An adaptive beamforming method, system and device

Abstract

The present invention discloses an adaptive beamforming method, system and device, comprising the following steps: establishing a vector signal model according to a received vector signal; iteratively updating weighting coefficients through an affine projection algorithm with a variable step length to determine an optimal weighting coefficient; wherein the weighting coefficients are updated using an adjusted step length, and the step length is dynamically adjusted based on the ratio of signal error power to received signal power; and forming a radiation pattern of an antenna array using the optimal weighting coefficient. The present invention has the advantages of fast convergence speed, good robustness and automatic parameter adjustment.

Description

Technical Field

[0001] The present invention relates to the field of wireless communications, and in particular to an adaptive beamforming method, system and device. Background Art

[0002] Beamforming technology is a key signal processing technology in fields such as wireless communications and radar systems. It adjusts the amplitude and phase of each antenna in the antenna array so that the antenna system can enhance or suppress signals in a specific direction. This technology is mainly used in radar and communication systems, and can achieve directional transmission and reception, improve the system's sensitivity to target signals, and reduce sensitivity to interference sources, thereby improving system performance. Traditional beamforming technology usually uses fixed weight and phase settings. When facing complex communication and radar scenarios, it often cannot effectively adapt to environmental changes, such as multipath propagation, interference signals, and target motion, and lacks adaptability to environmental changes and multipath effects. Adaptive filtering can automatically adjust the parameters of the filter according to the feedback information of the system to maximize or minimize specific performance indicators. By introducing adaptive filters, the beamforming system can dynamically adjust the beam shape of the antenna array to adapt to the signal characteristics in different directions and frequencies, so as to more flexibly cope with complex communication environments and interference situations and improve the performance and stability of the system.

[0003] The Least Mean Squares (LMS) algorithm based on gradient descent is one of the most commonly used adaptive filtering algorithms. It has the advantages of good robustness and easy implementation, but its convergence speed is slow. Although various variants based on the LMS algorithm provide better performance to some extent, they face the challenges of manual parameter adjustment and high computational cost.

[0004] Recursive Least Squares (RLS) is another commonly used adaptive filtering algorithm that can significantly improve the convergence speed and minimize the residual error, but it requires inverting an M×M matrix in each iteration, so the computational complexity is very high. Summary of the invention

[0005] According to one aspect of the present invention, an adaptive beamforming method, system and device are provided, which have the advantages of fast convergence speed, good robustness and automatic parameter adjustment.

[0006] In a first aspect, the present invention discloses an adaptive beamforming method, comprising the following steps:

[0007] establishing a vector signal model according to the received vector signal;

[0008] Iteratively updating the weighting coefficients through an affine projection algorithm with a variable step size to determine the optimal weighting coefficients; wherein the weighting coefficients are updated using an adjusted step size, and the step size is dynamically adjusted based on the ratio of the signal error power to the received signal power;

[0009] The radiation pattern of the antenna array is formed using the optimal weighting coefficients.

[0010] In some implementations, the variable step-size affine projection algorithm iteratively updates the weighting coefficients, comprising the following steps:

[0011] Initialize weighting coefficients, step size and control parameters;

[0012] After a preset number of iterations, an error vector is calculated based on the received signal vector and the reference signal vector;

[0013] The step size is adjusted according to the ratio of the signal error power to the received signal power;

[0014] Update the weighting coefficients based on the adjusted step size;

[0015] Repeat the iterative updating step until the weighting coefficient meets the convergence condition; the convergence condition is to minimize the mean square error between the beamformed signal and the reference signal vector.

[0016] In some embodiments, the weighting coefficient is:

[0017]

[0018] in, , is the signal vector, To control the parameters, is the identity matrix, , is the error vector, for the moment; is the projection order; It is the step size adjusted according to the ratio of signal error power to received signal power.

[0019] In some implementations, the step size is adjusted according to the ratio of the signal error power to the received signal power, specifically:

[0020]

[0021] in, , is the signal error power; , is the received signal power, is the signal vector; is the control variable.

[0022] In some embodiments, the radiation pattern of the antenna array is obtained by multiplying an array control matrix and a weighting coefficient.

[0023] In some embodiments, the radiation pattern of the antenna array is expressed as

[0024]

[0025] in, , is the array control matrix; , , for The control vector of the signal source is , and They are the antenna element spacing and the electromagnetic wave propagation speed; is the optimal weighting coefficient after complex transposition; and , .

[0026] In some implementations, the vector signal model is

[0027]

[0028] in , , is the signal vector; represents the complex transpose; and , , represent the complex envelopes of the useful signal and the interference signal respectively; , , is the The control vector of the signal source is , and They are the antenna element spacing and the electromagnetic wave propagation speed; is an independent and identically distributed antenna array with a mean of zero and a variance of Additive white Gaussian noise.

[0029] In some embodiments, the vector signal is received by a linear antenna array that is evenly arranged at intervals of half a wavelength.

[0030] In a second aspect, the present invention discloses an adaptive beamforming system, including an antenna array signal processing module and a beamforming control module;

[0031] The antenna array is used to receive vector signals;

[0032] The signal processing module establishes a vector signal model according to the received vector signal;

[0033] Iteratively updating the weighting coefficients through an affine projection algorithm with a variable step size to determine the optimal weighting coefficient; wherein the weighting coefficients are updated using a new step size, and the step size is dynamically adjusted based on the ratio of the signal error power to the received signal power;

[0034] Beamforming control is used to form the radiation pattern of the antenna array using the optimal weighting coefficients to achieve directional enhancement or suppression of the signal source.

[0035] In a third aspect, the present invention discloses an adaptive beamforming device, characterized by a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, an adaptive beamforming method as described above is implemented.

[0036] The present application discloses an adaptive beamforming method, system, and device, which iteratively updates the weighting coefficients by adjusting the variable step-size affine projection method, so that the radiation pattern of the antenna array is optimally aligned with the useful signal source, while suppressing the interference signal. While maintaining a high array gain, the present application significantly reduces the mean square error of the output signal, improves the signal-to-noise-plus-interference ratio (SNR), improves the overall performance of the communication system, and has the advantages of fast convergence speed, good robustness, and automatic parameter adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A schematic diagram of a flow chart of an adaptive beamforming method according to an embodiment of the present invention;

[0038] Figure 2 A schematic diagram of array gain comparison of an adaptive beamforming method according to an embodiment of the present invention;

[0039] Figure 3 A schematic diagram showing a comparison between an output signal and a noise plus interference ratio of an adaptive beamforming method according to Embodiment 2 of the present invention;

[0040] Figure 4 A schematic diagram showing a comparison of mean square errors of an adaptive beamforming method according to the second embodiment of the present invention;

[0041] Figure 5 A schematic diagram comparing the computational complexity of an adaptive beamforming method according to the second embodiment of the present invention. DETAILED DESCRIPTION

[0042] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0043] It should be noted that, in this article, the terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0044] It should be further understood that the terms "comprise", "include" indicate the presence of the described features, steps, operations, elements, components, projects, kinds and / or groups, but do not exclude the presence, occurrence or addition of one or more other features, steps, operations, elements, components, projects, kinds and / or groups. The terms "or", "and / or", "including at least one of the following" etc. used in this application may be interpreted as inclusive, or mean any one or any combination. Only when the combination of elements, functions, steps or operations inherently excludes each other in some manner, the exception to this definition will occur.

[0045] It should be understood that, although the various steps in the flowchart in the embodiment of the present application are displayed in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and it can be performed in other orders. Moreover, at least a portion of the steps in the figure may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and their execution order is not necessarily performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0046] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0047] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0048] The present application provides an adaptive beamforming method which is accurate, has the advantages of fast convergence speed, good robustness, and automatic parameter adjustment.

[0049] Specifically, the method comprises the following steps:

[0050] Step S1: Establish a vector signal model based on the received vector signal. The signal source is a linear antenna array evenly arranged at half-wavelength intervals at time The received vector signal constructs the vector signal model as follows:

[0051]

[0052] in , , is a signal vector; represents the complex transpose; and , , represent the complex envelopes of the useful signal and the interference signal respectively; , , is the The control vector of the signal source is , and They are the antenna element spacing and the electromagnetic wave propagation speed; is independently distributed on the antenna array, with a mean of zero and a variance of Additive white Gaussian noise.

[0053] Represent the above received vector signal in matrix form:

[0054]

[0055] is called the signal matrix, where is the projection order.

[0056] Step S2, iteratively updating the weighting coefficients through an affine projection algorithm with a variable step size to determine the optimal weighting coefficients; wherein the weighting coefficients are updated using a new step size, and the step size is dynamically adjusted based on the ratio of the signal error power to the received signal power.

[0057] The goal of solving the optimal beamforming problem is to determine the optimal weighting coefficients , so that the signal after beamforming The mean square error between the reference signal vector is the smallest, that is:

[0058]

[0059] in, , is the reference signal vector.

[0060] The optimal weighting coefficients for the above beamforming problem are It can be gradually approximated by the recursive formula of the affine projection algorithm:

[0061]

[0062] in is a fixed step size, is the control parameter to ensure the stability of matrix inversion, which is selected based on experience and is a constant; is the identity matrix, is the error vector:

[0063]

[0064] After several iterations, the weighting coefficients The convergence condition is met, so that the mean square error between the beamformed signal and the reference signal vector is minimized. The biggest problem faced by this traditional affine projection method is that it requires manual selection of a fixed step size. and projection order , and the high computational complexity caused by matrix inversion at each iteration.

[0065] Therefore, in this application, the weighting coefficients are dynamically adjusted by a variable step-size affine projection algorithm. In the initial stage, the step size is set to a larger value to quickly approach the optimal solution. As the iteration proceeds, the step size is adjusted according to the ratio of the signal error power to the received signal power to refine the update of the weighting coefficients and finally converge to the optimal value. In this way, the antenna array can effectively enhance the signal in a specific direction while suppressing interference from other directions, thereby improving the performance and stability of the communication system.

[0066] Specifically, the variable step-size affine projection algorithm iteratively updates the weighted coefficients, comprising the following steps:

[0067] Step S21, initializing weighting coefficients, step lengths and control parameters;

[0068] Step S22: After a preset number of iterations, an error vector is calculated based on the received signal vector and the reference signal vector.

[0069] The preset number is generally the projection order , after iterating Update the weighting coefficient after the first time and keep it unchanged at other times.

[0070] The error vector is calculated based on the received signal vector and the reference signal vector using the following formula:

[0071]

[0072] .

[0073] Step S23, adjusting the step size according to the ratio of the signal error power to the received signal power;

[0074] The step size is adjusted according to the ratio of signal error power to received signal power, which is:

[0075]

[0076] in, , is the signal error power; , is the received signal power, is the signal vector; is the control variable.

[0077] Step S24: update the weighting coefficient based on the adjusted step size and error vector.

[0078] Specifically, the weighting coefficient is updated based on the adjusted step size and error vector using the following formula:

[0079]

[0080] in, , is the signal vector, To control the parameters, is the identity matrix, , is the error vector, for the moment; is the projection order; It is the step size adjusted according to the ratio of signal error power to received signal power.

[0081] In each The adjusted step size is used for the iteration Update the weighting coefficient once , faster convergence speed and better convergence accuracy can be obtained without each iteration, while greatly reducing the computational complexity.

[0082] Step S25, repeat the iterative updating step until the weighting coefficient meets the convergence condition; the convergence condition is to minimize the mean square error between the beamformed signal and the reference signal vector.

[0083] Step S3: using the optimal weighting coefficient to form a radiation pattern of the antenna array to achieve directional enhancement or suppression of the signal source.

[0084] The radiation pattern of the antenna array is expressed as

[0085]

[0086] in, , is the array control matrix; , , for The control vector of the signal source is , and They are the antenna element spacing and the electromagnetic wave propagation speed; is the optimal weighting coefficient after complex transposition; and .

[0087] This method can adaptively adjust beamforming to adapt to dynamically changing communication environments. After several iterations, the system converges to a set of optimal weighting coefficients, so that the radiation pattern of the antenna array is optimally aligned with the useful signal source while suppressing interference signals. This method significantly reduces the mean square error of the output signal while maintaining high array gain, and improves the signal-to-noise-plus-interference ratio (SNR), thereby improving the overall performance of the communication system.

[0088] like Figure 2-5 As shown in the figure, this method has obvious technical advantages over the published methods in terms of array gain, interference suppression, mean square error and computational complexity. The published methods include the traditional affine projection method (APA) and various deformation methods based on minimum mean square error, LMS1, LMSS, nonparametric variable step size normalized LMS (VSSNLMS) and shrinkage widely linear complex-valued LMS (SWLCLMS). Compared with the above methods, this method has higher array gain, signal to noise plus interference ratio (SINR) reflects the interference suppression advantage mean square error, small mean square error, accuracy advantage, and low computational complexity.

[0089] Based on the same inventive concept, the present invention also provides an adaptive beamforming system including an antenna array, a signal processing module and a beamforming control module;

[0090] The antenna array is used to receive vector signals;

[0091] The signal processing module establishes a vector signal model according to the received vector signal;

[0092] Iteratively updating the weighting coefficients through an affine projection algorithm with a variable step size to determine the optimal weighting coefficients; wherein the weighting coefficients are updated using an adjusted step size, and the step size is dynamically adjusted based on the ratio of the signal error power to the received signal power;

[0093] Beamforming control is used to form the radiation pattern of the antenna array using the optimal weighting coefficients to achieve directional enhancement or suppression of the signal source.

[0094] The specific implementation process of this system is the same as that of the above embodiment and will not be described in detail here.

[0095] The present application discloses an adaptive beamforming method and system, which iteratively updates the weighting coefficients by adjusting the variable step-size affine projection method, so that the radiation pattern of the antenna array is optimally aligned with the useful signal source, while suppressing the interference signal. While maintaining a high array gain, the present application significantly reduces the mean square error of the output signal, improves the signal-to-noise-plus-interference ratio (SNR), improves the overall performance of the communication system, and has the advantages of fast convergence speed, good robustness, and automatic parameter adjustment.

[0096] Based on the same inventive concept, the present application also provides a device, which may include: a memory storing executable program code;

[0097] a processor coupled to the memory;

[0098] A transceiver used to communicate with other devices or communication networks and to receive or send network messages;

[0099] A bus used to connect memory, processors, and transceivers for internal communication.

[0100] The transceiver receives messages transmitted over the network and passes them to the processor through the bus. The processor calls the executable program code stored in the memory through the bus for processing, and passes the processing results to the transceiver through the bus for sending, thereby realizing the method provided in the above embodiment of the present application.

[0101] The present invention discloses a computer-readable storage medium, which stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute the method provided by the above described embodiment.

[0102] The present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program can be operated to enable a computer to execute the described adaptive beamforming method.

[0103] The embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0104] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0105] The above are only some embodiments of the present invention. For those skilled in the art, several modifications and improvements can be made without departing from the creative concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. An adaptive beamforming method, characterized in that: The following steps are involved: establishing a vector signal model according to the received vector signal; Iteratively updating the weighting coefficients through an affine projection algorithm with a variable step size to determine the optimal weighting coefficients; wherein the weighting coefficients are updated using an adjusted step size, and the step size is dynamically adjusted based on the ratio of the signal error power to the received signal power; Using optimal weighting coefficients to form a radiation pattern of the antenna array; The variable-step affine projection algorithm iteratively updates the weighted coefficients, including: Initialize weighting coefficients, step size and control parameters; After a preset number of iterations, an error vector is calculated based on the received signal vector and the reference signal vector; The step size is adjusted according to the ratio of the signal error power to the received signal power; Update the weighting coefficients based on the adjusted step size; Repeating the iterative updating step until the weight coefficient meets a convergence condition; the convergence condition is to minimize the mean square error between the beamformed signal and the reference signal vector; The weighting coefficient is: in, , is the signal vector, To control the parameters, is the identity matrix, , is the error vector, for the moment; is the projection order; It is the step size adjusted according to the ratio of signal error power to received signal power, specifically: in, , is the signal error power; , is the received signal power; , is the signal vector; is the control parameter.

2. The adaptive beamforming method according to claim 1, characterized in that: The radiation pattern of the antenna array is obtained by multiplying the array control matrix and the weighting coefficient.

3. The adaptive beamforming method according to claim 2, characterized in that: The radiation pattern of the antenna array is expressed as: in, , is the array control matrix; , , for The control vector of the signal source is , and They are the antenna element spacing and the electromagnetic wave propagation speed; is the optimal weighting coefficient after complex transposition; and , .

4. The adaptive beamforming method according to claim 1, characterized in that: The vector signal model is in , , is the signal vector; represents the complex transpose; and , , represent the complex envelopes of the useful signal and the interference signal respectively; , , is the The control vector of the signal source is , and They are the antenna element spacing and the electromagnetic wave propagation speed; is independently distributed on the antenna array, with a mean of zero and a variance of Additive white Gaussian noise.

5. The adaptive beamforming method according to claim 1, characterized in that: The vector signal is received by a linear antenna array that is evenly arranged at intervals of half a wavelength.

6. An adaptive beamforming system, characterized in that: It includes an antenna array, a signal processing module and a beamforming control module; The antenna array is used to receive vector signals; The signal processing module establishes a vector signal model according to the received vector signal; Iteratively updating the weighting coefficients through an affine projection algorithm with a variable step size to determine the optimal weighting coefficient; wherein the weighting coefficients are updated using a new step size, and the step size is dynamically adjusted based on the ratio of the signal error power to the received signal power; The beamforming control module is used to form the radiation pattern of the antenna array using the optimal weighting coefficient to achieve directional enhancement or suppression of the signal source; The variable-step affine projection algorithm iteratively updates the weighted coefficients, including: Initialize weighting coefficients, step size and control parameters; After a preset number of iterations, an error vector is calculated based on the received signal vector and the reference signal vector; The step size is adjusted according to the ratio of the signal error power to the received signal power; Update the weighting coefficients based on the adjusted step size; Repeating the iterative updating step until the weight coefficient meets a convergence condition; the convergence condition is to minimize the mean square error between the beamformed signal and the reference signal vector; The weighting coefficient is: in, , is the signal vector, To control the parameters, is the identity matrix, , is the error vector, for the moment; is the projection order; It is the step size adjusted according to the ratio of signal error power to received signal power, specifically: in, , is the signal error power; , is the received signal power; , is the signal vector; is the control parameter.

7. An adaptive beamforming device, characterized in that: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements an adaptive beamforming method as claimed in any one of claims 1 to 5 when executing the computer program.

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