SER6 Pro antenna with adaptive beam forming capability

Abstract

The invention discloses an SER6 Pro antenna with adaptive beam forming capability, comprising: a generation module for generating an initial beam directional diagram based on spatial-temporal correlation analysis of multipath signals; the calculation module is used for updating beam direction parameters in real time by using a Doppler frequency shift estimation algorithm so as to adapt to Doppler frequency shift change of a high-speed mobile user; the suppression module is used for carrying out noise suppression on the beam signal in a frequency domain by adopting an adaptive filter; and the optimization module is used for dynamically adjusting the beam forming matrix and optimizing the beam direction and intensity through an iterative optimization algorithm. The method has the beneficial effects that the problem of inaccurate beam direction identification caused by excessive reflected and scattered signals in a complex multi-path propagation environment is solved; the problem of unstable beam tracking caused by significant change of Doppler frequency shift in a high-speed mobile user scene is solved; and the problem that the signal-to-noise ratio is reduced due to the strong external noise source in a high-interference environment is solved.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a SER6 Pro antenna with adaptive beamforming capability. Background Art

[0002] Adaptive beamforming (ABF) is a signal processing technology mainly used in wireless communications, radar, sonar and other fields. Its purpose is to dynamically adjust the weights of the antenna array so that the radiation pattern of the array antenna forms a specific beam shape in space, thereby achieving enhanced reception of the desired signal and effective suppression of interference signals. Since the SER6 pro antenna is built into the communication equipment, in a complex multipath propagation environment, the beam direction identification is inaccurate due to excessive reflection and scattered signals; in the high-speed mobile user scenario, the beam tracking becomes unstable due to the significant change in Doppler frequency shift; in a high-interference environment, the strong influence of external noise sources leads to a decrease in the signal-to-noise ratio, further affecting the communication quality of the system. These issues are challenges faced by this technology in practical applications, which need to be solved through further research and technical optimization. Summary of the invention

[0003] The object of the present invention is to provide an adaptive beamforming method for a SER6 Pro antenna to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions: In a first aspect, the present invention provides an adaptive beamforming method for a SER6 Pro antenna, comprising: Generate initial beam pattern based on time-space correlation analysis of multipath signals; The Doppler frequency shift estimation algorithm is used to update the beam direction parameters in real time to adapt to the Doppler frequency shift changes of high-speed mobile users; Adopt adaptive filter to suppress the noise of beam signal in frequency domain and improve signal-to-noise ratio; Through the iterative optimization algorithm, the beamforming matrix is ​​dynamically adjusted to optimize the beam direction and intensity, thereby improving the beam direction recognition and tracking accuracy.

[0005] Preferably, the method for generating an initial beam pattern based on spatiotemporal correlation analysis of multipath signals comprises: Acquire the spatiotemporal characteristic data of multipath signals; The time delay and phase difference of each path are calculated through the time and space autocorrelation function of the multipath signal; Use phase difference and delay information to construct the spatial distribution map of multipath signals; Based on the spatial distribution map, an initial beam pattern is generated to better identify and process the beam direction in a complex multipath environment.

[0006] Preferably, the step of acquiring the spatiotemporal characteristic data of the multipath signal comprises: Collect multipath signal samples received by multiple antenna array elements; Calculate the phase difference and amplitude of the signal received by each antenna array element; Based on the phase difference and amplitude information, a spatiotemporal characteristic matrix is ​​constructed; If the signal-to-noise ratio (SNR) of the multipath signal satisfies ( text{SNR} geq T_1 ), where ( T_1 ) is a preset threshold, the signal quality is considered good and subsequent processing continues; where ( text{SNR} ) represents the signal-to-noise ratio, and (T_1 ) represents the signal-to-noise ratio threshold.

[0007] Preferably, the step of constructing a spatiotemporal characteristic matrix based on the phase difference and amplitude information comprises: Extract the signal phase difference and amplitude of each antenna array element; Construct a phase difference matrix (Phi), where (Phi_{ij}) represents the phase difference between the (i)th antenna array element and the (j)th antenna array element; Construct an amplitude matrix (A), where (A_{ij}) represents the amplitude between the (i)th antenna element and the (j)th antenna element; If the maximum element ( max(Phi_{ij}) ) in the phase difference matrix ( Phi ) is greater than the threshold ( T_2 ), multipath identification is performed; where ( max(Phi_{ij}) ) represents the maximum value of the phase difference matrix, and ( T_2 ) represents the phase difference threshold.

[0008] Preferably, if the maximum element (max(Phi_{ij})) in the phase difference matrix (Phi) is greater than the threshold (T_2), the method for performing multipath identification includes: Analyze the phase difference matrix (Phi) to determine the number of multipath signals; Calculate the delay and phase difference of each path; Generate multipath identification results using path delay and phase difference information; If the number of paths ( N ) is greater than or equal to ( T_3 ), further beamforming optimization is performed; where ( N ) represents the number of multipath signals and ( T_3 ) represents the path number threshold.

[0009] Preferably, the step of generating a multipath identification result by using path delay and phase difference information comprises: Determine the direction of the main path based on the delay information; Calculate the phase difference between the main path and other paths; Generate a path map, marking the main and other paths; If the strength of the main path (P) is greater than (T_4), the main lobe width is increased; where (P) represents the strength of the main path and (T_4) represents the path strength threshold.

[0010] Preferably, the phase center of the main path is calculated; Determine the phase offset of other paths relative to the primary path; Integrate phase offset information into path pattern; If the phase offset (Deltaphi) is greater than (T_5), the sidelobe suppression coefficient is adjusted; where (Deltaphi) represents the phase offset and (T_5) represents the phase offset threshold.

[0011] Preferably, the method for generating a path pattern based on a phase difference between a main path and other paths comprises: Mark the location of the main and other paths on the directional map; Adjust the gain distribution of the main lobe and side lobes; Further optimize beam shape to reduce interference; If the angle difference ( |theta_1 theta_2| ) between the main path direction ( theta_1 ) and the secondary path direction ( theta_2 ) of the pattern is less than ( T_6 ), the sidelobe level is reduced; where ( theta_1 ) and ( theta_2 ) represent the directions of the main path and the secondary path, respectively, and ( T_6 ) represents the path angle difference threshold.

[0012] Preferably, the method for further optimizing the beam shape and reducing interference includes: Adjust the main lobe direction; Reduce side lobe levels; Control beam shape to improve directivity; If the directivity index (D) of the optimized beam pattern is greater than (T_7), the beamforming effect is further improved; where (D) represents the directivity index of the beam, and (T_7) represents the directivity index threshold.

[0013] In a second aspect, the present invention provides a SER6 Pro antenna with adaptive beamforming capability, wherein the SER6 Pro antenna has a built-in system for implementing the method described in any of the above embodiments, wherein the system comprises: A generation module generates an initial beam pattern based on the spatiotemporal correlation analysis of multipath signals; The calculation module uses the Doppler frequency shift estimation algorithm to update the beam direction parameters in real time to adapt to the Doppler frequency shift changes of high-speed mobile users; The suppression module uses an adaptive filter to suppress the noise of the beam signal in the frequency domain to improve the signal-to-noise ratio; The optimization module dynamically adjusts the beamforming matrix through an iterative optimization algorithm, optimizes the beam direction and intensity, and improves the beam direction recognition and tracking accuracy.

[0014] The disclosed embodiment provides an adaptive beamforming method, including: generating an initial beam pattern based on the spatiotemporal correlation analysis of multipath signals; updating beam direction parameters in real time using a Doppler frequency shift estimation algorithm to adapt to the Doppler frequency shift changes of high-speed mobile users; using an adaptive filter to suppress noise on beam signals in the frequency domain to improve the signal-to-noise ratio; dynamically adjusting the beamforming matrix through an iterative optimization algorithm to optimize the beam direction and intensity and improve the beam direction identification and tracking accuracy. The solution of the disclosed embodiment can solve the following problems: 1. In complex multipath propagation environments, excessive reflection and scattering signals lead to inaccurate beam direction identification.

[0015] 2. In high-speed mobile user scenarios, beam tracking instability occurs due to significant changes in Doppler frequency shift.

[0016] 3. The problem of decreased signal-to-noise ratio due to strong external noise sources in high-interference environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is an overall flow chart of the method of the present invention; Figure 2 A diagram of the steps for generating an initial beam direction for the present invention; Figure 3 A specific flow chart of obtaining the spatiotemporal characteristic data of multipath signals according to the present invention; Figure 4 Constructing a space-time matrix flow chart for the present invention; Figure 5 This is a flow chart of phase difference matrix analysis of the present invention; Figure 6 A flow chart of the present invention for generating a multi-path identification result using path delay and phase difference information; Figure 7Flowchart for calculating the phase difference between the main path and other paths; Figure 8 A schematic diagram of system modules provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0019] The following describes the embodiments of the present disclosure through specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all of the embodiments. The present disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present disclosure.

[0020] It should be noted that various aspects of the embodiments within the scope of the appended claims are described below. It should be apparent that the aspects described herein may be embodied in a wide variety of forms, and any specific structure and / or function described herein is merely illustrative. Based on the present disclosure, it should be understood by those skilled in the art that an aspect described herein may be implemented independently of any other aspect, and two or more of these aspects may be combined in various ways. For example, any number of aspects described herein may be used to implement the device and / or practice the method. In addition, other structures and / or functionalities other than one or more of the aspects described herein may be used to implement this device and / or practice this method.

[0021] It should also be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present disclosure. The drawings only show components related to the present disclosure rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0022] Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects described may be practiced without these specific details.

[0023] Next, refer to the attached Figure 1, describes how an adaptive beamforming method of the present invention solves the problems of inaccurate beam direction identification, unstable beam tracking, and reduced signal-to-noise ratio in complex environments through a series of steps. The method includes the following steps: S100: Generate an initial beam pattern based on the time-space correlation analysis of multipath signals. This process involves estimating the delay and angle of each propagation path through multiple signals received by the antenna array, and then constructing an initial pattern. Specifically, in one embodiment, assuming that an antenna array includes multiple antenna units, the system will first collect the signals received by all units, and use the time and space distribution characteristics of these signals, such as the cross-correlation function or the spatial covariance matrix, to derive the direction of the main signal source. This can effectively reduce the impact of multipath effects caused by reflection and scattering, and improve the initial estimation accuracy of the beam direction.

[0024] S200: Use the Doppler shift estimation algorithm to update the beam direction parameters in real time to adapt to the Doppler shift changes of high-speed mobile users. The goal of this stage is to respond immediately to the rapidly changing user positions. For example, when a high-speed moving vehicle passes the edge of a cell, its speed will produce a significant Doppler effect, changing the signal frequency. The present invention extracts the Doppler shift information by performing a frequency domain transformation on the signal and incorporates it into the feedback loop of the beamforming algorithm. This ensures that the beam direction is continuously optimized as the user moves, and the tracking stability can be maintained even if the user's speed fluctuates significantly.

[0025] S300: Adaptive filters are used to suppress noise in the frequency domain of beam signals to improve the signal-to-noise ratio. The adaptive filter can adjust the filter parameters according to changes in the surrounding environment, thereby filtering out unnecessary interference signals and enhancing the clarity of the target signal. In one embodiment, the system may use a minimum mean square error (LMS) algorithm or other advanced adaptive filtering techniques to automatically adjust the filter coefficients by continuously monitoring the difference between the input and output signals. When an increase in external interference is detected, the adaptive filter will respond quickly to minimize the interference level, thereby maintaining good communication quality.

[0026] S400: Through the iterative optimization algorithm, the beamforming matrix is ​​dynamically adjusted to optimize the beam direction and intensity, and the beam direction identification and tracking accuracy are improved. In this step, the system not only considers the information collected in the previous stages, but also comprehensively weighs factors such as the beam directionality, gain, and sidelobe level in order to achieve optimal performance. Specifically, in each iteration, the algorithm will evaluate the actual effect of the current beamforming, and then adjust the antenna weighting vector according to the preset cost function, gradually approaching the optimal state. For example, in a specific application example, assuming that a base station in a certain cell needs to serve multiple high-speed mobile users at the same time, and faces severe multipath interference caused by dense buildings, by continuously iterating and optimizing the beamforming process, it is ultimately possible to accurately lock the position of each user, even in the face of a complex dynamic environment without losing its stability and efficiency.

[0027] In summary, this adaptive beamforming method effectively solves the communication challenges under complex multipath, high-speed mobile users and high interference conditions through the above four key steps. Through sophisticated technical means, this method not only enhances the robustness and flexibility of the system, but also greatly improves the quality of user communication experience.

[0028] In one embodiment of the present invention, please refer to the attached Figure 2 , the implementation of step S100 includes: S110: Acquire the time and space characteristic data of the multipath signal. This step refers to receiving signals from different propagation paths through the antenna array and recording the time and space characteristics of these signals, such as the time of signal arrival, signal strength, phase, etc. These data can be acquired in real time through a high-speed sampling system.

[0029] S120: Calculate the time delay and phase difference of each path through the time-space autocorrelation function of the multipath signal. The time-space autocorrelation function describes the relationship between the signals received at different time and space positions. The time delay τ and phase difference Δφ can be calculated using the following formula: [ R(tau, Deltaphi) = E[x(t) x^*(t + tau)] e^{jDeltaphi} ] Where (x(t)) is the received signal, (x^*(t + tau)) is the complex conjugate signal with a delay of τ, and (R(tau, Deltaphi)) is the autocorrelation function. By maximizing (R(tau, Deltaphi)), the delay τ and the phase difference Δφ can be estimated. The value of τ ranges from 0 to the maximum expected delay, and the optimal value depends on the propagation characteristics of the channel; the value of Δφ ranges from (-pi) to (pi), and the optimal value also depends on the multipath propagation of the channel.

[0030] S130: Use the phase difference and delay information to construct a spatial distribution map of the multipath signal. This step is to draw the distribution of each path in space based on the delay and phase difference information obtained in the previous step. The spatial distribution map can help identify the main multipath propagation direction. Specifically, the position of each path in two-dimensional or three-dimensional space can be determined based on the delay and phase difference of each path, and the corresponding distribution map can be drawn.

[0031] S140: Based on the spatial distribution diagram, an initial beam pattern is generated to better identify and process the beam direction in a complex multipath environment. The process of generating the initial beam pattern is to adjust the direction and gain of the beam according to the spatial distribution of the multipath signal so that it can be more effectively focused on the main multipath direction. The beam pattern can be achieved through an optimization algorithm, such as the minimum variance distortionless response (MVDR) algorithm. In this process, the main lobe direction of the beam points to the main signal source direction, and the side lobe direction is reduced as much as possible to reduce the impact of interference signals.

[0032] For example, in one embodiment, consider a uniform linear array containing 8 antenna elements, which receives multiple multipath signals. First, the signal data received by each antenna is obtained through a high-speed sampling system. Then, the time-space autocorrelation function of these signals is calculated to estimate the delay and phase difference of each path. Using this information, a distribution diagram of the multipath signal in two-dimensional space is drawn. Finally, the MVDR algorithm is used to optimize the beam direction so that the main lobe direction of the beam is aligned with the main signal source, and the side lobe direction suppresses interference from other directions. In this way, the beam direction can be more accurately identified and processed in a complex multipath environment, thereby improving the performance of the communication system.

[0033] In a specific embodiment of the present invention, please refer to the attached Figure 3 Step S110 involves four main steps: collecting signal samples, calculating signal phase difference and amplitude, constructing a spatiotemporal characteristic matrix, and evaluating the signal-to-noise ratio. Specifically, it includes: S111: Receive multipath signal samples from multiple antenna array elements. In this step, the system uses the deployed antenna array to capture multipath signals from different directions. Through these antenna array elements, the signal changes at different times and locations can be obtained. The collection of signal samples provides basic data for subsequent analysis.

[0034] S112: Calculate the phase difference and amplitude of the signal received by each antenna array element. In this step, the phase and amplitude of each signal sample are calculated based on the geometric relationship between the antenna array elements and the signal propagation model. These parameters reflect the transmission characteristics of the signal on different paths and are crucial for understanding the signal propagation environment.

[0035] S113: Based on the calculated phase difference and amplitude information, a time-space characteristic matrix is ​​constructed. This matrix summarizes the time domain and spatial distribution information of the received signals of all antenna array elements. The elements of this matrix usually include the signal amplitude and phase of each array element at a specific time point, providing a mathematical representation for subsequent signal processing.

[0036] S114: Evaluate the signal-to-noise ratio (SNR) of the multipath signal to determine whether it meets the preset threshold (T_1). If (text{SNR} geq T_1), the signal quality is good and can be further processed. The signal-to-noise ratio (text{SNR}) is used to measure the ratio of signal strength to background noise, and is usually defined as the ratio of signal power (P_{text{signal}}) to noise power (P_{text{noise}}), that is, (text{SNR} = frac{P_{text{signal}}}{P_{text{noise}}}). The threshold (T_1) is generally set between 10 and 20 dB, and the specific value depends on the application scenario to ensure signal quality and processing effect.

[0037] For example, in an adaptive beamforming application scenario, assume that an antenna array with 8 elements is used to receive multipath signals. Signal samples are collected once a second. After calculation, it is found that the phase and amplitude of the signal received by each element at a certain moment are ([1, 0.9 + 0.2i, 0.7 - 0.3i, 0.8 + 0.1i, 0.6 - 0.2i,0.9 + 0.3i, 0.5 - 0.4i, 0.8]) and ([1, 0.92, 0.65, 0.81, 0.67, 0.91, 0.52,0.83]). Based on these data, a spatiotemporal characteristic matrix is ​​constructed, and the signal-to-noise ratio is calculated to be 15 dB. Because the preset signal-to-noise ratio threshold (T_1) is 10 dB, the signal is considered to be of good quality and will continue to be processed.

[0038] Through the above steps, the temporal and spatial characteristic data of the multipath signal can be effectively acquired and processed, thereby improving the performance of the adaptive beamforming system.

[0039] In a specific embodiment of the present invention, please refer to the attached Figure 4 , further, step S113 includes: S1131: Extract the signal phase difference and amplitude of each antenna array element. This step aims to obtain the signal phase difference and amplitude information of each antenna array element from the received signal, which is the basis for the subsequent matrix construction. Specifically, assuming that the number of antenna array elements is (N), each antenna array element (i) will correspond to a phase (phi_i) and amplitude (a_i).

[0040] S1132: Construct a phase difference matrix (Phi), where (Phi_{ij}) represents the phase difference between the (i)th antenna element and the (j)th antenna element. This matrix reflects the relative phase relationship between the antenna elements and is very important for multipath propagation analysis. The size of the matrix (Phi) is (N times N), (Phi_{ij} =phi_i - phi_j), and the phases (phi_i) and (phi_j) usually range from (-pi) to (pi). By calculating the phase difference of each pair of antenna elements, a complete phase difference matrix is ​​formed.

[0041] S1133: Construct an amplitude matrix (A), where (A_{ij}) represents the amplitude between the (i)th antenna element and the (j)th antenna element. This matrix reflects the relative amplitude relationship between the antenna elements and helps to evaluate the change in signal strength. The size of the matrix (A) is also (N times N), (A_{ij} = a_i / a_j), and the amplitudes (a_i) and (a_j) are usually positive real numbers. Similarly, by calculating the amplitude ratio of each pair of antenna elements, a complete amplitude matrix is ​​formed.

[0042] S1134: If the maximum element (max(Phi_{ij})) in the phase difference matrix (Phi) is greater than the threshold (T_2), multipath identification is performed. Here, (max(Phi_{ij})) represents the maximum value in the phase difference matrix (Phi), and (T_2) represents a pre-set phase difference threshold, whose typical value range is usually between (0.1pi) and (0.5pi), and the optimal value depends on the specific application scenario. When (max(Phi_{ij}) > T_2), it indicates that the signal has a significant multipath effect, and further processing is required to improve the signal quality and anti-interference capability.

[0043] For example, in a specific embodiment, assume that there is an adaptive beamforming system with a set of 8 antenna elements. By extracting the signal phase difference and amplitude information of each antenna element, the corresponding phase difference matrix (Phi) and amplitude matrix (A) can be constructed. If it is detected that (max(Phi_{ij})) is greater than the set threshold (T_2), the system will further perform multipath identification and processing to optimize the beamforming effect and improve the communication quality. This method can effectively deal with multipath interference in complex environments and ensure the stability of signal transmission.

[0044] For further information, please see the attached Figure 5 , step S1134 includes the following four main steps: S11311: Analyze the phase difference matrix (Phi) to determine the number of multipath signals. In this step, by detecting the peak values ​​and their distribution in the phase difference matrix (Phi), the number of multipath signals present can be estimated. Specifically, the elements of the phase difference matrix (Phi) represent the phase differences between the received signals. By statistically analyzing these phase differences, the number of paths with significant differences is identified. For example, the presence of multiple signals can be determined by finding local maxima.

[0045] S11312: Calculate the delay and phase difference of each path. After determining the number of multipath signals, the next step is to accurately calculate the delay and phase difference of each path. The delay can be obtained by measuring the time difference of the signal arriving at different antennas, and the phase difference is obtained by calculating the corresponding elements in the phase difference matrix. This step needs to ensure high accuracy to improve the performance of subsequent beamforming. Specifically, if the delay of a path is (tau), it can be calculated by the formula (tau = c / (2 pi f)), where (c) is the speed of light and (f) is the carrier frequency.

[0046] S11313: Generate multipath identification results using path delay and phase difference information. In this step, the calculated delay and phase difference information is summarized to generate a detailed description of the multipath signal. This information will be used for subsequent beamforming optimization. For example, in a multipath environment, if three paths are identified, each with a delay (tau_1, tau_2, tau_3) and a phase difference (phi_1, phi_2, \phi_3), these data can be recorded in the system for subsequent processing.

[0047] S11314: If the number of paths (N) is greater than or equal to (T_3), further beamforming optimization is performed. Here, (N) represents the number of multipath signals identified, and (T_3) is a preset threshold for the number of paths. When the number of multipaths exceeds or equals this threshold, it indicates that the channel environment is complex and more complex beamforming optimization is required to improve communication quality and anti-interference capabilities. Specifically, assuming (T_3 = 3), if four paths are identified, additional optimization algorithms, such as adaptive algorithms, are initiated to adjust the beam direction and amplitude to maximize the signal-to-noise ratio and reduce the bit error rate.

[0048] In summary, through the above steps, this method can effectively identify multipath signals and optimize them according to their characteristics, thereby improving the performance of the beamforming system. For example, when it is detected that the maximum element in the phase difference matrix exceeds the threshold (T_2), the multipath identification process is started, which can significantly improve the communication effect in a multipath environment.

[0049] For further information, please see the attached Figure 6 , step S11313 includes: S101: Determine the direction of the main path based on the delay information. In a communication system, different delays are generated when a signal is transmitted through different paths. By analyzing these delays, the direction of the main path can be determined. The key to this step is to accurately measure the delays of different paths and use these data to locate the main path. For example, in one embodiment, a multi-antenna receiver can be used to capture the delays of multiple paths, and the direction of the main path can be determined by calculating the peak position of the delay distribution.

[0050] S102: Calculate the phase difference between the main path and other paths. The phase difference refers to the phase difference of the signal between the main path and other paths. This step helps distinguish the main path from other paths by calculating the phase difference of each path, providing key data for subsequent multipath identification. For example, specifically, assuming that the phase of the main path is (phi_0), the phase of the other paths is (phi_1, phi_2, dots, phi_n), and the phase difference is (Delta phi_i = phi_i- phi_0), where (i) is the index of the other path.

[0051] S103: Generate a path pattern, mark the main path and other paths. Generate a path pattern based on the data obtained in the first two steps, and intuitively display the location of each path. This pattern can not only help identify which paths are the most important, but also be used to optimize the beamforming strategy. For example, in a specific scenario, the path pattern can be represented by a polar coordinate diagram, where the radial distance represents the path strength and the angle represents the path direction. In this diagram, the main path and other paths are marked with different colors or symbols, so that multi-path identification is clear at a glance.

[0052] S104: If the strength of the main path (P) is greater than (T_4), increase the main lobe width. In this step, the parameter (P) represents the strength of the main path, which is usually a dimensionless relative value ranging from 0 to 1 or a greater value, depending on the definition of the specific system. Parameter (T_4) is a preset path strength threshold, and the range is also determined according to the specific situation. When the strength of the main path (P) is greater than the threshold (T_4), it indicates that the main path signal is very strong and the main lobe width needs to be appropriately increased to enhance the anti-interference capability. The meaning of the formula is to improve the robustness of the system in a high signal-to-noise ratio environment. When the main path strength is large enough, the main lobe width is expanded to cover more possible interference sources. For example, in one embodiment, if (T_4) is set to 0.8 and the main path strength is 0.9, the main lobe width needs to be increased so that the main lobe can better cover the potential strong reflection path.

[0053] The above steps not only improve the accuracy of multipath identification but also optimize the effect of adaptive beamforming by comprehensively utilizing path delay and phase difference information.

[0054] For further information, please see the attached Figure 7 , step S102 includes the following steps: S1021: Calculate the phase center of the main path. In this step, determine the reference point of the main path in space, that is, the phase center. The main path is usually the strongest or most direct signal path, and the phase center can be the main focus point when the array antenna receives the signal. Determining the phase center helps to calculate the relative phase offset later. For example, in one embodiment, the time difference between the signal reaching the phase center is calculated by the propagation time of the signal and the antenna array element spacing, thereby determining the phase center.

[0055] S1022: Determine the phase offset of other paths relative to the main path. In this step, the phase difference between the secondary path and the main path, that is, the phase offset (Deltaphi), is calculated. Specifically, assuming that the phase of the main path is (phi_{main}) and the phase of the secondary path is (phi_{secondary}), then (Deltaphi = phi_{secondary}- phi_{main}). In the calculation process, considering the propagation characteristics of electromagnetic waves in the air, the wavelength (lambda) and the path difference (d) are usually used to determine the phase offset, that is, (Deltaphi = frac{2pi}{lambda} d). In this formula, (lambda) represents the wavelength of the electromagnetic wave, ranging from a few millimeters to a few meters, and the optimal value depends on the operating frequency; (d) is the physical distance between the main path and the other paths, in meters, and the specific value depends on the signal environment.

[0056] S1023: Integrate the phase offset information into the path pattern. This step applies the calculated phase offset to the pattern of the antenna array to form a more accurate beamforming pattern. Specifically, the pattern adjusts the direction and width of the beam by controlling the weighting coefficients of each antenna element to optimize the signal transmission effect. For example, the phase offset can be applied to the weight matrix through a matrix calculation method to update the phase and amplitude of the antenna array element.

[0057] S1024: If the phase offset (Deltaphi) is greater than (T_5), adjust the sidelobe suppression coefficient. This step is to deal with the sidelobe problem caused by excessive phase offset. (T_5) is a preset phase offset threshold used to determine whether the phase offset is too large. When (Deltaphi) exceeds (T_5), increase the sidelobe suppression coefficient to reduce sidelobe interference. Specifically, the output performance of the antenna array can be optimized by adjusting the modulus and phase of the weighting vector. For example, the sidelobe suppression coefficient (S) can be calculated by the following formula: (S = 1 + k|Deltaphi - T_5|), where (k) is an adjustment factor, which usually ranges from 0 to 1, and the optimal value depends on the specific application scenario.

[0058] Through the above steps, the phase relationship between different paths can be effectively managed in the adaptive beamforming system to improve signal quality and transmission efficiency.

[0059] In a specific embodiment of the present invention, step S102 to step S103 also include the following steps: First, mark the positions of the main path and other paths in the beam pattern. In this step, the system needs to determine the specific directions of the main path and the secondary path, and mark these directions on the beam pattern. Specifically, the main path is the main way for signal transmission, and the secondary path may come from different reflections or scattering. For example, in a wireless communication system, the main signal direction received by the base station may be the direct path where the user equipment is located, and other directions may be multipath reflections caused by buildings, trees, etc.

[0060] Secondly, adjust the gain distribution of the main lobe and side lobes. The main lobe refers to the main radiation direction of the antenna array, while the side lobes are other radiation directions except the main lobe. The goal of this step is to increase the gain of the main lobe while reducing the gain of the side lobes to enhance signal reception on the main path and reduce interference. In one embodiment, the gain distribution can be optimized by adjusting the weights and phases of each antenna unit. Specifically, an adaptive algorithm (such as the least mean square algorithm) can be used to update the weights of the antenna units in real time so that the main lobe is aligned with the main path while minimizing the side lobes in other directions.

[0061] Further optimize the beam shape to reduce interference. In this phase, the goal is to further refine the shape of the beam so that it is more accurately aligned with the primary path and further reduce unwanted interference. This can be achieved by dynamically adjusting the weight distribution and phase distribution of the antenna array. For example, a machine learning algorithm can be used to train a model that automatically adjusts the configuration of the antenna array to achieve the optimal beam shape based on the actual measured phase and strength data.

[0062] If the angle difference (|theta_1 - theta_2|) between the main path direction (theta_1) and the secondary path direction (theta_2) of the directional pattern is less than (T_6), the sidelobe level is reduced. Here, (theta_1) represents the direction of the main path, (theta_2) represents the direction of the secondary path, and (T_6) is a preset angle difference threshold. The main purpose of this condition check is to increase the system's resolution when the angle difference between the two paths is very small, and to reduce interference to the main path by reducing the sidelobe level. Specifically, (T_6) can be set in the range of 0 to 10 degrees in practical applications, and the optimal value may depend on the specific communication environment and requirements. The formula (|theta_1 - theta_2| < T_6) is used to determine whether the directions of the two paths are too close, so as to determine whether additional measures are needed to optimize the beam pattern. This ensures that the system can still effectively distinguish between the main signal and the interference signal in a complex environment.

[0063] Next, the method of further optimizing the beam shape and reducing interference of the present invention is described. The method includes the following steps: adjusting the main lobe direction; reducing the side lobe level; controlling the beam shape and improving the directivity; if the directivity index (D) of the optimized beam pattern is greater than (T_7), the beam forming effect is further improved.

[0064] In the first step, adjusting the main lobe direction means adjusting the phase or power distribution of the antenna array so that the main energy of the beam is concentrated in the desired target direction, thereby improving the quality of the target signal. The best direction can be selected from multiple candidate directions through an optimization algorithm such as a genetic algorithm or a particle swarm optimization algorithm. For example, in one embodiment, the phase can be dynamically adjusted to achieve the best main lobe direction by monitoring the received signal strength in real time and feeding it back to the optimization algorithm.

[0065] In the second step, reducing the side lobe level means reducing the interference to other users or systems by reducing the energy radiation in the non-target direction. Excessive side lobe level will increase the interference between systems and affect the communication quality. This can be achieved by optimizing the weight distribution or using spatial filtering technology. Specifically, constraints can be introduced in the optimization process to limit the side lobe level to not exceed a predetermined threshold, thereby achieving the effect of reducing the side lobe level.

[0066] The third step involves controlling the beam shape to improve directivity. This step mainly uses optimization algorithms to accurately control the shape of the beam so that it is more focused in the target direction while reducing radiation in other directions. This not only improves the transmission efficiency of the signal, but also further reduces interference. For example, the beam shape can be controlled by designing an appropriate weighting function to make the main lobe narrower and more directive.

[0067] Finally, when the directivity index (D) of the optimized beam pattern is greater than (T_7), the beamforming effect is further improved. The directivity index (D) indicates the energy concentration of the main radiation direction of the beam compared with other directions in its surrounding environment. It is usually expressed in decibels (dB) ranging from 0 to infinity. The larger the value, the stronger the directivity. The threshold (T_7) is a preset standard to determine whether further optimization is needed. For example, if (T_7) is set to 20 dB, when (D) is greater than 20 dB, it means that the directivity of the beam is already quite good, but there is still room for improvement. At this time, the beam shape can be further optimized through more refined algorithm adjustments or additional constraints to achieve better communication effects. Such a design can ensure high-quality communication performance in different environments.

[0068] Next, the method of controlling the beam shape and improving the directivity of the present invention is described. The method achieves fine control of the beam shape through multiple steps, thereby improving the directivity of the beam. The specific steps include: adjusting the weighting factor of beamforming; optimizing the weight distribution of beamforming; ensuring the stability and reliability of beamforming; and determining the power efficiency of beamforming according to specific conditions.

[0069] Adjusting the weighting factor of beamforming means assigning different weights to the output signals of different antenna elements or array elements, thereby changing the direction and shape of the synthesized beam. Through this dynamic adjustment, the beam pointing can be precisely controlled to make it more focused on the target area and reduce energy leakage. The weighting factor usually takes a value between -1 and 1, and the optimal value depends on the specific application scenario and the location of the target signal.

[0070] Optimizing the weight distribution of beamforming involves calculating and adjusting the complex weights of each antenna element to maximize the gain of the beam in the desired direction while minimizing interference in undesired directions. This step usually uses an optimization algorithm, such as gradient descent or genetic algorithm, to obtain the optimal weight combination through repeated iterations.

[0071] Ensuring the stability and reliability of beamforming is to ensure that the beamforming system can work normally under different environmental conditions and is not affected by channel changes. This requires the introduction of robust design, such as increasing redundancy or adopting adaptive feedback mechanisms, to ensure that the system can maintain stable performance even when the channel changes.

[0072] If the adjusted beamforming matrix ( W ) satisfies the condition ( text{Trace}(W^H R_b W) leqP_{text{th}} ), the power efficiency of beamforming is ensured. In this formula, ( W ) represents the beamforming matrix, ( R_b ) represents the channel covariance matrix, ( text{Trace}(X) ) represents the trace of the matrix ( X ), and ( P_{text{th}} ) represents the power threshold of beamforming. The specific meanings of these parameters are as follows: ( W ) is a complex matrix representing the weight distribution of each antenna array element; ( R_b ) reflects the statistical correlation of the channel between different array elements; ( text{Trace}(X) ) calculates the sum of the diagonal elements of the matrix ( X ); ( P_{text{th}} ) is a preset power threshold used to control the total output power of the system. This formula means that by ensuring that the power consumption of the beamforming matrix does not exceed the preset threshold, the high power efficiency of beamforming is ensured.

[0073] For example, in one embodiment, assume that there is a phased array system consisting of 16 antenna elements, and a high-gain beam needs to be formed at a specific angle to cover a specific target area. The system gradually optimizes the beam shape by adjusting the weighting factor of each element to maximize the maximum gain in the target area while ensuring that background noise and interference in other directions are minimized. Specifically, the weight distribution is continuously updated using an adaptive algorithm, and after each iteration, it is verified whether the beamforming matrix satisfies ( text{Trace}(W^H R_b W) leq P_{text{th}} ), thereby ensuring that the system operates stably with high efficiency.

[0074] In addition, based on the method provided in the above embodiment, the present invention also provides a SER6 Pro antenna with adaptive beamforming capability, see the attached Figure 8 The antenna is built with a system capable of implementing the method provided in any of the above embodiments, the system comprising: A generating module 100 generates an initial beam pattern based on a spatiotemporal correlation analysis of multipath signals; The calculation module 200 uses a Doppler frequency shift estimation algorithm to update the beam direction parameters in real time to adapt to the Doppler frequency shift changes of high-speed mobile users; The suppression module 300 uses an adaptive filter to suppress the noise of the beam signal in the frequency domain to improve the signal-to-noise ratio; The optimization module 400 dynamically adjusts the beam forming matrix through an iterative optimization algorithm, optimizes the beam direction and intensity, and improves the beam direction recognition and tracking accuracy.

[0075] The system works like this: 1. Generate initial beam pattern: First, by analyzing the time-space correlation of multipath signals, the system can accurately identify the main signal propagation path and generate an initial beam pattern. This process utilizes the correlation information of the signal at different time and space positions and can effectively handle complex multipath environments. In this way, the system can improve the accuracy of beam direction recognition in the case of a large number of reflected and scattered signals, solving the problem of inaccurate beam direction recognition in complex multipath propagation environments.

[0076] 2. Real-time update of beam direction parameters: Next, the system uses the Doppler shift estimation algorithm to monitor and update the beam direction parameters in real time. In the scenario of high-speed mobile users, the Doppler shift effect is significantly enhanced due to the increase in mobile speed, causing the beam direction to change frequently. By continuously updating the beam direction parameters, the system can quickly adapt to these changes and ensure that the beam direction is always accurately aimed at the target user, thereby maintaining stable beam tracking performance and solving the problem of unstable beam tracking in the scenario of high-speed mobile users.

[0077] 3. Noise suppression and signal-to-noise ratio improvement: In order to improve the system's anti-interference ability in high-interference environments, the system also introduces an adaptive filter. The filter performs noise suppression on the beam signal in the frequency domain, effectively removing background noise and other interference signals. This adaptive noise suppression technology not only improves the system's signal-to-noise ratio, but also enhances the quality of the received signal, making beamforming more stable and reliable. By suppressing noise in the frequency domain, the problem of reduced signal-to-noise ratio caused by excessive external noise sources in high-interference environments is solved.

[0078] 4. Dynamically adjust the beamforming matrix: Finally, the system dynamically adjusts the beamforming matrix through an iterative optimization algorithm to further optimize the beam direction and strength. This process is a closed-loop feedback mechanism that enables the beam to focus on the target more accurately and optimize the signal strength through continuous evaluation and adjustment. Such dynamic adjustment can not only improve the direction recognition and tracking accuracy of the beam, but also always maintain the optimal beam shape in a changing environment, thereby improving the performance of the entire system.

[0079] In summary, the present invention effectively solves the technical challenges brought by complex multipath environments, high-speed mobile users and high-interference environments through the above four key modules, and provides a highly reliable and efficient adaptive beamforming method.

[0080] The above is a preferred embodiment of the present application. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. An adaptive beamforming method for a SER6 Pro antenna, characterized in that: include: Generate initial beam pattern based on time-space correlation analysis of multipath signals; The Doppler frequency shift estimation algorithm is used to update the beam direction parameters in real time to adapt to the Doppler frequency shift changes of high-speed mobile users; Adopt adaptive filter to suppress the noise of beam signal in frequency domain and improve signal-to-noise ratio; Through the iterative optimization algorithm, the beamforming matrix is ​​dynamically adjusted to optimize the beam direction and intensity, thereby improving the beam direction recognition and tracking accuracy.

2. The adaptive beamforming method of the SER6 Pro antenna according to claim 1, characterized in that: The method for generating an initial beam pattern based on time-space correlation analysis of multipath signals includes: Acquire the spatiotemporal characteristic data of multipath signals; The time delay and phase difference of each path are calculated through the time and space autocorrelation function of the multipath signal; Use phase difference and delay information to construct the spatial distribution map of multipath signals; Based on the spatial distribution map, an initial beam pattern is generated to better identify and process the beam direction in a complex multipath environment.

3. The adaptive beamforming method of the SER6 Pro antenna according to claim 2, characterized in that: The step of acquiring the spatiotemporal characteristic data of the multipath signal comprises: Collect multipath signal samples received by multiple antenna array elements; Calculate the phase difference and amplitude of the signal received by each antenna array element; Based on the phase difference and amplitude information, a spatiotemporal characteristic matrix is ​​constructed; If the signal-to-noise ratio (SNR) of the multipath signal satisfies ( text{SNR} geq T_1 ), where ( T_1 ) is a preset threshold, the signal quality is considered good and subsequent processing continues; where ( text{SNR} ) represents the signal-to-noise ratio, and ( T_1 ) represents the signal-to-noise ratio threshold.

4. The adaptive beamforming method of the SER6 Pro antenna according to claim 3, characterized in that: The step of constructing a spatiotemporal characteristic matrix based on phase difference and amplitude information includes: Extract the signal phase difference and amplitude of each antenna array element; Construct a phase difference matrix (Phi), where (Phi_{ij}) represents the phase difference between the (i)th antenna element and the (j)th antenna element; Construct an amplitude matrix (A), where (A_{ij}) represents the amplitude between the (i)th antenna element and the (j)th antenna element; If the maximum element ( max(Phi_{ij}) ) in the phase difference matrix ( Phi ) is greater than the threshold ( T_2 ), multipath identification is performed; where ( max(Phi_{ij}) ) represents the maximum value of the phase difference matrix, and ( T_2 ) represents the phase difference threshold.

5. The adaptive beamforming method of the SER6 Pro antenna according to claim 4, characterized in that: If the maximum element (max(Phi_{ij})) in the phase difference matrix (Phi) is greater than the threshold (T_2), multipath identification is performed, including: Analyze the phase difference matrix (Phi) to determine the number of multipath signals; Calculate the delay and phase difference of each path; Generate multipath identification results using path delay and phase difference information; If the number of paths ( N ) is greater than or equal to ( T_3 ), further beamforming optimization is performed; where ( N ) represents the number of multipath signals and ( T_3 ) represents the path number threshold.

6. The adaptive beamforming method of the SER6 Pro antenna according to claim 5, characterized in that: The method of generating a multipath identification result by using path delay and phase difference information includes: Determine the direction of the main path based on the delay information; Calculate the phase difference between the main path and other paths; Generate a path map, marking the main and other paths; If the strength of the main path (P) is greater than (T_4), the main lobe width is increased; where (P) represents the strength of the main path and (T_4) represents the path strength threshold.

7. The adaptive beamforming method of the SER6 Pro antenna according to claim 6, characterized in that: The calculating of the phase difference between the main path and other paths comprises: Calculate the phase center of the main path; Determine the phase offset of other paths relative to the primary path; Integrate phase offset information into path pattern; If the phase offset (Deltaphi) is greater than (T_5), the sidelobe suppression coefficient is adjusted; where (Deltaphi) represents the phase offset and (T_5) represents the phase offset threshold.

8. The adaptive beamforming method of a SER6 Pro antenna according to claim 1, characterized in that: Based on the phase difference between the main path and other paths, a path pattern is generated, including: Mark the location of the main and other paths on the directional map; Adjust the gain distribution of the main lobe and side lobes; Further optimize beam shape to reduce interference; If the angle difference ( |theta_1 theta_2| ) between the main path direction ( theta_1 ) and the secondary path direction ( theta_2 ) of the pattern is less than ( T_6 ), the sidelobe level is reduced; where ( theta_1 ) and ( theta_2 ) represent the directions of the main path and the secondary path, respectively, and ( T_6 ) represents the path angle difference threshold.

9. The adaptive beamforming method of the SER6 Pro antenna according to claim 8, characterized in that: The method for further optimizing the beam shape and reducing interference includes: Adjust the main lobe direction; Reduce side lobe levels; Control beam shape to improve directivity; If the directivity index (D) of the optimized beam pattern is greater than (T_7), the beamforming effect is further improved; where (D) represents the directivity index of the beam, and (T_7) represents the directivity index threshold.

10. A SER6 Pro antenna with adaptive beamforming capability, the SER6 Pro antenna having a built-in system for implementing any of the methods of claims 1 to 9, characterized in that: The system comprises: A generation module generates an initial beam pattern based on the spatiotemporal correlation analysis of multipath signals; The calculation module uses the Doppler frequency shift estimation algorithm to update the beam direction parameters in real time to adapt to the Doppler frequency shift changes of high-speed mobile users; The suppression module uses an adaptive filter to suppress the noise of the beam signal in the frequency domain to improve the signal-to-noise ratio; The optimization module dynamically adjusts the beamforming matrix through an iterative optimization algorithm, optimizes the beam direction and intensity, and improves the beam direction recognition and tracking accuracy.

Images