Cross-type movable antenna array, communication system, communication parameter optimization method and related devices

Abstract

The cross-type movable antenna array, communication system, communication parameter optimization method and related devices proposed in the embodiments of the present application. The cross-type movable antenna array includes: a plurality of first sliding tracks parallel to each other, a plurality of second sliding tracks parallel to each other, at least one antenna unit, and a drive control device; the first sliding tracks and the second sliding tracks cross to form a two-dimensional plane grid structure, and antenna units are arranged at the cross points of the first sliding tracks and the second sliding tracks; when the drive control device receives a drive request for a target position, the drive control device is used to control the first sliding tracks and / or the second sliding tracks to slide, driving the plurality of antenna units to move to the target position, greatly reducing the hardware cost, energy consumption and maintenance difficulty of the movable antenna array.

Description

Technical Field

[0001] This application relates to the field of wireless communication technologies, and in particular, to a cross-type movable antenna array, a communication system, a communication parameter optimization method, and related devices. Background Art

[0002] With the rapid development of wireless communication technologies, users' demands for communication quality, data transmission rate, and network capacity are increasing day by day. Due to the fixed position of the antennas in traditional fixed-position antenna systems, they cannot flexibly adapt to the dynamically changing wireless channel environment, resulting in limited communication performance. Especially in multi-user communication scenarios, the layout of fixed antennas is difficult to effectively address issues such as user position changes, channel fading, and interference.

[0003] In related technologies, movable antenna technology has the potential to improve wireless communication performance. By allowing the antennas to move in three-dimensional space, movable antenna technology can dynamically adjust the antenna positions to optimize the signal transmission channels, thereby significantly improving communication performance. However, movable antenna systems usually need to be equipped with independent drive components for each antenna unit in the antenna unit array, so that each antenna unit can move independently, resulting in a significant increase in usage costs including hardware costs, energy consumption, and maintenance difficulty as the number of antennas increases. Summary of the Invention

[0004] Embodiments of this application provide a cross-type movable antenna array, a communication system, a communication parameter optimization method, and related devices, which can reduce the usage cost of the movable antenna array.

[0005] To achieve the above object, a first aspect of the embodiments of this application proposes a cross-type movable antenna array, including:

[0006] Multiple mutually parallel first sliding rails, multiple mutually parallel second sliding rails, at least one antenna unit, and a drive control device;

[0007] The first sliding rails and the second sliding rails form a two-dimensional plane grid structure after crossing, and the antenna units are arranged at the intersection points of the first sliding rails and the second sliding rails;

[0008] When the drive control device receives a drive request for a target position, the drive control device is configured to control the first sliding rails and / or the second sliding rails to slide, and drive the multiple antenna units to move to the target position.

[0009] In some embodiments, the drive control device includes a first drive motor and a second drive motor, the first drive motor is connected to the first sliding rails, and the second drive motor is connected to the second sliding rails;

[0010] There are multiple antenna units. When the drive control device receives a drive request for the target position of the target antenna unit, the drive control device is configured to determine the target antenna unit from the multiple antenna units. The intersection point where the antenna unit is located is the target intersection point, and the target intersection point includes a first direction position corresponding to the moving direction of the first sliding track and a second direction position corresponding to the moving direction of the second sliding track;

[0011] The first drive motor is configured to control the first sliding track to move to the first direction position, and the second drive motor is configured to control the second sliding track to move to the second direction position.

[0012] To achieve the above object, a second aspect of the embodiments of the present application provides a communication system, including:

[0013] A base station, where the base station is provided with at least one cross-type movable antenna array as described in the first aspect;

[0014] At least one terminal, and the base station communicates based on the cross-type movable antenna array and the terminal.

[0015] To achieve the above object, a third aspect of the embodiments of the present application provides a method for optimizing communication parameters of a communication system. The communication system is as described in the second aspect, and the method for optimizing communication parameters is applied to the base station. The method includes:

[0016] Based on the cross-type movable antenna array, obtain the instantaneous channel vector between the base station and the terminal, and generate a signal-to-interference-plus-noise ratio model corresponding to the base station receiving the uplink signal of the terminal based on the instantaneous channel vector;

[0017] Generate a communication parameter optimization model based on the signal-to-interference-plus-noise ratio model and the transmission power parameter of the terminal;

[0018] Solve the communication parameter optimization model to obtain an optimized antenna position vector, an optimized base station receiving and combining matrix, and an optimized terminal transmission power;

[0019] Adjust at least one of the cross-type movable antenna arrays based on the optimized antenna position vector, and adjust the receiving parameters of the base station based on the optimized base station receiving and combining matrix;

[0020] Send the optimized terminal transmission power to the terminal so that the terminal transmits transmission information to the base station according to the optimized terminal transmission power.

[0021] In some embodiments, the obtaining the instantaneous channel vector between the base station and the terminal based on the cross-type movable antenna array includes:

[0022] Generate a first direction field response matrix between the terminal and the base station based on a first movement parameter of a first sliding track, a pitch arrival angle, and an azimuth arrival angle between the terminal and the base station.

[0023] Generate a second direction field response matrix between the terminal and the base station based on a second movement parameter of a second sliding track, a pitch arrival angle, and an azimuth arrival angle between the terminal and the base station.

[0024] Obtain the instantaneous channel vector based on the Khatri-Rao product of the first direction field response matrix and the second direction field response matrix.

[0025] In some embodiments, there are multiple terminals, and generating a communication parameter optimization model based on the signal-to-interference-plus-noise ratio model and the transmission power parameters of the terminals includes:

[0026] Generate an optimization objective function based on minimizing the transmission power parameters of all the terminals.

[0027] Generate optimization variable parameters based on the antenna position vector parameters of the base station, the base station receive combining matrix parameters of the base station, and the transmission power parameters.

[0028] Generate a transmission rate constraint based on the signal-to-interference-plus-noise ratio model, and generate optimization constraint conditions based on the transmission rate constraint, the variable optional domain of the optimization variable parameters, and the position distance constraint of the antenna position vector parameters.

[0029] Generate the communication parameter optimization model based on the optimization objective function, the optimization variable parameters, and the optimization constraint conditions.

[0030] In some embodiments, solving the communication parameter optimization model to obtain an optimized antenna position vector, an optimized base station receive combining matrix, and an optimized terminal transmission power includes:

[0031] Generate a single-parameter optimization condition based on the number of terminals and the number of tracks of the sliding track.

[0032] Directly solve the communication parameter optimization model based on a single transmission path condition and the single-parameter optimization condition to obtain the optimized antenna position vector, the optimized base station receive combining matrix, and the optimized terminal transmission power.

[0033] In some embodiments, when there are multiple transmission paths between the terminal and the base station, solving the communication parameter optimization model to obtain an optimized antenna position vector, an optimized base station receive combining matrix, and an optimized terminal transmission power includes:

[0034] Discretize the antenna position vector parameters to obtain discrete antenna position parameters, and construct an initial antenna position matrix based on the discrete antenna position parameters;

[0035] Perform iterative elimination on the initial antenna position matrix to obtain a screened antenna position matrix;

[0036] Perform iterative optimization on the screened antenna position matrix based on the optimization objective function to obtain the optimized antenna position vector;

[0037] Based on the optimized antenna position vector and the instantaneous channel matrix model, obtain the optimized base station receive combining matrix, where the instantaneous channel matrix model is generated based on the instantaneous channel vectors of all terminals;

[0038] Generate the optimized terminal transmit power based on the instantaneous channel matrix model and the receive noise power of the base station.

[0039] In some embodiments, the performing iterative elimination on the initial antenna position matrix to obtain a screened antenna position matrix includes:

[0040] Take each initial antenna position in the initial antenna position matrix as a test antenna position one by one;

[0041] Calculate the first lowest total transmit power corresponding to the test antenna position;

[0042] Delete the corresponding row vector and / or column vector in the initial antenna position matrix where the test antenna position is located, and adjust the test antenna position to obtain an adjusted test antenna position, and calculate the second lowest total transmit power corresponding to the adjusted test antenna position;

[0043] When the second lowest total transmit power is less than the first lowest total transmit power, update the test antenna position in the initial antenna position matrix based on the adjusted test antenna position.

[0044] In some embodiments, the performing iterative optimization on the screened antenna position matrix based on the optimization objective function to obtain the optimized antenna position vector includes:

[0045] Based on the optimization objective function, calculate the third lowest total transmit power corresponding to the screened antenna position matrix;

[0046] Based on the identity matrix, replace the row vectors in the screened antenna position matrix one by one to obtain the first replaced and screened antenna position, and calculate the fourth lowest total transmission power corresponding to the first replaced and screened antenna position. When the fourth lowest total transmission power is less than the third lowest total transmission power and the first replaced and screened antenna position satisfies the position distance constraint, update the screened antenna position matrix based on the first replaced and screened antenna position, and use the fourth lowest total transmission power as the new third lowest total transmission power;

[0047] Based on the updated screened antenna position matrix, obtain the optimized antenna position vector.

[0048] To achieve the above object, a fourth aspect of the embodiments of the present application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the communication parameter optimization method of the communication system as described in the third aspect.

[0049] To achieve the above object, a fifth aspect of the embodiments of the present application provides a storage medium, which is a computer-readable storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, it implements the communication parameter optimization method of the communication system as described in the third aspect.

[0050] The cross-type movable antenna array, communication system, communication parameter optimization method and related devices proposed in the embodiments of the present application. The cross-type movable antenna array includes: a plurality of mutually parallel first sliding tracks, a plurality of mutually parallel second sliding tracks, at least one antenna unit and a drive control device; the first sliding tracks and the second sliding tracks cross to form a two-dimensional plane grid structure, and antenna units are arranged at the intersection points of the first sliding tracks and the second sliding tracks; when the drive control device receives a drive request for a target position, the drive control device is used to control the first sliding tracks and / or the second sliding tracks to slide, driving a plurality of antenna units to move to the target position. The embodiments of the present application use the mutually crossed first sliding tracks and second sliding tracks to form a two-dimensional plane grid structure, and arrange the antenna units at the intersection points in the two-dimensional plane grid structure. By controlling the sliding of the first sliding tracks and the second sliding tracks, the coordinated movement of the antenna units is realized. This design significantly reduces the number of required drive control devices, greatly reduces the hardware cost, energy consumption and maintenance difficulty, makes the deployment and application of large-scale movable antenna arrays more economically feasible, and thus greatly reduces the use cost of the movable antenna array; at the same time, this solution can still flexibly adjust the antenna position to adapt to the dynamically changing wireless channel environment, effectively cope with problems such as terminal position change, channel fading and interference, so as to improve the communication quality, data transmission rate and network capacity in multi-terminal communication scenarios, etc.

[0051] Other features and advantages of the present application will be described in the subsequent specification, and in part will become apparent from the specification, or will be understood by implementing the present application. The objectives and other advantages of the present application can be achieved and obtained through the structures specifically pointed out in the specification, claims, and drawings. Description of the Drawings

[0052] Figure 1 is a schematic structural diagram of a cross-type movable antenna array provided by an embodiment of the present application.

[0053] Figure 2 is a schematic structural diagram of a communication system loaded with a cross-type movable antenna array provided by another embodiment of the present application.

[0054] Figure 3 is a schematic diagram of the channel space angle provided by another embodiment of the present application.

[0055] Figure 4 is a flowchart of a method for optimizing communication parameters of a communication system provided by another embodiment of the present application.

[0056] Figure 5 is Figure 4 a flowchart of step 401 in

[0057] Figure 6 is Figure 4 a flowchart of step 402 in

[0058] Figure 7 is Figure 4 a flowchart of step 403 in

[0059] Figure 8 is Figure 4 another flowchart of step 403 in

[0060] Figure 9 is Figure 8 a flowchart of step 802 in

[0061] Figure 10 is Figure 8 a flowchart of step 803 in

[0062] Figure 11 is a simulation schematic diagram of the first communication system loaded with a cross-type movable antenna array provided by another embodiment of the present application.

[0063] Figure 12 is a simulation schematic diagram of the second communication system loaded with a cross-type movable antenna array provided by another embodiment of the present application.

[0064] Figure 13It is a schematic diagram of the hardware structure of an electronic device provided by another embodiment of the present application. Detailed implementation manners

[0065] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0066] It should be noted that although functional module division is performed in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order from the module division in the device or the flowchart.

[0067] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0068] First, explanations are given for the symbols that appear below: , , and respectively represent conjugate, inverse, conjugate transpose and transpose operations; represents taking the expectation of a random variable; represents the identity matrix of; represents the dot product of vector a and vector b; , and respectively represent the Euclidean norm, Frobenius norm and infinity norm of a complex vector; represents the vector the j-th element of.

[0069] With the rapid development of wireless communication technology, users' demands for communication quality, data transmission rate and network capacity are increasing day by day. Due to the fixed position of its antennas, the traditional fixed-position antenna system cannot flexibly adapt to the dynamically changing wireless channel environment, resulting in limited communication performance. Especially in multi-user communication scenarios, the layout of fixed antennas is difficult to effectively cope with problems such as user position changes, channel fading and interference.

[0070] In the related art, the movable antenna technology has the potential to improve wireless communication performance. By allowing the antenna to move in three-dimensional space, the movable antenna technology can dynamically adjust the antenna position to optimize the signal transmission channel, thereby significantly improving communication performance. However, a movable antenna system usually needs to be equipped with an independent driving component for each antenna unit in the antenna unit array, so that each antenna unit can move independently, resulting in a significant increase in the usage cost including hardware cost, energy consumption, and maintenance difficulty as the number of antennas increases.

[0071] To reduce the usage cost of the movable antenna array, the embodiments of the present application utilize a two-dimensional plane grid structure formed by mutually intersecting first sliding tracks and second sliding tracks, and arrange the antenna units at the intersection points in the two-dimensional plane grid structure. By controlling the sliding of the first sliding tracks and the second sliding tracks, the cooperative movement of the antenna units is realized. This design significantly reduces the number of required drive control devices, greatly reduces the hardware cost, energy consumption, and maintenance difficulty, makes the deployment and application of large-scale movable antenna arrays more economically feasible, and thus greatly reduces the usage cost of the movable antenna array. At the same time, this solution can still flexibly adjust the antenna position to adapt to the dynamically changing wireless channel environment, effectively cope with problems such as terminal position change, channel fading, and interference, thereby improving the communication quality, data transmission rate, and network capacity in multi-terminal communication scenarios, etc.

[0072] Next, the cross-type movable antenna array, communication system, communication parameter optimization method, and related devices proposed in the embodiments of the present application will be further described. First, a cross-type movable antenna array will be described. Refer to Figure 1 , which is a schematic structural diagram of a cross-type movable antenna array provided by the embodiments of the present application. As Figure 1 shown, the cross-type movable antenna array proposed by the embodiments of the present application includes sliding tracks, antenna units, and drive control devices. Among them, the sliding tracks include M first sliding tracks in the vertical direction and N second sliding tracks in the horizontal direction, which together form a two-dimensional plane grid structure. Each antenna unit is installed at the intersection point of the first sliding track and the second sliding track and is connected to the radio frequency chain through a flexible cable to realize the movement of the antenna. Each first sliding track and second sliding track is driven by an independent drive control device for controlling the movement of the antenna in the horizontal and vertical directions.

[0073] Among them, the drive control device can be as Figure 1The drive motor shown in the figure is arranged at one end of each sliding track, wherein the drive control device corresponding to the vertical first drive track is the first drive motor, and the drive control device corresponding to the horizontal second drive track is the second drive motor. When the drive control device receives a drive request for the target position, the first drive motor controls the vertical first sliding track to move horizontally, thereby changing the horizontal position of each column of antenna units to the first direction position; the second drive motor controls the horizontal second sliding track to move vertically, thereby changing the vertical position of each row of antenna units to the second direction position, completing the coordinated movement of the target antenna unit at the target intersection corresponding to the target position to the target position. Unlike traditional movable antenna systems, the cross-type movable antenna array significantly reduces hardware complexity and motion overhead through a coordinated movement mechanism. In addition, the cross-type movable antenna array realizes the coordinated movement of multiple antenna units through horizontal and vertical sliding tracks. Compared with traditional movable antenna systems, the cross-type movable antenna array significantly reduces the number of drive components, reduces hardware complexity and cost.

[0074] It is understandable that the first sliding track and the second sliding track can not only be in the vertical and horizontal directions, but also in the upward and downward directions, and do not have to be perpendicular to each other, but only need to intersect on a plane to form a two-dimensional plane grid structure.

[0075] By utilizing mutually intersecting first and second sliding rails to form a two-dimensional planar grid structure, and setting the antenna units at the intersection of the two-dimensional planar grid structure, the coordinated movement of the antenna units is achieved by controlling the sliding of the first and second sliding rails. This design significantly reduces the number of required driving and control devices, greatly reduces hardware costs, energy consumption and maintenance difficulties, making the deployment and application of large-scale movable antenna arrays more economical and feasible, thereby greatly reducing the cost of using movable antenna arrays; at the same time, the solution can still flexibly adjust the antenna position, adapt to the dynamically changing wireless channel environment, and effectively deal with problems such as terminal position changes, channel fading and interference, thereby improving communication quality, data transmission rate and network capacity in multi-terminal communication scenarios.

[0076] Reference Figure 2 , is a schematic diagram of the structure of a communication system equipped with a cross-type movable antenna array provided by an embodiment of the present application. Figure 2 As shown in , the communication system includes a base station equipped with a cross-type movable antenna array and K terminals, and then the base station communicates with the terminals based on the loaded cross-type movable antenna array.

[0077] In this embodiment, a field response channel model is used to describe the channel variation with antenna position. It is assumed that the channel between the base station and the terminal satisfies the far field condition, because the size of the moving area of the antenna unit (usually several wavelengths) is much smaller than the signal propagation distance. , assuming the base station receives channel paths. Figure 3 , is a channel space angle diagram provided by an embodiment of the present application. Figure 3 As shown in the terminal No. The physical pitch arrival angle and azimuth arrival angle of each path are expressed as and .

[0078] Based on the above description of the cross-type movable antenna array and the communication system, the following further describes a communication parameter optimization method of a communication system provided by an embodiment of the present application. The communication parameter optimization method of the communication system provided in the embodiment of the present application can be applied to a base station in the communication system or a processor connected to the base station, etc. Figure 4 , which is an optional flowchart of the communication parameter optimization method of the communication system provided in an embodiment of the present application, Figure 4 The method in the embodiment may include but is not limited to steps 401 to 405. Figure 4 The order of step 401 to step 405 is not specifically limited, and the order of steps can be adjusted or some steps can be reduced or added according to actual needs.

[0079] Step 401: Based on a cross-type movable antenna array, an instantaneous channel vector between a base station and a terminal is obtained, and a signal-to-interference-and-noise ratio model corresponding to an uplink signal received by the base station from the terminal is generated based on the instantaneous channel vector.

[0080] Step 401 is described in detail below.

[0081] In some embodiments, in order to improve the communication efficiency and reduce the communication cost of the terminals between a base station equipped with a cross-type movable antenna array and multiple terminals, it is first necessary to perform data modeling on the instantaneous channel vector between the base station and the terminal in the communication system based on the cross-type movable antenna array, so as to facilitate the subsequent use of the instantaneous channel vector to generate a signal-to-noise ratio model corresponding to the uplink signal received by the base station from the terminal.

[0082] The following further describes how to model the instantaneous channel vector between the base station and the terminal based on the cross-type movable antenna array.

[0083] Reference Figure 5, based on a cross-type movable antenna array, an instantaneous channel vector between a base station and a terminal is obtained, including the following steps 501 to 503.

[0084] Step 501: Based on the first movement parameter of the first sliding track, the elevation angle of arrival and the azimuth angle of arrival between the terminal and the base station, generate a first direction field response matrix between the terminal and the base station.

[0085] Step 502: Based on the second movement parameter of the second sliding track, the elevation angle of arrival and the azimuth angle of arrival between the terminal and the base station, generate a second direction field response matrix between the terminal and the base station.

[0086] Step 503: Based on the Khatri-Rao product of the first direction field response matrix and the second direction field response matrix, obtain the instantaneous channel vector.

[0087] The following is a detailed description of steps 501 to 503.

[0088] Based on the Figure 3 shown channel space angle schematic, based on the first movement parameter of the first sliding track and the elevation angle of arrival and azimuth angle of arrival of the ℓ-th path between the k-th terminal and the base station, generate the first direction field response vector in the horizontal direction between the k-th terminal and the base station, as shown in the following formula (1). As shown in the following formula (1).

[0089] (1)

[0090] Similarly, based on the second movement parameter of the second sliding track, the elevation angle of arrival and azimuth angle of arrival of the -th path between the k-th terminal and the base station, generate the second direction field response vector in the vertical direction between the k-th terminal and the base station, as shown in the following formula (2). in the vertical direction between the k-th terminal and the base station, as shown in the following formula (2). As shown in the following formula (2).

[0091] (2)

[0092] where and respectively represent the virtual angles of arrival of the ℓ-th path reaching the k-th terminal and the base station. Therefore, the corresponding first direction field response matrix in the horizontal direction and the second direction field response matrix in the vertical direction are respectively shown in the following formula (3) and formula (4).

[0093] (3)

[0094] (4)

[0095] Then, based on the first directional field response matrix and the second directional field response matrix The Khatri-Rao product (⊙) is used to obtain the instantaneous channel vector between terminal k and the base station. As shown in the following formula (5).

[0096] (5)

[0097] Through steps 501 to 503 above, channel variations caused by terminal motion can be more accurately captured, particularly by taking into account the spatial characteristics of the antenna unit at different positions (the positional movement of the antenna unit represented by the first and second sliding tracks). Compared to constructing the directional field response matrix using only AoA and AoD, introducing the motion parameter and using the Katli-Rao product can more precisely characterize the spatial correlation of the channel, thereby improving the accuracy of the instantaneous channel vector estimation between the terminal and the base station, ultimately contributing to improved communication system performance.

[0098] Based on the instantaneous channel vector between each terminal and the base station , the instantaneous channel matrix between the base station and all terminals It can be shown as the following formula (6).

[0099] (6)

[0100] Next, based on the instantaneous channel vector between terminal k and the base station , base station and terminal The instantaneous channel vector between Expressed as a function of the antenna position vector, based on space division multiple access technology, the base station receives the terminal The transmitted uplink signal is determined by the receiving combining matrix, the channel matrix and the user's transmit power. Representative terminal The transmit power is defined as Represents the base station receiving combining matrix. Definition Represents the additive Gaussian noise power when the base station receives the signal. Based on this, we can further obtain the terminal received by the base station The signal to interference noise ratio (SINR) model corresponding to the transmitted uplink signal to interference noise ratio is shown in the following formula (7).

[0101] (7)

[0102] Step 402: Generate a communication parameter optimization model based on the signal-to-interference-plus-noise ratio (SINR) model and the transmission power parameters of the terminal.

[0103] The following provides a detailed description of Step 402.

[0104] After obtaining the SINR model (7) between the terminal and the base station through modeling, in order to further optimize the energy consumption cost of the terminal when the base station communicates with multiple terminals, a communication transmission optimization model will be further generated based on this SINR model (7) and the transmission power of each terminal to optimize the transmission power of each terminal under the condition of meeting the communication requirements between each terminal and the base station. The following will further describe how to construct this communication transmission optimization model.

[0105] Referring to Figure 6 , generating a communication parameter optimization model based on the SINR model and the transmission power parameters of the terminal includes the following steps 601 to 604.

[0106] Step 601: Generate an optimization objective function based on minimizing the transmission power parameters of all terminals.

[0107] Step 602: Generate optimization variable parameters based on the antenna position vector parameters of the base station, the base station receive combining matrix parameters of the base station, and the transmission power parameters.

[0108] Step 603: Generate a transmission rate constraint based on the SINR model, and generate optimization constraint conditions based on the transmission rate constraint, the variable selection domain of the optimization variable parameters, and the position distance constraint of the antenna position vector parameters.

[0109] Step 604: Generate a communication parameter optimization model based on the optimization objective function, the optimization variable parameters, and the optimization constraint conditions.

[0110] The following provides a detailed description of Steps 601 to 604.

[0111] In some embodiments, first, an optimization objective function is generated based on minimizing the transmission power parameters of all terminals in the communication system .

[0112] Next, optimization variable parameters are generated based on the antenna position vector parameters of the base station , the base station receive combining matrix parameters of the base station , and the transmission power parameters . .

[0113] After that, a transmission rate constraint is generated based on the SINR model , based on the transmission rate constraint, the variable optional domain of the optimization variable parameters (including , and ), and the position distance constraint of the antenna position vector parameters (including and ), the optimization constraint conditions are generated.

[0114] Finally, based on the optimization objective function, the optimization variable parameters, and the optimization constraint conditions, a communication parameter optimization model is generated as shown in the following formula (8).

[0115] (8)

[0116] Among them, constraint condition (a) ensures that each terminal meets the minimum rate requirement, , constraint condition (b) ensures that the transmit power of each terminal is non - negative, and constraint conditions (c) and (d) limit the movement of the antenna elements within the region while constraint conditions (d) and (f) respectively constrain that the antenna spacing in the horizontal and vertical directions is not less than the minimum thresholds and .

[0117] Through the above steps 601 to 604, by taking the minimization of the transmit power of all terminals as the optimization objective, and comprehensively considering the base station antenna position, the receive combining matrix, and the transmit power as optimization variables, while introducing the transmission rate constraint based on the signal - to - interference - plus - noise ratio (SINR) and the antenna position distance constraint, a complete communication parameter optimization model is finally formed. This model can minimize the total transmit power of all terminals and optimize the position of the base station antenna and the receive combining matrix on the premise of ensuring that each terminal reaches the target transmission rate. This optimization method can improve the spectral efficiency, reduce the energy consumption, reduce the interference, and enhance the overall network performance.

[0118] Step 403: Solve the communication parameter optimization model to obtain the optimized antenna position vector, the optimized base station receive combining matrix, and the optimized terminal transmit power.

[0119] The following is a detailed description of step 403.

[0120] Next, the communication parameter optimization model (8) will be further solved to obtain the optimized communication parameters, which include the optimized antenna position vector , the optimized base station receive combining matrix and the optimized terminal transmit power , so as to facilitate the subsequent adjustment of the base station and terminals in the communication system using these optimized communication parameters, thereby making the communication performance better when the adjusted base station and terminals communicate.

[0121] In this embodiment, first, for the case where there is only a single communication channel path between the terminal and the base station, the optimal solution of the communication parameter optimization model (8) and the theoretical optimal performance bound are considered. Then, a practical algorithm is designed to obtain a suboptimal solution of the communication parameter optimization model (8) under a multipath channel.

[0122] The solution in the case of a single communication channel path is described first below.

[0123] Referring to Figure 7 , solving the communication parameter optimization model to obtain the optimized antenna position vector, the optimized base station receive combining matrix, and the optimized terminal transmission power includes the following steps 701 to 702.

[0124] Step 701: Generate a single-parameter optimization condition based on the number of terminals of the terminal and the number of orbits of the sliding orbit.

[0125] Step 702: Based on the single transmission path condition and the single-parameter optimization condition, directly solve the communication parameter optimization model to obtain the optimized antenna position vector, the optimized base station receive combining matrix, and the optimized terminal transmission power.

[0126] Steps 701 to 702 are described in detail below.

[0127] For the case where there is only one communication transmission path between the terminal and the base station (i.e., the single transmission path condition), first, based on the number of terminals K of the terminal and the number of orbits of the sliding orbit (including the number of orbits M of the first sliding orbit and the number of orbits N of the second sliding orbit), the single-parameter optimization condition is generated as ; then, based on this single-parameter optimization condition and the single transmission path condition, the optimized antenna position vector in the optimal case is as shown in the following formula (9).

[0128] (9)

[0129] (10)

[0130] (11)

[0131] where and and represent the virtual arrival angles of the paths arriving at the terminal . For a multi-terminal communication system with randomly distributed terminals and scatterers, the probability that two terminals have exactly the same arrival angle is zero. Therefore, it is assumed that the path arrival angles of different terminals are different. The matrix and represent the factorization coefficient matrices respectively. Among them, the vector It can be obtained by prime factorizing the number M of the first sliding tracks. For any integer M, the prime factorization is expressed as . Among them, is the i-th prime factor (arranged in non-decreasing order), is the total number of prime factors. Define . For any positive integer m, , it can be uniquely determined by the factorization coefficient vector , satisfying , and . The vector is the integer quotient obtained by successively dividing the number (m - 1) by each element in m (from back to front). Similarly, can be obtained by prime factorizing the integer N. and represent any two non-overlapping sets of terminal pairs, the union of which is all terminal pairs, and the number of elements in the two sets does not exceed and , respectively. Define to represent the i-th element of the set , where . Similarly, define to represent the i-th element of the set , where, .

[0132] Under the condition that the channel transmission path between each terminal and the base station is a single path (i.e., the single transmission path condition), when , the minimum value of the transmission power of each terminal in the communication parameter optimization model (8) is as shown in the following formula (12).

[0133] (12)

[0134] Among them, and respectively represent the total number of prime factors of the number M of the first sliding tracks and the number N of the second sliding tracks. For any channel, the lower bound of the transmission power represented by the objective function of the communication parameter optimization model (8) is as shown in the following formula (13).

[0135] (13)

[0136] Through the above steps 701 to 702, by introducing the number of terminals, the number of sliding tracks, and the single transmission path condition to generate a single parameter optimization condition, the originally complex multi-variable optimization problem is simplified, so that the communication parameter optimization model can be directly solved.

[0137] Next, an embodiment of the present application provides a design method for a cross-type movable antenna in a multipath channel to obtain a suboptimal solution to problem (P1).

[0138] At any given antenna position parameter This application proposes a zero-forcing receive combining method to minimize total transmit power while meeting terminal communication requirements. The specific steps include receive combining matrix design, minimum transmit power, and antenna position optimization.

[0139] For the design of optimized receiving combining matrix: for a given channel matrix , the zero-forcing receiving combining matrix is adopted as shown in the following formula (14).

[0140] (14)

[0141] For minimum transmit power: To meet the terminal The rate requirement, the minimum transmit power (i.e., the optimized terminal transmit power) is calculated by the following formula (15).

[0142] (15)

[0143] Based on this, the total transmit power of all terminals can be expressed as the following formula (16).

[0144] (16)

[0145] in, From the above steps of optimizing the design of the receive combining matrix and the minimum transmit power, it can be seen that the terminal transmit power and the base station receive combining matrix are functions related to the antenna position vector. The embodiment of the present application optimizes the antenna position vector according to the method described below for antenna position optimization, and then determines the corresponding optimized terminal transmit power and optimized base station receive combining matrix.

[0146] The following further describes the steps involved in optimizing the antenna position.

[0147] Reference Figure 8 When there are multiple transmission paths between the terminal and the base station, the communication parameter optimization model is solved to obtain the optimized antenna position vector, the optimized base station receiving combining matrix, and the optimized terminal transmission power, including the following steps 801 to 805.

[0148] Step 801: Discretize the antenna position vector parameters to obtain discrete antenna position parameters, and construct an initial antenna position matrix based on the discrete antenna position parameters.

[0149] Step 802: Iteratively eliminate the initial antenna position matrix to obtain a screening antenna position matrix.

[0150] The following provides a detailed description of steps 801 to 802.

[0151] Regarding the optimization of the antenna position, first, discretize the antenna movement area, that is, the horizontal movement area of the antenna position vector parameter and the vertical movement area are respectively discretized into and candidate positions, that is, discrete antenna position parameters, as shown in the following formula (17) respectively.

[0152] (17)

[0153] Wherein, and .

[0154] Then, construct a channel matrix and a binary selection matrix, that is, based on the discretized discrete antenna position parameters, construct the channel vector from the candidate positions corresponding to all discrete antenna position parameters to terminal k. Merge the channel vectors of all terminals into a channel matrix

[0155] Construct two binary matrices as the initial antenna position matrix, that is and , which respectively represent the antenna position selection in the horizontal and vertical directions. Among them, only one element in each row of each binary selection matrix is 1, indicating the antenna position of that row or column.

[0156] Initialize the binary selection matrix as the identity matrix, denoted as and .

[0157] Next, perform iterative elimination on the initial antenna position selection matrix, that is, gradually eliminate rows from the initial antenna position selection matrix corresponding to the initial binary matrix until and only have M rows and N rows left respectively, as described in detail below.

[0158] Referring to Figure 9 , perform iterative elimination on the initial antenna position matrix to obtain a screened antenna position matrix, including the following steps 901 to 904.

[0159] Step 901: Take each initial antenna position in the initial antenna position matrix as the test antenna position one by one.

[0160] Step 902: Calculate the first minimum total transmission power corresponding to the test antenna position.

[0161] Step 903: Delete the corresponding row vector and / or column vector in the initial antenna position matrix where the test antenna position is located, adjust the test antenna position to obtain an adjusted test antenna position, and calculate the second minimum total transmission power corresponding to the adjusted test antenna position.

[0162] Step 904: When the second lowest total transmit power is less than the first lowest total transmit power, update the verification antenna position in the initial antenna position matrix based on the adjusted verification antenna position.

[0163] Steps 901 to 904 are described in detail below.

[0164] In some embodiments, the iteration parameter i is first set to 0, and the initial antenna position matrix is substituted based on the iteration parameter i, so that each initial antenna position in the initial antenna position matrix is used as a test antenna position one by one, and then it is determined whether i satisfies If satisfied , then calculate the current test antenna position and The total transmission power under the condition of , as the first minimum total transmission power, is recorded as As shown in the following formula (18).

[0165] (18)

[0166] Then, the iteration parameter i corresponds to Assigned to . Set another iteration parameter j=1 and perform the following operations in a loop.

[0167] For the corresponding row vector in the initial antenna position matrix where the test antenna is located, delete The jth row of the matrix is assigned to Calculate the current adjustment to verify antenna position and The total transmission power under the second lowest total transmission power As shown in the following formula (19).

[0168] (19)

[0169] If the second total transmission power at the current adjustment test antenna position Less than the first lowest total transmit power , then based on the adjustment of the test antenna position, the test antenna position in the initial antenna position matrix is updated, that is, Assign to , and Updated to the total transmit power at the current antenna position .

[0170] Increment the value of j by 1 for each loop until it reaches

[0171] Conversely, if it does not satisfy , enter the next level of loop and determine whether the iteration parameter i satisfies . If it is satisfied, calculate the total transmission power at the current test antenna position and which is the first minimum total transmission power, denoted as as shown in the following formula (20).

[0172] (20)

[0173] Then, assign the value corresponding to the iteration parameter i to . Set j = 1 and loop to execute the following operations

[0174] For the corresponding column vector in the initial antenna position matrix where the test antenna position is located, delete the j-th row of the matrix and assign it to . Calculate the total transmission power at the current adjusted test antenna position and which is used as the second minimum total transmission power as shown in the following formula (21).

[0175] (21)

[0176] If the second total transmission power at the current adjusted test antenna position is less than the first minimum total transmission power , then update the test antenna position in the initial antenna position matrix based on the adjusted test antenna position, that is, assign to , and update to the total transmission power at the current antenna position .

[0177] Increment the value of j by 1 for each loop until it reaches

[0178] After ending the above loop, if it does not satisfy , increment the value of the iteration parameter i by 1 and determine whether it satisfies . If it is satisfied, repeat the above loop. Otherwise, jump out of the loop to complete the iterative elimination of the initial antenna position selection matrix

[0179] Step 803: Iteratively optimize the selected antenna position matrix based on the optimization objective function to obtain the optimized antenna position vector

[0180] The following is a detailed description of step 803.

[0181] Next, based on the optimization objective function, the screened antenna position matrix and are iteratively optimized. On the premise of satisfying the minimum spacing constraint between antennas, the total transmission power is further reduced to obtain the optimized antenna position vector, which is specifically described as follows.

[0182] Referring to Figure 10 , based on the optimization objective function, the screened antenna position matrix is iteratively optimized to obtain the optimized antenna position vector, including the following steps 1001 to 1003.

[0183] Step 1001: Based on the optimization objective function, calculate the third lowest total transmission power corresponding to the screened antenna position matrix.

[0184] Step 1002: Based on the identity matrix, replace the row vectors of the screened antenna position matrix one by one to obtain the first replacement screened antenna position, and calculate the fourth lowest total transmission power corresponding to the first replacement screened antenna position. When the fourth lowest total transmission power is less than the third lowest total transmission power and the first replacement screened antenna position satisfies the position distance constraint, update the screened antenna position matrix based on the first replacement screened antenna position and use the fourth lowest total transmission power as the new third lowest total transmission power.

[0185] Step 1003: Based on the updated screened antenna position matrix, obtain the optimized antenna position vector.

[0186] The following is a detailed description of steps 1001 to 1003.

[0187] First, initialize the parameter iteration optimization parameter to positive infinity, and then assign the total transmission power under the previous antenna position selection matrix, that is, the screened antenna position matrix and , that is, the third lowest total transmission power to . Set i = 1.

[0188] After that, judge whether is satisfied. If is satisfied, then execute the setting to positive infinity, assign to . Set j = 1 and loop to execute the following row replacement process. In each row replacement process, replace the i-th row of the matrix with the identity matrix The j-th row vector of, and assign the new matrix after replacement to the matrix , to obtain the first replacement and screening antenna position. Calculate the new antenna position selection matrix and (i.e., the first replacement and screening antenna position) of the total transmission power, that is, the fourth lowest total transmission power . If the position selection matrix corresponding to the first replacement and screening antenna position satisfies the position distance constraint, that is , and the fourth lowest total transmission power is less than , then update the screening antenna position matrix based on the first replacement and screening antenna position, that is, change to . At the same time, take the fourth lowest total transmission power as the new third lowest total transmission power, that is, change to . At the end of each row replacement loop, increment j by 1 until ends the row replacement process.

[0189] On the contrary, if it does not satisfy , determine whether it satisfies . If it satisfies , set to positive infinity, and assign to . Set j = 1 and loop to execute the following row replacement process. In each row replacement process, replace the i-th row of the matrix with the j-th row of the identity matrix , and assign the new matrix after replacement to the matrix , to obtain the first replacement and screening antenna position. Calculate the new antenna position selection matrix and under (i.e., the first replacement and screening antenna position) of the total transmission power, that is, the fourth lowest total transmission power . If the position selection matrix corresponding to the first replacement and screening antenna position satisfies the position distance constraint, that is , and the fourth lowest total transmission power is less than , then update the screening antenna position matrix based on the first replacement and screening antenna position, that is, change to . At the same time, take the fourth lowest total transmission power as the new third lowest total transmission power, that is, change to . At the end of each row replacement loop, increment j by 1 until ends the row replacement process.

[0190] If it does not satisfy , and does not satisfy judge and front antenna position selection matrix and Total transmit power under Is the absolute value of the difference less than the predetermined threshold? If so, repeat the above iterative optimization process, otherwise jump out of the above iterative optimization process. And return the updated screening antenna position matrix and , to obtain the optimized antenna position vector.

[0191] Step 804: Based on the optimized antenna position vector and the instantaneous channel matrix model, an optimized base station receiving combining matrix is obtained.

[0192] Step 805: Generate optimized terminal transmit power based on the instantaneous channel matrix model and the base station's receive noise power.

[0193] Steps 804 to 805 are described in detail below.

[0194] In some embodiments, after obtaining the optimized antenna position vector, combined with the above formula (14) for optimizing the receiving merging matrix, the corresponding optimized base station receiving merging matrix can be obtained based on the optimized antenna position vector and the instantaneous channel matrix model. . In combination with the above formula (6), the instantaneous channel matrix model It can be further determined based on the optimized antenna position vector.

[0195] And, based on the above minimum transmit power formula (15), after obtaining the optimized antenna position vector, we can further calculate the instantaneous channel matrix model based on and the base station's receiving noise power , generating optimized terminal transmit power .

[0196] Through steps 801 to 805, 901 to 904, and 1001 to 1004, the continuous antenna position parameters are first discretized to construct an initial antenna position matrix. Then, through an iterative elimination process, each initial antenna position is examined one by one, and the effects of removing and adjusting the position on the system's total transmit power are compared. If the adjusted position reduces the total transmit power, the antenna position matrix is updated. This step compares the performance of different antenna positions to preliminarily screen out antenna positions that are more conducive to reducing transmit power, reducing the computational complexity of subsequent optimization. Furthermore, based on the screened antenna position matrix, further iterative optimization is performed. By comparing the antenna positions in the screened matrix with the identity matrix, the antenna positions are replaced row by row, and the total transmit power after the replacement is calculated. If the replacement position further reduces the total transmit power while satisfying the position distance constraint, the screened matrix is updated. Based on this initial screening, this step further refines the antenna positions to find the globally optimal antenna position vector. After determining the optimal antenna position vector, the optimal base station receive combining matrix is calculated based on the instantaneous channel matrix model of all terminals. Finally, the optimal terminal transmit power is determined by combining the instantaneous channel matrix model and the base station's received noise power. This allows the global optimal solution to be gradually approached through a phased, iterative optimization approach. While ensuring system performance (such as minimizing total transmit power), the complexity of the optimization problem is reduced and computational efficiency is improved. This method is particularly suitable for multipath transmission environments, can effectively utilize spatial resources, optimize antenna layout, and thus improve the overall performance of the communication system.

[0197] In practical applications, as the terminal and / or environmental scatterers move, the channel coefficients such as the angle of arrival of the channel path from each terminal to the base station change over time. Therefore, the instantaneous channel vector of each terminal exhibits random fading. In these scenarios, frequent adjustments to the antenna position based on the instantaneous channel will result in higher mobility overhead and energy consumption. To solve the above problems, the embodiment of the present application further expands the above-mentioned cross-type movable antenna system optimization method and proposes a cross-type movable antenna array assisted communication system based on a dual-time-scale optimization strategy. This system optimizes the antenna position based on statistical channel parameters, and the design of the terminal's transmit power and the base station's receive combining matrix is still based on the instantaneous channel. Among them, the dual-time-scale optimization problem of minimizing the terminal's expected transmit power is shown in the following formula (22).

[0198] (twenty two)

[0199] in, Represents the expected transmit power of the terminal over time.

[0200] In solving the above dual-time-scale optimization problem (22), the expected value of the total transmit power is approximated by Monte Carlo simulation. Specifically, based on the given statistical channel distribution of the terminals, a sufficient number of instantaneous channel samples are randomly generated. Let the random realization of the s-th instantaneous channel mapping be , where S represents the total number of samples. Each sample represents an instantaneous channel realization. For each instantaneous channel realization, the optimized terminal transmit power is calculated by the following formula (23).

[0201] (23)

[0202] where, represents all the row element data of the -th column in the matrix data.

[0203] And a zero-forcing receive combining matrix as shown in the following formula (24) is adopted to obtain the optimized base station receive combining matrix.

[0204] (24)

[0205] After that, the average value of the total transmit power of S instantaneous channels is calculated as the expected value of the total transmit power over time, that is, an approximation of the objective function of the dual-time-scale optimization problem. The antenna position optimization design is completed by using the relevant optimized antenna position steps in the above steps 801 to 803. Finally, the optimized antenna position vector is substituted into the design of the optimized receive combining matrix and the minimum transmit power calculation to optimize the terminal transmit power and the optimized base station receive combining matrix.

[0206] Step 404: Adjust at least one cross-type movable antenna array based on the optimized antenna position vector, and adjust the receive parameters of the base station based on the optimized base station receive combining matrix.

[0207] Step 405: Send the optimized terminal transmit power to the terminal so that the terminal sends transmission information to the base station according to the optimized terminal transmit power.

[0208] The following gives a detailed description of steps 404 to 405.

[0209] In some embodiments, after calculating the optimized antenna position vector, the optimized base station receive combining matrix, and the optimized terminal transmit power for the communication parameter optimization model, using the optimal antenna position vector, physically adjust the positions of at least one cross-type movable antenna array in the base station, and adjust the receive parameters of the base station according to the optimal base station receive combining matrix, so that the communication system actually operates in the optimal state of the antenna position and the receive combining method. At the same time, send the optimal terminal transmit power to the terminal to guide the terminal to use this power for data transmission, so that the entire system achieves the optimized best performance.

[0210] To verify the effectiveness of the method proposed in this application, Monte Carlo simulations and performance comparison tests were conducted in this embodiment. The experimental parameters are as follows: The size of the cross-type movable antenna array is M×N = 6 m × 6 m, which is installed on a base station at a height of 10 m. The size of the antenna movement area is 20λ×20λ, and in the optimization algorithm, it is discretized at the resolution of . There are three cubic buildings located at m respectively within the 120° azimuth sector covered by the base station. The height of the buildings is 30 m, and the side length of the bottom is 10 m. The total number of terminals is K = 18. Among them, half of the terminals are randomly distributed on the ground, and the distance range from the base station is 5 to 50 m; the other half of the terminals are randomly distributed inside the buildings. The carrier frequency is 30 GHz. The line-of-sight signal path gain between the base station and each terminal is , where is the distance between the base station and terminal k. The noise power is -80 dBm, and the minimum rate requirement for each terminal is 3 bps / Hz. The total number of Monte Carlo simulations for terminal distribution and instantaneous channel realization is S = 1000.

[0211] In addition to the communication parameter optimization scheme of the communication system equipped with a cross-type movable antenna proposed in this application embodiment, the following three methods are used as control group schemes.

[0212] (1) Antenna-by-antenna movement scheme: Independent antenna movement is carried out in a two-dimensional rectangular area. For each antenna element, the antenna position is adjusted by the antenna position optimization method proposed in the present invention.

[0213] (2) Dense UPA scheme: A uniform planar array (UPA) is adopted, and the antenna spacing is .

[0214] (3) Sparse UPA scheme: A uniform planar array is adopted, and the antenna spacing is 4 .

[0215] For fair comparison, all benchmark schemes use the same number of antennas as the cross-type movable antenna array.

[0216] Referring to Figure 11 , it is the simulation schematic diagram of the first communication system equipped with a cross-type movable antenna array provided in this application embodiment. As shown in Figure 11As shown in the figure, the total transmit power achieved by the scheme proposed in this embodiment and the baseline scheme varies with the number of terminals. The results show that the total transmit power of different large-scale antenna and planar phased array schemes increases with the number of terminals. However, the performance of the movable antenna scheme is far superior to the dense and sparse uniform planar array schemes. For example, when the number of terminals K = 30, more than 30 dB of power can be saved. This is because the movable antenna array can effectively reduce the channel correlation between terminals by optimizing the antenna position. In addition, the cross-type movable antenna scheme based on statistical channels performs better than the dense and sparse uniform planar array schemes. At the same time, it can avoid the frequent movement of the antenna due to the random movement of the terminal, significantly reducing the energy consumption caused by antenna movement, and providing a feasible solution for the design of base stations using cross-type movable antenna arrays.

[0217] Reference Figure 12 , is a simulation diagram of a second communication system equipped with a cross-type movable antenna array provided in an embodiment of the present application. Figure 12 Figure 2 shows the results of antenna position optimization for a cross-shaped movable antenna array based on statistical channel parameters. On the one hand, the antennas are distributed throughout the movable area, maximizing the array aperture while reducing channel correlation between multiple terminals. On the other hand, the non-uniform antenna layout reduces beam sidelobes.

[0218] An embodiment of the present application further provides an electronic device, including:

[0219] at least one memory;

[0220] at least one processor;

[0221] at least one program;

[0222] The program is stored in the memory, and the processor executes the at least one program to implement the communication parameter optimization method and communication parameter optimization method of the communication system implemented in the present application. The electronic device can be any intelligent terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), an in-vehicle computer, etc.

[0223] See also Figure 13 , Figure 13 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0224] The processor 1301 can be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0225] The memory 1302 can be implemented in the form of a ROM (Read Only Memory), a static storage device, a dynamic storage device, or a RAM (Random Access Memory), etc. The memory 1302 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1302 and are called by the processor 1301 to execute the communication parameter optimization method and the communication parameter optimization method of the communication system in the embodiments of the present application;

[0226] The input / output interface 1303 is used to implement information input and output;

[0227] The communication interface 1304 is used to implement communication interaction between this device and other devices, and can implement communication through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.);

[0228] The bus 1305 transmits information between the various components of the device (such as the processor 1301, the memory 1302, the input / output interface 1303, and the communication interface 1304);

[0229] Among them, the processor 1301, the memory 1302, the input / output interface 1303, and the communication interface 1304 achieve communication connections with each other inside the device through the bus 1305.

[0230] The embodiments of the present application also provide a storage medium, which is a computer-readable storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, it implements the communication parameter optimization method and the communication parameter optimization method of the above-mentioned communication system.

[0231] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0232] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0233] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0234] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0235] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0236] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0237] It should be understood that in this application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects and indicates that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Here, A and B can be singular or plural. The character " / " generally indicates an "or" relationship between the associated objects before and after. "At least one (one)" or a similar expression below means any combination of these items, including any combination of single item (one) or plural items (ones). For example, at least one (one) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or plural.

[0238] In several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the above division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0239] The units described above as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0240] In addition, in each embodiment of this application, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0241] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes: various media that can store programs, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0242] The preferred embodiments of the embodiments of this application have been described above with reference to the accompanying drawings, which does not limit the scope of rights of the embodiments of this application. Any modifications, equivalent replacements, and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of this application shall be within the scope of rights of the embodiments of this application.

Claims

1. A cross-type movable antenna array, characterized in that, Comprising: A plurality of mutually parallel first sliding tracks, a plurality of mutually parallel second sliding tracks, at least one antenna unit, and a drive control device; The first sliding tracks and the second sliding tracks cross to form a two-dimensional plane grid structure, and the antenna unit is disposed at the intersection of the first sliding tracks and the second sliding tracks; When the drive control device receives a drive request for a target position, the drive control device is configured to control the first sliding tracks and / or the second sliding tracks to slide, driving a plurality of the antenna units to move to the target position; The drive control device includes a first drive motor and a second drive motor, the first drive motor is connected to the first sliding tracks, and the second drive motor is connected to the second sliding tracks; There are a plurality of the antenna units. When the drive control device receives a drive request for the target position of the target antenna unit, the drive control device is configured to determine the target antenna unit from a plurality of the antenna units. The intersection point where the antenna unit is located is the target intersection point. The target intersection point includes a first direction position corresponding to the moving direction of the first sliding tracks and a second direction position corresponding to the moving direction of the second sliding tracks; The first drive motor is configured to control the first sliding tracks to move to the first direction position, and the second drive motor is configured to control the second sliding tracks to move to the second direction position, so as to realize the cooperative movement of the antenna units by controlling the sliding of the first sliding tracks and the second sliding tracks.

2. A communication system, characterized in that, Comprising: A base station, the base station being provided with at least one cross-type movable antenna array as described in claim 1; At least one terminal, the base station communicating based on the cross-type movable antenna array and the terminal.

3. A method for optimizing communication parameters of a communication system, characterized in that, The communication system is as described in claim 2, the method is applied to the base station, and the method includes: Based on the cross-type movable antenna array, obtaining an instantaneous channel vector between the base station and the terminal, and generating a signal-to-interference-plus-noise ratio model corresponding to the base station receiving the uplink signal of the terminal based on the instantaneous channel vector. The optimization variable parameters of the signal-to-interference-plus-noise ratio model include an antenna position vector parameter, and the antenna position vector parameter includes a first movement parameter of the first sliding tracks and a second movement parameter of the second sliding tracks; Generating a communication parameter optimization model based on the signal-to-interference-plus-noise ratio model and the transmission power parameter of the terminal; Solving the communication parameter optimization model to obtain an optimized antenna position vector, an optimized base station receive combining matrix, and an optimized terminal transmission power; Adjusting at least one of the cross-type movable antenna arrays based on the optimized antenna position vector, and adjusting the receive parameters of the base station based on the optimized base station receive combining matrix; Sending the optimized terminal transmission power to the terminal, so that the terminal sends transmission information to the base station according to the optimized terminal transmission power.

4. The method for optimizing communication parameters of the communication system according to claim 3, wherein The obtaining the instantaneous channel vector between the base station and the terminal based on the cross-type movable antenna array includes: Generate a first direction field response matrix between the terminal and the base station based on a first movement parameter of a first sliding track, an elevation angle of arrival and an azimuth angle of arrival between the terminal and the base station; Generate a second direction field response matrix between the terminal and the base station based on a second movement parameter of a second sliding track, an elevation angle of arrival and an azimuth angle of arrival between the terminal and the base station; Obtain the instantaneous channel vector based on the Khatri-Rao product of the first direction field response matrix and the second direction field response matrix; 5. The method for optimizing communication parameters of the communication system according to claim 3, characterized in that, There are multiple terminals, and generating a communication parameter optimization model based on the signal-to-interference-plus-noise ratio model and the transmission power parameters of the terminals includes: Generate an optimization objective function based on minimizing the transmission power parameters of all the terminals; Generate optimization variable parameters based on the antenna position vector parameters of the base station, the base station receive combining matrix parameters of the base station, and the transmission power parameters; Generate a transmission rate constraint based on the signal-to-interference-plus-noise ratio model, and generate optimization constraint conditions based on the transmission rate constraint, the variable selection domain of the optimization variable parameters, and the position distance constraint of the antenna position vector parameters; Generate the communication parameter optimization model based on the optimization objective function, the optimization variable parameters, and the optimization constraint conditions; 6. The method for optimizing communication parameters of the communication system according to claim 5, characterized in that Solving the communication parameter optimization model to obtain an optimized antenna position vector, an optimized base station receive combining matrix, and an optimized terminal transmission power includes: Generate a single parameter optimization condition based on the number of terminals and the number of tracks of the sliding track; Directly solve the communication parameter optimization model based on a single transmission path condition and the single parameter optimization condition to obtain the optimized antenna position vector, the optimized base station receive combining matrix, and the optimized terminal transmission power; 7. The method for optimizing communication parameters of the communication system according to claim 5, characterized in that, When there are multiple transmission paths between the terminal and the base station, solving the communication parameter optimization model to obtain an optimized antenna position vector, an optimized base station receive combining matrix, and an optimized terminal transmission power includes: Discretize the antenna position vector parameters to obtain discrete antenna position parameters, and construct an initial antenna position matrix based on the discrete antenna position parameters; Perform iterative elimination on the initial antenna position matrix to obtain a screened antenna position matrix; Iteratively optimize the screened antenna position matrix based on the optimization objective function to obtain the optimized antenna position vector; Obtain the optimized base station receive combining matrix based on the optimized antenna position vector and the instantaneous channel matrix model, and the instantaneous channel matrix model is generated based on the instantaneous channel vectors of all terminals; Generate the optimized terminal transmission power based on the instantaneous channel matrix model and the receive noise power of the base station; 8. The method for optimizing communication parameters of the communication system according to claim 7, characterized in that, The performing iterative elimination on the initial antenna position matrix to obtain a screened antenna position matrix includes: One by one, use each initial antenna position in the initial antenna position matrix as a test antenna position; Calculate a first minimum total transmission power corresponding to the test antenna position; Delete the corresponding row vector and / or column vector in the initial antenna position matrix where the test antenna position is located, adjust the test antenna position to obtain an adjusted test antenna position, and calculate the second lowest total transmission power corresponding to the adjusted test antenna position; When the second lowest total transmission power is less than the first lowest total transmission power, update the test antenna position in the initial antenna position matrix based on the adjusted test antenna position.

9. The method for optimizing communication parameters of the communication system according to claim 7, characterized in that, The iterative optimization of the selected antenna position matrix based on the optimization objective function to obtain the optimized antenna position vector includes: Based on the optimization objective function, calculate the third lowest total transmission power corresponding to the selected antenna position matrix; Based on the identity matrix, replace the row vectors in the selected antenna position matrix one by one to obtain a first replaced selected antenna position, and calculate the fourth lowest total transmission power corresponding to the first replaced selected antenna position. When the fourth lowest total transmission power is less than the third lowest total transmission power and the first replaced selected antenna position satisfies the position distance constraint, update the selected antenna position matrix based on the first replaced selected antenna position and use the fourth lowest total transmission power as the new third lowest total transmission power; Based on the updated selected antenna position matrix, obtain the optimized antenna position vector.

10. An electronic device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the communication parameter optimization method of the communication system according to any one of claims 3 to 9.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the communication parameter optimization method of the communication system according to any one of claims 3 to 9.

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