An adaptive filtering and noise reduction system based on variable step-size least mean square error algorithm

Abstract

This invention discloses an adaptive filtering and noise reduction system based on a variable step-size minimum mean square error algorithm, comprising a main signal link and a closed-loop control module. The main signal link integrates a low-noise amplifier, an analog-to-digital converter, and an adaptive filter, responsible for precise signal amplification, efficient analog-to-digital conversion, and flexible filtering. The closed-loop control system, through the close collaboration of a noise detection unit, a spectrum analysis unit, and a parameter adjustment unit, captures and analyzes the noise spectrum characteristics in real time, and then dynamically optimizes the filtering parameters to achieve the best noise reduction effect. The parameter adjustment unit of this invention employs a variable step-size minimum mean square error algorithm. Leveraging its unique real-time amplitude feedback mechanism for the error signal, combined with the nonlinear characteristics of the hyperbolic tangent function, it cleverly achieves dynamic adjustment of the step size, thus achieving an ideal adaptive balance between rapid convergence and low steady-state error.

Description

Technical Field

[0001] This invention belongs to the field of communication technology, specifically relating to the design of an adaptive filtering and noise reduction system based on a variable step size minimum mean square error algorithm. Background Technology

[0002] In wireless communication systems, the quality of signal processing at the receiver front end directly affects the reliability and sensitivity of the communication link. With the rapid development of 5G, IoT, and high-frequency communication technologies, receivers need to handle multi-band, time-varying noise interference in complex electromagnetic environments, including white noise, narrowband interference, and impulse noise. Traditional fixed-parameter filters lack dynamic adaptability and are difficult to effectively suppress non-stationary noise, leading to signal distortion, increased bit error rate, and degraded overall system performance.

[0003] Currently, adaptive filtering technology is widely used for dynamic noise suppression, but its core drawback lies in the fact that the traditional Least Mean Square (LMS) algorithm uses a fixed step size parameter, making it difficult to simultaneously meet the requirements of balancing convergence speed and steady-state error. Furthermore, existing adaptive filtering schemes often rely on adjusting parameters based on a single noise feature, resulting in insufficient adaptability to scenarios with complex spectral distributions and thus limited noise suppression effectiveness.

[0004] In recent years, researchers have proposed improved algorithms based on LMS to address the aforementioned problems, but these still suffer from low dynamic adjustment efficiency, imperfect closed-loop control, and high hardware implementation complexity. Therefore, there is an urgent need for an adaptive filtering and noise reduction circuit that balances convergence speed, steady-state accuracy, and hardware efficiency to meet the stringent requirements of modern wireless communication systems for high sensitivity, low bit error rate, and complex noise suppression capabilities. Summary of the Invention

[0005] The purpose of this invention is to address the problem of low noise reduction performance of existing adaptive filtering and noise reduction techniques based on the LMS algorithm, and to propose an adaptive filtering and noise reduction system based on the variable step size minimum mean square error algorithm.

[0006] The technical solution of the present invention is: an adaptive filtering and noise reduction system based on the variable step size minimum mean square error algorithm, including a main signal link and a closed-loop control module.

[0007] The main signal link includes a low-noise amplifier, an analog-to-digital converter, and an adaptive filter connected in sequence. The low-noise amplifier amplifies the input signal to obtain an amplified signal; the analog-to-digital converter performs analog-to-digital conversion on the amplified signal to obtain a digital signal; and the adaptive filter filters the digital signal to obtain an output signal.

[0008] The closed-loop control module includes a noise detection unit, a spectrum analysis unit, and a parameter adjustment unit. The input of the noise detection unit is connected to the output of the analog-to-digital converter (ADC) to extract noise components from the digital signal and calculate their root mean square (RMS) values. The input of the spectrum analysis unit is connected to the output of the noise detection unit to analyze the noise power spectral density using a fast Fourier transform based on the RMS value of the noise components, and to adjust the smoothing and scaling parameters of the variable step-size minimum mean square error (MMS) algorithm based on the noise power spectral density. The input of the parameter adjustment unit is connected to the output of the spectrum analysis unit, and its output is connected to an adaptive filter to dynamically adjust the tap coefficients of the adaptive filter using the variable step-size MMS algorithm, thus forming a closed-loop control of the main signal link.

[0009] Furthermore, the formula for the noise detection unit to extract the noise component from the digital signal is as follows:

[0010]

[0011] in n For representing discrete time series, Indicates noise components, Represents an ideal reference signal. This represents the output vector generated by performing an inner product of the input signal vector and the tap coefficients of the adaptive filter.

[0012] Furthermore, the formula for calculating the root mean square value of the noise component in the noise detection unit is:

[0013]

[0014] in Represents the root mean square value of the noise component. N This indicates the number of sampling points for signal analysis. This represents the noise component.

[0015] Furthermore, the root mean square value of the response to the noise component in the spectrum analysis unit. The noise power spectral density is analyzed using Fast Fourier Transform.

[0016] Furthermore, the formula for the analytical noise power spectral density is:

[0017]

[0018] in Indicates frequency k Noise power density at that location Represents a single time-domain sampling point. n For representing discrete time series, N This indicates the number of sampling points for signal analysis. jIt represents the imaginary unit.

[0019] Furthermore, the method for adjusting the smoothing and scaling parameters of the variable step size minimum mean square error algorithm based on the noise power spectral density in the noise detection unit is as follows:

[0020] If the variance of the noise power spectral density at each frequency point is s If 2 ≤ 0.1, the noise is determined to be flat noise, and the smoothing parameter of the variable step size minimum mean square error algorithm is adjusted accordingly. Increase the initial value from 0.5 to 0.8 to accelerate convergence; if the variance of the noise power spectral density at each frequency point... s If ² > 0.1, the noise is determined to be non-flat noise, and the scaling parameters of the variable step size minimum mean square error algorithm are adjusted accordingly. The value was reduced from the initial value of 0.05 to 0.02 to improve steady-state accuracy.

[0021] Furthermore, the variable step size minimum mean square error algorithm in the parameter adjustment unit includes the following steps:

[0022] S1. Initialize the parameters of the variable step size minimum mean square error algorithm.

[0023] S2, based on noise components Calculate filter step size parameters .

[0024] S3. Based on the filter step size parameter The tap coefficients of the adaptive filter are dynamically adjusted.

[0025] Further, step S1 specifically involves: initializing and setting the maximum value of the filter step size parameter. m max =0.1, the minimum value of the filter step size parameter. m min =0.001, smoothing parameter The initial value is 0.5, scaling parameter The initial value is 0.05, and the initial value of the tap coefficient vector of the adaptive filter is 0.

[0026] Furthermore, the filter step size parameter in step S2 The calculation formula is:

[0027]

[0028] in Indicates the smoothing parameter. This represents the scaling parameter. This represents the hyperbolic tangent function.

[0029] Furthermore, the formula for dynamically adjusting the tap coefficients of the adaptive filter in step S3 is as follows:

[0030]

[0031] in This represents the filter tap coefficient vector before adjustment. This represents the adjusted filter tap coefficient vector. This represents the input signal vector.

[0032] The beneficial effects of this invention are:

[0033] (1) The adaptive filtering noise reduction system in this invention is based on the dynamic step size adjustment of the variable step size minimum mean square error algorithm, which significantly improves the convergence speed. At the same time, it achieves a high signal-to-noise ratio gain, effective narrowband interference suppression and extremely low bit error rate. It has efficient noise reduction performance and significantly enhances the communication reliability in multi-band and time-varying noise environments. It provides an efficient and reliable solution for the front-end signal preprocessing of high-sensitivity receivers.

[0034] (2) This invention achieves a fast closed-loop response of the adaptive filtering and noise reduction system by using fast Fourier transform processing and dynamic adaptive filter parameter update, thereby improving the real-time performance of the system and reducing the system energy efficiency.

[0035] (3) This invention achieves efficient resource scheduling and low-latency operation through a real-time closed-loop control strategy. At the same time, it adopts parallel processing technology and pipeline structure to significantly shorten the operation time of key modules. Attached Figure Description

[0036] Figure 1 The figure shown is a block diagram of an adaptive filtering and noise reduction system based on a variable step size minimum mean square error algorithm provided in an embodiment of the present invention.

[0037] Figure 2 The diagram shows a comparison of the bit error rate suppression performance of different algorithms provided in the embodiments of the present invention.

[0038] Figure 3 The diagram shows a comparison of the convergence performance of different algorithms provided in the embodiments of the present invention. Detailed Implementation

[0039] Exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be understood that the embodiments shown and described in the drawings are merely exemplary and are intended to illustrate the principles and spirit of the invention, and are not intended to limit the scope of the invention.

[0040] This invention provides an adaptive filtering and noise reduction system based on the Variable Step Size Minimum Mean Square Error (VSLMS) algorithm, such as... Figure 1 As shown, it includes the main signal link and the closed-loop control module.

[0041] like Figure 1 As shown, the main signal link includes a low-noise amplifier (LNA), an analog-to-digital converter (ADC), and an adaptive filter connected in sequence.

[0042] The low-noise amplifier is used to amplify the input signal to obtain an amplified signal. In this embodiment of the invention, the gain range of the low-noise amplifier is 10-30dB, and the noise figure is less than 2dB.

[0043] An analog-to-digital converter (ADC) is used to convert amplified signals from analog to digital to obtain digital signals. In this embodiment of the invention, the ADC's sampling rate is adjustable from 5MHz to 100MHz, and the resolution is not less than 12 bits.

[0044] An adaptive filter is used to filter digital signals to obtain an output signal. In this embodiment of the invention, the adaptive filter is a 64-tap lateral structure adaptive filter.

[0045] like Figure 1 As shown, the closed-loop control module includes a noise detection unit, a spectrum analysis unit, and a parameter adjustment unit.

[0046] The noise detection unit is connected to the output of the analog-to-digital converter and is used to extract noise components from the digital signal and calculate the root mean square value of the noise components.

[0047] In this embodiment of the invention, the formula for the noise detection unit to extract noise components from the digital signal is as follows:

[0048]

[0049] in n For representing discrete time series, Indicates noise components, Represents an ideal reference signal. This represents the output vector generated by performing an inner product of the input signal vector and the tap coefficients of the adaptive filter.

[0050] The formula for calculating the root mean square value of the noise component in the noise detection unit is:

[0051]

[0052] in Represents the root mean square value of the noise component. N This indicates the number of sampling points for signal analysis. This represents the noise component.

[0053] The input of the spectrum analysis unit is connected to the output of the noise detection unit. It is used to analyze the noise power spectral density using Fast Fourier Transform (FFT) based on the root mean square value of the noise components, and to adjust the smoothing and scaling parameters of the variable step size minimum mean square error algorithm based on the noise power spectral density.

[0054] In this embodiment of the invention, the root mean square value in response to the noise component The noise power spectral density is analyzed using Fast Fourier Transform, and the specific formula is as follows:

[0055]

[0056] in Indicates frequency k Noise power density at that location This represents a single time-domain sampling point, i.e., the first time-domain sampling point in the digital signal sequence after analog-to-digital conversion. n Each sampled value (scalar). n For representing discrete time series, N This indicates the number of sampling points for signal analysis. j It represents the imaginary unit.

[0057] In this embodiment of the invention, if the variance of the noise power spectral density at each frequency point... s If ² ≤ 0.1, then since the power values ​​at each frequency point are nearly uniformly distributed, it indicates that the noise power spectrum fluctuates little, and the noise is determined to be flat noise (i.e., white noise, whose power spectral density takes nearly constant values ​​at all frequency points, resulting in a flat spectrum curve). The smoothing parameters of the variable step size minimum mean square error algorithm will then be adjusted accordingly. Increase the initial value from 0.5 to 0.8 to accelerate convergence; if the variance of the noise power spectral density at each frequency point... s If ² > 0.1, then a significant peak exists ( , This represents the maximum power value in the noise power spectral density. The average power value of the noise power spectral density (or multiple peaks and valleys) indicates that the noise is non-flat. Non-flat noise power spectral density will exhibit obvious peaks at certain frequency points, at which point the variance... s 2 The scaling parameters of the variable step size minimum mean square error algorithm are relatively large and the spectrum curve is not flat. The initial value was reduced from 0.05 to 0.02 to improve steady-state accuracy. The updated smoothing parameters were then transmitted via the SPI interface. and scaling parameters Write to the FPGA registers to ensure the changes take effect in the next clock cycle.

[0058] The input of the parameter adjustment unit is connected to the output of the spectrum analysis unit, and its output is connected to the adaptive filter. It is used to dynamically adjust the tap coefficients of the adaptive filter using a variable step size minimum mean square error algorithm to form a closed-loop control of the main signal link.

[0059] In this embodiment of the invention, the variable step size minimum mean square error algorithm includes the following steps S1~S3:

[0060] S1. Initialize the parameters of the variable step size minimum mean square error algorithm.

[0061] In this embodiment of the invention, the maximum value of the filter step size parameter is initialized. m max =0.1, the minimum value of the filter step size parameter. m min =0.001, smoothing parameter The initial value is 0.5, scaling parameter The initial value is 0.05, and the initial value of the tap coefficient vector of the adaptive filter is 0.

[0062] S2, based on noise components Calculate filter step size parameters :

[0063]

[0064] in Indicates the smoothing parameter. This represents the scaling parameter. The hyperbolic tangent function is expressed as:

[0065]

[0066] The hyperbolic tangent function nonlinearly maps the error magnitude to the interval [0,1), and then applies a scaling parameter. After scaling .

[0067] S3. Based on the filter step size parameter The tap coefficients of the adaptive filter are dynamically adjusted using the following formula:

[0068]

[0069] in This represents the filter tap coefficient vector before adjustment. This represents the adjusted filter tap coefficient vector. This represents the input signal vector. In this embodiment of the invention, an additional limiter ensures... To avoid step size exceeding the limit and causing divergence.

[0070] In this embodiment of the invention, the hardware optimization design employs parallelization and pipeline techniques to improve efficiency. The FFT processor is optimized based on the radix-2 algorithm, compressing the computation time for 1024 points to 5 seconds. μs The adaptive filter coefficients are updated using a four-stage pipeline structure, with a single update delay of ≤10ns. Priority-based thread scheduling is implemented in the FPGA: the parameter adjustment thread has a response time of ≤50ns, the spectrum analysis period is 1ms, and noise detection is a background task. An interrupt is triggered during frequency transitions, forcibly reloading the parameters.

[0071] The performance verification of the VSLMS algorithm provided in this embodiment of the invention can be completed through simulation and actual measurement.

[0072] In actual hardware testing, when the 2.4GHz Wi-Fi signal is subjected to -70dBm narrowband interference, the interference suppression depth reaches 27dB. Figure 2 As shown, the VSLMS algorithm provided in this embodiment of the invention reduces the bit error rate from 10% to 10% compared to the traditional LMS algorithm. -3 Reduced to 1.3×10 -7 .

[0073] In MATLAB simulations, when a 5G NR signal is superimposed with 0dB Gaussian white noise, the VSLMS algorithm provided in this embodiment of the invention converges within 200 iterations. Figure 3 As shown, compared to the traditional LMS algorithm, the steady-state error is reduced to 10. -4 The signal-to-noise ratio is improved by 12dB.

[0074] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. An adaptive filtering and noise reduction system based on a variable step-size minimum mean square error algorithm, characterized in that, Includes the main signal link and closed-loop control module; The main signal link includes a low-noise amplifier, an analog-to-digital converter, and an adaptive filter connected in sequence. The low-noise amplifier amplifies the input signal to obtain an amplified signal; the analog-to-digital converter performs analog-to-digital conversion on the amplified signal to obtain a digital signal; and the adaptive filter filters the digital signal to obtain an output signal. The closed-loop control module includes a noise detection unit, a spectrum analysis unit, and a parameter adjustment unit; The input terminal of the noise detection unit is connected to the output terminal of the analog-to-digital converter, and is used to extract noise components from the digital signal and calculate the root mean square value of the noise components. The input of the spectrum analysis unit is connected to the output of the noise detection unit. It is used to analyze the noise power spectral density using fast Fourier transform based on the root mean square value of the noise component, and to adjust the smoothing and scaling parameters of the variable step size minimum mean square error algorithm based on the noise power spectral density. The input of the parameter adjustment unit is connected to the output of the spectrum analysis unit, and its output is connected to the adaptive filter. It is used to dynamically adjust the tap coefficients of the adaptive filter using a variable step size minimum mean square error algorithm to form a closed-loop control of the main signal link.

2. The adaptive filtering and noise reduction system based on the variable step size minimum mean square error algorithm according to claim 1, characterized in that, The formula for the noise detection unit to extract noise components from the digital signal is as follows: in n For representing discrete time series, Indicates noise components, Represents an ideal reference signal. This represents the output vector generated by performing an inner product of the input signal vector and the tap coefficients of the adaptive filter.

3. The adaptive filtering and noise reduction system based on the variable step size minimum mean square error algorithm according to claim 1, characterized in that, The formula for calculating the root mean square value of the noise component in the noise detection unit is as follows: in Represents the root mean square value of the noise component. N This indicates the number of sampling points for signal analysis. This represents the noise component.

4. The adaptive filtering and noise reduction system based on the variable step size minimum mean square error algorithm according to claim 1, characterized in that, The root mean square value of the response to the noise component in the spectrum analysis unit The noise power spectral density is analyzed using Fast Fourier Transform.

5. The adaptive filtering and noise reduction system based on the variable step size minimum mean square error algorithm according to claim 4, characterized in that, The formula for the analytical noise power spectral density is: in Indicates frequency k Noise power density at that location Represents a single time-domain sampling point. n For representing discrete time series, N This indicates the number of sampling points for signal analysis. j It represents the imaginary unit.

6. The adaptive filtering and noise reduction system based on the variable step size minimum mean square error algorithm according to claim 1, characterized in that, The method for adjusting the smoothing and scaling parameters of the variable step size minimum mean square error algorithm based on the noise power spectral density in the spectrum analysis unit is as follows: If the variance of the noise power spectral density at each frequency point is σ If 2 ≤ 0.1, the noise is determined to be flat noise, and the smoothing parameter of the variable step size minimum mean square error algorithm is adjusted accordingly. Increase the initial value from 0.5 to 0.8 to accelerate convergence; If the variance of the noise power spectral density at each frequency point is σ If ² > 0.1, the noise is determined to be non-flat noise, and the scaling parameters of the variable step size minimum mean square error algorithm are adjusted accordingly. The value was reduced from the initial value of 0.05 to 0.02 to improve steady-state accuracy.

7. The adaptive filtering and noise reduction system based on the variable step size minimum mean square error algorithm according to claim 1, characterized in that, The variable step size minimum mean square error algorithm in the parameter adjustment unit includes the following steps: S1. Initialize the parameters of the variable step size minimum mean square error algorithm; S2, based on noise components Calculate filter step size parameters ; S3. Based on the filter step size parameter The tap coefficients of the adaptive filter are dynamically adjusted.

8. The adaptive filtering and noise reduction system based on the variable step size minimum mean square error algorithm according to claim 7, characterized in that, Step S1 specifically involves: initializing and setting the maximum value of the filter step size parameter. μ max =0.1, the minimum value of the filter step size parameter. μ min =0.001, smoothing parameter The initial value is 0.5, scaling parameter The initial value is 0.05, and the initial value of the tap coefficient vector of the adaptive filter is 0.

9. The adaptive filtering and noise reduction system based on the variable step size minimum mean square error algorithm according to claim 7, characterized in that, The filter step size parameter in step S2 The calculation formula is: in Indicates the smoothing parameter. This represents the scaling parameter. This represents the hyperbolic tangent function.

10. The adaptive filtering and noise reduction system based on the variable step size minimum mean square error algorithm according to claim 7, characterized in that, The formula for dynamically adjusting the tap coefficients of the adaptive filter in step S3 is as follows: in This represents the filter tap coefficient vector before adjustment. This represents the adjusted filter tap coefficient vector. This represents the input signal vector.

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