antennas, the method includes: Based on the location coordinates of the base station and mobile terminal in the communication system, a two-path channel model of the communication system is constructed, which includes the dominant path and the diffusion path; wherein, the location coordinates of each movable antenna in the two-path channel model are unknowns; Using the position coordinates of each of the movable antennas as unknowns, an expression for the traversal reachable rate is determined based on the two-path channel model; With the objective of minimizing the traversal reachable rate, the target position coordinates of each of the movable antennas are solved; The movable antennas in the communication system are adjusted according to the target position coordinates of each movable antenna.

2. The method according to claim 1, characterized in that, The expression for the traversal reachability includes a term corresponding to the beamforming matrix and a term corresponding to the channel vector; the channel vector is the output of the two-path channel model, and the method further includes: Based on the zero-forcing beamforming algorithm, the channel vector is used to construct the corresponding terms of the beamforming matrix; By combining the terms corresponding to the constructed beamforming matrix with the channel vector, an expression for the traversal reachable rate is obtained.

3. The method according to claim 1, characterized in that, The step of solving for the target position coordinates of each of the movable antennas with the objective of minimizing the traversal reachability includes: Based on Jensen's inequality, determine the tight lower bound expression of the traversal reachability rate; Let the components of the denominator in the tightly bounded expression be surrogate functions, and construct the surrogate functions into a target surrogate function using the Monte Carlo method; the target surrogate function approximates the surrogate function when the number of instantaneous channel implementations is sufficiently large; The target position coordinates of each of the movable antennas are solved with the goal of minimizing the target surrogate function.

4. The method according to claim 3, characterized in that, The step of solving for the target position coordinates of each of the movable antennas with the objective of minimizing the target surrogate function includes: The objective function is determined by subtracting the product of the penalty function and the iteration coefficient from the objective surrogate function; the iteration coefficient is related to the number of iterations when solving for the target position coordinates. The position coordinates of the movable antenna are set as the initial position coordinates, and a gradient descent algorithm is performed on the objective function a preset number of times to obtain the position coordinates of the first iteration; Repeat the following steps until the iteration termination condition is met, and determine the position coordinates of the last iteration as the target position coordinates of the movable antenna: Based on the number of iterations, the iteration coefficient is reduced, and the objective function for this iteration is determined. The position coordinates of the movable antenna are set to the position coordinates of the previous iteration, and the gradient descent algorithm is executed on the objective function of the current iteration a preset number of times to obtain the position coordinates of the current iteration; The iteration ends when the distance between the position coordinates of the current iteration and the position coordinates of the previous iteration is less than or equal to a preset distance threshold.

5. The method according to claim 4, characterized in that, The step of performing a preset number of gradient descent algorithms on the objective function to obtain the position coordinates of the first iteration includes: The following steps are repeated a preset number of times for the objective function, and the position coordinates of the last iteration are determined as the position coordinates of the first iteration: Based on the current position coordinates, the normalized gradient of the objective function is calculated, and the position coordinates to be updated and the current objective function value are determined according to the normalized gradient and the step size; the first position coordinates are the initial position coordinates. If the location coordinates to be updated meet the movable range of the movable antenna, and the difference between the previous target function value and the current target function value is greater than or equal to a preset threshold, then the next location coordinates will be determined as the location coordinates to be updated. If the position coordinates to be updated do not meet the movable range of the movable antenna, or the difference between the previous objective function value and the current objective function value is less than a preset threshold, then the step size is updated, and the steps of determining the position coordinates to be updated and the second function value based on the normalized gradient and the step size are executed.

6. The method according to any one of claims 1-5, characterized in that, The construction of a two-path channel model for the communication system, comprising a dominant path and a diffusion path, based on the location coordinates of the base station and the mobile terminal in the communication system includes: For each of the movable antennas, based on the spatial geometric relationship between the position coordinates of the movable antenna and the position coordinates of the mobile terminal, the first channel vector when the transmitted signal of the movable antenna propagates to the mobile terminal via the dominant path is determined; The first channel vectors of each of the movable antennas are concatenated to obtain the first channel matrix; For each of the movable antennas, the second channel vector when the transmitted signal of the movable antenna propagates to the mobile terminal through the diffusion path is determined by multiplying the preset spatial covariance matrix by a Gaussian random vector after performing Koleski decomposition on the preset spatial covariance matrix. The second channel vectors of each of the movable antennas are concatenated to obtain the second channel matrix; The first channel matrix and the second channel matrix are added together to obtain the two-path channel model.

7. The method according to any one of claims 1-5, characterized in that, Before constructing the two-path channel model including the dominant path and the diffusion path, the method further includes: Using the base station in the communication system as the origin, two straight lines parallel to the ground and pointing in a preset direction and perpendicular to each other are used as the horizontal and vertical axes, and a straight line perpendicular to the ground is used as the vertical axis, to construct the Cartesian coordinate system of the communication system. Acquire historical trajectories from multiple mobile devices; the historical trajectories include multiple location points. Based on the historical trajectory, multiple position coordinates corresponding to the mobile terminals are generated in the Cartesian coordinate system.

8. A device for adjusting the position of a movable antenna, characterized in that, Applied to a communication system, the communication system including multiple movable antennas, the device includes: The model construction module is used to construct a two-path channel model of the communication system, including the dominant path and the diffusion path, based on the location coordinates of the base station and the mobile terminal in the communication system; wherein, the location coordinates of each of the movable antennas in the two-path channel model are unknowns; The traversal reachable rate representation module is used to determine the expression of the traversal reachable rate based on the two-path channel model, using the position coordinates of each of the movable antennas as unknowns. The target location solution module is used to solve for the target location coordinates of each of the movable antennas with the objective of maximizing the minimum value of the traversal reachable rate; A movable antenna adjustment module is used to adjust the movable antennas in the communication system according to the target position coordinates of each movable antenna.

9. A communication system, characterized in that, include: Base station, multiple movable antennas, and control unit; The control unit is used to perform the method as described in any one of claims 1-7.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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