Intelligent polarized shaped antenna, communication system, positioning method, communication parameter optimization method and related equipment

Abstract

The embodiment of the invention provides an intelligent polarization shaped antenna, a communication system, a positioning method, a communication parameter optimization method and related equipment, and the intelligent polarization shaped antenna comprises a splitter, a first attenuator, a second attenuator, a first phase shifter, a second phase shifter, a first polarization unit and a second polarization unit. The splitter is connected with the first attenuator and the second attenuator, the first attenuator is connected with the first phase shifter, the second attenuator is connected with the second phase shifter, the first phase shifter is connected with the first polarization unit, and the second phase shifter is connected with the second polarization unit; when the antenna receives a processing signal, the splitter, the attenuator, the phase shifter and the planning unit are respectively used for carrying out splitting processing, amplitude processing and phase shifting processing on the processing signal and sending the processed signal so as to improve the reliability of wireless signal transmission.

Description

Technical Field

[0001] The present application relates to the field of wireless communication technology, and in particular to an intelligent polarization shaping antenna, a communication system, a positioning method, a communication parameter optimization method and related equipment. Background Art

[0002] In order to meet the demand for higher-speed wireless communication and higher-precision wireless perception brought about by the surge in the number of IoT devices in the upcoming sixth-generation wireless networks, the current trend of multiple-input multiple-output (MIMO) technology is to equip base stations and wireless terminals with more antennas. In related technologies, more antennas than existing large-scale MIMO are usually deployed on base stations (i.e., ultra-large-scale MIMO), which can significantly improve the spatial degrees of freedom, thereby enhancing the communication and perception performance of wireless systems.

[0003] However, since wireless signals may experience random depolarization due to scattering and other environmental factors when propagating in wireless channels, this fixed large-scale antenna array cannot fully align all signal components, causing serious signal attenuation and resulting in low data transmission reliability of wireless signals. Summary of the invention

[0004] The embodiments of the present application provide an intelligent polarization shaping antenna, a communication system, a positioning method, a communication parameter optimization method and related equipment, which can improve the reliability of data transmission of wireless signals.

[0005] To achieve the above object, a first aspect of an embodiment of the present application proposes a smart polarization shaped antenna, comprising: A splitter, a first attenuator, a second attenuator, a first phase shifter, a second phase shifter, a first polarization unit, and a second polarization unit, wherein the splitter is connected to the first attenuator and the second attenuator, the first attenuator is connected to the first phase shifter, the second attenuator is connected to the second phase shifter, the first phase shifter is connected to the first polarization unit, and the second phase shifter is connected to the second polarization unit; When the antenna receives a processed signal, the splitter is used to split the processed signal to obtain a first processed signal and a second processed signal, and send the first processed signal to the first attenuator and send the second processed signal to the second attenuator; The first attenuator is used to perform a first amplitude processing on the first processed signal to obtain a first amplitude processed signal, and send the first amplitude processed signal to the first phase shifter; the second attenuator is used to perform a second amplitude processing on the second processed signal to obtain a second amplitude processed signal, and send the second amplitude processed signal to the second phase shifter; The first phase shifter is used to perform a first phase shift processing on the first amplitude processed signal, and send the first phase shift processed signal to the first polarization unit after obtaining the first phase shift processed signal. The first polarization unit is used to send the first phase shift processed signal. The second phase shifter is used to perform a second phase shift processing on the second amplitude processed signal, and send the second phase shift processed signal to the second polarization unit after obtaining the second phase shift processed signal. The polarization directions of the first phase shift processed signal and the second phase shift processed signal are perpendicular to each other.

[0006] In some embodiments, the smart polarization shaping antenna further includes: a digital signal processor and a radio frequency chain connected to each other, wherein the radio frequency chain is connected to the splitter; When the antenna receives the processed signal, the digital signal processor and the radio frequency chain are used to perform digital signal processing and radio frequency processing on the processed signal in sequence, and send the processed signal to the splitter.

[0007] To achieve the above object, a second aspect of an embodiment of the present application proposes a base station, including: At least one antenna surface, on which at least one smart polarization shaping antenna as described in the first aspect is provided; At least one movable rotating rod is connected to the antenna surface, and the movable rotating rod is used to adjust the position of the antenna surface and the antenna rotation angle.

[0008] To achieve the above-mentioned purpose, a third aspect of an embodiment of the present application proposes a communication system, including: The base station as described in the second aspect; At least one terminal, wherein the terminal is provided with the intelligent polarization shaping antenna as described in the first aspect; The base station communicates with the terminal.

[0009] To achieve the above-mentioned purpose, a fourth aspect of an embodiment of the present application proposes a terminal positioning method for a communication system, the communication system is as shown in the third aspect, the terminal positioning method is applied to a base station, and the method includes: Obtain at least one terminal polarization shaping vector sent by the terminal to be located, obtain the antenna position and antenna rotation angle of each antenna surface, and obtain a base station receiving signal obtained by receiving a pilot signal sent by the terminal to be located, wherein the base station receiving signal is obtained based on the terminal polarization shaping vector and the pilot signal; Performing estimation calculation based on the base station received signal and the pilot signal to obtain a non-polarized channel matrix; Based on the non-polarized channel matrix, the antenna position and the antenna rotation angle, obtaining an arrival direction vector between the terminal to be located and the base station; Determine an effective antenna gain corresponding to the antenna rotation angle, and obtain a non-polarization shaped channel model between the terminal and the base station based on the effective antenna gain, the antenna position and the antenna rotation angle; Performing distance estimation based on the non-polarization shaped channel model and the effective antenna gain to obtain an estimated distance between the terminal to be located and the base station; Based on the estimated distance and the arrival direction vector, the location information of the terminal to be located is obtained.

[0010] In some embodiments, performing estimation calculation based on the base station received signal and the pilot signal to obtain a polarization-free channel matrix includes: Generate a side slice matrix and a front slice matrix based on the pilot signal, the complex-valued matrix parameters and the polarization-free channel parameters; generating a positive estimation parameter based on a Katli-Rao product of the pilot signal and the polarization-free channel parameter; Based on minimization of the difference between the frontal slice matrix and the frontal estimation parameter, a complex value matrix is ​​obtained; generating a side estimation parameter based on a Katli-Rao product of the complex-valued matrix and the pilot signal; The non-polarized channel matrix is ​​obtained based on minimization processing of the difference between the side slicing matrix and the side estimation parameter.

[0011] In some embodiments, obtaining the arrival direction vector between the terminal to be located and the base station based on the non-polarized channel matrix, the antenna position and the antenna rotation angle includes: Generate a channel covariance matrix based on the product of the polarization-free channel matrix and the transposed matrix of the polarization-free channel matrix; Performing eigenvalue decomposition on the covariance matrix to obtain matrix eigenvectors; Obtaining a steering vector parameter based on the antenna position, the antenna rotation angle, and an arrival direction parameter; Based on the product of the steering vector parameter and the matrix eigenvector, an inverse process is performed to obtain an arrival direction function, and the arrival direction function is optimized to obtain the arrival direction vector.

[0012] In some embodiments, the performing distance estimation based on the non-polarization shaped channel model and the effective antenna gain to obtain the estimated distance between the terminal to be located and the base station includes: A distance numerator parameter is obtained by multiplying the product of the number of antennas and the unit channel power of the antenna by the accumulated value of the effective antenna gain; Accumulating the products of all the non-polarization shaped channel models and the effective antenna gain to obtain a distance denominator parameter; The estimated distance is obtained based on the ratio of the distance numerator parameter and the distance denominator parameter.

[0013] To achieve the above-mentioned purpose, a fifth aspect of an embodiment of the present application proposes a communication parameter optimization method for a communication system, wherein the communication system is as shown in the third aspect, and the communication parameter optimization method is applied to a base station, and the method includes: In a coherent time period, based on the terminal positioning method of the communication system described in the fourth aspect, terminal distances between multiple terminals and the base station are obtained, wherein the coherent time period includes a slow time scale and at least one fast time scale in a time sequence; Generate a achievable transmission rate model between the base station and each of the terminals based on the terminal distance, and generate a communication parameter optimization model based on the achievable transmission rate model; Decomposing the communication parameter optimization model based on the slow time scale and the fast time scale to obtain a position rotation optimization model and a polarization shaping optimization model; Solving the position rotation optimization model to obtain an optimized position rotation angle, and adjusting at least one intelligent polarization shaping antenna in the base station in the slow time scale based on the optimized position rotation angle; The polarization shaping optimization model is solved to obtain the optimized polarization shaping corresponding to each fast time scale, and based on the optimized polarization shaping, at least one intelligent polarization shaping antenna in the base station and / or at least one intelligent polarization shaping antenna of at least one terminal is adjusted in the corresponding fast time scale.

[0014] In some embodiments, generating a communication parameter optimization model based on the achievable transmission rate model includes: Based on maximizing the achievable transmission rate model as a rate optimization objective function; Based on the antenna position parameters, antenna rotation angle parameters, base station polarization shaping vector parameters, precoding vector parameters of the base station and terminal polarization shaping vector parameters of each of the terminals as rate optimization variables; Generate rate constraint conditions based on the variable optional domain of the rate optimization variable, the position distance constraint between the antenna position parameters, the signal non-reflection constraint of the antenna position parameters and the antenna rotation angle parameters, and the non-forward center constraint; The communication parameter optimization model is generated based on the optimization objective function, the rate optimization variable, and the rate constraint condition.

[0015] In some embodiments, solving the polarization shaping optimization model to obtain an optimized polarization shaping corresponding to each fast time scale includes: The polarization shaping optimization model is converted into a Lagrangian format to obtain a converted polarization shaping optimization model; Using a block coordinate descent method, the conversion polarization shaping optimization model is divided into a terminal polarization shaping optimization model, a base station polarization shaping optimization model, and a transmit precoding optimization model; The terminal polarization shaping optimization model, the base station polarization shaping optimization model and the transmit precoding optimization model are iteratively solved to obtain the optimized polarization shaping.

[0016] To achieve the above-mentioned purpose, the sixth aspect of an embodiment of the present application proposes 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 terminal positioning method of the communication system as described in the fourth aspect or the communication parameter optimization method of the communication system as described in the fifth aspect.

[0017] To achieve the above-mentioned purpose, the seventh aspect of an embodiment of the present application proposes a storage medium, which is a computer-readable storage medium, and the storage medium stores a computer program. When the computer program is executed by a processor, it implements the terminal positioning method of the communication system as described in the fourth aspect or the communication parameter optimization method of the communication system as described in the fifth aspect.

[0018] The intelligent polarization shaping antenna, communication system, positioning method, communication parameter optimization method and related equipment proposed in the embodiments of the present application, the intelligent polarization shaping antenna includes: a splitter, a first attenuator, a second attenuator, a first phase shifter, a second phase shifter, a first polarization unit and a second polarization unit, the splitter is connected to the first attenuator and the second attenuator, the first attenuator is connected to the first phase shifter, the second attenuator is connected to the second phase shifter, the first phase shifter is connected to the first polarization unit, and the second phase shifter is connected to the second polarization unit; when the antenna receives a processed signal, the splitter is used to split the processed signal to obtain a first processed signal and a second processed signal, and send the first processed signal to the first attenuator, and send the second processed signal to the second attenuator; the first attenuator is used to The first processing signal is subjected to a first amplitude processing to obtain a first amplitude processing signal, and the first amplitude processing signal is sent to the first phase shifter. The second attenuator is used to perform a second amplitude processing on the second processing signal to obtain a second amplitude processing signal, and the second amplitude processing signal is sent to the second phase shifter. The first phase shifter is used to perform a first phase shift processing on the first amplitude processing signal, and the first phase shift processing signal is sent to the first polarization unit after obtaining the first phase shift processing signal. The first polarization unit is used to send the first phase shift processing signal. The second phase shifter is used to perform a second phase shift processing on the second amplitude processing signal, and the second phase shift processing signal is sent to the second polarization unit after obtaining the second phase shift processing signal. The polarization directions of the first phase shift processing signal and the second phase shift processing signal are perpendicular to each other. The embodiment of the present application realizes the fine joint regulation of the amplitude and phase of the polarization signal by setting a splitter, a dual-path independently controllable attenuator and a phase shifter collaborative architecture in the intelligent polarization shaping antenna, which can effectively solve the signal attenuation problem caused by polarization mismatch in the traditional large-scale MIMO system, and radiates the dynamically adjusted polarization processing signal through the orthogonally arranged dual-polarization units, so that the subsequent communication system equipped with the intelligent polarization shaping antenna can compensate for the random depolarization effect in the wireless channel in real time, thereby significantly improving the polarization matching accuracy without increasing the number of antennas, so as to improve the reliability of wireless signal transmission; in addition, the structure breaks through the limitation of the traditional polarization reconstruction antenna that only adjusts the amplitude, and fully taps the potential of polarization diversity through phase-amplitude collaborative control, so that the base station equipped with the intelligent polarization shaping antenna can adaptively track the spatial orientation changes of the terminal device, maintain a stable polarization alignment state in a complex propagation environment, thereby enhancing the signal transmission reliability in the multi-terminal scenario, and providing better communication quality assurance for high-density Internet of Things devices, while improving the channel space utilization rate through dynamic optimization of the polarization state.

[0019] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a structural schematic diagram of a communication system equipped with an intelligent polarization shaping antenna provided in one embodiment of the present application.

[0021] Figure 2 This is a schematic diagram of the geometric position of an intelligent polarization shaping antenna provided in another embodiment of the present application.

[0022] Figure 3 This is a schematic diagram comparing the performance of an intelligent polarization shaping antenna provided by another embodiment of the present application with existing antenna technologies.

[0023] Figure 4 It is a protocol diagram of wireless communication and perception of a communication system provided with an intelligent polarization shaping antenna provided in another embodiment of the present application.

[0024] Figure 5 This is a schematic diagram of structured pilot polarization time in a coherent time domain provided by another embodiment of the present application.

[0025] Figure 6 This is a flowchart of a terminal positioning method for a communication system provided in another embodiment of the present application.

[0026] Figure 7 yes Figure 6 Flowchart of step 602 in FIG.

[0027] Figure 8 yes Figure 6 Flowchart of step 603 in FIG.

[0028] Fig. 9 yes Figure 6 Flowchart of step 605 in FIG.

[0029] Fig.10 This is a flowchart of a communication parameter optimization method for a communication system provided by another embodiment of the present application.

[0030] Fig.11 yes Fig.10 Flowchart of step 1002 in FIG.

[0031] Fig.12 yes Fig.10 Flowchart of step 1005 in FIG.

[0032] Fig.13It is a simulation schematic diagram of a first communication system equipped with an intelligent polarization shaping antenna provided by another embodiment of the present application.

[0033] Fig.14 It is a simulation schematic diagram of a second communication system equipped with an intelligent polarization shaping antenna provided in yet another embodiment of the present application.

[0034] Fig.15 It is a simulation schematic diagram of a third communication system equipped with an intelligent polarization shaping antenna provided in yet another embodiment of the present application.

[0035] Fig.16 This is a schematic diagram of the hardware structure of an electronic device provided in yet another embodiment of the present application. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0037] It should be noted that although the functional modules are divided in the device schematic and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart.

[0038] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0039] In order to meet the demand for higher-speed wireless communication and higher-precision wireless perception brought about by the surge in the number of IoT devices in the upcoming sixth-generation wireless networks, the current trend of multiple-input multiple-output (MIMO) technology is to equip base stations and wireless terminals with more antennas. In related technologies, more antennas than existing large-scale MIMO are usually deployed on base stations (i.e., ultra-large-scale MIMO), which can significantly improve the spatial degrees of freedom, thereby enhancing the communication and perception performance of wireless systems.

[0040] However, since wireless signals may experience random depolarization due to scattering and other environmental factors when propagating in wireless channels, this fixed large-scale antenna array cannot fully align all signal components, causing serious signal attenuation and resulting in low data transmission reliability of wireless signals.

[0041] In order to improve the reliability of data transmission of wireless signals, the embodiment of the present application realizes the fine joint control of the amplitude and phase of the polarization signal by setting a splitter, a dual-path independently controllable attenuator and a phase shifter collaborative architecture in the intelligent polarization shaping antenna, which can effectively solve the signal attenuation problem caused by polarization mismatch in the traditional large-scale MIMO system, and radiate the dynamically adjusted polarization processing signal through the orthogonally arranged dual-polarization units, so that the subsequent communication system equipped with the intelligent polarization shaping antenna can compensate for the random depolarization effect in the wireless channel in real time, thereby significantly improving the polarization matching accuracy without increasing the number of antennas, so as to improve the reliability of wireless signal transmission; in addition, the structure breaks through the limitation of the traditional polarization reconstruction antenna that only adjusts the amplitude, and fully taps the potential of polarization diversity through phase-amplitude collaborative control, so that the base station equipped with the intelligent polarization shaping antenna can adaptively track the spatial orientation changes of the terminal device, maintain a stable polarization alignment state in a complex propagation environment, thereby enhancing the signal transmission reliability in the multi-terminal scenario, and providing better communication quality assurance for high-density Internet of Things devices, while improving the channel space utilization through dynamic optimization of the polarization state.

[0042] The following will further describe the intelligent polarization shaping antenna, communication system, positioning method, communication parameter optimization method and related equipment proposed in the embodiments of the present application. First, an intelligent polarization shaping antenna is described, referring to Figure 1 , is a schematic diagram of the structure of a communication system equipped with an intelligent polarization shaping antenna provided in an embodiment of the present application. Figure 1 As shown, the overall architecture of a communication system equipped with an intelligent polarization shaping antenna is demonstrated. The communication system includes a base station and multiple terminals, wherein the base station is provided with multiple antenna surfaces, and each antenna surface is provided with multiple intelligent polarization shaping antennas; in addition, the terminal is also provided with an intelligent polarization shaping antenna accordingly, so that the terminal and the base station can perform better two-way communication through the intelligent polarization shaping antenna.

[0043] The intelligent polarforming antenna (IPA). This is a new antenna structure that improves wireless sensing and communication performance without increasing the number of antennas and incurring additional costs and energy consumption. Specifically, the intelligent polarforming antenna controls the polarization state of each antenna at the transmitting and / or receiving end by adjusting the polarization shaping vector with adjustable signal amplitude and phase, thereby using polarization diversity to achieve dynamic adjustment of antenna polarization to cope with real-time channel conditions and depolarization effects. This process is achieved through an electronically adjustable polarization former, which integrates a phase shifter and attenuator to collaboratively adjust the phase and amplitude of the transmit / receive signal (see Figure 1). In addition, each IPA can be independently adjusted in position and / or rotation to adapt to the spatial distribution of channels. This can be achieved through mechanical control, that is, physical movement through actuators such as motors and precision gears, as well as external mechanical structures.

[0044] Specifically, if Figure 1 As shown in , each smart polarization shaping antenna is provided with a digital signal processor, a radio frequency chain and a polarization shaper connected in sequence. When the smart polarization shaping antenna needs to send a processed signal of a device (such as a base station or a terminal device), the digital signal processor is used to perform corresponding digital signal processing on the processed signal, and then send the processed signal after digital signal processing to the radio frequency chain. The radio frequency chain then performs radio frequency processing on the processed signal, and sends the processed signal after radio frequency processing to the polarization shaper for polarization processing, and then sends the processed signal after polarization processing. Correspondingly, when the smart polarization shaping antenna needs to receive a processed signal, the polarization shaper receives the processed signal for polarization processing, and then transmits the processed signal after polarization processing to the radio frequency chain for corresponding radio frequency processing, and then transmits the processed signal after radio frequency processing to the digital signal processor for corresponding digital signal processing, and then transmits the processed signal back to the device (such as a base station or a terminal device).

[0045] like Figure 1 As shown in, the polarization shaper includes a splitter, a first attenuator, a second attenuator, a first phase shifter, a second phase shifter, a first polarization unit and a second polarization unit; wherein the splitter is connected to the first attenuator and the second attenuator, the first attenuator is connected to the first phase shifter, the second attenuator is connected to the second phase shifter, the first phase shifter is connected to the first polarization unit, and the second phase shifter is connected to the second polarization unit. When the antenna receives the processed signal, the splitter is used to split the processed signal to obtain a first processed signal and a second processed signal, and send the first processed signal to the first attenuator, and send the second processed signal to the second attenuator; the first attenuator is used to perform a first amplitude processing on the first processed signal to obtain a first amplitude processed signal, and send the first amplitude processed signal to the first phase shifter, the second attenuator is used to perform a second amplitude processing on the second processed signal to obtain a second amplitude processed signal, and send the second amplitude processed signal to the second phase shifter; the first phase shifter is used to perform a first phase shift processing on the first amplitude processed signal, and then send the first phase shift processed signal to the first polarization unit, the first polarization unit is used to send the first phase shift processed signal, the second phase shifter is used to perform a second phase shift processing on the second amplitude processed signal, and then send the second phase shift processed signal to the second polarization unit, the second polarization unit is used to send the second phase shift processed signal, and the polarization directions of the first phase shift processed signal and the second phase shift processed signal are perpendicular to each other.

[0046] Therefore, by setting a splitter, a dual-path independently controllable attenuator and a phase shifter collaborative architecture in the intelligent polarization shaping antenna, the refined joint control of the amplitude and phase of the polarization signal is realized, which can effectively solve the signal attenuation problem caused by polarization mismatch in traditional large-scale MIMO systems, and radiate the dynamically adjusted polarization processing signal through orthogonally arranged dual-polarization units, so that the subsequent communication system equipped with the intelligent polarization shaping antenna can compensate for the random depolarization effect in the wireless channel in real time, thereby significantly improving the polarization matching accuracy without increasing the number of antennas, so as to improve the reliability of wireless signal transmission; in addition, the structure breaks through the limitation of the traditional polarization reconstruction antenna that only adjusts the amplitude, and fully explores the potential of polarization diversity through phase-amplitude collaborative control, so that the base station equipped with the intelligent polarization shaping antenna can adaptively track the spatial orientation changes of the terminal equipment, maintain a stable polarization alignment state in a complex propagation environment, thereby enhancing the signal transmission reliability in multi-terminal scenarios, providing better communication quality assurance for high-density Internet of Things devices, and improving the channel space utilization through dynamic optimization of the polarization state.

[0047] Reference Figure 3 , is a performance comparison diagram of a smart polarization shaping antenna provided in an embodiment of the present application and existing antenna technology. Figure 3 As shown, the intelligent polarization shaping antenna is significantly different from the existing six-dimensional mobile antenna and the traditional polarizable reconfigurable antenna. First, the six-dimensional mobile antenna is composed of multiple antennas / subarrays with three-dimensional rotatable and movable positions, which are mechanically controlled, but the six-dimensional mobile antenna either lacks antenna polarization control or is designed under fixed polarization conditions. Second, although traditional polarizable reconfigurable antennas are able to adjust antenna polarization, they only adjust the amplitude of the signal, but cannot adjust the signal phase, and fail to fully utilize polarization diversity. In addition, the polarizable reconfigurable antenna with fixed antenna position / rotation cannot obtain additional spatial degrees of freedom through antenna movement. In contrast, the IPA with polarization shaping function aims to simultaneously adjust the amplitude and phase of the polarized signal to fully exploit the advantages of polarization diversity, while adjusting the position / rotation of the antenna to fully utilize the spatial degrees of freedom.

[0048] First, explain the symbols that appear below: , , as well as denote conjugate, inverse, conjugate transpose, and transpose operations respectively; It means to find the expectation of a random variable; express The identity matrix of ; a⋅b represents the dot product of vector a and vector b; and ∘ denote the Kronecker product and Khatri-Rao product, respectively; , as well as They represent the Euclidean norm, Frobenius norm and infinite norm of a complex vector respectively; Representation vector The j-th element of .

[0049] Specifically, if Figure 1 As shown, each intelligent polarization shaping antenna can independently apply a certain phase offset (through the phase shifter included in the polarization shaper) and amplitude change (through the attenuator included in the polarization shaper) to the corresponding processed signal for transmission / reception, thereby adjusting the polarization state of the antenna. The phase shifter and attenuator in the polarization shaper achieve precise polarization control by independently adjusting the phase and amplitude of the signal. In this way, the IPA can make full use of polarization diversity by using polarization shaping, and adaptively control the polarization of the antenna so that the polarization between the transmitting and receiving antennas remains consistent. We assume that each transmitting / receiving antenna consists of two orthogonally arranged linear polarization units, one of which is used for vertical polarization ( element), and another polarization unit for horizontal polarization ( Specifically, each terminal Each is equipped with a single intelligent polarization shaping antenna, and its receiving polarization shaping vector is shown in the following formula (1).

[0050]

[0051] Among them, the terminal corresponds to the first polarization unit in the intelligent polarization shaping antenna (i.e. element) and the second polarization unit (i.e. ) have amplitude coefficients of and In addition, the terminal Components and The phase shifts of the components are and .

[0052] The base station has B antenna surfaces (i.e., intelligent polarization antenna (IPA) subarrays), denoted as Each IPA subarray consists of N (≥1) IPAs, denoted as Therefore, the total number of transmitting antennas in the base station is BN. Each IPA subarray is a uniform planar array with a fixed size. All antennas in the same base station subarray have the same polarization characteristics determined by the propagation environment, so their polarization can be controlled by the same polarization shaping vector. The transmit polarization shaping vector of an IPA sub-array is expressed as shown in the following formula (2).

[0053]

[0054] in, and Respectively represent Each corresponding antenna in the transmit IPA subarray Components and The amplitude coefficient of the element. Similarly, and Respectively represent The antennas in the transmitting IPA subarray Components and The phase offset of the element. The phase and amplitude of the polarization shaping vector can be controlled continuously or discretely. For ease of implementation, discrete control of amplitude and phase offset is adopted in this embodiment. and Respectively represent the polarization shaping amplitude and phase offset control bits of each smart polarization shaping antenna. Therefore, the receiving polarization shaping vector and the transmitting polarization shaping vector are expressed as .

[0055] in , express The set of all possible values ​​of and Phase constitute; ,in Here, the discrete phase values ​​are assumed to be in the interval Uniform distribution within represents the controllable amplitude set, whose size is ,and In the interval Evenly distributed within. Note that when hour, Degenerates to the case of no attenuation, that is .

[0056] Each IPA can be rotated and / or repositioned within a given space. In this embodiment, it is assumed that the IPA of each terminal can be rotated while maintaining a fixed position. This can be achieved through actuators or auxiliary components (such as rotary motors) or through manual adjustment to achieve physical movement. In contrast, all antennas in each IPA subarray in the base station can be moved in a convex 3D space given by the base station. This process is achieved by connecting the subarrays to the central processing unit of the base station through movable rotating rods with flexible wires, allowing the central processing unit to precisely control their three-dimensional position and rotation angle, thus achieving mechanical control.

[0057] In order to facilitate the description of the movement of the terminal / base station antenna, the present embodiment establishes three Cartesian coordinate systems. Figure 2 , is a schematic diagram of the geometric position of an intelligent polarization shaping antenna provided in an embodiment of the present application. Figure 2 As shown, the global coordinate system is recorded as , where the central processing unit of the base station (i.e., the base station center) is located at the origin The local coordinate system of each IPA subarray is denoted by , where the center of the subarray is the origin The local coordinate system of each terminal is denoted by , where the origin is defined as the center of the terminal antenna. IPA subarrays ( ) can be described by the position vector and rotation vector shown in the following formula (3) respectively.

[0058]

[0059] in, , ,and Indicates The coordinates of the IPA centers in the global coordinate system; , ,and The average value is , respectively representing the The IPA sub-array is relative to the global coordinate system axis, Axis and The rotation angle of the axis. , the corresponding rotation matrix can be written as shown in the following formula (4).

[0060]

[0061] in and .make Indicates the first The position of the antenna in its local coordinate system, then the first IPA subarray The position of each antenna in the global coordinate system can be expressed as shown in the following formula (5).

[0062]

[0063] Next, on the terminal side, the terminal local coordinate system Relative to the global coordinate system The rotation angle vector is shown in the following formula (6).

[0064]

[0065] in , ,and The average value is , respectively representing the The terminals are relative to the global coordinate system axis, Axis and The rotation angle of the axis. Similar to , given After that, the corresponding rotation matrix can be recorded as .

[0066] Based on the above, the following is a mathematical modeling of the signal model in the communication system equipped with the smart polarization shaping antenna.

[0067] For ease of explanation, in the embodiments of the present application, it is assumed that the channel between the base station and each terminal is a far-field line-of-sight link channel. In addition, the position of the terminal changes slowly, and it can be considered that it remains approximately unchanged during the coherence time of each position, while their rotation (orientation) may change arbitrarily during this period. Each terminal is equipped with an omnidirectional intelligent polarization shaping antenna. Let and Respectively represent the terminals The azimuth and elevation of the signal arriving at the center of the base station. The corresponding pointing vector is as shown in the following formula (7).

[0068]

[0069] Then, the base station The IPA subarray is targeted at The steering vector of a terminal can be expressed as shown in the following formula (8).

[0070]

[0071] in, represents the carrier wavelength. Next, to determine the effective antenna gain In the present application, Projection to In the local coordinate system of the IPA sub-matrix, it is recorded as Then, in spherical coordinates, we can express for ,in and Respectively represent the corresponding arrival directions in the local coordinate system (such as Figure 2 Finally, IPA subarrays along the direction Effective antenna gain on a linear scale As shown in the following formula (9).

[0072]

[0073] in, It represents the effective antenna gain determined by the antenna radiation pattern (unit: dBi).

[0074] Let the base station The IPA subarray and The non-polarized line-of-sight link channel between the terminals is shown in the following formula (10).

[0075]

[0076] in, is the free space path loss, where Indicates the reference distance The channel power at meters, Indicates The distance between the terminal location and the base station center.

[0077] In such Figure 3 In the local coordinate system shown, the vertical Component edge positive - Axis or - Axis alignment, while horizontal Component edge positive - Axis or -axis direction, and their unit vectors are shown in the following formula (11).

[0078]

[0079] In addition, the polarization state of an electromagnetic wave can be described by any two orthogonal electric field components on the wavefront, which are represented by the following orthogonal unit vectors in the global coordinate system as shown in the following formula (12).

[0080]

[0081] The transmission field component of the line-of-sight link is generated by projecting the time-varying electric field of the transmitting antenna onto the direction of the line-of-sight link signal. The corresponding transformation is shown in the following formula (13).

[0082]

[0083] Similarly, the received field component is obtained by projecting the line-of-sight link signal direction onto the receiving antenna, and its projection matrix is ​​shown in the following formula (14).

[0084]

[0085] Therefore, the The terminal and the base station The dual-polarization response matrix between the dual-polarization antennas on the IPA sub-array is expressed as the following formula (15).

[0086]

[0087] Then, by receiving the polarization shaping vector and the transmit polarization shaping vector Introducing the polarization effect for each antenna, we can get The expression of the IPA polarization shaping channel is shown in the following formula (16).

[0088] (16) Next, in an embodiment of the present application, a communication-aware integrated communication system based on intelligent polarization shaping antenna (IPA) enhancement is studied, which utilizes terminal position awareness to facilitate communication between terminals equipped with IPA and base stations equipped with IPA. In this embodiment, a new type of actual channel setting is considered, that is, the position of the terminal is basically static, but the orientation of the terminal may change frequently. This channel setting is applicable to many scenarios, such as when spectators in a stadium watch a football game, their smartphones are fixed in position but often rotated to take pictures in different directions. Another example is a terminal that plays a virtual reality (VR) game in a fixed position, and their VR devices rotate frequently to enhance the gaming experience. In this scenario, a dual-time-scale transmission protocol for an IPA-enhanced wireless communication and perception system is proposed.

[0089] Reference Figure 4 , is a schematic diagram of a wireless communication and sensing protocol of a communication system provided with a smart polarization shaping antenna provided in an embodiment of the present application. Figure 4 As shown in , the protocol consists of two phases, namely a slow time scale phase and a fast time scale phase.

[0090] In the first phase (slow time scale phase): In this initial phase, the terminal position is first sensed, and then the position and rotation of the base station antenna are determined based on the sensed terminal position. Figure 5 , is a schematic diagram of pilot polarization time structured in a coherent time domain provided by an embodiment of the present application. Figure 5 As shown in , the base station receives multiple pilot signals from the terminal device, which are shaped by the terminal polarization vector that varies with time. The transmission is performed to achieve the terminal positioning based on polarization shaping. Subsequently, based on the perceived terminal position, the antenna position and rotation of all IPAs in the base station are optimized to maximize the average achievable rate of the terminal. The optimized antenna position and rotation will be implemented on the base station and remain unchanged during the second phase of each positioning coherence time.

[0091] Step 2 (fast time scale phase): The remaining time of each localization coherence time is given by The first phase consists of channel coherence intervals. During each channel coherence interval, the rotation of the terminal (or the resulting channel) remains unchanged. During each channel coherence interval, the transmit and receive polarization shaping vectors are determined jointly based on the instantaneous terminal rotation / channel to maximize the achievable rate for all terminals. The terminal position sensed in the first phase helps to more efficiently obtain the instantaneous channel information within each channel coherence interval, because the terminal position remains unchanged and only the terminal orientation changes during different channel coherence times.

[0092] Based on the communication system modeled above, the terminal positioning method of the communication system provided in the embodiment of the present application will be further described below. The terminal positioning 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 6 , which is an optional flowchart of the terminal positioning method of the communication system provided in an embodiment of the present application, Figure 6 The method in the embodiment may include but is not limited to steps 601 to 606. Figure 6 The order of step 601 to step 606 is not specifically limited, and the order of steps can be adjusted or some steps can be reduced or added according to actual needs.

[0093] Step 601: obtaining at least one terminal polarization shaping vector sent by the terminal to be located, obtaining the antenna position and antenna rotation angle of each antenna surface, and obtaining a base station receiving signal obtained by receiving a pilot signal sent by the terminal to be located.

[0094] The following is a detailed description of step 601.

[0095] In such Figure 4 In the protocol design shown, in order to obtain the location information of each terminal at the beginning of the coherence time in a communication system equipped with an intelligent polarization shaping antenna, so as to facilitate subsequent communication parameter optimization, the embodiment of the present application needs to locate each terminal first.

[0096] In order to realize the positioning perception of the terminal using a small number of base station antenna positions and rotation arrangements, the IPA subarray of the base station will be in a group Different training positions - rotate the pairing to collect pilot signals of all terminals. In order to determine the terminal position of the terminal to be located, the embodiment of the present application proposes that the terminal to be located A set of randomly configured terminal polarization shaping vectors ,in Represents the total number of pilot signal blocks used for the terminal to be located. Base station polarization shaping vector , , set to a fixed value for perception. Figure 5 As shown in , it is assumed that during the position training period (normalized to the symbol period) in step 1 of the proposed protocol, the channels between all terminals and the base station remain unchanged. The position training duration is recorded as ,in , Indicates the number of time slots in each pilot signal block. Each terminal sends a known pilot signal to the base station , and in The pilot signal is repeated within a block. The structured pilot-polarization shaping pattern is as follows: Figure 5 As shown, the terminal polarization shaping vector of the terminal to be located is In the Block The t_slot remains constant within each time slot and varies between blocks.

[0097] Furthermore, in this case, the antenna position and antenna rotation angle (ie, training position-rotation pairing) of each antenna surface (ie, IPA subarray) in the base station are obtained as shown in the above formula (3).

[0098] Based on this, in the proposed pilot-polarization shaping mode, the base station receives the pilot signal sent by the terminal to be positioned, and the base station receiving signal (for the mth training position-rotation pairing) is as shown in the following formula (17).

[0099]

[0100] in Represents a complex-valued matrix parameter No. Here, the dynamic polarization shaping channel component vector is shown in the following formula (18).

[0101]

[0102] in .also, represents the horizontal stacking of all terminal pilot signals, Indicates IPA training positions - all in the rotation pair The aggregate channel from each terminal to all antennas (i.e., non-polarized channel parameters); is a complex-valued additive white Gaussian noise matrix whose independent and identically distributed elements obey a Gaussian distribution with zero mean.

[0103] Step 602: performing estimation calculation based on the base station received signal and the pilot signal to obtain a non-polarized channel matrix.

[0104] Step 602 is described in detail below.

[0105] Next, in order to accurately locate the terminal to be positioned at the initial moment of the coherence time, it is necessary to further receive the signal based on the base station. and pilot signal Perform an estimation calculation to obtain the non-polarized channel matrix between the terminal to be located and the base station without polarization processing , so as to facilitate the subsequent use of the non-polarized channel matrix Determine the distance information between the terminal to be located and the base station How to obtain the non-polarized channel matrix will be further described below.

[0106] Reference Figure 7 , performing estimation calculation based on the base station received signal and the pilot signal to obtain the non-polarized channel matrix, including the following steps 701 to 705.

[0107] Step 701: Generate a side slice matrix and a front slice matrix based on a pilot signal, a complex-valued matrix parameter, and a polarization-free channel parameter.

[0108] Step 702: Generate positive estimation parameters based on the Catterelli-Rao product of the pilot signal and the polarization-free channel parameter.

[0109] Step 703: Obtain a complex value matrix based on minimization of the difference between the frontal slice matrix and the frontal estimation parameters.

[0110] Step 704: Generate side estimation parameters based on the Catterelli-Rao product of the complex-valued matrix and the pilot signal.

[0111] Step 705: Based on minimization of the difference between the side slicing matrix and the side estimation parameter, a non-polarized channel matrix is ​​obtained.

[0112] Steps 701 to 705 are described in detail below.

[0113] In some embodiments, after obtaining the base station receiving signal and pilot signal Afterwards, Based on this, we can further , complex-valued matrix parameters And the polarization-free channel parameters Constructed a three-dimensional matrix , which contains all Matrix . The corresponding expanded representations of mode-1, mode-2 and mode-3 are shown in the following formula (19).

[0114]

[0115] in, and Respectively horizontal, lateral and frontal slices of , side slice matrix and the frontal slice matrix . Among them, the side slice matrix and the frontal slice matrix The matrix expansion affected by noise can be rewritten as shown in the following formula (20).

[0116]

[0117] in, and represents the additive white Gaussian noise matrix. Assume that the pilot signal Known, then by alternately minimizing the corresponding offset square function, iteratively estimate and .based on , in In the iteration, IPA training position-rotation paired channels The estimated value of is obtained by slicing the matrix sideways and the side estimation parameters With polarization-free channel parameters The following offset function is obtained by minimizing the difference of the product of as shown in the following formula (21).

[0118]

[0119] The side estimation parameters Based on complex-valued matrices and the pilot signal The Cattri-Lao product is obtained, that is, After solving, we get The closed expression for the estimated value of is shown in the following formula (22).

[0120]

[0121] Complex-valued matrices The estimated value of the matrix is ​​sliced ​​positively With positive estimated parameters The offset function that minimizes the difference of is obtained, as shown in the following formula (23).

[0122]

[0123] The positive estimated parameter Based on pilot signal and polarization-free channel parameters The Catterelli-Lao product of After solving, we get The closed-form expression for the estimated value of is

[0124] Based on the estimated stable non-polarized channel (in , the user location is determined by extracting the corresponding arrival direction at the base station and the distance between the user and the base station. To apply the multiple signal classification (MUSIC) algorithm, we first construct the sample covariance matrix based on the channel estimate. The matrices are stacked into a large matrix, i.e. a non-polarized channel matrix. .

[0125] Through the above steps 701 to 705, by generating the side slice matrix and the front slice matrix, combining the pilot signal and the non-polarized channel parameters, and using mathematical methods such as the Cattery-Rao product, the complex value matrix and the non-polarized channel matrix can be effectively estimated. The acquisition of these key parameters provides the basis for the subsequent determination of the arrival direction and distance of the terminal. Finally, through the difference minimization processing, the accuracy of parameter estimation can be improved, thereby achieving more accurate terminal positioning. This positioning method based on intelligent polarization adjustable antenna can greatly improve the positioning performance and service quality of the communication system, and is of great value in high-density Internet of Things applications such as 6G.

[0126] Step 603: Based on the non-polarization channel matrix, the antenna position and the antenna rotation angle, an arrival direction vector between the terminal to be located and the base station is obtained.

[0127] Step 603 is described in detail below.

[0128] In some embodiments, after obtaining the non-polarized channel matrix After that, it is necessary to further base on the non-polarized channel matrix , the antenna position of the current base station and antenna rotation angle , in order to obtain the estimated arrival direction vector between the terminal to be located and the base station, so as to facilitate the subsequent use of the arrival direction vector The following will further describe how to determine the arrival direction vector between the terminal to be located and the base station. .

[0129] Reference Figure 8 , based on the non-polarized channel matrix, the antenna position and the antenna rotation angle, obtaining the arrival direction vector array between the terminal to be located and the base station, including the following steps 801 to 804.

[0130] Step 801: Generate a channel covariance matrix based on the product of the non-polarization channel matrix and the transposed matrix of the non-polarization channel matrix.

[0131] Step 802: Perform eigenvalue decomposition on the covariance matrix to obtain matrix eigenvectors.

[0132] Step 803: Obtain steering vector parameters based on the antenna position, antenna rotation angle and arrival direction parameters.

[0133] Step 804: Based on the product of the steering vector parameter and the matrix eigenvector, an inverse process is performed to obtain an arrival direction function, and the arrival direction function is optimized to obtain an arrival direction vector.

[0134] Steps 801 to 804 are described in detail below.

[0135] In some embodiments, a channel covariance matrix is ​​first generated based on the product of the non-polarized channel matrix and the transposed matrix of the non-polarized channel matrix as shown in the following formula (24).

[0136]

[0137] against Antenna positions corresponding to training positions-rotations and antenna rotation angle The steering vector transmission of the pairing structure is shown in the following formula (25).

[0138]

[0139] Next, the covariance matrix Perform eigenvalue decomposition, and its expression is shown in the following formula (26).

[0140]

[0141] in is included maximum The diagonal matrix of the eigenvalues ​​of the matrix, Contains corresponding to this The matrix eigenvector of the largest eigenvalue, and the remaining matrix eigenvalues ​​and matrix eigenvectors constitute and According to the multi-signal classification algorithm, all The arrival direction vector of each user can be obtained by finding the previous Peak values ​​are obtained, that is, based on the guidance vector parameter and matrix eigenvectors The product of , and then the inverse processing, get the arrival direction function, and then optimize the arrival direction function to get the arrival direction vector As shown in the following formula (27).

[0142]

[0143] Through the above steps 801 to 804, first, the channel covariance matrix is ​​calculated by the non-polarized channel matrix, and the eigenvalue decomposition is performed to obtain the matrix eigenvectors. These eigenvectors contain the direction information of the signal; then, the steering vector parameters are calculated in combination with the antenna position, rotation angle and other parameters. The direction of arrival function can be obtained by multiplying the steering vector parameters with the eigenvector and performing the inverse processing; finally, the direction of arrival function is optimized to obtain the final direction of arrival vector. This method based on the characteristic analysis of the channel covariance matrix can effectively estimate the direction of arrival of the terminal and provide key parameters for subsequent precise positioning. This direction of arrival estimation technology based on intelligent polarization adjustable antennas can greatly improve the positioning accuracy and robustness of the communication system, and has important application value in application scenarios such as high-density Internet of Things and autonomous driving.

[0144] Step 604: Determine the effective antenna gain corresponding to the antenna rotation angle, and obtain a non-polarization shaped channel model between the terminal and the base station based on the effective antenna gain, the antenna position and the antenna rotation angle.

[0145] Step 604 is described in detail below.

[0146] Next, based on the effective antenna gain formula shown in formula (9) above, it can be determined that at the current antenna rotation angle The corresponding effective antenna gain Then, the non-polarized line-of-sight link channel formula corresponding to the above formula (10) is further used to calculate the effective antenna gain. , Antenna location and antenna rotation angle , we get the non-polarized shaped channel model between the terminal and the base station .

[0147] Step 605: Distance estimation is performed based on the non-polarization shaped channel model and the effective antenna gain to obtain an estimated distance between the terminal to be located and the base station.

[0148] Step 605 is described in detail below.

[0149] The non-polarized shaped channel model between the current base station and the terminal to be located is obtained. and effective antenna gain Then, using the non-polarization shaped channel model and the corresponding effective antenna gain Perform distance estimation processing to obtain the estimated distance between the terminal to be located and the base station , so that it is convenient to use the estimated distance later Determine the location information of the terminal to be located. The following will further describe how to estimate the distance between the terminal to be located and the base station.

[0150] Reference Fig. 9 , perform distance estimation based on the non-polarization shaped channel model and the effective antenna gain to obtain the estimated distance between the terminal to be located and the base station, and follow the steps 901 to 903.

[0151] Step 901: A distance numerator parameter is obtained by multiplying the product of the number of antennas and the unit channel power by the accumulated value of the effective antenna gain.

[0152] Step 902: Accumulate the products of all non-polarization shaped channel models and effective antenna gains to obtain a distance denominator parameter.

[0153] Step 903: Obtain an estimated distance based on the ratio of the distance numerator parameter to the distance denominator parameter.

[0154] Steps 901 to 903 are described in detail below.

[0155] In some embodiments, after obtaining the non-polarization shaped channel model Then, the non-polarization shaped channel model is By performing modulo processing, we can obtain the channel amplitude observed in the line-of-sight link channel estimation , and then use all The observed channel amplitude in the line-of-sight link channel estimation And the geometric relationship between the terminal to be located and the base station, which represents the two-norm , we can further construct the following least squares-based method to estimate the distance The estimation of is shown in the following formula (28).

[0156] (28) Next, by solving formula (28), we can get the analytical value of the estimated distance: . That is, the number of antennas based on the antenna and unit channel power The product of the effective antenna gain , and get the distance numerator parameter Then, the channel amplitudes corresponding to all non-polarization shaped channel models are accumulated and the root of the effective antenna gain The product of , get the distance denominator parameter ; Finally, based on the ratio of the distance numerator parameter and the distance denominator parameter, the estimated distance is obtained As shown in the following formula (29).

[0157]

[0158] Through the above steps 901 to 903, by comprehensively considering the number of antennas, unit channel power, effective antenna gain and non-polarization shaping channel model, the distance numerator and distance denominator parameters are constructed, and their ratio is used to estimate the distance. This method can more comprehensively utilize channel information and antenna characteristics, thereby more accurately estimating the distance between the terminal and the base station, providing more accurate distance information for subsequent terminal positioning, and thus improving positioning accuracy.

[0159] Step 606: Based on the estimated distance and the arrival direction vector, obtain the location information of the terminal to be located.

[0160] Step 606 is described in detail below.

[0161] In some embodiments, after obtaining the estimated distance of each terminal to be located and the arrival direction vector Afterwards, the arrival direction vector can be further used Determine the position of each terminal to be located relative to the base station, and then use the estimated distance between each terminal to be located and the base station and the corresponding position, the position information of each terminal to be located relative to the base station can be accurately determined, so as to facilitate the subsequent use of the estimated distance Optimize the communication parameters between the base station and the terminal.

[0162] By implementing the terminal positioning method of the communication system provided by the above steps 601 to 606, the non-polarization channel matrix is ​​first estimated by comprehensively utilizing the user polarization shaping vector, antenna position and rotation angle, base station receiving signal and pilot signal, and then the arrival direction vector is calculated. In addition, the non-polarization shaping channel model is constructed in combination with the effective antenna gain, and finally the distance is estimated, so that the terminal location information can be obtained more comprehensively and accurately, and the positioning accuracy and robustness can be effectively improved. Especially in a complex wireless channel environment, a more reliable positioning service can be provided.

[0163] Further, based on the communication system and terminal positioning method proposed above, the present application embodiment proposes a communication parameter optimization method for the communication system. Fig.10 , which is an optional flow chart of the communication parameter optimization method of the communication system provided in the embodiment of the present application, Fig.10 The method in the embodiment may include but is not limited to steps 1001 to 1005. Fig.10 The order of steps 1001 to 1005 is not specifically limited, and the order of steps can be adjusted or some steps can be reduced or increased according to actual needs. Figure 1 The base station in the communication system shown in the figure, or a processor, a server, etc. connected to the communication system.

[0164] Step 1001: In a coherent time period, based on a terminal positioning method of a communication system, obtain terminal distances between multiple terminals and a base station.

[0165] The following is a detailed description of step 1001.

[0166] In some embodiments, Figure 4 In the protocol design shown, at the beginning of the coherent time period, the terminal positioning method of the communication system mentioned above is used to determine the terminal distance between each terminal that needs to optimize the communication parameters and the base station. .

[0167] Step 1002: Generate a achievable transmission rate model between the base station and each terminal based on the terminal distance, and generate a communication parameter optimization model based on the achievable transmission rate model.

[0168] The following is a detailed description of step 1002.

[0169] Next, the terminal distance between each terminal and the base station is used , combined with the non-polarized line-of-sight link channel formula shown in formula (10) above, the non-polarized line-of-sight link channel between each terminal and the base station can be obtained: , and further based on the IPA polarization shaping channel shown in the above formula (16), the terminal The overall IPA channel representation between all IPA subarrays of the base station , and further based on the overall IPA channel representation, the achievable transmission rate model between the base station and each terminal can be further obtained as shown in the following formula (30).

[0170]

[0171] Indicates user The transmit precoder of and are the antenna position vector and antenna rotation angle vector of all IPA sub-arrays of the base station respectively; is the noise variance, Then, based on the achievable transmission rate model (30), a communication parameter optimization model for transmission optimization between the base station and all terminals is further generated, as described in detail below.

[0172] Reference Fig.11 , generating a communication parameter optimization model based on the achievable transmission rate model, including the following steps 1101 to 1104.

[0173] Step 1101: The maximum achievable transmission rate model is used as the rate optimization objective function.

[0174] Step 1102: The antenna position parameters, antenna rotation angle parameters, base station polarization shaping vector parameters, precoding vector parameters of the base station and the terminal polarization shaping vector parameters of each terminal are used as rate optimization variables.

[0175] Step 1103: Generate rate constraints based on the variable optional domain of the rate optimization variable, the position distance constraint between the antenna position parameters, the signal non-reflection constraint of the antenna position parameters and the antenna rotation angle parameters, and the non-forward center constraint.

[0176] Step 1104: Generate a communication parameter optimization model based on the optimization objective function, rate optimization variables, and rate constraints.

[0177] Steps 1101 to 1104 are described in detail below.

[0178] In some embodiments, according to Figure 4 The dual time scale protocol shown in the figure aims to jointly optimize the antenna positions on the base station side in the slow time scale. and antenna rotation angle , and jointly optimize the transmit polarization shaping vector at the base station side on a fast time scale With the precoding vector And the polarization shaping vector on the terminal side , thereby maximizing the weighted total rate of all terminals. That is, using the maximum achievable transmission rate model (30) as the rate optimization objective function, based on the antenna position parameters of the base station , Antenna rotation angle parameters , Base station polarization shaping vector parameters , precoding vector parameters And the terminal polarization shaping vector parameters of each terminal As rate-optimized variables; optional fields based on rate-optimized variables , , , the position distance constraints between antenna position parameters , antenna position parameters and antenna rotation angle parameters signal non-reflection constraints , non-positive center constraint , generating rate constraints. Thus, the corresponding communication parameter optimization model is generated by using the above rate optimization objective function, rate optimization variables and rate constraints as shown in the following formula (31).

[0179]

[0180] in Indicates terminal The rate weights are, ζ represents the total transmit power of the base station, and the expected value is taken from the random channel changes caused by arbitrary terminal rotation. The first two constraints ensure that the receive and transmit polarization shaping vectors meet the discrete amplitude and phase requirements. The fourth constraint ensures that the center of each IPA subarray is located in the convex three-dimensional space of the base station. The fifth constraint enforces the minimum distance Overlap and coupling between IPA subarrays are prevented; the sixth constraint is used to alleviate mutual signal reflections between base station antennas, and the seventh constraint prevents the front of each IPA subarray from facing the base station center of the base station, as that may cause signal blocking.

[0181] Through the above steps 1101 to 1104, by constructing an optimization function with the goal of maximizing the achievable transmission rate, and comprehensively considering various communication parameters such as antenna position, rotation angle, polarization shaping vector and precoding vector as optimization variables, and introducing actual physical limitations such as position distance constraints, signal non-reflection constraints and non-forward center constraints, a comprehensive communication parameter optimization model is finally generated. This model can more effectively coordinate and optimize various communication parameters, thereby maximizing the system transmission rate and improving wireless communication performance while meeting actual constraints.

[0182] Step 1003: Decompose the communication parameter optimization model based on the slow time scale and the fast time scale to obtain a position rotation optimization model and a polarization shaping optimization model.

[0183] The following is a detailed description of step 1003.

[0184] Next, according to Figure 4 The dual time scale protocol shown in the figure decomposes the communication parameter optimization model (31) based on the slow time scale and the fast time scale to obtain a position rotation optimization model for optimizing the antenna position and the antenna rotation angle, and a polarization shaping model for the base station. , precoding And the polarization shaping of the terminal The optimized polarization shaping optimization model is described in detail as follows.

[0185] For fast time scale optimization, in each channel coherence interval, the base station first estimates the instantaneous channel of all terminals, which is the channel for all possible terminal polarization shaping vector parameters and base station polarization shaping vector parameters The antenna position parameters are estimated , Antenna rotation angle parameters and the terminal position remain fixed. Then, the base station determines its transmit polarization shaping vector , precoding vector And the terminal polarization shaping vector parameters In order to facilitate the polarization shaping vector and Each element in is updated in parallel and element by element, thereby simplifying their optimization process. In the embodiment of the present application, auxiliary variables are introduced and Therefore, for fast time scales, there is a polarization shaping optimization model as shown in the following formula (32).

[0186] (32) The mean square error term of the terminal As shown in the following formula (33).

[0187] The mean square error term By combining the weighted minimum mean square error (WMMSE) method, is the equilibrium parameter, Indicates terminal The weighting factor of .

[0188] For the optimization of slow time scale, the antenna position parameter in the communication parameter optimization model (31) is and antenna rotation angle parameters The slow time scale optimization of becomes a stochastic optimization problem due to the expectation operation in the objective function. Since the objective function is difficult to solve, it is approximated as a deterministic function in the embodiment of the present application. Specifically, we generate independently for all terminals A set of random channel samples is generated, and the average achievable rate on these channel samples is used as an approximation of the expected rate in the objective function of the communication parameter optimization model (31). Figure 4 The base station can obtain the line-of-sight channel of the terminal under all possible IPA position-rotation pairs by estimating the terminal position at the beginning of the first phase of the proposed protocol, and based on this, it can change the rotation angle of all terminals independently and randomly. To generate A set of random channel samples.

[0189] Assume that the IPA time-varying channel The samples are . No. The average rate of a terminal can be approximated as ,in Indicates all Thus, the communication parameter optimization model (31) is reformulated as a position rotation optimization model as shown in the following formula (33).

[0190] (33) Step 1004: Solve the position rotation optimization model to obtain an optimized position rotation angle, and adjust at least one intelligent polarization shaping antenna in the base station in a slow time scale based on the optimized position rotation angle.

[0191] The following is a detailed description of step 1004.

[0192] In some embodiments, the position rotation optimization model (33) is a non-convex optimization problem, which is due to the non-concave objective function and the non-convex constraints of the 2nd to 4th constraints. Traditional convex optimization methods do not have practical efficiency when solving the position rotation optimization model (33). Inspired by the low complexity of the particle swarm optimization algorithm, the particle swarm optimization algorithm is used in the embodiment of the present application to solve the optimized position rotation angle in the position rotation optimization model (33), that is, including optimizing the antenna position and optimizing the antenna rotation angle, and then adjusting at least one intelligent polarization shaping antenna in the base station based on the optimized antenna position and the optimized antenna rotation angle in the slow time scale within the coherence time.

[0193] Step 1005: Solve the polarization shaping optimization model to obtain the optimized polarization shaping corresponding to each fast time scale, and adjust at least one intelligent polarization shaping antenna in the base station and / or at least one intelligent polarization shaping antenna of at least one terminal in the corresponding fast time scale based on the optimized polarization shaping.

[0194] The following is a detailed description of step 1005.

[0195] In some embodiments, a penalty dual decomposition framework is used to solve the polarization shaping optimization model (32) for a fast time scale, and a two-layer iterative algorithm is developed to solve the polarization shaping optimization model (32). The inner loop uses a block-based minimization method to solve the enhanced Lagrangian problem, and the outer loop updates the dual variables and penalty coefficients according to the constraint violation until convergence, so as to obtain the optimized polarization shaping corresponding to each fast time scale, that is, including the optimized base station polarization shaping vector, the optimized precoding vector of the base station, and the optimized terminal polarization shaping vector of each terminal, so that in each corresponding fast time scale, the intelligent polarization shaping antenna in the base station is adjusted based on the optimized base station polarization shaping vector and the optimized precoding vector parameters, and the intelligent polarization shaping antenna of the terminal is adjusted based on the optimized terminal polarization shaping vector.

[0196] How to solve the polarization shaping optimization model (32) will be further described below.

[0197] Reference Fig.12 , solve the polarization shaping optimization model to obtain the optimized polarization shaping corresponding to each fast time scale, including the following steps 1201 to 1203.

[0198] Step 1201: convert the polarization shaping optimization model into a Lagrangian format to obtain a converted polarization shaping optimization model.

[0199] Step 1202: using the block coordinate descent method, the conversion polarization shaping optimization model is divided into a terminal polarization shaping optimization model, a base station polarization shaping optimization model and a transmit precoding optimization model.

[0200] Step 1203: Iteratively solve the terminal polarization shaping optimization model, the base station polarization shaping optimization model and the transmit precoding optimization model to obtain optimized polarization shaping.

[0201] Steps 1201 to 1203 are described in detail below.

[0202] Specifically, in the inner loop of the penalized dual decomposition, the block coordinate descent method is applied in the embodiment of the present application to solve the problem. First, the polarization shaping optimization model (32) is converted into a Lagrangian format to obtain the converted polarization shaping optimization model as shown in the following formula (34).

[0203] (34) in, and Representation and constraint and The corresponding dual variable vector, and is the penalty coefficient. By dividing the optimization variables into the following blocks: , , , , , and , that is, including the terminal polarization shaping optimization model, the base station polarization shaping optimization model and the transmit precoding optimization model, each block can be optimized separately while other blocks are fixed.

[0204] (1) First, the terminal polarization shaping vector parameter The update of is achieved by solving the terminal polarization shaping optimization model corresponding to the unconstrained quadratic programming problem shown in the following formula (35).

[0205] (35) in Therefore, the closed-form optimal solution of the terminal polarization shaping optimization model (35) can be expressed as shown in the following formula (36).

[0206]

[0207]

[0208] Next, The sub-problem is shown in formula (38).

[0209]

[0210] because The elements of are independent of each other in the objective function and constraints, so the optimal solution can be calculated in parallel, as shown in the following formula (39).

[0211]

[0212]

[0213] (2) Base station polarization shaping vector parameters The update of can be performed by solving the base station polarization shaping optimization model corresponding to the unconstrained quadratic programming problem shown in the following formula (41).

[0214]

[0215] in , , ,here From the vector Based on this, the optimal solution of the base station polarization shaping optimization model (41) is shown in the following formula (42).

[0216]

[0217]

[0218] (3) The sub-problem is defined as follows:

[0219] Similar to the above formula (38), the optimal solution of formula (44) can be efficiently obtained through parallel computing, and its specific expression is omitted here.

[0220] (4) When other variables are fixed, by minimizing Get the linear minimum mean square error equalization coefficient As shown in the following formula (45).

[0221]

[0222] (5) Unit channel power for the kth terminal The optimal solution of is shown in the following formula (46).

[0223]

[0224] (6) Base station precoding parameters The update of is obtained by solving the transmit precoding optimization model shown in the following formula (47).

[0225] (47) For the transmit precoding optimization model (47), the optimal solution can be derived using the following first-order optimality condition as shown in the following formula (48).

[0226]

[0227] in is the dual variable of the transmit power constraint. If ,but is the optimal solution; otherwise, the optimal Based on this, by iteratively solving the above six components, the solution of the conversion polarization shaping optimization model (34) can be achieved.

[0228] In the outer loop of the penalized dual decomposition framework, the update rule of the dual variables is shown in the following formula (49).

[0229]

[0230] Through the above steps 1201 to 1203, by performing Lagrangian transformation on the polarization shaping optimization model and decomposing it into multiple sub-problems using the block coordinate descent method, and then solving them iteratively, the complexity of the optimization problem can be effectively reduced, and the polarization shaping can be optimized, thereby better matching the channel characteristics and improving the signal transmission quality and system performance.

[0231] By implementing the communication parameter optimization method of the communication system corresponding to the above steps 1001 to 1005, a reachable transmission rate model is constructed by combining the terminal positioning results, and it is decomposed into position rotation optimization under a slow time scale and polarization shaping optimization under a fast time scale, thereby realizing dynamic adjustment of the base station antenna position, rotation angle and polarization shaping. This method can flexibly optimize communication parameters on different time scales according to channel changes and terminal positions, thereby maximizing the system transmission rate, improving wireless communication performance, and adapting to complex and changing wireless environments.

[0232] In order to evaluate the performance of the proposed solution, a communication system equipped with a smart polarization shaping antenna is simulated and verified in the embodiments of the present application, and compared with other solutions.

[0233] First, in this embodiment, the performance of the terminal positioning method of the communication system equipped with the intelligent polarization shaping antenna proposed in this scheme is evaluated. It is compared with the direct positioning method. The direct positioning method adopts maximum likelihood estimation and iteratively optimizes the arrival angle vector of the terminal. , the distance from the terminal to the base station To maximize the log-likelihood function. The positioning error is defined as ,in Indicates the real location of all terminals, indicates its estimated location In the positioning stage, this embodiment uses the matrix Designed to meet The semi-orthogonal matrix is ​​set based on the Fourier transform matrix . Reference Fig.13 , is a simulation schematic diagram of the first communication system equipped with a smart polarization shaping antenna provided in the embodiment of the present application. Fig.13As shown in the figure, it is shown that the proposed positioning scheme based on polarization shaping (i.e., the terminal positioning method of the communication system equipped with intelligent polarization shaping antenna proposed in this scheme) has higher accuracy than the direct positioning method. This is because by providing a controllable polarization shaping vector, the proposed method makes full use of the measurement diversity of the received signal, thereby improving the estimation accuracy. In addition, the proposed method also effectively utilizes the Khatri-Rao channel structure and converts the stable channel into and dynamic coefficient The estimation of is separated, so that the closed-form solution can be used to locate the terminal based on the decoupled estimation results. Since the proposed algorithm requires less sensing time for positioning, the effective data rate of the terminal can be significantly improved.

[0234] Next, this example verifies the performance of the proposed communication parameter optimization method for a communication system equipped with an intelligent polarization shaping antenna and the performance of the proposed polarization shaping algorithm (fixed antenna position and rotation). The comparison scheme is a fixed parameter scheme, where , , and are fixed. Precoding vector The maximum rate transmission is adopted. This embodiment adopts a three-sector base station configuration, where the number of sectors is , each sector covers 120°. Fig.14 , is a simulation schematic diagram of a second communication system equipped with a smart polarization shaping antenna provided in an embodiment of the present application. Fig.14 As shown in , the proposed scheme of optimizing polarization-based beamforming only is studied for the average total rate that can be achieved when the number of terminals varies, and the impact of different polarization-based beamforming amplitude / phase quantization levels on the achievable rate is also shown. Assume that the number of quantization bits at the base station and the terminal is the same. It can be observed that the performance of polarization-based beamforming optimization with joint amplitude and phase control is better than that of phase control only (i.e. ) and amplitude control only (i.e. ). In addition, the proposed scheme including amplitude control performs better than phase control alone. Furthermore, as the number of terminals increases, The performance improvement brought by amplitude control is more significant as the number of channels increases, because the multi-terminal interference caused by the channel state information estimation error will be more serious. This result shows that in systems with a large number of terminals, polarization shaping using amplitude-phase joint control may be more advantageous than polarization shaping using only amplitude or only phase control.

[0235] Reference Fig.15 , is a simulation schematic diagram of a third communication system equipped with a smart polarization shaping antenna provided in an embodiment of the present application. Fig.15As shown in , the achievable rates of different schemes under the change of base station transmit power are plotted. The results show that compared with the fixed parameter scheme, whether only polarization shaping optimization, only position-rotation optimization, or the algorithm of joint antenna position / rotation and polarization shaping optimization is adopted, the achievable rate can be improved. Among them, the joint optimization scheme (i.e., the communication parameter optimization method of the communication system equipped with intelligent polarization shaping antenna proposed in this application) obtains the highest performance gain. In particular, when only the polarization shaping optimization scheme is adopted, its average rate is significantly higher than that of the fixed parameter scheme. This is because the polarization shaping antenna can dynamically adjust the polarization state of the terminal and the base station to maximize the instantaneous channel gain, thereby effectively utilizing the additional degrees of freedom provided by polarization diversity. In addition, even at the same transmit power, the position-rotation optimization scheme alone can achieve a higher rate than the fixed parameter scheme, because the IPA system with position-rotation adjustment has more spatial degrees of freedom and can more reasonably deploy antenna resources to match the spatial distribution of the terminal channel. Furthermore, as the base station transmit power increases, the performance gap between the proposed scheme and the fixed parameter scheme will continue to widen. This is an expected phenomenon, because as the transmit power increases, the total rate will be limited by more interference. By adjusting the polarization shaping of the terminal / base station and the position and rotation of the base station antenna, the interference suppression conditions of the multi-terminal MIMO channel can be effectively improved with the help of base station precoding.

[0236] Therefore, the wireless system enhanced by the intelligent polarization shaping antenna proposed in the present invention (i.e., the communication parameter optimization method of the communication system equipped with the intelligent polarization shaping antenna proposed in the present application) can provide an efficient communication / perception solution for the wireless network.

[0237] The present application also provides an electronic device, including: at least one memory; at least one processor; at least one program; The program is stored in the memory, and the processor executes the at least one program to implement the terminal positioning 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), a car computer, etc.

[0238] See also Fig.16 , Fig.16 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes: The processor 1601 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application; The memory 1602 can be implemented in the form of ROM (Read Only Memory), static storage device, dynamic storage device or RAM (Random Access Memory). The memory 1602 can store operating systems and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1602, and the processor 1601 calls and executes the terminal positioning method and communication parameter optimization method of the communication system of the embodiments of this application; Input / output interface 1603, used to implement information input and output; Communication interface 1604, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.); A bus 1605 that transmits information between the various components of the device (e.g., the processor 1601, the memory 1602, the input / output interface 1603, and the communication interface 1604); The processor 1601 , the memory 1602 , the input / output interface 1603 and the communication interface 1604 are connected to each other in communication within the device via the bus 1605 .

[0239] An embodiment of the present application also 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, the terminal positioning method and communication parameter optimization method of the above-mentioned communication system are implemented.

[0240] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0241] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0242] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0243] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0244] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0245] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0246] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0247] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0248] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0249] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0250] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0251] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. An intelligent polarization shaping antenna, characterized in that: include: A splitter, a first attenuator, a second attenuator, a first phase shifter, a second phase shifter, a first polarization unit, and a second polarization unit, wherein the splitter is connected to the first attenuator and the second attenuator, the first attenuator is connected to the first phase shifter, the second attenuator is connected to the second phase shifter, the first phase shifter is connected to the first polarization unit, and the second phase shifter is connected to the second polarization unit; When the antenna receives a processed signal, the splitter is used to split the processed signal to obtain a first processed signal and a second processed signal, and send the first processed signal to the first attenuator and send the second processed signal to the second attenuator; The first attenuator is used to perform a first amplitude processing on the first processed signal to obtain a first amplitude processed signal, and send the first amplitude processed signal to the first phase shifter; the second attenuator is used to perform a second amplitude processing on the second processed signal to obtain a second amplitude processed signal, and send the second amplitude processed signal to the second phase shifter; The first phase shifter is used to perform a first phase shift processing on the first amplitude processed signal, and send the first phase shift processed signal to the first polarization unit after obtaining the first phase shift processed signal. The first polarization unit is used to send the first phase shift processed signal. The second phase shifter is used to perform a second phase shift processing on the second amplitude processed signal, and send the second phase shift processed signal to the second polarization unit after obtaining the second phase shift processed signal. The polarization directions of the first phase shift processed signal and the second phase shift processed signal are perpendicular to each other.

2. The intelligent polarization shaped antenna according to claim 1, characterized in that: Also includes: a digital signal processor and a radio frequency chain connected to each other, wherein the radio frequency chain is connected to the splitter; When the antenna receives the processed signal, the digital signal processor and the radio frequency chain are used to perform digital signal processing and radio frequency processing on the processed signal in sequence, and send the processed signal to the splitter.

3. A base station, characterized in that: include: At least one antenna surface, wherein the antenna surface is provided with at least one smart polarization shaping antenna according to claim 1; At least one movable rotating rod, the movable rotating rod is connected to the antenna surface, and the movable rotating rod is used to adjust the three-dimensional position of the antenna surface and the antenna rotation angle.

4. A communication system, characterized in that: include: The base station as claimed in claim 3; At least one terminal, wherein the terminal is provided with the intelligent polarization shaping antenna according to claim 1; The base station communicates with the terminal.

5. A terminal positioning method for a communication system, characterized in that: The communication system is as shown in claim 4, the terminal positioning method is applied to a base station, and the method comprises: Acquire at least one user polarization shaping vector sent by the user to be located, acquire the antenna position and antenna rotation angle of each antenna surface, and acquire a base station receiving signal obtained by receiving the pilot signal sent by the user to be located, wherein the base station receiving signal is obtained based on the user polarization shaping vector and the pilot signal; Performing estimation calculation based on the base station received signal and the pilot signal to obtain a non-polarized channel matrix; Based on the non-polarized channel matrix, the antenna position and the antenna rotation angle, obtaining an arrival direction vector between the user to be located and the base station; Determine an effective antenna gain corresponding to the antenna rotation angle, and obtain a non-polarization shaped channel model between a user and a base station based on the effective antenna gain, the antenna position and the antenna rotation angle; Performing distance estimation based on the non-polarization shaped channel model and the effective antenna gain to obtain an estimated distance between the user to be located and the base station; Based on the estimated distance and the arrival direction vector, the location information of the user to be located is obtained.

6. The terminal positioning method of the communication system according to claim 5, characterized in that: The performing estimation calculation based on the base station received signal and the pilot signal to obtain a non-polarized channel matrix includes: Generate a side slice matrix and a front slice matrix based on the pilot signal, the complex-valued matrix parameters and the polarization-free channel parameters; generating a positive estimation parameter based on a Katli-Rao product of the pilot signal and the polarization-free channel parameter; Based on minimization of the difference between the frontal slice matrix and the frontal estimation parameter, a complex value matrix is ​​obtained; generating a side estimation parameter based on a Katli-Rao product of the complex-valued matrix and the pilot signal; The non-polarized channel matrix is ​​obtained based on minimization processing of the difference between the side slicing matrix and the side estimation parameter.

7. The terminal positioning method of the communication system according to claim 5, characterized in that: The obtaining, based on the non-polarized channel matrix, the antenna position and the antenna rotation angle, a direction of arrival vector between the terminal to be located and the base station, comprises: Generate a channel covariance matrix based on the product of the polarization-free channel matrix and the transposed matrix of the polarization-free channel matrix; Performing eigenvalue decomposition on the covariance matrix to obtain matrix eigenvectors; Obtaining a steering vector parameter based on the antenna position, the antenna rotation angle, and an arrival direction parameter; Based on the product of the steering vector parameter and the matrix eigenvector, an inverse process is performed to obtain an arrival direction function, and the arrival direction function is optimized to obtain the arrival direction vector.

8. The terminal positioning method of the communication system according to claim 5, characterized in that: The performing distance estimation based on the non-polarization shaped channel model and the effective antenna gain to obtain an estimated distance between the terminal to be located and the base station includes: A distance numerator parameter is obtained by multiplying the product of the number of antennas and the unit channel power of the antenna by the accumulated value of the effective antenna gain; Accumulating the products of all the non-polarization shaped channel models and the effective antenna gain to obtain a distance denominator parameter; The estimated distance is obtained based on the ratio of the distance numerator parameter and the distance denominator parameter.

9. A communication parameter optimization method for a communication system, characterized in that: The communication system is as shown in claim 4, the communication parameter optimization method is applied to a base station, and the method comprises: In a coherent time period, based on the terminal positioning method of the communication system according to claim 5, the terminal distances between the multiple terminals and the base station are obtained, and the coherent time period includes a slow time scale and at least one fast time scale in a time sequence; Generate a achievable transmission rate model between the base station and each of the terminals based on the terminal distance, and generate a communication parameter optimization model based on the achievable transmission rate model; Decomposing the communication parameter optimization model based on the slow time scale and the fast time scale to obtain a position rotation optimization model and a polarization shaping optimization model; Solving the position rotation optimization model to obtain an optimized position rotation angle, and adjusting at least one intelligent polarization shaping antenna in the base station in the slow time scale based on the optimized position rotation angle; The polarization shaping optimization model is solved to obtain the optimized polarization shaping corresponding to each fast time scale, and based on the optimized polarization shaping, at least one intelligent polarization shaping antenna in the base station and / or at least one intelligent polarization shaping antenna of at least one terminal is adjusted in the corresponding fast time scale.

10. The communication parameter optimization method of a communication system according to claim 9, characterized in that: The generating a communication parameter optimization model based on the achievable transmission rate model comprises: Based on maximizing the achievable transmission rate model as a rate optimization objective function; Based on the antenna position parameters, antenna rotation angle parameters, base station polarization shaping vector parameters, precoding vector parameters of the base station and terminal polarization shaping vector parameters of each of the terminals as rate optimization variables; Generate rate constraint conditions based on the variable optional domain of the rate optimization variable, the position distance constraint between the antenna position parameters, the signal non-reflection constraint of the antenna position parameters and the antenna rotation angle parameters, and the non-forward center constraint; The communication parameter optimization model is generated based on the optimization objective function, the rate optimization variable, and the rate constraint condition.

11. The communication parameter optimization method of a communication system according to claim 10, characterized in that: Solving the polarization shaping optimization model to obtain the optimized polarization shaping corresponding to each fast time scale includes: The polarization shaping optimization model is converted into a Lagrangian format to obtain a converted polarization shaping optimization model; Using a block coordinate descent method, the conversion polarization shaping optimization model is divided into a terminal polarization shaping optimization model, a base station polarization shaping optimization model, and a transmit precoding optimization model; The terminal polarization shaping optimization model, the base station polarization shaping optimization model and the transmit precoding optimization model are iteratively solved to obtain the optimized polarization shaping.

12. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the terminal positioning method of the communication system according to any one of claims 5 to 8 or the communication parameter optimization method of the communication system according to claims 9 to 11 is implemented.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the terminal positioning method of the communication system as described in any one of claims 5 to 8 or the communication parameter optimization method of the communication system as described in any one of claims 9 to 11 is implemented.

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