Intelligent polarization shaping antenna, communication system, positioning method, communication parameter optimization method and related devices

Abstract

The intelligent polarization shaping antenna, communication system, positioning method, communication parameter optimization method and related devices 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 both the first attenuator and the second attenuator. The first attenuator is connected to the first phase shifter, and 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 processing signal, the splitter, attenuator, phase shifter and planning unit are respectively used to perform splitting processing, amplitude processing, phase shift processing on the processing signal in sequence and send the processed processing signal, so as to improve the reliability of wireless signal transmission.

Description

Technical Field

[0001] This application relates to the field of wireless communication technologies, and in particular, to intelligent polarization shaping antennas, communication systems, positioning methods, communication parameter optimization methods, and related devices. Background Art

[0002] In order to meet the demands for higher-rate wireless communication and higher-precision wireless sensing brought about by the surge in the number of Internet of Things devices in the upcoming sixth-generation wireless network, the current trend in 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), enabling a significant increase in the spatial degrees of freedom, thereby enhancing the communication and sensing performance of wireless systems.

[0003] However, when wireless signals propagate in a wireless channel, they may experience random depolarization due to scattering and other environmental factors, resulting in the inability of such a fixed large-scale antenna array to fully align with all signal components, causing severe signal attenuation and low data transmission reliability of wireless signals. Summary of the Invention

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

[0005] To achieve the above object, a first aspect of the embodiments of this application proposes an intelligent polarization shaping antenna, including:

[0006] 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;

[0007] When the antenna receives a processing signal, the splitter is configured to split the processing signal to obtain a first processing signal and a second processing signal, and send the first processing signal to the first attenuator and the second processing signal to the second attenuator;

[0008] 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 a 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 a second phase shifter;

[0009] The first phase shifter is used to perform a first phase shift processing on the first amplitude processed signal to obtain a first phase shift processed signal and then send it 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 to obtain a second phase shift processed signal and then send it to the second polarization unit. The second polarization unit is used to send 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.

[0010] In some embodiments, the intelligent polarization shaping antenna further includes:

[0011] A digital signal processor and a radio frequency chain connected to each other, and the radio frequency chain is connected to the splitter;

[0012] When the antenna receives a processed signal, the digital signal processor and the radio frequency chain are sequentially used to perform digital signal processing and radio frequency processing on the processed signal, and send the processed signal to the splitter.

[0013] To achieve the above object, a second aspect of the embodiments of the present application proposes a base station, including:

[0014] At least one antenna surface, on which at least one intelligent polarization shaping antenna as described in the first aspect is provided;

[0015] 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 position of the antenna surface and the antenna rotation angle.

[0016] To achieve the above object, a third aspect of the embodiments of the present application proposes a communication system, including:

[0017] A base station as described in the second aspect;

[0018] At least one terminal, on which an intelligent polarization shaping antenna as described in the first aspect is provided;

[0019] Communication is carried out between the base station and the terminal.

[0020] To achieve the above object, a fourth aspect of the embodiments of the present application proposes a method for terminal positioning in 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:

[0021] Obtain at least one terminal polarization shaping vector sent by the terminal to be positioned, obtain the antenna positions and antenna rotation angles on each antenna surface, and obtain the base station received signal obtained by receiving the pilot signal sent by the terminal to be positioned. The base station received signal is obtained based on the terminal polarization shaping vector and the pilot signal;

[0022] Perform estimation calculations based on the base station received signal and the pilot signal to obtain an unpolarized channel matrix;

[0023] Based on the unpolarized channel matrix, the antenna positions, and the antenna rotation angles, obtain the direction-of-arrival vector between the terminal to be positioned and the base station;

[0024] Determine the effective antenna gain corresponding to the antenna rotation angle, and based on the effective antenna gain, the antenna positions, and the antenna rotation angles, obtain an unpolarized shaping channel model between the terminal and the base station;

[0025] Perform distance estimation based on the unpolarized shaping channel model and the effective antenna gain to obtain the estimated distance between the terminal to be positioned and the base station;

[0026] Based on the estimated distance and the direction-of-arrival vector, obtain the position information of the terminal to be positioned.

[0027] In some embodiments, the performing estimation calculations based on the base station received signal and the pilot signal to obtain an unpolarized channel matrix includes:

[0028] Generate a side slice matrix and a front slice matrix based on the pilot signal, complex-valued matrix parameters, and unpolarized channel parameters;

[0029] Generate front estimation parameters based on the Khatri-Rao product of the pilot signal and the unpolarized channel parameters;

[0030] Obtain a complex-valued matrix based on the minimization of the difference between the front slice matrix and the front estimation parameters;

[0031] Generate side estimation parameters based on the Khatri-Rao product of the complex-valued matrix and the pilot signal;

[0032] Obtain the unpolarized channel matrix based on the minimization of the difference between the side slice matrix and the side estimation parameters.

[0033] In some embodiments, obtaining the direction-of-arrival vector between the terminal to be located and the base station based on the non-polarized channel matrix, the antenna positions, and the antenna rotation angles includes:

[0034] Generating a channel covariance matrix based on the product of the non-polarized channel matrix and the transpose matrix of the non-polarized channel matrix;

[0035] Performing eigenvalue decomposition on the covariance matrix to obtain matrix eigenvectors;

[0036] Obtaining a steering vector parameter based on the antenna positions, the antenna rotation angles, and the direction-of-arrival parameters;

[0037] Based on the product of the steering vector parameter and the matrix eigenvectors, and then performing a reciprocal operation to obtain a direction-of-arrival function, and performing an optimization process on the direction-of-arrival function to obtain the direction-of-arrival vector.

[0038] In some embodiments, estimating the distance between the terminal to be located and the base station based on the non-polarized shaping channel model and the effective antenna gain includes:

[0039] Obtaining a distance numerator parameter based on the product of the number of antennas of the antenna and the unit channel power, and then multiplying by the cumulative value of the effective antenna gain;

[0040] Accumulating the products of all the non-polarized shaping channel models and the effective antenna gain to obtain a distance denominator parameter;

[0041] Obtaining the estimated distance based on the ratio of the distance numerator parameter to the distance denominator parameter.

[0042] To achieve the above object, a fifth aspect of the embodiments of the present application proposes a method for optimizing communication parameters of a communication system. The communication system is as shown in the third aspect. The method for optimizing communication parameters is applied to a base station, and the method includes:

[0043] During a coherent time period, based on the terminal positioning method of the communication system described in the fourth aspect, obtaining the terminal distances between multiple terminals and the base station. The coherent time period includes a slow time scale and at least one fast time scale in chronological order;

[0044] Generating an achievable transmission rate model between the base station and each terminal based on the terminal distances, and generating a communication parameter optimization model based on the achievable transmission rate model;

[0045] 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;

[0046] 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 based on the optimized position rotation angle in the slow time scale;

[0047] Solve the polarization shaping optimization model to obtain an 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 based on the optimized polarization shaping in the corresponding fast time scale.

[0048] In some embodiments, generating the communication parameter optimization model based on the achievable transmission rate model includes:

[0049] Based on maximizing the achievable transmission rate model as the rate optimization objective function;

[0050] 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 terminal as rate optimization variables;

[0051] Based on the variable optional domain of the rate optimization variables, the position distance constraint between the antenna position parameters, the signal non-reflection constraint and non-forward center constraint between the antenna position parameters and the antenna rotation angle parameters, generate rate constraint conditions;

[0052] Generate the communication parameter optimization model based on the optimization objective function, the rate optimization variables, and the rate constraint conditions.

[0053] In some embodiments, solving the polarization shaping optimization model to obtain an optimized polarization shaping corresponding to each fast time scale includes:

[0054] Convert the polarization shaping optimization model into a Lagrangian format to obtain a converted polarization shaping optimization model;

[0055] Using the block coordinate descent method, divide the converted polarization shaping optimization model into a terminal polarization shaping optimization model, a base station polarization shaping optimization model, and a transmit precoding optimization model;

[0056] Iteratively solve the terminal polarization shaping optimization model, the base station polarization shaping optimization model, and the transmit precoding optimization model to obtain the optimized polarization shaping.

[0057] To achieve the above object, a sixth aspect of the embodiments of the present application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the terminal positioning method of the communication system described in the fourth aspect or the communication parameter optimization method of the communication system described in the fifth aspect.

[0058] To achieve the above object, a seventh aspect of the embodiments of the present application provides a storage medium, which is a computer-readable storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, it implements the terminal positioning method of the communication system described in the fourth aspect or the communication parameter optimization method of the communication system described in the fifth aspect.

[0059] The intelligent polarization shaping antenna, communication system, positioning method, communication parameter optimization method and related devices 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 respectively. The first attenuator is connected to the first phase shifter, and 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 processing signal, the splitter is used to split the processing signal to obtain a first processing signal and a second processing signal, and send the first processing signal to the first attenuator and the second processing signal to the second attenuator. The first attenuator is used to perform a first amplitude processing on the first processing 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 processing 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 obtained first phase shift processed signal to the first polarization unit. The first polarization unit is used to transmit 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 obtained second phase shift processed signal to the second polarization unit. The second polarization unit is used to transmit 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. In the embodiments of the present application, by setting a splitter, a cooperative architecture of dual-channel independently controllable attenuators and phase shifters in the intelligent polarization shaping antenna, fine-grained 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 the dynamically adjusted polarization processing signals are radiated through the orthogonally arranged dual-polarization units, so that the subsequent communication system equipped with this intelligent polarization shaping antenna can compensate 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, this structure breaks through the limitation of traditional polarization reconstruction antennas that only adjust the amplitude, and fully exploits the polarization diversity potential through phase-amplitude cooperative control, enabling the base station equipped with the intelligent polarization shaping antenna to adaptively track the spatial orientation change of the terminal device and maintain a stable polarization alignment state in a complex propagation environment, thereby enhancing the signal transmission reliability in a multi-terminal scenario, providing better communication quality guarantee for high-density Internet of Things devices, and at the same time improving the channel space utilization rate through dynamic optimization of the polarization state.

[0060] Other features and advantages of the present application will be described in the following specification, and in part will become apparent from the specification, or will be understood by implementing the present application. The objectives and other advantages of the present application can be achieved and obtained through the structures specifically pointed out in the specification, claims, and drawings. Description of the Drawings

[0061] Figure 1 is a schematic structural diagram of a communication system equipped with an intelligent polarization shaping antenna provided by an embodiment of the present application.

[0062] Figure 2 is a schematic diagram of the geometric position of an intelligent polarization shaping antenna provided by another embodiment of the present application.

[0063] Figure 3 is a schematic comparison chart of the performance of an intelligent polarization shaping antenna provided by another embodiment of the present application with existing antenna technologies.

[0064] Figure 4 is a schematic protocol diagram of wireless communication and sensing of a communication system provided with an intelligent polarization shaping antenna according to another embodiment of the present application.

[0065] Figure 5 is a schematic diagram of the structured pilot polarization time in the coherent time domain provided by another embodiment of the present application.

[0066] Figure 6 is a flowchart of a method for terminal positioning in a communication system provided by another embodiment of the present application.

[0067] Figure 7 is Figure 6 a flowchart of step 602 in

[0068] Figure 8 is Figure 6 a flowchart of step 603 in

[0069] Figure 9 is Figure 6 a flowchart of step 605 in

[0070] Figure 10 is a flowchart of a method for optimizing communication parameters of a communication system provided by another embodiment of the present application.

[0071] Figure 11 is Figure 10 a flowchart of step 1002 in

[0072] Figure 12 is Figure 10 a flowchart of step 1005 in

[0073] Figure 13It is a simulation schematic diagram of the first communication system equipped with an intelligent polarization shaping antenna provided by another embodiment of the present application.

[0074] Figure 14 It is a simulation schematic diagram of the second communication system equipped with an intelligent polarization shaping antenna provided by another embodiment of the present application.

[0075] Figure 15 It is a simulation schematic diagram of the third communication system equipped with an intelligent polarization shaping antenna provided by another embodiment of the present application.

[0076] Figure 16 It is a schematic diagram of the hardware structure of an electronic device provided by another embodiment of the present application. Detailed implementation manners

[0077] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0078] It should be noted that although functional module division is performed in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from the module division in the device or the flowchart.

[0079] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application, and are not intended to limit the present application.

[0080] In order to meet the requirements for higher-rate wireless communication and higher-precision wireless sensing brought about by the surge in the number of Internet of Things devices in the upcoming sixth-generation wireless network, the current trend of multiple-input multiple-output (MIMO) technology is to equip base stations and wireless terminals with more antennas. In the related art, more antennas than existing large-scale MIMO (i.e., ultra-large-scale MIMO) are usually deployed on the base station, so that the spatial degrees of freedom can be significantly increased, thereby enhancing the communication and sensing performance of the wireless system.

[0081] However, when wireless signals propagate in the wireless channel, random depolarization may occur due to scattering and other environmental factors, resulting in the inability of this fixed large-scale antenna array to fully align with all signal components, causing serious signal attenuation and low data transmission reliability of wireless signals.

[0082] To improve the reliability of data transmission of wireless signals, in the embodiments of the present application, a splitter, a dual-channel independently controllable attenuator and a phase shifter are arranged in an intelligent polarization shaping antenna to realize fine-grained joint control of the amplitude and phase of polarization signals, effectively solving the problem of signal attenuation caused by polarization mismatch in traditional large-scale MIMO systems. The polarization processing signals with dynamic adjustment are radiated by orthogonally arranged dual-polarization units, enabling the subsequent communication system equipped with the intelligent polarization shaping antenna to compensate for the random depolarization effect in the wireless channel in real time. Thus, without increasing the number of antennas, the polarization matching accuracy can be significantly improved to enhance the reliability of wireless signal transmission. In addition, this structure breaks through the limitation of traditional polarization reconstruction antennas that only adjust the amplitude, fully exploiting the potential of polarization diversity through phase-amplitude coordinated control, enabling the base station equipped with the intelligent polarization shaping antenna to adaptively track the spatial azimuth change of the terminal device and 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 guarantee for high-density Internet of Things devices, and improving the channel space utilization through dynamic optimization of the polarization state.

[0083] The intelligent polarization shaping antenna, communication system, positioning method, communication parameter optimization method and related devices proposed in the embodiments of the present application will be further described below. First, an intelligent polarization shaping antenna will be described. Refer to Figure 1 , which is a schematic structural diagram of a communication system equipped with an intelligent polarization shaping antenna provided by the embodiments of the present application. As Figure 1 shown, it shows the overall architecture of the communication system equipped with the intelligent polarization shaping antenna. The communication system includes a base station and multiple terminals. Multiple antenna surfaces are arranged on the base station, and multiple intelligent polarization shaping antennas are arranged on each antenna surface. In addition, intelligent polarization shaping antennas are also correspondingly arranged on the terminals, enabling better two-way communication between the terminals and the base station through the intelligent polarization shaping antennas.

[0084] The intelligent polarization shaping 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 polarization shaping antenna adjusts the polarization shaping vector with adjustable signal amplitude and phase to control the polarization states of the antennas at the transmitter and / or receiver, thereby realizing dynamic adjustment of antenna polarization using polarization diversity to cope with real-time channel conditions and depolarization effects. This process is achieved through an electronically tunable polarization shaper, which integrates a phase shifter and an attenuator and can coordinately adjust the phase and amplitude of the transmitted / received signals (see Figure 1). In addition, each IPA can also be independently adjusted in terms of position and / or rotation angle to adapt to the channel spatial distribution. This can be achieved through mechanical control, i.e., by means of actuators such as electric motors and precision gears, as well as external mechanical structures, to achieve physical movement.

[0085] Specifically, as Figure 1 shown, in each intelligent polarization shaping antenna, there is a digital signal processor, a radio frequency chain, and a polarization shaper connected in sequence. When the intelligent polarization shaping antenna needs to send a processing 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 processing 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 processing signal and sends the processed signal after radio frequency processing to the polarization shaper for polarization processing, and sends the processed signal after polarization processing. Correspondingly, when the intelligent polarization shaping antenna needs to receive a processing signal, the polarization shaper receives the processing signal for polarization processing, 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 sends the processing signal back to the device (such as a base station or a terminal device).

[0086] As Figure 1 shown, 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 both 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 processing signal, the splitter is used to split the processing signal to obtain a first processing signal and a second processing signal, and send the first processing signal to the first attenuator and the second processing signal to the second attenuator; the first attenuator is used to perform a first amplitude processing on the first processing 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 processing 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 to obtain a first phase shift processed signal and then send it 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 to obtain a second phase shift processed signal and then send it to the second polarization unit, and the second polarization unit is used to send 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.

[0087] Thus, by setting up a splitter, a dual-channel independently controllable attenuator, and a phase shifter in the intelligent polarization shaping antenna to work together, the refined joint regulation of the amplitude and phase of the polarization signal is achieved. This can effectively solve the signal attenuation problem caused by polarization mismatch in traditional large-scale MIMO systems. The polarization processing signal with dynamic adjustment is radiated through the orthogonally arranged dual-polarization units, enabling the subsequent communication system equipped with this intelligent polarization shaping antenna to compensate for the random depolarization effect in the wireless channel in real time. Thus, without increasing the number of antennas, the polarization matching accuracy can be significantly improved to enhance the reliability of wireless signal transmission. In addition, this structure breaks through the limitation of traditional polarization reconstruction antennas that only adjust the amplitude, fully explores the potential of polarization diversity through phase-amplitude collaborative control, enables the base station equipped with the intelligent polarization shaping antenna to adaptively track the spatial orientation change of the terminal device, maintains a stable polarization alignment state in a complex propagation environment, thereby enhancing the signal transmission reliability in a multi-terminal scenario, providing better communication quality guarantee for high-density Internet of Things devices, and at the same time improving the channel space utilization through dynamic optimization of the polarization state.

[0088] Refer to Figure 3 , which is a schematic diagram showing the performance comparison between an intelligent polarization shaping antenna provided by an embodiment of the present application and existing antenna technologies. As Figure 3 shown, the intelligent polarization shaping antenna is significantly different from existing six-dimensional mobile antennas and traditional polarization reconfigurable antennas. First, the six-dimensional mobile antenna consists of multiple antennas / sub-arrays with three-dimensional rotatable and movable positions, which is mechanically controlled. However, the six-dimensional mobile antenna either lacks antenna polarization control or is designed under fixed polarization conditions. Second, although traditional polarization reconfigurable antennas can adjust the antenna polarization, they only adjust the amplitude of the signal and cannot adjust the signal phase, failing to fully utilize polarization diversity. In addition, the polarization reconfigurable antenna with a 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 polarization signal to fully explore the advantages of polarization diversity, and at the same time adjust the position / rotation of the antenna to fully utilize the spatial degrees of freedom.

[0089] First, the symbols that will appear below are explained: , , and respectively represent the conjugate, inverse, conjugate transpose, and transpose operations; represents taking the expectation of a random variable; represents 's identity matrix; a⋅b represents the dot product of vectors a and b; and ∘ respectively represent the Kronecker product and the Khatri-Rao product; , and represent the Euclidean norm, Frobenius norm, and infinity norm of a complex vector, respectively; represents the vector of the j-th element.

[0090] Specifically, as Figure 1 shown, each intelligent polarization shaping antenna can independently apply a certain phase shift (through the phase shifter attached to the polarization shaper) and amplitude change (through the attenuator attached to 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, IPA can make full use of polarization diversity through polarization shaping and adaptively control the antenna polarization so that the polarization between the transmitting and receiving antennas remains consistent. We assume that each transmitting / receiving antenna consists of two linearly polarized elements arranged orthogonally, where one polarized element is for vertical polarization ( element), and the other polarized element is for horizontal polarization ( element). Specifically, each terminal is equipped with a single intelligent polarization shaping antenna, and its received polarization shaping vector is as shown in the following formula (1).

[0091]

[0092] where the amplitude coefficients of the first polarized element (i.e., element) and the second polarized element (i.e., ) in the intelligent polarization shaping antenna corresponding to the terminal are and , respectively. In addition, the phase shifts of the element and the element in the terminal are and , respectively.

[0093] The base station has B antenna surfaces (i.e., intelligent polarization shaping antenna (IPA) subarrays), denoted as . Each IPA subarray consists of N (≥1) IPAs, denoted as . Therefore, the total number of transmitting antennas of the base station is BN. Each IPA subarray is a uniform planar array with a fixed size. All antennas within 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. For the -th IPA subarray in the base station, its transmitted polarization shaping vector is expressed as shown in the following formula (2).

[0094]

[0095] Among them, and respectively represent the element and element amplitude coefficient of each corresponding antenna in the th transmitting IPA sub - array. Similarly, and respectively represent the element and element phase offset of the antenna in the th transmitting IPA sub - array. The phase and amplitude of the polarization shaping vector can be controlled continuously or discretely. For the sake of easy implementation, discrete control of amplitude and phase offset is adopted in this embodiment. Let and respectively represent the number of bits of polarization shaping amplitude and phase offset control for each intelligent polarization shaping antenna. Therefore, for the received polarization shaping vector and the transmitted polarization shaping vector, they are expressed as .

[0096] Among them , represents the set of all possible values of and phase ; , among which . Here, the discrete phase values are assumed to be uniformly distributed in the interval ; represents the set of controllable amplitudes, whose size is , and is uniformly distributed in the interval . Note that when , degenerates into the case of no attenuation, that is .

[0097] Each IPA can rotate and / or re - position within a given space. In this embodiment, it is assumed that the IPA of each terminal can rotate while maintaining a fixed position. This can be achieved by an actuator or an accessory component (such as a rotary motor) or by manual adjustment to achieve physical movement. In contrast, all the antennas within each IPA sub - array in the base station can be re - positioned and rotated together within the convex 3D space given by the base station. This process is achieved by connecting the sub - array to the central processing unit of the base station through a movable rotating rod with flexible wires, so that the central processing unit can precisely control their three - dimensional positions and rotation angles to achieve mechanical control.

[0098] For the convenience of describing the movement of the terminal / base station antenna, three Cartesian coordinate systems are established in the embodiments of this application. Refer toFigure 2 , which is a schematic diagram of the geometric position of an intelligent polarization shaping antenna provided by an embodiment of the present application. As Figure 2 shown, the global coordinate system is denoted as , where the central processing unit of the base station (i.e., the center of the base station) is located at the origin . The local coordinate system of each IPA subarray is denoted as , where the center of the subarray is used as the origin . The local coordinate system of each terminal is denoted as , where the origin is defined as the center of the terminal antenna. On the base station side, the position and rotation of the th IPA subarray ( ) can be described by the position vector and rotation vector shown in the following formula (3).

[0099]

[0100] where, , , and represent the coordinates of the th IPA center in the global coordinate system; , , and all take values in , and respectively represent the rotation angles of the th IPA subarray relative to the axis, axis, and axis in the global coordinate system. Given , the corresponding rotation matrix can be written as shown in the following formula (4).

[0101]

[0102] where and . Let represent the position of the th antenna in the local coordinate system of the IPA subarray. Then, the position of the th antenna in the th IPA subarray in the base station in the global coordinate system can be expressed as shown in the following formula (5).

[0103]

[0104] Next, on the terminal side, the rotation angle vector of the terminal local coordinate system relative to the global coordinate system is as shown in the following formula (6).

[0105]

[0106] Among them , , and all take values from , respectively representing the rotation angles of the -th terminal relative to the axis, axis and axis in the global coordinate system. Similar to , given , the corresponding rotation matrix can be denoted as .

[0107] Based on the above, the following conducts relevant mathematical modeling on the signal model in the communication system equipped with the intelligent polarization shaping antenna.

[0108] For the convenience of description, 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 positions of the terminals change slowly and can be considered approximately unchanged within each position coherence time, while their rotations (orientations) may vary arbitrarily during this period. Each terminal is equipped with an omnidirectional intelligent polarization shaping antenna. Let and respectively represent the azimuth angle and elevation angle of the signal arriving at the base station center from the terminal . Therefore, the steering vector corresponding to the direction is as shown in the following formula (7).

[0109]

[0110] Then, the steering vector of the -th IPA subarray in the base station with respect to the -th terminal can be expressed as shown in the following formula (8).

[0111]

[0112] Among them, represents the carrier wavelength. Next, in order to determine the effective antenna gain , in the embodiments of the present application, is projected onto the local coordinate system of the -th IPA subarray, denoted as . Then, is represented in the spherical coordinate system as , where and respectively represent the corresponding directions of arrival in the local coordinate system (as shown in Figure 2 ). Finally, the -th IPA subarray along the direction The effective antenna gain on the linear scale As shown in the following formula (9).

[0113]

[0114] Wherein, represents the effective antenna gain determined by the antenna radiation pattern (unit: dBi).

[0115] Let the unpolarized line-of-sight link channel between the th IPA subarray and the th terminal in the base station be as shown in the following formula (10).

[0116]

[0117] Wherein, is the free space path loss, where represents the channel power at the reference distance meters, and represents the distance between the position of the th terminal and the center of the base station.

[0118] In the local coordinate system as Figure 3 shown, the vertical element of the intelligent polarization shaping antenna is aligned along the positive -axis or -axis, while the horizontal element is arranged along the positive -axis or -axis direction, and their unit vectors are respectively as shown in the following formula (11).

[0119]

[0120] In addition, the polarization state of the electromagnetic wave can be described by any two orthogonal electric field components on the wavefront, and these two components are represented by the following orthogonal unit vectors in the global coordinate system as shown in the following formula (12).

[0121]

[0122] The transmitting field component of the line-of-sight link is generated by projecting the time-varying electric field of the transmitting antenna onto the line-of-sight link signal direction. The corresponding transformation is shown in the following formula (13).

[0123]

[0124] Similarly, the receiving field component is obtained by projecting the line-of-sight link signal direction onto the receiving antenna, and its projection matrix is as shown in the following formula (14).

[0125]

[0126] Therefore, the th terminal and the th IPA subarray in the base station, the dual-polarization response matrix between the dual-polarized antennas is represented by the following formula (15).

[0127]

[0128] Then, by receiving the polarization shaping vector and the transmitting polarization shaping vector introduce polarization effects to each antenna, an -dimensional IPA polarization shaping channel can be obtained, and its expression is shown in the following formula (16).

[0129] (16)

[0130] Next, in the embodiment of the present application, a communication perception integrated communication system enhanced based on intelligent polarization shaping antenna (IPA) is studied. This communication system uses terminal location perception 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. For example, when the audience in the stadium watches a football game, their smartphones are fixed in position but often rotated to take photos in different directions. Another example is the terminal for virtual reality (VR) games at a fixed position. Their VR devices are frequently rotated to enhance the gaming experience. In this scenario, a dual-time-scale transmission protocol for IPA-enhanced wireless communication and sensing systems is proposed.

[0131] Referring to Figure 4 , it is a schematic diagram of the protocol for wireless communication and sensing of a communication system equipped with an intelligent polarization shaping antenna provided by the embodiment of the present application. As Figure 4 shown, this protocol includes two stages, namely the slow time scale stage and the fast time scale stage.

[0132] In the first stage (slow time scale stage): In this initial stage, first, the terminal position is sensed, and then based on the sensed terminal position, the position and rotation of the base station antenna are determined. Referring to Figure 5 , it is a schematic diagram of the structured pilot polarization time in the coherent time domain provided by the embodiment of the present application. As Figure 5 shown, the base station receives multiple pilot signals from the terminal device, and these signals pass through the time-varying terminal polarization shaping vector Transmissions are carried out to achieve terminal positioning based on polarization shaping. Subsequently, based on the sensed terminal position, the antenna positions and rotations of all IPAs in the base station are optimized to maximize the average achievable rate of the terminal. The optimized antenna positions and rotations will be implemented on the base station and remain unchanged during the second phase of each positioning coherence time.

[0133] Step 2 (fast time scale phase): The remaining time of each positioning coherence time consists of channel coherence intervals. Within 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 will be jointly determined according to the instantaneous terminal rotation / channel to maximize the achievable rate of 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 in different channel coherence times.

[0134] Based on the above-modeled communication system, the terminal positioning method of the communication system provided by the embodiments of the present application will be further described below. The terminal positioning method of the communication system provided by the embodiments of the present application can be applied to a base station in the communication system or a processor connected to the base station, etc. Referring to Figure 6 which is an optional flowchart of the terminal positioning method of the communication system provided by the embodiments of the present application, Figure 6 the method in Figure 6 may include but is not limited to steps 601 to 606. At the same time, it can be understood that the order of steps 601 to 606 in this embodiment is not specifically limited, and the order of steps can be adjusted according to actual needs or some steps can be reduced or added.

[0135] Step 601: 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 the base station received signal obtained by receiving the pilot signal sent by the terminal to be located.

[0136] The following gives a detailed description of step 601.

[0137] In the protocol design as shown in Figure 4 in order to obtain the position information of each terminal at the initial stage of the coherence time in a communication system provided with intelligent polarization shaping antennas, so as to facilitate subsequent communication parameter optimization, the embodiments of the present application need to first locate each terminal.

[0138] To achieve positioning and sensing of terminals using a small number of base station antenna position-rotation arrangements, the IPA sub-array of the base station will be in a group of Move among different training position-rotation pairings to collect the pilot signals of all terminals. To determine the terminal position of the terminal to be located, the embodiments of this application propose a set of randomly configured terminal polarization shaping vectors of the terminal to be located where represents the total number of pilot signal blocks for the terminal to be located. The base station polarization shaping vector , , is set to a fixed value for sensing. As shown in , it is assumed that during the position training in the first step of the proposed protocol (normalized to the symbol period), the channels between all terminals and the base station remain unchanged. The duration of this position training is denoted as Figure 5 , where , , represents the number of time slots in each pilot signal block. Each terminal sends a known pilot signal to the base station and repeats this pilot signal within blocks. The structured pilot-polarization shaping pattern is as shown in Figure 5 , where the terminal polarization shaping vector of the terminal to be located remains unchanged within the th time slot of the th block and varies between blocks.

[0139] Furthermore, obtain the antenna positions and antenna rotation angles (i.e., training position-rotation pairings) of each antenna surface (i.e., IPA subarray) in the base station in this case as shown in the above formula (3).

[0140] Based on this, under the proposed pilot-polarization shaping pattern, the base station received signal obtained by the base station receiving the pilot signal sent by the terminal to be located (for the mth training position-rotation pairing) is as shown in the following formula (17).

[0141]

[0142] where represents the th row of the complex-valued matrix parameter . Here, the dynamic polarization shaping channel component vector is as shown in the following formula (18).

[0143]

[0144] where . In addition, represents the horizontal stacking of all terminal pilot signals, represents all in the The aggregated channel from a terminal to all antennas (i.e., the non-polarized channel parameters); is a complex-valued additive white Gaussian noise matrix, and its independently and identically distributed elements follow a Gaussian distribution with a mean of 0.

[0145] Step 602: Perform estimation calculations based on the base station received signal and the pilot signal to obtain the non-polarized channel matrix.

[0146] The following provides a detailed description of Step 602.

[0147] Next, in order to accurately locate the terminal to be located at the initial moment of the coherence time, it is necessary to further perform estimation calculations based on the base station received signal and the pilot signal to obtain the non-polarized channel matrix between the terminal to be located and the base station without polarization processing so as to facilitate subsequent determination of the distance information between the terminal to be located and the base station . The following will further describe how to obtain this non-polarized channel matrix.

[0148] Referring to Figure 7 , perform estimation calculations 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.

[0149] Step 701: Generate a side slice matrix and a front slice matrix based on the pilot signal, the complex-valued matrix parameter, and the non-polarized channel parameter.

[0150] Step 702: Generate a front estimation parameter based on the Khatri-Rao product of the pilot signal and the non-polarized channel parameter.

[0151] Step 703: Obtain a complex-valued matrix based on the minimization of the difference between the front slice matrix and the front estimation parameter.

[0152] Step 704: Generate a side estimation parameter based on the Khatri-Rao product of the complex-valued matrix and the pilot signal.

[0153] Step 705: Obtain the non-polarized channel matrix based on the minimization of the difference between the side slice matrix and the side estimation parameter.

[0154] The following provides a detailed description of Steps 701 to 705.

[0155] In some embodiments, after obtaining the base station received signal and the pilot signal , let . Based on this, it is possible to further perform calculations based on the pilot signal , the complex-valued matrix parameter and non-polarized channel parameters A three-dimensional matrix is constructed which contains all the matrices in the third dimension . The expanded representations of the corresponding mode-1, mode-2, and mode-3 are shown in the following formula (19) respectively.

[0156]

[0157] Wherein,[[]] and represent 's horizontal, side, and front slices respectively, that is, the horizontal slice matrix , the side slice matrix and the front slice matrix . Among them, the matrix expansion of the side slice matrix and the front slice matrix affected by noise can be rewritten as shown in the following formula (20).

[0158]

[0159] Wherein,[[]] and represent the additive white Gaussian noise matrix. Assuming that the pilot signal is known, then by alternately minimizing the corresponding offset square function, and can be estimated iteratively. Based on , in the th iteration, the channel of the th IPA training position-rotation pairing 's estimated value is obtained by minimizing the following offset function of the difference between the product of the side slice matrix and the side estimation parameter and the non-polarized channel parameter as shown in the following formula (21).

[0160]

[0161] Where the side estimation parameter is obtained based on the Khatri-Rao product of the complex-valued matrix and the pilot signal , that is . The closed expression of the estimated value of obtained after solving is shown in the following formula (22).

[0162]

[0163] The estimated value of the complex-valued matrix is obtained through the front slice matrix and the front estimation parameter by minimizing the offset function of the difference, as shown in the following formula (23).

[0164]

[0165] where the front estimation parameter is generated based on the pilot signal and the Khatri-Rao product of the non-polarized channel parameter , that is . The closed-form expression of the estimated value of obtained after solving is

[0166]

[0167] Based on the estimated stable non-polarized channel (where , the user location is determined by extracting the corresponding direction of arrival 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 according to the channel estimation. Stack all matrices into a large matrix, that is, the non-polarized channel matrix .

[0168] 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 parameter, and using mathematical methods such as the Khatri-Rao product, the complex-valued matrix and the non-polarized channel matrix can be effectively estimated. The acquisition of these key parameters provides a basis for subsequent determination of the direction of arrival and distance of the terminal. Finally, through the difference minimization process, the accuracy of parameter estimation can be improved, thus realizing more accurate terminal positioning. This positioning method based on the intelligent polarization tunable antenna can greatly improve the positioning performance and service quality of the communication system, and has important value in high-density Internet of Things applications such as 6G.

[0169] Step 603: Based on the non-polarized channel matrix, the antenna position, and the antenna rotation angle, obtain the direction-of-arrival vector between the terminal to be located and the base station.

[0170] The following provides a detailed description of step 603.

[0171] In some embodiments, after obtaining the non-polarized channel matrix , it is necessary to further based on the non-polarized channel matrix , the antenna position of the current base station , so as to obtain an estimated direction-of-arrival vector between the terminal to be located and the base station, facilitating subsequent use of this direction-of-arrival vector to locate the terminal to be located. Next, how to determine the direction-of-arrival vector between the terminal to be located and the base station will be further described .

[0172] Referring to Figure 8 , based on the non-polarized channel matrix, antenna positions, and antenna rotation angles, a direction-of-arrival vector array between the terminal to be located and the base station is obtained, including the following steps 801 to step 804.

[0173] Step 801: Generate a channel covariance matrix based on the product of the non-polarized channel matrix and the transpose matrix of the non-polarized channel matrix.

[0174] Step 802: Perform eigenvalue decomposition on the covariance matrix to obtain matrix eigenvectors.

[0175] Step 803: Based on the antenna positions, antenna rotation angles, and direction-of-arrival parameters, obtain steering vector parameters.

[0176] Step 804: Based on the product of the steering vector parameters and the matrix eigenvectors, and then perform a reciprocal process to obtain a direction-of-arrival function, and perform an optimization process on the direction-of-arrival function to obtain a direction-of-arrival vector.

[0177] Next, steps 801 to 804 will be described in detail.

[0178] In some embodiments, first, a channel covariance matrix is generated based on the product of the non-polarized channel matrix and the transpose matrix of the non-polarized channel matrix as shown in the following formula (24).

[0179]

[0180] For the antenna positions and antenna rotation angles paired and constructed for each training position-rotation, the steering vector transmission is as shown in the following formula (25).

[0181]

[0182] Next, the covariance matrix is subjected to eigenvalue decomposition, and its expression is as shown in the following formula (26).

[0183]

[0184] Where is the one containing the largest a diagonal matrix of matrix eigenvalues, including the matrix eigenvectors corresponding to these largest eigenvalues, while the remaining matrix eigenvalues and matrix eigenvectors respectively form and . According to the multiple signal classification algorithm, the direction-of-arrival vectors of all users can be obtained by finding the first peaks in the pseudo-spectrum, that is, based on the steering vector parameter and the matrix eigenvector product, and then taking the reciprocal to obtain the direction-of-arrival function, and optimizing the direction-of-arrival function to obtain the direction-of-arrival vector as shown in the following formula (27).

[0185]

[0186] Through the above steps 801 to 804, first, the channel covariance matrix is calculated from the unpolarized channel matrix and its eigenvalue decomposition is performed to obtain the matrix eigenvectors. These eigenvectors contain the direction information of the signals; then, combining parameters such as antenna position and rotation angle, the steering vector parameter is calculated. Multiplying the steering vector parameter by the eigenvector and taking the reciprocal can obtain the direction-of-arrival function; finally, optimizing this direction-of-arrival function can obtain the final direction-of-arrival vector. This method based on the eigenvalue analysis of the channel covariance matrix can effectively estimate the direction of arrival of the terminal, providing key parameters for subsequent precise positioning. This direction-of-arrival estimation technology based on intelligent polarization-tunable antennas can greatly improve the positioning accuracy and robustness of communication systems and has important application value in application scenarios such as high-density Internet of Things and autonomous driving.

[0187] Step 604: Determine the effective antenna gain corresponding to the antenna rotation angle, and based on the effective antenna gain, antenna position, and antenna rotation angle, obtain the unpolarized shaping channel model between the terminal and the base station.

[0188] The following details Step 604.

[0189] Next, based on the effective antenna gain formula shown in the above formula (9), the effective antenna gain corresponding to the current antenna rotation angle can be determined. Then, further using the unpolarized line-of-sight link channel formula corresponding to the above formula (10), based on the effective antenna gain , antenna position , and antenna rotation angle , the unpolarized shaping channel model between the terminal and the base station is obtained.

[0190] Step 605: Based on the non-polarized shaped channel model and the effective antenna gain, perform distance estimation to obtain the estimated distance between the terminal to be located and the base station.

[0191] The following provides a detailed description of Step 605.

[0192] After obtaining the non-polarized shaped channel model between the current base station and the terminal to be located and the effective antenna gain then, utilize the non-polarized shaped channel model and the corresponding effective antenna gain to perform distance estimation processing to obtain the estimated distance between the terminal to be located and the base station so as to facilitate subsequent determination of the location information of the terminal to be located using this estimated distance. The following will further describe how to perform distance estimation between the terminal to be located and the base station.

[0193] Figure 9 Refer to , based on the non-polarized shaped channel model and the effective antenna gain, perform distance estimation to obtain the estimated distance between the terminal to be located and the base station, in the following steps 901 to step 903.

[0194] Step 901: Based on the product of the number of antennas of the antenna and the unit channel power, and then multiply by the accumulated value of the effective antenna gain, to obtain the distance numerator parameter.

[0195] Step 902: Accumulate the products of all non-polarized shaped channel models and effective antenna gains to obtain the distance denominator parameter.

[0196] Step 903: Based on the ratio of the distance numerator parameter and the distance denominator parameter, obtain the estimated distance.

[0197] The following provides a detailed description of steps 901 to 903.

[0198] In some embodiments, after obtaining the non-polarized shaped channel model then, perform modulo operation on the non-polarized shaped channel model to obtain the channel amplitude observed in the line-of-sight link channel estimation , and then utilize all channel amplitudes observed in the line-of-sight link channel estimations and the geometric relationship between the terminal to be located and the base station, where represents the two-norm , can further construct the following least squares-based method to perform the estimation of the estimated distance as shown in the following formula (28).

[0199] (28)

[0200] Next, solve the formula (28) to obtain the analytical value of the estimated distance . That is, the product of the number of antennas based on the antenna and the unit channel power , and then multiply by the accumulated value of the effective antenna gain to obtain the distance numerator parameter . Then, accumulate the product of the channel amplitude corresponding to all non-polarized shaping channel models and the square root of the effective antenna gain to obtain the distance denominator parameter ; finally, based on the ratio of the distance numerator parameter and the distance denominator parameter, obtain the estimated distance as shown in the following formula (29).

[0201]

[0202] Through the above steps 901 to 903, by comprehensively considering the number of antennas, unit channel power, effective antenna gain, and non-polarized shaping channel model, the distance numerator and distance denominator parameters are constructed, and their ratio is used to estimate the distance. This method can make more comprehensive use of 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 further improving the positioning accuracy.

[0203] Step 606: Based on the estimated distance and the direction-of-arrival vector, obtain the position information of the terminal to be located.

[0204] The following provides a detailed description of step 606.

[0205] In some embodiments, after obtaining the estimated distance and the direction-of-arrival vector of each terminal to be located, the direction-of-arrival vector can be further used to determine the azimuth of each terminal to be located relative to the base station. Then, using the estimated distance between each terminal to be located and the base station and the corresponding azimuth, the position information of each terminal to be located relative to the base station can be accurately determined, facilitating subsequent optimization of communication parameters between the base station and the terminal using the estimated distance .

[0206] By implementing the terminal positioning method of the communication system provided in the above steps 601 to 606, by comprehensively utilizing the user polarization shaping vector, antenna position and rotation angle, base station received signal, and pilot signal, first estimate the unpolarized channel matrix, then calculate the direction-of-arrival vector, and construct an unpolarized shaping channel model in combination with the effective antenna gain, and finally perform distance estimation, so as to be able to obtain the position information of the terminal more comprehensively and accurately, effectively improve the positioning accuracy and robustness, especially in a complex wireless channel environment, and can provide a more reliable positioning service.

[0207] Further, based on the above-mentioned communication system and terminal positioning method, an embodiment of the present application proposes a communication parameter optimization method for a communication system. Refer to Figure 10 FIG. Figure 10 is an optional flowchart of the communication parameter optimization method for the communication system provided in the embodiment of the present application. Figure 10 The method in Figure 1 may include but is not limited to steps 1001 to 1005. At the same time, it can be understood that the present embodiment does not specifically limit the order of steps 1001 to 1005 in

[0208] and the step order can be adjusted according to actual needs, or some steps can be reduced or added. This method can be applied to a base station in a communication system as shown in

[0209] or a processor, server, etc. connected to the communication system.

[0210] In some embodiments, in the protocol design as shown in Figure 4 , at the beginning of the coherent time period, use the above-mentioned terminal positioning method of the communication system to determine the terminal distance between each terminal that needs to optimize communication parameters and the base station .

[0211] Step 1002: Generate an 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.

[0212] The following details step 1002.

[0213] Next, use the terminal distance between each terminal and the base station , and in combination with the unpolarized line-of-sight link channel formula shown in the above formula (10), the unpolarized 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 overall IPA channel representation between the terminal and all IPA sub-arrays of the base station can be obtained. , and further based on this 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).

[0214]

[0215] denotes the transmit precoder of user ; and are respectively the antenna position vector and the antenna rotation angle vector of all IPA sub-arrays of the base station; is the noise variance, . Then next, based on this achievable transmission rate model (30), a communication parameter optimization model for transmission optimization between the base station and all terminals is further generated, which is described in detail as follows.

[0216] Referring to Figure 11 , a communication parameter optimization model is generated based on the achievable transmission rate model, including the following steps 1101 to 1104.

[0217] Step 1101: Based on maximizing the achievable transmission rate model as the rate optimization objective function.

[0218] Step 1102: Based on 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 as the rate optimization variables.

[0219] Step 1103: Based on the variable optional domain of the rate optimization variables, the position distance constraint between the antenna position parameters, the signal non-reflection constraint between the antenna position parameters and the antenna rotation angle parameters, and the non-forward center constraint, generate the rate constraint conditions.

[0220] Step 1104: Based on the optimization objective function, rate optimization variables, and rate constraint conditions, generate the communication parameter optimization model.

[0221] The following is a detailed description of steps 1101 to 1104.

[0222] In some embodiments, according to the two-time scale protocol shown in Figure 4 , the design goal is to jointly optimize the antenna position and the antenna rotation angle on the base station side on the slow time scale, and jointly optimize the transmit polarization shaping vector and the polarization shaping vector at the terminal side , so as to maximize the weighted total rate of all terminals. That is, the maximization of the achievable transmission rate model (30) is used as the rate optimization objective function, based on the antenna position parameters of the base station , the antenna rotation angle parameter , the base station polarization shaping vector parameter , the precoding vector parameter and the terminal polarization shaping vector parameter of each terminal as the rate optimization variables; based on the variable selection domain of the rate optimization variables , , , the position distance constraint between the antenna position parameters , the signal non-reflection constraint between the antenna position parameter and the antenna rotation angle parameter , the non-forward center constraint , rate constraint conditions are generated. Thus, the corresponding communication parameter optimization model is generated by using the above rate optimization objective function, rate optimization variables and rate constraint conditions as shown in the following formula (31).

[0223]

[0224] where represents the rate weight of terminal , ζ represents the total transmit power of the base station, and the expected value is taken from the random channel variation caused by the rotation of any terminal. The first two constraints ensure that the received and transmitted polarization shaping vectors meet the discrete amplitude and phase requirements. The 4th constraint ensures that the center of each IPA subarray is located within the convex three-dimensional space of the base station. The 5th constraint enforces the minimum distance to prevent overlap and coupling between IPA subarrays; the 6th constraint is used to mitigate the mutual signal reflection between the base station antennas, and the 7th constraint prevents the front of each IPA subarray from facing the base station center of the base station, because that may cause signal blockage.

[0225] Through the above steps 1101 to 1104, by constructing an optimization function aiming at 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 at the same time introducing actual physical limitations such as position distance constraint, signal non-reflection constraint and non-forward center constraint, a comprehensive communication parameter optimization model is finally generated. This model can more effectively coordinate and optimize various communication parameters, so as to maximize the transmission rate of the system and improve the wireless communication performance on the premise of meeting the actual constraint conditions.

[0226] 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.

[0227] The following is a detailed description of Step 1003.

[0228] Next, according to the Figure 4 dual-time scale protocol shown, decompose 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 , precoding and polarization shaping of the terminal

[0229] For the optimization of the fast time scale, within each channel coherence interval, the base station first estimates the instantaneous channels of all terminals, and this channel is estimated for all possible terminal polarization shaping vector parameters and base station polarization shaping vector parameters , and the antenna position parameters , antenna rotation angle parameters and the terminal positions are kept fixed. Subsequently, the base station determines its transmit polarization shaping vector , precoding vector and terminal polarization shaping vector parameters . To facilitate the parallel and element-by-element update of each element in the polarization shaping vectors and and thus simplify their optimization process, auxiliary variables and are introduced in the embodiments of the present application. Therefore, for the fast time scale, there is a polarization shaping optimization model as shown in the following formula (32).

[0230] (32)

[0231] where the mean square error term of the terminal is as shown in the following formula (33).

[0232] This mean square error term is obtained by combining the weighted minimum mean square error (WMMSE) method, is the equalization parameter, represents the weighting factor of the terminal.

[0233] For the optimization of the slow time scale, the antenna position parameter in the communication parameter optimization model (31) and the antenna rotation angle parameter The slow time scale optimization of becomes a stochastic optimization problem due to the expectation operation in the objective function. Since this objective function is difficult to solve, in the embodiments of the present application, it is approximated as a deterministic function. Specifically, we independently generate sets of random channel samples for all terminals, and use the average achievable rate over these channel samples as an approximation of the expected rate in the objective function of the communication parameter optimization model (31). According to the estimated terminal positions at the beginning of the first phase of the protocol proposed in Figure 4 , the base station can obtain the line-of-sight channels of the terminals under all possible IPA position-rotation pairs, and accordingly generate by independently and randomly changing the rotation angles of all terminals sets of random channel samples.

[0234] Assume that the th sample of the IPA time-varying channel is . The average rate of the th terminal can be approximated as , where represents the set of all channel samples. Thus, the communication parameter optimization model (31) is reformulated as a position-rotation optimization model as shown in the following formula (33).

[0235] (33)

[0236] Step 1004: Solve the position-rotation optimization model to obtain the optimized position-rotation angle, and adjust at least one intelligent polarization shaping antenna in the base station in the slow time scale based on the optimized position-rotation angle.

[0237] The following provides a detailed description of step 1004.

[0238] In some embodiments, the position-rotation optimization model (33) is a non-convex optimization problem because the objective function is non-concave and the 2nd - 4th constraints are non-convex. Traditional convex optimization methods are not practically efficient in solving the position-rotation optimization model (33). Inspired by the low complexity of the particle swarm optimization algorithm, in the embodiments of the present application, the particle swarm optimization algorithm is used to solve the optimized position-rotation angle in the position-rotation optimization model (33), that is, including optimizing the antenna position and the antenna rotation angle, and then adjusting at least one intelligent polarization shaping antenna in the base station in the slow time scale within the coherence time based on the optimized antenna position and the optimized antenna rotation angle.

[0239] 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.

[0240] The following gives a detailed description of Step 1005.

[0241] In some embodiments, for the solution of the polarization shaping optimization model (32) for the fast time scale, a penalty dual decomposition framework is adopted, 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 augmented Lagrangian problem, while the outer loop updates the dual variables and penalty coefficients according to the constraint violation until convergence to obtain the optimized polarization shaping corresponding to each fast time scale, that is, including the optimized base station polarization shaping vector, optimized precoding vector of the base station, and optimized terminal polarization shaping vector of each terminal, so as to facilitate subsequent adjustment of the intelligent polarization shaping antenna in the base station based on the optimized base station polarization shaping vector and optimized precoding vector parameters in each corresponding fast time scale, and adjustment of the intelligent polarization shaping antenna of the terminal based on the optimized terminal polarization shaping vector.

[0242] The following will further describe how to solve the polarization shaping optimization model (32).

[0243] Refer to Figure 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.

[0244] Step 1201: Convert the polarization shaping optimization model into Lagrangian form to obtain the converted polarization shaping optimization model.

[0245] Step 1202: Use the block coordinate descent method to divide the converted polarization shaping optimization model into a terminal polarization shaping optimization model, a base station polarization shaping optimization model, and a transmit precoding optimization model.

[0246] 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 the optimized polarization shaping.

[0247] The following gives a detailed description of Steps 1201 to 1203.

[0248] Specifically, in the inner loop of the penalty dual decomposition, the block coordinate descent method is applied in the embodiments of the present application to solve. First, the polarization shaping optimization model (32) is converted into Lagrangian form to obtain the converted polarization shaping optimization model as shown in the following formula (34).

[0249] (34)

[0250] Among them, and respectively represent the dual variable vectors corresponding to the constraints and while 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 when the other blocks are fixed.

[0251] (1) First, the update of the terminal polarization shaping vector parameter is achieved by solving the terminal polarization shaping optimization model corresponding to the unconstrained quadratic programming problem shown in the following formula (35).

[0252] (35)

[0253] Among them . Therefore, the closed-form optimal solution of the terminal polarization shaping optimization model (35) can be expressed as shown in the following formula (36).

[0254]

[0255]

[0256] Next, the sub-problem is as shown in the following formula (38).

[0257]

[0258] Since each element of is independent of each other in the objective function and constraints, the optimal solution can be calculated in parallel, specifically as shown in the following formula (39).

[0259]

[0260]

[0261] (2) The update of the base station polarization shaping vector parameter can be carried out by solving the base station polarization shaping optimization model corresponding to the unconstrained quadratic programming problem shown in the following formula (41).

[0262]

[0263] Among them , , Here can be obtained 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).

[0264]

[0265]

[0266] (3) The sub-problem is defined as shown in the following formula (44)

[0267]

[0268] 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.

[0269] (4) With other variables fixed, by minimizing the linear minimum mean square error equalization coefficient is obtained as shown in the following formula (45).

[0270]

[0271] (5) For the unit channel power of the k-th terminal, the optimal solution is shown in the following formula (46).

[0272]

[0273] (6) The update of the precoding parameter of the base station is obtained by solving the transmit precoding optimization model shown in the following formula (47).

[0274] (47)

[0275] For this transmit precoding optimization model (47), the optimal solution can be derived as shown in the following formula (48) by using the following first-order optimality condition.

[0276]

[0277] Among them is the dual variable of the transmit power constraint. If , then is the optimal solution; otherwise, the optimal can be obtained by solving the above six components iteratively to solve the transformed polarization shaping optimization model (34).

[0278] In the outer loop of the penalty dual decomposition framework, the update rule of the dual variable is shown in the following formula (49).

[0279]

[0280] Through the above steps 1201 to 1203, by performing Lagrangian transformation on the polarization shaping optimization model and using the block coordinate descent method to decompose it into multiple sub-problems, and then performing iterative solution, the complexity of the optimization problem can be effectively reduced, the polarization shaping can be optimized, so as to better match the channel characteristics, improve the signal transmission quality and system performance.

[0281] By implementing the communication parameter optimization method of the communication system corresponding to the above steps 1001 to 1005, by combining the terminal positioning results, constructing an achievable transmission rate model, and decomposing it into position rotation optimization under the slow time scale and polarization shaping optimization under the fast time scale, the dynamic adjustment of the base station antenna position, rotation angle and polarization shaping is realized. This method can flexibly optimize communication parameters on different time scales according to channel changes and terminal positions, so as to maximize the system transmission rate, improve wireless communication performance, and adapt to complex and changeable wireless environments.

[0282] To evaluate the performance of the proposed scheme, in the embodiment of the present application, the proposed communication system equipped with an intelligent polarization shaping antenna is simulated and verified, and compared with other schemes.

[0283] First, in this embodiment, the performance of the terminal positioning method of the proposed communication system equipped with an intelligent polarization shaping antenna in this scheme is evaluated. It is compared with the direct positioning method. The direct positioning method uses maximum likelihood estimation to iteratively optimize 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 , where represents the true positions of all terminals, and represents its estimated position . In the positioning stage, in this embodiment, the matrix is designed to satisfy of the semi-orthogonal matrix, and is set based on the Fourier transform matrix. Referring to Figure 13 , it is the simulation schematic diagram of the first communication system equipped with an intelligent polarization shaping antenna provided by the embodiment of the present application. As Figure 13As shown, it is demonstrated that the proposed polarization shaping - based positioning scheme (i.e., the terminal positioning method of the communication system equipped with intelligent polarization - shaping antennas 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, thus improving the estimation accuracy. In addition, the method proposed in this scheme also efficiently utilizes the Khatri - Rao channel structure, separating the estimation of the stable channel from the dynamic coefficients , so that the decoupled estimation results can be used for terminal positioning with a closed - form solution. Since the proposed algorithm requires less sensing time for positioning, the effective data rate of the terminal can be significantly improved.

[0284] Next, this embodiment verifies the performance of the communication parameter optimization method of the proposed communication system equipped with intelligent polarization - shaping antennas and the performance of the proposed polarization - shaping algorithm (fixed antenna position and rotation). The comparison scheme is the fixed - parameter scheme, where , , and are all fixed. The precoding vector adopts maximum ratio transmission. This embodiment uses a three - sector base - station configuration, where the number of sectors , and each sector covers 120°. Referring to Figure 14 , it is the simulation schematic diagram of the second communication system equipped with intelligent polarization - shaping antennas provided by the embodiments of this application. As Figure 14 shown, the achievable average total rate of the proposed polarization - shaping scheme that only optimizes polarization is studied under the change of the number of terminals, and at the same time, the influence of different polarization - shaping amplitude / phase quantization levels on the achievable rate is shown. It is assumed that the number of quantization bits at the base - station and the terminal is the same. It can be observed that the polarization - shaping optimization performance of joint amplitude and phase control is better than that of only phase control (i.e., ) and only amplitude control (i.e., ). In addition, the proposed scheme including amplitude control has better performance than only phase control. Furthermore, as the number of terminals increases, the performance improvement brought by amplitude control is more significant because the multi - terminal interference caused by the channel state information estimation error will be more serious. This result shows that in a system with a large number of terminals, polarization - shaping with joint amplitude - phase control may be more advantageous than polarization - shaping with only amplitude or only phase control.

[0285] Referring to Figure 15 , it is the simulation schematic diagram of the third communication system equipped with intelligent polarization - shaping antennas provided by the embodiments of this application. As Figure 15As shown, the achievable rates of different schemes are plotted against the base station transmit power. The results show that compared with the fixed-parameter scheme, whether using only polarization shaping optimization, only position-rotation optimization, or the algorithm combining antenna position / rotation and polarization shaping optimization, 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 antennas proposed in this application) obtains the highest performance gain. In particular, when only the polarization shaping optimization scheme is adopted, its average rate is already significantly higher than that of the fixed-parameter scheme. This is because the polarization shaping antenna can dynamically adjust the polarization states of the terminal and the base station to maximize the instantaneous channel gain, thus effectively utilizing the additional degrees of freedom provided by polarization diversity. In addition, even at the same transmit power, the rate achieved by the position-rotation optimization scheme alone is higher than that of the fixed-parameter scheme, because the IPA system with position-rotation adjustment has more spatial degrees of freedom and can deploy antenna resources more reasonably to match the spatial distribution of the terminal channel. Further, as the base station transmit power increases, the performance gap between the proposed scheme and the fixed-parameter scheme also continues to widen. This is an expected phenomenon, because as the transmit power increases, the total rate is more limited by interference, and by adjusting the polarization shaping of the terminal / base station and the position and rotation of the base station antennas, the interference suppression conditions of the multi-terminal MIMO channel can be effectively improved by means of base station precoding.

[0286] Therefore, the intelligent polarization shaping antenna-enhanced wireless system proposed in the present invention (i.e., the communication parameter optimization method of the communication system equipped with intelligent polarization shaping antennas proposed in this application) can provide an efficient communication / sensing scheme for wireless networks.

[0287] The embodiments of this application also provide an electronic device, including:

[0288] At least one memory;

[0289] At least one processor;

[0290] At least one program;

[0291] The program is stored in the memory, and the processor executes the at least one program to implement the terminal positioning method and the communication parameter optimization method of the communication system described above in this application. This electronic device can be any intelligent terminal including a mobile phone, a tablet computer, a personal digital assistant (Personal Digital Assistant, abbreviated as PDA), a vehicle-mounted computer, etc.

[0292] Please refer to Figure 16 , Figure 16 which schematically shows the hardware structure of an electronic device in another embodiment. The electronic device includes:

[0293] The processor 1601 can be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0294] The memory 1602 can be implemented in the form of a ROM (Read Only Memory), a static storage device, a dynamic storage device, or a RAM (Random Access Memory), etc. The memory 1602 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1602 and are called by the processor 1601 to execute the terminal positioning method and communication parameter optimization method of the communication system in the embodiments of the present application;

[0295] The input / output interface 1603 is used to implement information input and output;

[0296] The communication interface 1604 is used to implement communication interaction between this device and other devices, and can implement communication through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.);

[0297] The bus 1605 transmits information between various components of the device (such as the processor 1601, the memory 1602, the input / output interface 1603, and the communication interface 1604);

[0298] Among them, the processor 1601, the memory 1602, the input / output interface 1603, and the communication interface 1604 achieve communication connections with each other inside the device through the bus 1605.

[0299] The embodiments of the present application also provide a storage medium, which is a computer-readable storage medium. The storage medium stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned terminal positioning method and communication parameter optimization method of the communication system.

[0300] The memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory may optionally include memories remotely located relative to the processor, and these remote memories can be connected to the processor through 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.

[0301] The embodiments described in the embodiments of the present application are for more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art will know that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0302] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than those shown in the figures, or combine certain steps, or different steps.

[0303] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0304] Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, and appropriate combinations thereof.

[0305] 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 do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0306] It should be understood that in this application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Among them, A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one (one) of the following" or its similar expression refers to any combination of these items, including any combination of single item (one) or plural items (ones). For example, at least one (one) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0307] In several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the above division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of devices or units can be in electrical, mechanical or other forms.

[0308] The units described above as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0309] In addition, each functional unit in each embodiment of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0310] When an integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes: various media that can store programs, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0311] The preferred embodiments of the embodiments of this application have been described above with reference to the accompanying drawings, which does not limit the scope of the rights of the embodiments of this application. Any modifications, equivalent replacements, and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of this application shall be within the scope of the rights of the embodiments of this application.

Claims

1. An intelligent polarization shaping antenna, characterized in that, Including: 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. The second phase shifter is connected to the second polarization unit. When the antenna receives a processing signal, the splitter is configured to split the processing signal to obtain a first processing signal and a second processing signal, and send the first processing signal to the first attenuator and the second processing signal to the second attenuator. The first attenuator is configured to perform a first amplitude processing on the first processing signal to obtain a first amplitude processed signal, and send the first amplitude processed signal to the first phase shifter. The second attenuator is configured to perform a second amplitude processing on the second processing 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 configured to perform a first phase shift processing on the first amplitude processed signal to obtain a first phase shift processed signal and send it to the first polarization unit. The first polarization unit is configured to send the first phase shift processed signal. The second phase shifter is configured to perform a second phase shift processing on the second amplitude processed signal to obtain a second phase shift processed signal and send it to the second polarization unit. The second polarization unit is configured to send 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 shaping antenna according to claim 1, wherein Further including: A digital signal processor and a radio frequency chain connected to each other. The radio frequency chain is connected to the splitter. When the antenna receives a processing signal, the digital signal processor and the radio frequency chain are sequentially configured to perform digital signal processing and radio frequency processing on the processing signal, and send the processed processing signal to the splitter.

3. A base station, characterized in that, Including: At least one antenna surface, on which at least one intelligent polarization shaping antenna as claimed in claim 1 is provided. At least one movable rotating rod, which is connected to the antenna surface and is used to adjust the three-dimensional position and the antenna rotation angle of the antenna surface.

4. A communication system, characterized in that, Including: A base station as claimed in claim 3. At least one terminal, on which an intelligent polarization shaping antenna as claimed in claim 1 is provided. Communication occurs between the base station and the terminal.

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

6. The terminal positioning method of the communication system according to claim 5, characterized in that, The estimating and calculating based on the received signal of the base station and the pilot signal to obtain the non-polarized channel matrix includes: Generate a side slice matrix and a front slice matrix based on the pilot signal, complex-valued matrix parameters, and non-polarized channel parameters; Generate front estimation parameters based on the Khatri-Rao product of the pilot signal and the non-polarized channel parameters; Obtain a complex-valued matrix based on the minimization of the difference between the front slice matrix and the front estimation parameters; Generate side estimation parameters based on the Khatri-Rao product of the complex-valued matrix and the pilot signal; Obtain the non-polarized channel matrix based on the minimization of the difference between the side slice matrix and the side estimation parameters.

7. The terminal positioning method of the communication system according to claim 5, characterized in that, The obtaining the direction-of-arrival vector between the user to be located and the base station based on the non-polarized channel matrix, the antenna positions, and the antenna rotation angles includes: Generate a channel covariance matrix based on the product of the non-polarized channel matrix and the transposed matrix of the non-polarized channel matrix; Perform eigenvalue decomposition on the covariance matrix to obtain matrix eigenvectors; Based on the antenna positions, the antenna rotation angles, and the direction-of-arrival parameters, obtain steering vector parameters; Based on the product of the steering vector parameters and the matrix eigenvectors, and then perform a reciprocal operation to obtain a direction-of-arrival function, and perform an optimization process on the direction-of-arrival function to obtain the direction-of-arrival vector.

8. The terminal positioning method of the communication system according to claim 5, wherein The performing distance estimation based on the non-polarized shaping channel model and the effective antenna gain to obtain the estimated distance between the user to be located and the base station includes: Obtain a distance numerator parameter based on the product of the number of antennas of the antenna and the unit channel power, and then multiply by the cumulative value of the effective antenna gain; Accumulate the products of all the non-polarized shaping channel models and the effective antenna gain to obtain a distance denominator parameter; Based on the ratio of the distance numerator parameter and the distance denominator parameter, obtain the estimated distance.

9. A method for optimizing communication parameters of a communication system, characterized in that, The communication parameter optimization method is applied to a base station, and the method includes: During a coherent time period, based on the terminal positioning method of the communication system according to claim 5, obtain the terminal distances between multiple terminals and the base station, where the coherent time period includes a slow time scale and at least one fast time scale in chronological order; Generate an achievable transmission rate model between the base station and each terminal based on the terminal distances, and generate a communication parameter optimization model based on the achievable transmission rate model; 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; 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 the slow time scale based on the optimized position rotation angle; Solve the polarization shaping optimization model to obtain an 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.

10. The method for optimizing communication parameters of the communication system according to claim 9, characterized in that, The generating of the communication parameter optimization model based on the achievable transmission rate model includes: Based on maximizing the achievable transmission rate model as the 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 terminal as rate optimization variables; Based on the variable optional domain of the rate optimization variables, the position distance constraint between the antenna position parameters, the signal non-reflection constraint and non-forward center constraint between the antenna position parameters and the antenna rotation angle parameters, generate rate constraint conditions; Generate the communication parameter optimization model based on the optimization objective function, the rate optimization variables, and the rate constraint conditions.

11. The method for optimizing communication parameters of the communication system according to claim 10, characterized in that, The solving of the polarization shaping optimization model to obtain an optimized polarization shaping corresponding to each fast time scale includes: Convert the polarization shaping optimization model into a Lagrangian format to obtain a converted polarization shaping optimization model; Using the block coordinate descent method, divide the converted polarization shaping optimization model into a terminal polarization shaping optimization model, a base station polarization shaping optimization model, and a transmit precoding optimization model; Iteratively solve the terminal polarization shaping optimization model, the base station polarization shaping optimization model, and the transmit precoding optimization model to obtain the optimized polarization shaping.

12. An electronic device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the 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 any one of claims 9 to 11.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the 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 any one of claims 9 to 11.

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