Multi-channel cooperative control method based on RIS phased array

Abstract

The invention discloses a multi-channel cooperative control method based on an RIS phased array, and relates to the technical field of phased arrays, and the method comprises the steps: S1, enabling a target RIS phased array to be provided with a plurality of independent channels, and calculating a first efficiency value representing the ideal beam forming efficiency; s2, activating the independent channel as a cooperative multi-channel, and constructing a beam coupling model for the cooperative multi-channel; s3, predicting predicted beam parameters of the cooperative multiple channels by using the beam coupling model; and S4, calculating a second efficiency value representing the predicted beam forming efficiency based on the predicted beam parameter, and comparing the second efficiency value with the first efficiency value to judge whether the calibration takes effect or not. According to the method, the beam coupling model is constructed after closed-loop calibration, meanwhile, a comparison and judgment mechanism of the beam generation efficiency value is introduced, the multi-channel calibration result is evaluated in real time, and the method has the advantages of reducing beam side lobes and improving the main lobe focusing degree and has the beneficial effects of greatly reducing the multi-channel phase error amplification effect.

Description

Technical Field

[0001] The present invention relates to the technical field of phased arrays, and in particular to a multi-channel collaborative control method based on a RIS phased array. Background Art

[0002] Existing channel signal calibration techniques for RIS phased arrays are mostly based on the assumption of channel independence. This means that during single-channel calibration, other channels are assumed to be closed or ideally matched. The calibration process typically involves injecting test signals channel by channel using an external measurement system (such as a vector network analyzer) and adjusting the amplitude and phase parameters of each element based on reflected signal feedback. This approach is suitable for sparse arrays or low-frequency bands. However, when RIS phased arrays are deployed in dense, high-frequency millimeter-wave or terahertz arrays, near-field electromagnetic coupling effects can easily lead to nonlinear fluctuations in multi-channel coupling strength when the RIS phased array is bent due to the non-uniform variations in element spacing during multi-channel coordinated control.

[0003] Under the traditional linear system assumption, the multi-channel joint response is the vector superposition of the single-channel response. However, actual measurements show that in a 128-element RIS phased array, when 16 channels are activated simultaneously, the multi-channel phase error of the synthesized beam reaches 6.3 times the single-channel calibration error, resulting in an average decrease of 7.2dB in beam gain, which seriously deviates from theoretical expectations. In the existing technology, when correcting the process of beam gain reduction caused by multi-channel phase error, the main method is to fine-tune the amplitude and phase parameters of independent channels one by one, which easily leads to ignoring the cross-effect of coupling interference under multi-channel coordination. After the existing method adjusts the single-channel phase error, the multi-channel coordination may cause the error to increase or the beam gain to further decrease because the single-channel calibration process is not effective for the multi-channel coordination, resulting in poor correction effect for the beam gain reduction, which in turn reduces the energy efficiency of dense array deployment of RIS phased arrays. Summary of the Invention

[0004] The present invention provides a multi-channel cooperative control method based on the RIS phased array, which solves the problem that in the dense array deployment of the RIS phased array, when the existing technology calibrates the independent channel errors, the multi-channel cooperative beam gain decreases due to the lack of interference correction for multi-channel cooperation.

[0005] The present invention is achieved through the following technical solutions: A multi-channel collaborative control method based on a RIS phased array, the method comprising: Step S1: Dividing the array elements of the target RIS phased array into a plurality of independent channels, presetting ideal beam parameters for each independent channel, and calculating a first efficiency value representing an ideal beamforming efficiency based on the ideal beam parameters; Step S2: Mark the original reflection coefficient of each independent channel, perform closed-loop calibration on all the original reflection coefficients, activate all the independent channels after the closed-loop calibration as cooperative multi-channels, and build a beam coupling model for the cooperative multi-channels; Step S3: using the beam coupling model to predict and generate the synthetic beam of the coordinated multi-channel to represent array control, and marking the generated data of the synthetic beam as the predicted beam parameter representing the tendency of the coordinated multi-channel beam result; Step S4: Based on the predicted beam parameters, a second efficiency value representing the predicted beamforming efficiency is calculated, and the second efficiency value is compared with the first efficiency value. When the first efficiency value is lower than the second efficiency value, it is determined that the calibration is effective; when the first efficiency value is higher than the second efficiency value, it is determined that the calibration is not effective, and the process returns to step S2.

[0006] Furthermore, the process of closed-loop calibration of the original reflection coefficient is set to: detect and mark the coupling strength of different independent channels, and allocate the calibration time slot length in a positive correlation manner according to the coupling strength between channels; construct a TDM scheduling table, and use the TDM scheduling table to perform closed-loop parallel calibration of the original reflection coefficient in each independent channel according to the calibration time slot length.

[0007] Furthermore, the contents of the TDM scheduling table include: dividing independent channels into a number of channel groups of equal number, measuring the electromagnetic coupling strength between every two channel groups and expressing it as a coupling strength matrix, calculating the coupling weight value of each channel group through the coupling strength matrix, and using the coupling weight value to allocate a calibration time to each channel group.

[0008] Furthermore, the original reflection coefficient is calibrated in a closed loop using an iterative least squares method. The process includes: And let the iteration number in the calibration process be expressed as k, set the calibration reflection coefficient Г, set the original reflection coefficient as the initial value of the iterative reflection coefficient; preset the target reflection coefficient Г for each independent channel t and control parameter θ, set the Jacobian matrix and represent it as J; set the error threshold for the iterative process; The calculation formula of the calibration reflection coefficient Г for each iteration is expressed as: , Among them, the θ k-1 represents the control parameter value of the previous iteration number, the θ k Indicates the control parameter value of the current iteration sequence; k Represents the calibration reflection coefficient of the current iteration number, the Г k-1 represents the calibration reflection coefficient of the previous iteration number; k-1 The Jacobian matrix representing the ordinal number of the previous iteration; When calibrating the reflection coefficient Г kand the target reflection coefficient Г t When the difference is less than the error threshold, the iteration process stops and the calibration is completed.

[0009] Furthermore, the correction error is defined as ε, and its calculation formula is expressed as: , where ε k Indicates the correction error of the current iteration number; Assume that the least squares target value of the control parameter is expressed as Δθ and is solved using the standard least squares formula, Then the calculation formula of the least squares target value Δθ is expressed as: , Among them J H is the conjugate transpose of the Jacobian matrix.

[0010] Furthermore, the calculation formula of the Jacobian matrix J is expressed as: , where J k Indicates the value of the Jacobian matrix at the current iteration.

[0011] Furthermore, the beam coupling model includes a Riemannian manifold algorithm and a beam generator; the working process of the beam coupling model includes: The original reflection coefficient after closed-loop calibration is constructed as the complex reflection coefficient of the coordinated multi-channel, the phase amplitude space is modeled as a complex Hilbert manifold, the complex reflection coefficient is mapped to the complex Hilbert manifold using nonlinear mapping, the manifold loss function is constructed based on the complex reflection coefficient, and the Riemann gradient of the manifold loss function is calculated; The control parameters of the beamformer are obtained, and the Riemann gradient and control parameters are mapped to the tangent space of the complex Hilbert manifold for geodesic update analysis. The control parameters are updated using exponential mapping in the tangent space. The updated control parameters are input into the beamformer to output a synthetic beam. The generated data of the synthetic beam is collected and annotated as the predicted beam parameters.

[0012] Furthermore, an inverse distortion parameter is added to the updated control parameter and then input into a beamformer to output a synthesized beam.

[0013] Furthermore, it is assumed that the ideal beam parameters and the predicted beam parameters are consistent in content; the data content of the ideal beam parameters and the predicted beam parameters includes: The main lobe gain value indicates the radiation intensity in the main lobe direction corresponding to the peak value of the beam gain; The main lobe pointing error value indicates the deviation of the actual pointing direction of the main lobe from the predetermined direction; The sidelobe level value represents the ratio of the radiated power in the sidelobe direction to the peak power of the main lobe; The sidelobe pseudo-lobes value indicates the parasitic high-gain lobe value caused by the phase quantization error of the independent channel.

[0014] Furthermore, the calculation process of the first efficiency value and the second efficiency value is set as follows: The data contents of the ideal beam parameters and the predicted beam parameters are normalized to generate a scoring function. The scoring functions of the main lobe gain value, the main lobe pointing error value, the side lobe level value, and the side lobe pseudo lobe value are labeled as the first function f1, the second function f2, the third function f3, and the fourth function f4, respectively. The first weight value m1, the second weight value m2, the third weight value m3, and the fourth weight value m4 are set accordingly. The beam efficiency function is represented by η. The beam efficiency function η is calculated as: η = m1∙f1+ m2∙f2+ m3∙f3+ m4∙f4, Among the four weight values, the first weight value m1 has the largest value; the calculation process of the first efficiency value and the second efficiency value are the same as the calculation formula of the beam efficiency function η.

[0015] Compared with the existing technology, the present invention constructs a beam coupling model after closed-loop calibration to explicitly characterize the near-field electromagnetic coupling between channels and the nonlinear changes under the curved structure, thereby making up for the defect of traditional methods in ignoring the mutual interference of multiple channels. At the same time, a comparative judgment mechanism of the beam generation efficiency value is introduced to evaluate the multi-channel calibration results in real time, avoiding the accumulation of new errors that may be caused by a single fine-tuning of the RIS phased array. It can reduce the beam sidelobes and improve the focusing degree of the mainlobe, and has the advantages of improving the multi-channel collaborative beam gain of the RIS phased array and the beneficial effect of significantly reducing the multi-channel phase error amplification effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings: Figure 1 This is a structural diagram of the present invention. DETAILED DESCRIPTION

[0017] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with examples and drawings. The exemplary embodiments of the present invention and their descriptions are only used to explain the present invention and are not intended to limit the present invention. Example

[0018] like Figure 1 As shown, this embodiment is a multi-channel collaborative control method based on a RIS phased array, the method comprising: Step S1: Dividing the array elements of the target RIS phased array into a plurality of independent channels, presetting ideal beam parameters for each independent channel, and calculating a first efficiency value representing an ideal beamforming efficiency based on the ideal beam parameters; Step S2: Mark the original reflection coefficient of each independent channel, perform closed-loop calibration on all the original reflection coefficients, activate all the independent channels after the closed-loop calibration as cooperative multi-channels, and build a beam coupling model for the cooperative multi-channels; Step S3: using the beam coupling model to predict and generate the synthetic beam of the coordinated multi-channel to represent array control, and marking the generated data of the synthetic beam as the predicted beam parameter representing the tendency of the coordinated multi-channel beam result; Step S4: Based on the predicted beam parameters, a second efficiency value representing the predicted beamforming efficiency is calculated, and the second efficiency value is compared with the first efficiency value. When the first efficiency value is lower than the second efficiency value, it is determined that the calibration is effective; when the first efficiency value is higher than the second efficiency value, it is determined that the calibration is not effective, and the process returns to step S2.

[0019] Dividing the array elements of the target RIS phased array into several independent channels means dividing the entire large-scale array into several small unit groups that can be driven, calibrated, and optimized independently at the physical or control level. In specific implementations, common division methods include division according to physical topology, division according to RF links, and division according to beam segments. Array channel division is usually performed using one or more hybrid methods. The original reflection coefficient refers to the reflection amplitude and phase parameters of each independent channel before precise adjustment or collaborative calibration. The closed-loop calibration refers to the introduction of a closed-loop feedback control mechanism of measurement-feedback-adjustment-verification to continuously optimize the original reflection coefficient until the system performance meets the preset target or convergence condition. Activating independent channels as collaborative multi-channels means that after completing the closed-loop amplitude and phase calibration of each independent channel, each channel is no longer tested or controlled separately, but all channels participate in a complete beamforming task together. In practice, there are various conventional methods for activating independent channels into coordinated multi-channels. For example, these methods involve unified addressing through a controller, connecting independent channels to a central controller through address mapping and driver circuits. Alternatively, a unified power supply network can be used to converge the bus to a single master control chip via phase shifters to complete the transition from single-channel to multi-channel operation. A beam coupling model is used to predict the resulting composite beam. This involves using the beam coupling model to predict the resulting composite beam when multiple channels are simultaneously activated under given array control parameters. This predictive approach allows for an assessment of whether the current coordinated multi-channel composite beam meets the RIS phased array's requirements for minimizing beam gain drop.

[0020] The beam coupling model is a computational simulation model constructed in this embodiment, which is used to evaluate the impact of coupling interference between channels in collaborative multi-channels on the generation of synthetic beams. The first efficiency value and the second efficiency value are both beam forming efficiency values. The first efficiency value represents the theoretical forming efficiency calculated based on ideal beam parameters; the second efficiency value represents the beam forming efficiency predicted by the target RIS phased array based on the beam coupling model after this calibration process. When the first efficiency value is greater than the second efficiency value, the predicted forming performance of the calibration configuration after considering coupling interference is better than or exceeds the ideal state, indicating that the current closed-loop calibration plus coupling modeling has an optimization effect. In a specific implementation, to avoid excessively long overall operating times for the target RIS phased array, the ideal beam parameters and first efficiency value can be set to the lower limit of the ideal state. Specifically, each parameter can be set to a relatively low value. This improves the pass rate of predicted beam parameter verification, while ensuring stable beam gain and avoiding lengthy calibration times. To ensure the generation quality of the target RIS phased array, the ideal beam parameters and first efficiency value can be set to a higher level of the ideal state. Specifically, each parameter can be set to a relatively high value. This maximizes the quality of the predicted generated beam, stabilizes the beam gain to a greater extent, and avoids beam gain degradation. This implementation ensures that the target RIS phased array consistently uses optimal or suboptimal amplitude and phase settings in high-frequency, dense, and structurally variable scenarios, reducing manual intervention and testing costs and improving the efficiency of verification and calibration of array unit generated beams.

[0021] Furthermore, as a feasible implementation method, the process of closed-loop calibration of the original reflection coefficient is set to: detect and mark the coupling strength of different independent channels, and allocate the calibration time slot length in a positive correlation manner according to the coupling strength between channels; construct a TDM scheduling table, and use the TDM scheduling table to perform closed-loop parallel calibration of the original reflection coefficient in each independent channel according to the calibration time slot length.

[0022] The coupling strength of different independent channels is detected and labeled. This typically involves measuring S-parameters and performing EM simulations on the individual channels. The S-parameter amplitude is used as an indicator of coupling strength, and the field coupling coefficients of adjacent cells are calculated using EM excitation for each channel. Calibration slot lengths are assigned in a positive correlation based on the coupling strength between channels. The greater the coupling strength, the longer the calibration slot allocated to that channel. This gives highly coupled channels more time for measurement and adjustment, reducing interference errors during the calibration process. TDM, which stands for time division multiplexing, divides time into several consecutive, non-overlapping time slots, allowing different channels or signals to use different time slots to avoid interference. The TDM schedule specifies when each channel receives time resources for calibration operations. It typically takes the form of a time sequence table, which can be configured as "Channel 1 occupies time slots 1-3, Channel 2 occupies time slots 4-6, etc." Calibration slot lengths are dynamically allocated based on channel coupling strength, with channels with higher coupling strength receiving longer slots to ensure more time for calibration.

[0023] Furthermore, as a feasible implementation method, the contents of the TDM scheduling table include: dividing independent channels into a number of channel groups of equal number, measuring the electromagnetic coupling strength between every two channel groups and expressing it as a coupling strength matrix, calculating the coupling weight value of each channel group through the coupling strength matrix, and using the coupling weight value to allocate a corresponding calibration time to each channel group.

[0024] The electromagnetic coupling strength describes the degree of mutual influence of the electromagnetic fields between two channel groups, reflecting the degree of interference or energy transfer caused by the signal of one channel group to the other channel group in space. In specific implementation, the measurement method can use a vector network analyzer to measure the transmission parameters between the two channel groups to obtain the coupling coefficient, and use electromagnetic simulation to use the signal injection and feedback measurement results to indirectly infer the coupling strength. The purpose of calculating the coupling weight value of each channel group is to first quantify the coupling influence of each channel group in the entire array based on the coupling relationship between groups, and then distribute the total calibration time to each channel group according to the proportion of coupling influence.

[0025] Example 2 In this embodiment, an iterative least squares method is used to perform closed-loop parallel calibration on the original reflection coefficients, and the process includes: And let the iteration number in the calibration process be expressed as k, set the calibration reflection coefficient Г, set the original reflection coefficient as the initial value of the iterative reflection coefficient; preset the target reflection coefficient Г for each independent channel t and control parameter θ, set the Jacobian matrix and represent it as J; set the error threshold for the iterative process; The calculation formula of the calibration reflection coefficient Г for each iteration is expressed as: , Among them, the θ k-1 represents the control parameter value of the previous iteration number, the θ k Indicates the control parameter value of the current iteration sequence; k Represents the calibration reflection coefficient of the current iteration number, the Г k-1 represents the calibration reflection coefficient of the previous iteration number; k-1 The Jacobian matrix representing the ordinal number of the previous iteration; When calibrating the reflection coefficient Г k and the target reflection coefficient Г t When the difference is less than the error threshold, the iteration process stops and the calibration is completed.

[0026] The calibration reflection coefficient represents the reflection coefficient value of the original reflection coefficient in each iteration process. The target reflection coefficient represents the reference target value of the reflection coefficient iteration, which can be set according to historical data and empirical rules, including, for example, the reflection coefficient amplitude representing the intensity of the reflected energy and the reflection coefficient phase that determines the directionality of the reflected wave. The control parameter represents a physical input that can be actually adjusted, representing the physical parameters corresponding to the independent channel in the target RIS phased array, and is used to initialize the initial control configuration of the calibration or simulation modeling. The physical quantity represented by the control parameter can be one or more, depending on the control complexity of the reflection characteristics of each RIS unit or channel. For RIS units with simple structures, for example, only the reflection phase is controlled, or there is only one adjustable device, such as a PIN switch, a 1-bit capacitive load, etc.; for RIS units with complex structures, such as dual-control units with independently adjustable amplitude and phase, the control parameter is in the form of a multivariable function. The Г k-1 Indicates the current estimated calibration reflection coefficient obtained based on the electromagnetic response under the current control parameters in the previous round of iterative calibration. k -θ k-1 Indicates the adjustment amount of each independent channel control parameter in the actual system, such as the voltage increased by 0.2V, the phase control bit flipped, etc. k-1 The Jacobian matrix represents the sensitivity of the reflection response to the control variables. Its value is the value in the previous iteration process. Its essence is the gradient of the reflection coefficient caused by a small change in each control parameter.

[0027] Furthermore, as a feasible implementation method, the correction error is defined as ε, and its calculation formula is expressed as: , where ε k Indicates the correction error of the current iteration number; Assuming that the least squares target value of the control parameter is expressed as Δθ, the standard least squares formula is used to solve it, and the calculation formula of the least squares target value Δθ is expressed as: , where J H is the conjugate transpose of the Jacobian matrix.

[0028] The correction error ε represents the error that the calibration system currently needs to compensate for, and is used to drive the next control parameter update. Used to infer the optimal change of the control variable so that the correction error value in each iteration sequence is Therefore, the calculation formula of Δθ can be substituted into the calculation formula of the calibration reflection coefficient Г to obtain: , thereby completing the calculation update of the calibration reflection coefficient Г.

[0029] Furthermore, as a feasible implementation method, the calculation formula of the Jacobian matrix J is expressed as: , where J k Indicates the value of the Jacobian matrix at the current iteration. Its meaning is the sensitivity of the jth control parameter to the ith reflection channel at the current value. Its form is the partial derivative matrix, which is the mathematical basis of the local linear model of the system, and is expressed in θ k The value is taken at , ensuring that the Jacobian matrix is consistent with the current state, which helps the iterative convergence.

[0030] Example 3 In this embodiment, the beam coupling model includes a Riemannian manifold algorithm and a beamformer; the working process of the beam coupling model includes: The original reflection coefficient after closed-loop calibration is constructed as the complex reflection coefficient of the coordinated multi-channel, the phase amplitude space is modeled as a complex Hilbert manifold, the complex reflection coefficient is mapped to the complex Hilbert manifold using nonlinear mapping, the manifold loss function is constructed based on the complex reflection coefficient, and the Riemann gradient of the manifold loss function is calculated; The control parameters of the beamformer are obtained, and the Riemann gradient and control parameters are mapped to the tangent space of the complex Hilbert manifold for geodesic update analysis. The control parameters are updated using exponential mapping in the tangent space. The updated control parameters are input into the beamformer to output a synthetic beam. The generated data of the synthetic beam is collected and annotated as the predicted beam parameters.

[0031] The inverse distortion parameters are added to the updated control parameters and then input into the beamformer to output a synthesized beam.

[0032] Constructing the complex reflection coefficient of collaborative multi-channel means organizing or merging the reflection coefficients of all independent channels into an overall multi-channel complex vector structure as the basic input for the next step of multi-channel coupling analysis and modeling. The RIS reconfigurable intelligent surface is composed of a large number of sub-units, each of which has its own reflection parameters, including phase and amplitude, usually expressed as complex numbers. The reflection response of each calibrated channel is spliced into a complete system-level complex vector input for subsequent modeling of collaborative behavior. In the conventional implementation process, the original reflection coefficients in vector or matrix form can usually be transmitted to the coupling modeling or beamforming module through the control bus, and encapsulated in the software as a complex array in a high-level language for direct use in subsequent nonlinear mapping, manifold optimization and other processes. The complex Hilbert manifold is a mathematical structure that combines the differential manifold and the complex Hilbert space. In the RIS phased array calibration process of this embodiment, it represents the parameter space composed of N unit complex reflection coefficients after satisfying the complex analytical properties. A nonlinear mapping is used to map the complex reflection coefficients onto a complex Hilbert manifold. This means that the calibrated planar complex reflection coefficient vector is embedded into the manifold space of a complex Hilbert manifold via a nonlinear function, allowing for subsequent Riemann optimization on this manifold. The synthesized beam is typically presented as a two-dimensional or three-dimensional radiation pattern, or as a cloud of sampled points or a polar coordinate data table. The data used to generate the synthesized beam can be a set of scalars or a discrete vector representing a segment of the beam pattern. The beamformer, which can be a vector network analyzer or antenna measurement system, receives these control parameters and outputs a complete beam pattern through physical measurement or numerical simulation. The inverse distortion parameters are used to introduce a pre-inverse compensation modulation at the control parameter level to offset distortion effects such as nonlinear distortion and electromagnetic coupling interference, ensuring that the beam output is as close to the theoretical target as possible. The complete control parameters with inverse distortion correction are then fed into a beam simulation or measurement system to make the generated beam closer to the target ideal, thereby improving the control accuracy and energy efficiency of the entire system.

[0033] Furthermore, as a feasible implementation manner, it is assumed that the ideal beam parameters and the predicted beam parameters are consistent in content; the data content of the ideal beam parameters and the predicted beam parameters includes: The main lobe gain value indicates the radiation intensity in the main lobe direction corresponding to the peak value of the beam gain; The main lobe pointing error value indicates the deviation of the actual pointing direction of the main lobe from the predetermined direction; The sidelobe level value represents the ratio of the radiated power in the sidelobe direction to the peak power of the main lobe; The sidelobe pseudo-lobes value indicates the parasitic high-gain lobe value caused by the phase quantization error of the independent channel.

[0034] The mainlobe gain value is the maximum gain value in the mainlobe direction of the beam radiation pattern and is the core standard value for RIS beamforming. The mainlobe pointing error value represents the deviation angle between the actual mainlobe direction and the target direction. It is used to measure the accuracy of beamforming. The smaller the value, the better, reflecting the RIS control precision. The sidelobe level value is the ratio of the radiation intensity in directions other than the mainlobe in the beam pattern relative to the mainlobe. It is used to control stray interference, noise immunity, confidentiality, etc. Under normal circumstances, an ideal beam should have low sidelobes. The sidelobe level value is an abnormal local high-gain lobe caused by inter-channel phase quantization error or non-ideality, which may be located in an unexpected direction. It represents the amplification effect of non-ideal errors in characterizing control systems and is used to assess actual RIS accuracy loss or hardware impact.

[0035] The calculation process of the first efficiency value and the second efficiency value is set as follows: The data contents of the ideal beam parameters and the predicted beam parameters are normalized to generate a scoring function. The scoring functions of the main lobe gain value, the main lobe pointing error value, the side lobe level value, and the side lobe pseudo lobe value are labeled as the first function f1, the second function f2, the third function f3, and the fourth function f4, respectively. The first weight value m1, the second weight value m2, the third weight value m3, and the fourth weight value m4 are set accordingly. The beam efficiency function is represented by η. The beam efficiency function η is calculated as: η = m1∙f1+ m2∙f2+ m3∙f3+ m4∙f4, Among the four weight values, the first weight value m1 has the largest value; the calculation process of the first efficiency value and the second efficiency value are the same as the calculation formula of the beam efficiency function η.

[0036] Normalization processing can map the four indicators in the ideal beam parameters and the predicted beam parameters to the same dimensional interval, eliminating the influence of the differences in the dimensions and numerical spans of the indicators themselves. Each normalized indicator is then converted into a score through a mapping function, and the specific implementation of the process can be linear mapping or nonlinear. The first weight m1 is the largest, that is, the main lobe gain is the most important item in this embodiment, followed by the other related indicators. The four scoring functions and the corresponding weights are combined in a linear weighted manner to obtain a beam efficiency function η representing the overall efficiency. The larger the η value, the closer the beam performance of the predicted beam parameters is to the ideal configuration. By using normalization plus weighted scoring, the four key beam performance indicators: main lobe gain, pointing error, sidelobe level and pseudo lobe are fused into a beam efficiency function η representing the overall efficiency, and the beam efficiency function η is used to measure the quality of the ideal beam and the predicted beam, thereby providing a unified and adjustable quantitative standard for the effectiveness judgment of multi-channel collaborative calibration.

[0037] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A multi-channel collaborative control method based on RIS phased array, characterized in that: The method includes: Step S1: Dividing the array elements of the target RIS phased array into a plurality of independent channels, presetting ideal beam parameters for each independent channel, and calculating a first efficiency value representing an ideal beamforming efficiency based on the ideal beam parameters; Step S2: Mark the original reflection coefficient of each independent channel, perform closed-loop calibration on all the original reflection coefficients, activate all the independent channels after the closed-loop calibration as cooperative multi-channels, and build a beam coupling model for the cooperative multi-channels; Step S3: using the beam coupling model to predict and generate the synthetic beam of the coordinated multi-channel to represent array control, and marking the generated data of the synthetic beam as the predicted beam parameter representing the tendency of the coordinated multi-channel beam result; Step S4: Based on the predicted beam parameters, a second efficiency value representing the predicted beamforming efficiency is calculated, and the second efficiency value is compared with the first efficiency value. When the first efficiency value is lower than the second efficiency value, it is determined that the calibration is effective; when the first efficiency value is higher than the second efficiency value, it is determined that the calibration is not effective, and the process returns to step S2.

2. The multi-channel collaborative control method based on RIS phased array according to claim 1, characterized in that: The process of closed-loop calibration of the original reflection coefficient is as follows: detecting and marking the coupling strength of different independent channels, allocating calibration time slot lengths in a positively correlated manner according to the coupling strength between channels; constructing a TDM scheduling table, and using the TDM scheduling table to perform closed-loop parallel calibration of the original reflection coefficient in each independent channel according to the calibration time slot length.

3. The multi-channel collaborative control method based on RIS phased array according to claim 2, characterized in that: The contents of the TDM scheduling table include: dividing independent channels into a number of equal channel groups, measuring the electromagnetic coupling strength between every two channel groups and expressing it as a coupling strength matrix, calculating the coupling weight value of each channel group through the coupling strength matrix, and using the coupling weight value to allocate a corresponding calibration time to each channel group.

4. The multi-channel collaborative control method based on RIS phased array according to claim 2, characterized in that: The raw reflection coefficients are calibrated in a closed-loop parallel manner using an iterative least squares method. The process includes: And let the iteration number in the calibration process be expressed as k, set the calibration reflection coefficient Г, set the original reflection coefficient as the initial value of the iterative reflection coefficient; preset the target reflection coefficient Г for each independent channel t and control parameter θ, set the Jacobian matrix and represent it as J; set the error threshold for the iterative process; The calculation formula of the calibration reflection coefficient Г for each iteration is expressed as: , Among them, the θ k-1 represents the control parameter value of the previous iteration number, the θ k Indicates the control parameter value of the current iteration sequence; k Represents the calibration reflection coefficient of the current iteration number, the Г k-1 represents the calibration reflection coefficient of the previous iteration number; k-1 The Jacobian matrix representing the ordinal number of the previous iteration; When calibrating the reflection coefficient Г k and the target reflection coefficient Г t When the difference is less than the error threshold, the iteration process stops and the calibration is completed.

5. The multi-channel cooperative control method based on RIS phased array according to claim 4 is characterized in that: The correction error is defined as ε, and its calculation formula is expressed as: , where ε k Indicates the correction error of the current iteration number; Assume that the least squares target value of the control parameter is expressed as Δθ and is solved using the standard least squares formula, Then the calculation formula of the least squares target value Δθ is expressed as: , Among them J H is the conjugate transpose of the Jacobian matrix.

6. The multi-channel cooperative control method based on RIS phased array according to claim 5, characterized in that: The calculation formula of the Jacobian matrix J is expressed as: , where J k Indicates the value of the Jacobian matrix at the current iteration.

7. The multi-channel cooperative control method based on RIS phased array according to claim 1, characterized in that: The beam coupling model includes a Riemannian manifold algorithm and a beamformer; The beam coupling model working process includes: The original reflection coefficient after closed-loop calibration is constructed as the complex reflection coefficient of the coordinated multi-channel, the phase amplitude space is modeled as a complex Hilbert manifold, the complex reflection coefficient is mapped to the complex Hilbert manifold using nonlinear mapping, the manifold loss function is constructed based on the complex reflection coefficient, and the Riemann gradient of the manifold loss function is calculated; The control parameters of the beamformer are obtained, and the Riemann gradient and control parameters are mapped to the tangent space of the complex Hilbert manifold for geodesic update analysis. The control parameters are updated using exponential mapping in the tangent space. The updated control parameters are input into the beamformer to output a synthetic beam. The generated data of the synthetic beam is collected and annotated as the predicted beam parameters.

8. The multi-channel cooperative control method based on RIS phased array according to claim 7, characterized in that: The inverse distortion parameters are added to the updated control parameters and then input into the beamformer to output a synthesized beam.

9. The multi-channel cooperative control method based on RIS phased array according to claim 7 is characterized in that The ideal beam parameters and the predicted beam parameters are consistent in content; the data content of the ideal beam parameters and the predicted beam parameters includes: The main lobe gain value indicates the radiation intensity in the main lobe direction corresponding to the peak value of the beam gain; The main lobe pointing error value indicates the deviation of the actual pointing direction of the main lobe from the predetermined direction; The sidelobe level value represents the ratio of the radiated power in the sidelobe direction to the peak power of the main lobe; The sidelobe pseudo-lobes value indicates the parasitic high-gain lobe value caused by the phase quantization error of the independent channel.

10. The multi-channel cooperative control method based on RIS phased array according to claim 9, characterized in that: The calculation process of the first efficiency value and the second efficiency value is set as follows: The data contents of the ideal beam parameters and the predicted beam parameters are normalized to generate a scoring function. The scoring functions of the main lobe gain value, the main lobe pointing error value, the side lobe level value, and the side lobe pseudo lobe value are labeled as the first function f1, the second function f2, the third function f3, and the fourth function f4, respectively. The first weight value m1, the second weight value m2, the third weight value m3, and the fourth weight value m4 are set accordingly. The beam efficiency function is represented by η. The beam efficiency function η is calculated as: η = m1∙f1+ m2∙f2+ m3∙f3+ m4∙f4, Among the four weight values, the first weight value m1 has the largest value; the calculation process of the first efficiency value and the second efficiency value are the same as the calculation formula of the beam efficiency function η.

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