A method for suppressing interference signals in a high-bandwidth, low-noise amplifier chip

Abstract

This application relates to the field of amplifier noise interference suppression technology, specifically to a method for suppressing interference signals in a high-bandwidth, low-noise amplifier chip. The method includes: determining frequency domain characteristic values ​​based on the distribution of the disorder level of each sub-signal in each frequency band among the disorder levels of all sub-signals in the corresponding frequency band, and the disorder level itself; determining comprehensive characteristic values ​​based on the distance from the observation window of each sub-signal to the current time, and based on the distribution of the signal characteristic values ​​of each sub-signal's corresponding frequency band among the signal characteristic values ​​of all frequency bands; and employing an adaptive filtering algorithm to suppress interference signals in the time-domain signal at the current time. This application solves the limitations of series negative feedback on the bandwidth extension of low-frequency noise amplifiers and the interference of fixed threshold denoising on signal quality in low-noise amplifiers, thereby improving the operating bandwidth range and signal quality of low-frequency noise amplifiers.

Description

Technical Field

[0001] This application relates to the field of amplifier noise interference suppression technology, specifically to a method for suppressing interference signals in a high-bandwidth, low-noise amplifier chip. Background Technology

[0002] Modern communication systems demand high-speed data transmission, multi-band compatibility, and strong anti-interference capabilities. Radio frequency (RF) receivers must have a wide operating frequency range to ensure signal integrity and reliability. The primary function of a low-noise amplifier (LNO) chip is to amplify the weak RF signal received by the RF receiver antenna, enabling subsequent circuitry to process the signal. As the first active device encountered by the RF signal entering the receiver, the performance characteristics of the LNO chip, such as gain flatness and noise figure, directly affect the receiver's operation.

[0003] Traditional methods for widening the operating frequency of low-noise amplifiers (LNAs) include using series negative feedback to change the transistor's input impedance, thereby achieving simultaneous matching of minimum noise and minimum input standing wave ratio (VSWR). Because the change in transistor input impedance expands the operating bandwidth of the designed amplifier, this approach is limited and cannot meet the needs of applications requiring a wider frequency range. Furthermore, traditional denoising methods for LNAs typically employ fixed thresholds, resulting in limited suppression of time-varying interference caused by multi-source noise, thus degrading the signal quality within the LNA. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a method for suppressing interference signals in a high-bandwidth, low-noise amplifier chip, thereby resolving the existing issues.

[0005] The interference signal suppression method for a high-bandwidth, low-noise amplifier chip disclosed in this application adopts the following technical solution:

[0006] The time-domain signal output by the high-bandwidth low-noise amplifier chip within a preset time period before each time moment is obtained and denoted as the time-domain signal at each time moment. The time-domain signal of the preset number of time moments before the current time moment is obtained. The frequency domain signal of the time-domain signal at each time moment is divided into sub-signals of multiple frequency bands. The time-domain signal at the current time moment and all time moments before it is traversed to obtain all sub-signals under each frequency band.

[0007] All sub-signals at each time point are numbered in ascending order of frequency. Based on the numbering of each sub-signal in each frequency band, the feature weights of each sub-signal in each frequency band are determined. By analyzing the average distribution of the frequency intervals between all adjacent peaks in each sub-signal in each frequency band, the structural uniformity of each sub-signal in each frequency band is determined. Combined with the feature weights, the disorder level of each sub-signal in each frequency band is determined. Based on the distribution of the disorder level of each sub-signal in each frequency band among the disorder levels of all sub-signals in the corresponding frequency band and the disorder level itself, the frequency domain feature values ​​of each sub-signal in each frequency band are determined.

[0008] The time-domain signals of the current moment and all previous moments are divided into multiple observation windows according to time sequence. By analyzing the average distribution of the frequency domain characteristic values ​​of all sub-signals corresponding to each frequency band in each observation window, the signal characteristic value of each frequency band is determined. Based on the distance from the observation window of each sub-signal to the current moment, the time distance weight of each sub-signal is determined. Based on the distribution of the signal characteristic values ​​of the corresponding frequency band of each sub-signal among the signal characteristic values ​​of all frequency bands, the signal characteristic coefficient of each sub-signal is determined. Combined with the time distance weight, the comprehensive characteristic value of each sub-signal is determined.

[0009] Based on the comprehensive feature values, an adaptive filtering algorithm is used to suppress interference signals in the time-domain signal at the current moment.

[0010] Preferably, the expression for the feature weights of each sub-signal in each frequency band is: In the formula, This represents the feature weight of sub-signal j in the i-th frequency band; This represents the number of all sub-signals in the sub-segment containing sub-signal j in the i-th frequency band; This represents the number of all sub-signals at time j in the i-th frequency band; , These represent the preset multiplicative factor and the preset additive factor, respectively.

[0011] Preferably, the structural uniformity of each sub-signal in each frequency band is the average frequency interval between all adjacent peaks in each sub-signal in each frequency band.

[0012] Preferably, the disorder level of each sub-signal in each frequency band is the ratio of the feature weight to the structural uniformity of each sub-signal in each frequency band.

[0013] Preferably, the frequency domain feature value of each sub-signal in each frequency band is the result of positively fusing the proportion of the disorder level of each sub-signal in each frequency band to the maximum disorder level of all sub-signals in the corresponding frequency band with the disorder level.

[0014] Preferably, the method for determining the signal characteristic values ​​in each frequency band is as follows:

[0015] Number all observation windows sequentially, and the signal characteristic value in the i-th frequency band. The expression is In the formula, This represents the mean of the frequency domain eigenvalues ​​of all sub-signals corresponding to the i-th frequency band within the observation window k; k represents the number of the observation window k; and K represents the total number of observation windows.

[0016] Preferably, the time distance weight of each sub-signal is the ratio of the observation window number of each sub-signal to the corresponding observation window number of the time-domain signal at the current time.

[0017] Preferably, the signal characteristic coefficient of each sub-signal is the proportion of the signal characteristic value of the corresponding frequency band of each sub-signal to the maximum value of the signal characteristic value of all frequency bands.

[0018] Preferably, the comprehensive characteristic value of each sub-signal is the product of the time distance weight of each sub-signal and the signal characteristic coefficient.

[0019] Preferably, the step of using an adaptive filtering algorithm to suppress interference signals in the time-domain signal at the current moment includes:

[0020] In the frequency domain signal of the time domain signal at the current moment, the corresponding parts of each sub-signal in the time domain signal are used as the input of the adaptive filtering algorithm. The forgetting factor of the adaptive filtering algorithm is set as the normalized value of the inverse of the comprehensive feature value of the corresponding sub-signal. The corresponding parts of each sub-signal in the time domain signal after filtering are output, and the corresponding parts of all sub-signals in the time domain signal are recombined to obtain the filtered time domain signal.

[0021] One embodiment of this application provides a method for suppressing interference signals in a high-bandwidth, low-noise amplifier chip, the method comprising the following steps:

[0022] This application has at least the following beneficial effects:

[0023] This application first employs a parallel negative feedback mechanism to address the shortcomings of traditional series feedback mechanisms, thereby improving the operating bandwidth of the low-frequency noise amplifier. Furthermore, by analyzing the frequency position and structural characteristics of sub-signals, it quantitatively assesses the combined impact of low-noise amplifier gain fluctuations and external interference on signals in different frequency bands. By calculating feature weights, structural uniformity, and disorder, it ultimately obtains frequency domain feature values ​​that reflect signal interference risk, helping to identify high-risk interference frequency bands and providing a basis for subsequent interference suppression and signal quality improvement. Further, by analyzing the frequency domain feature values ​​within the observation window and combining time distance and interference severity, this application calculates the comprehensive feature value of the sub-signals, thereby more accurately identifying the signal with the most severe interference. This provides a basis for subsequent adaptive interference suppression and effectively improves the problem of insufficient interference suppression by low-noise amplifiers in different frequency bands. Finally, this application inversely maps the comprehensive feature value to the forgetting factor of the adaptive filtering algorithm. This allows the filter to dynamically adjust its response speed according to the interference risk of the signal, responding quickly to strong interference and remaining stable to weak interference, thus effectively suppressing interference from low-noise amplifiers to signals in different frequency bands and improving signal quality in low-frequency amplifiers. Attached Figure Description

[0024] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart illustrating the steps of an interference signal suppression method for a high-bandwidth, low-noise amplifier chip provided in this application;

[0026] Figure 2 This is a schematic diagram of a low-noise amplifier operating bandwidth widening circuit provided in one embodiment of this application. Figure 2 It includes an input matching circuit unit X; a negative feedback circuit unit Y; an output matching circuit unit Z; a bias circuit unit Q; resistors R1 and R2; capacitors C1, C2, C3, C4, C5, and C6; inductors L1, L2, L3, L4, L5, and L6; microstrip transmission lines ML1, ML2, ML3, and ML4; a radio frequency input RFin; a digital signal power supply Vdd; a radio frequency output RFout; and a high electron mobility transistor PHEMT. Detailed Implementation

[0027] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an interference signal suppression method for a high-bandwidth, low-noise amplifier chip proposed according to this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0029] The following description, in conjunction with the accompanying drawings, details a specific scheme for an interference signal suppression method for a high-bandwidth, low-noise amplifier chip provided in this application.

[0030] This application provides an embodiment of an interference signal suppression method for a high-bandwidth, low-noise amplifier chip. Specifically, it provides the following method for suppressing interference signals in a high-bandwidth, low-noise amplifier chip. Please refer to [link to relevant documentation]. Figure 1 The method includes the following steps:

[0031] Step S1: Obtain the time-domain signal output by the high-bandwidth low-noise amplifier chip within a preset time period before each time, and record it as the time-domain signal at each time. Obtain the time-domain signal at a preset number of times before the current time. Divide the frequency domain signal of the time-domain signal at each time into sub-signals of multiple frequency bands. Traverse the time-domain signal at the current time and all times before to obtain all sub-signals under each frequency band.

[0032] The operating frequency range, or bandwidth, of a low-noise amplifier is a key technical specification of the amplifier circuit. This embodiment significantly widens the operating bandwidth of the low-noise amplifier while optimizing gain flatness by adding an LC parallel resonator to the resistor in the traditional series negative feedback circuit.

[0033] In this embodiment, the circuit used to broaden the operating bandwidth of the low-noise amplifier includes: an input matching circuit, an output matching circuit, a bias circuit, and a negative feedback circuit. The circuit used to broaden the operating bandwidth of the low-noise amplifier uses GaAs as the substrate material of the MMIC.

[0034] The input matching circuit is connected to the gate of the transistor to achieve optimal transmission of the radio frequency signal and is used to receive the radio frequency signal; the output matching circuit is connected to the drain of the transistor to achieve optimal transmission of the radio frequency signal and is used to output the amplified signal; the bias circuit is connected to the source of the transistor to achieve gate voltage bias of the transistor, and the drain voltage Vdd is applied to the drain through an inductor to provide DC voltage to the transistor; the negative feedback circuit is connected in series between the drain and gate of the transistor to form negative feedback and is used to extend the operating bandwidth of the low noise amplifier chip.

[0035] In the circuit of this embodiment, a set of LC parallel resonators is introduced into the negative feedback circuit unit, which can extend the operating frequency of the low-noise amplifier chip to a higher range. At the same time, a set of RC parallel resonators is introduced, which can extend the operating frequency of the low-noise amplifier chip to a lower range, thereby expanding the operating bandwidth of the amplifier.

[0036] The schematic diagram of the low-noise amplifier bandwidth widening circuit provided in this embodiment is as follows: Figure 2 As shown, the circuit configuration used to broaden the operating bandwidth of the low-noise amplifier is as follows:

[0037] The microstrip transmission line ML1, capacitor C1, inductor L1, and inductor L2 constitute the input matching circuit unit X;

[0038] Resistor R2, capacitor C3, capacitor C6, inductor L4, and inductor L5 constitute the negative feedback circuit unit Y;

[0039] Inductor L6, microstrip transmission line ML4, and capacitor C4 constitute the output matching circuit unit Z;

[0040] Resistor R1 and capacitor C2 constitute the bias circuit unit Q;

[0041] After the radio frequency signal enters through the input matching circuit unit, it is amplified by the transistor. Then, part of the output signal enters the transistor gate through the feedback unit, with a phase difference of 180°, forming negative feedback. The other part of the radio frequency signal is output through the output matching unit.

[0042] In the negative feedback circuit unit, L5 and C6 form an LC parallel resonator, which can extend the operating frequency of the noise amplifier chip to a higher frequency. At the same time, C6 and R2 form an RC parallel resonator, which can extend the operating frequency of the low noise amplifier chip to a lower frequency band, thereby widening the operating frequency range of the low noise amplifier chip.

[0043] It should be noted that there are many commonly used time-frequency conversion algorithms. In this embodiment, the Fourier transform method is used to convert the time domain signal to the frequency domain signal. In practical applications, as other implementation methods, implementers may also use other time-frequency conversion algorithms such as wavelet transform according to specific circumstances. This embodiment does not impose any special restrictions on the selection of time-frequency conversion algorithms.

[0044] The Fourier transform method is a well-known technique, and the specific process of using it to convert time-domain signals into frequency-domain signals will not be elaborated here.

[0045] It should be understood that the preset quantity and the number of frequency bands N are set manually. In this embodiment, the preset quantity is 20 and the number of frequency bands N is 30. In actual applications, as other implementation methods, implementers can also set them according to specific circumstances. This embodiment does not impose any special restrictions.

[0046] It should be noted that the frequency range of the sub-signals after time-frequency conversion is 0~ ,in, The Nyquist frequency is used in the process of converting the acquired signal into a digital signal. The Nyquist frequency and the A / D analog-to-digital converter are both well-known technologies, and their specific principles will not be elaborated here.

[0047] Step S2: Number all sub-signals at each time point in ascending frequency order. Based on the numbering of each sub-signal in each frequency band, determine the feature weight of each sub-signal in each frequency band. By analyzing the average distribution of the frequency intervals between all adjacent peaks in each sub-signal in each frequency band, determine the structural uniformity of each sub-signal in each frequency band. Combined with the feature weights, determine the disorder level of each sub-signal in each frequency band. Based on the distribution of the disorder level of each sub-signal in each frequency band among the disorder levels of all sub-signals in the corresponding frequency band and the disorder level, determine the frequency domain feature value of each sub-signal in each frequency band.

[0048] Expanding the operating frequency range of a low-noise amplifier by introducing LC and RC parallel resonators will introduce gain fluctuations at both high and low frequencies. Typically, the LC parallel resonator operates at high frequencies, while the RC parallel resonator operates at low frequencies. Their resonant frequencies determine the gain characteristics of the RF signal in the high and low frequency ranges. Near the resonant frequency, the resonator exhibits high impedance, resulting in higher gain for the RF signal. However, further away from the resonant frequency, the impedance decreases, leading to a decrease in gain. This non-linear response causes gain fluctuations in both high and low frequencies, thus affecting gain flatness.

[0049] Due to environmental and power consumption variations, the operation of low-noise amplifier chips varies considerably, leading to slight changes in the resonant frequency. Especially in the high-frequency range of the operating frequency, the parasitic inductance and capacitance in the MMIC exacerbate the changes in the resonant frequency, resulting in significant variations in the gain of the low-noise amplifier.

[0050] Different communication protocols use different frequency ranges and modulation methods, resulting in dynamic changes in the frequency distribution of signals received by RF receivers at different times. This leads to relatively random variations in adjacent-channel interference between signals. The interference signal is further amplified by the gain fluctuations of the low-noise amplifier (LNA), causing the frequency distribution and statistical characteristics of the signal in the LNA to change continuously over time. However, at shorter timescales, considering the relatively slow changes in the modulation methods of communication protocols, the behavior of interference sources, and environmental factors, the frequency distribution and statistical characteristics of the RF signal change little over a short period and can be approximated as a stationary random signal.

[0051] Therefore, based on the above analysis, this embodiment numbers all sub-signals in ascending frequency order at each time point, and determines the feature weights of each sub-signal in each frequency band based on the numbering of each sub-signal in each frequency band; by analyzing the average distribution of frequency intervals between all adjacent peaks in each sub-signal in each frequency band, the structural uniformity of each sub-signal in each frequency band is determined, and combined with the feature weights, the disorder level of each sub-signal in each frequency band is determined; based on the distribution of the disorder level of each sub-signal in each frequency band among the disorder levels of all sub-signals in the corresponding frequency band and the disorder level itself, the frequency domain feature values ​​of each sub-signal in each frequency band are determined, so as to identify and distinguish signal characteristics in different frequency bands, and ultimately provide a basis for the process of suppressing interference to low-noise amplifiers. The specific process is as follows:

[0052] First, due to the varying levels of adjacent-channel interference across different frequency bands in broadband communication, and the influence of the parallel resonator of the low-noise amplifier, signals in the high-frequency and low-frequency bands exhibit greater variations and are subject to stronger interference. In this embodiment, all sub-signals at each time point are numbered in ascending frequency order. Furthermore, based on the numbering of each sub-signal within each frequency band, the characteristic weights of each sub-signal within each frequency band are determined, specifically as follows:

[0053] As one implementation method, in this embodiment, the feature weights of sub-signal j in the i-th frequency band are... The expression is: In the formula, This represents the number of all sub-signals in the sub-segment containing sub-signal j in the i-th frequency band; This represents the number of all sub-signals at time j in the i-th frequency band; , These represent the preset multiplicative factor and the preset additive factor, respectively.

[0054] It should be noted that the multiplicative factor is preset. and preset additive factors The values ​​are all manually set. In this embodiment, the multiplicative factor is preset. The value is 0.005, and the preset additive factor is... The value of is 1. In practical applications, as other implementation methods, implementers can also set it according to specific circumstances. This embodiment does not impose any special restrictions.

[0055] Based on the characteristic weights of each sub-signal in each frequency band, it can be understood that the characteristic weights reflect the likelihood of interference from internal gain fluctuations of the low-noise amplifier in different frequency bands of the signal; if the sub-signal j's... The further away from the median This indicates that the edge portions of the frequency domain signal band are more likely to be interfered with by the gain fluctuations of the low-noise amplifier. This is because the LC and RC parallel resonators function in the high and low frequency bands respectively, resulting in more pronounced gain fluctuations in these edge bands. Conversely, if the sub-signal j... The closer to the median value This indicates that the center of the frequency band of the signal is less likely to be interfered with by the gain fluctuations inside the low-noise amplifier. This is because the center frequency band is far from the resonant points of the LC and RC resonators, and the impedance change of the resonator at this point is relatively gradual. The gain of the LNA is less affected by this, so the gain fluctuation is also relatively small, and the possibility of signal interference is reduced.

[0056] Furthermore, this embodiment determines the structural uniformity of each sub-signal in each frequency band by analyzing the average distribution of the frequency intervals between all adjacent peaks in each sub-signal within each frequency band. This determination is used to characterize the frequency structural uniformity of the signal within each frequency band. Specifically:

[0057] In this embodiment, the average frequency interval between all adjacent peaks in each sub-signal of each frequency band is used as the structural uniformity of each sub-signal in each frequency band. The structural uniformity is used to characterize the frequency structural uniformity of the signal in each frequency band, reflecting the distribution of frequency components of the signal in that frequency band. If the average frequency interval between all adjacent peaks in the sub-signal of the current frequency band is larger, it usually means that the frequency structure of the signal in the current frequency band is simpler, and the signal may be smoother or more uniform, and the corresponding structural uniformity is larger. Conversely, if the average frequency interval between all adjacent peaks in the sub-signal of the current frequency band is smaller, it usually means that the frequency structure of the signal in the current frequency band is more complex, and the signal may contain denser frequency components or faster changing packets, such as multiple closely arranged peaks. The corresponding structural complexity is also relatively large, and the structural uniformity is lower.

[0058] Furthermore, this embodiment determines the degree of disorder of each sub-signal in each frequency band based on the structural uniformity of each sub-signal and in combination with the aforementioned feature weights, specifically as follows:

[0059] In this embodiment, the ratio of the feature weight to the structural uniformity of each sub-signal in each frequency band is used as the disorder level of each sub-signal in each frequency band. The disorder level is used to comprehensively evaluate the inherent interference risk of the frequency band and the complexity of the signal itself. If the feature weight of the sub-signal in the current frequency band is larger, it means that the edge part of the signal band is more likely to be interfered with by the gain fluctuation of the low-noise amplifier, and therefore, the disorder level is larger. At the same time, if the structural uniformity of the sub-signal in the current frequency band is smaller, it means that the frequency structure of the signal in the current frequency band is simpler and the signal is smoother and more uniform, and therefore, the disorder level of the corresponding sub-signal is smaller. Conversely, if the structural complexity of the sub-signal in the current frequency band is larger, it means that the frequency structure of the signal in the current frequency band is more complex, the signal contains more frequency components or changes more drastically, and therefore, the disorder level of the corresponding sub-signal is larger.

[0060] Furthermore, this embodiment determines the frequency domain feature value of each sub-signal in each frequency band based on the distribution of the disorder level of each sub-signal in the corresponding frequency band and the disorder level itself, specifically as follows:

[0061] In this embodiment, the ratio of the disorder level of each sub-signal in each frequency band to the maximum disorder level of all sub-signals in the corresponding frequency band is positively fused with the disorder level, and the result is used as the frequency domain feature value of each sub-signal in each frequency band.

[0062] It should be understood that positive fusion refers to combining two or more indicators through addition or multiplication to obtain a comprehensive indicator, thereby more comprehensively and accurately assessing a phenomenon or problem. This fusion method is not limited to simple arithmetic operations, but can also include more complex statistical models and analytical methods. Implementers can choose according to specific circumstances, and this embodiment does not impose any special restrictions.

[0063] Preferably, as a specific implementation method, in this embodiment, the product of the proportion of the disorder level of each sub-signal in each frequency band to the maximum disorder level of all sub-signals in the corresponding frequency band and the disorder level is used as the frequency domain feature value of each sub-signal in each frequency band. In actual application, the implementer may also adopt the addition or other positive fusion methods according to the specific situation. This embodiment does not impose any special restrictions.

[0064] Based on the frequency domain characteristic values ​​of each sub-signal in each frequency band, it can be understood that the frequency domain characteristic values ​​reflect the overall interference level of the sub-signal in each frequency band. If the proportion of the disorder level of the sub-signal in the current frequency band to the maximum disorder level of all sub-signals in the corresponding frequency band is larger, it indicates that the energy of the corresponding signal in the current frequency band is stronger. Strong signals are more easily interfered with by other strong signal sources. Therefore, the risk and signal complexity of the current frequency band itself are generally moderate, and the corresponding frequency domain characteristic value is larger. At the same time, if the disorder level of the sub-signals in the current frequency band is larger, it indicates that the current frequency band itself is more easily affected by low-noise amplifiers and has high signal complexity, resulting in a higher degree of interference to the signal in this frequency band. Therefore, the corresponding frequency domain characteristic value is correspondingly larger.

[0065] Conversely, if the proportion of the disorder level of the sub-signal in the current frequency band to the maximum disorder level of all sub-signals in the corresponding frequency band is smaller, it indicates that the energy of the corresponding signal in the current frequency band is relatively weak, and the weak signal is less likely to be interfered with by other strong signal sources. At the same time, if the disorder level of the sub-signal in the current frequency band is smaller, it indicates that the current frequency band itself is less affected by the gain fluctuation of the low-noise amplifier, and the frequency structure of the signal itself is relatively simple. Therefore, the overall interference level of the signal in this frequency band is lower, and the corresponding frequency domain characteristic value is also smaller.

[0066] Thus, this embodiment quantifies the combined impact of low-noise amplifier gain fluctuations and external interference on signals in different frequency bands by analyzing the frequency position and structural characteristics of sub-signals. By calculating feature weights, structural uniformity, and disorder, frequency domain feature values ​​that reflect signal interference risk are finally obtained, which helps to identify high-risk interference frequency bands and provides a basis for subsequent interference suppression and signal quality improvement.

[0067] Step S3: Divide the time-domain signals of the current time and all previous times into multiple observation windows according to time sequence. By analyzing the average distribution of the frequency domain characteristic values ​​of all sub-signals corresponding to each frequency band in each observation window, determine the signal characteristic value of each frequency band. Based on the distance from the observation window of each sub-signal to the current time, determine the time distance weight of each sub-signal. Based on the distribution of the signal characteristic values ​​of the corresponding frequency band of each sub-signal among the signal characteristic values ​​of all frequency bands, determine the signal characteristic coefficient of each sub-signal. Combined with the time distance weight, determine the comprehensive characteristic value of each sub-signal.

[0068] Currently, low-noise amplifier chips do not make sufficient use of the prior characteristics of interference in different frequency bands within a short period of time, and the degree of interference suppression for each frequency band is relatively small. This results in insufficient suppression of interference in the high-frequency and low-frequency bands of radio frequency signals, making it difficult to improve the frequency distortion of radio frequency signals.

[0069] Therefore, to address the aforementioned issues, this embodiment divides the time-domain signals of the current moment and all previous moments into multiple observation windows according to time sequence. By analyzing the average distribution of the frequency domain characteristic values ​​of all sub-signals corresponding to each frequency band within each observation window, the signal characteristic value of each frequency band is determined. Based on the distance from the observation window of each sub-signal to the current moment, the time distance weight of each sub-signal is determined. Based on the distribution of the signal characteristic values ​​of each sub-signal's corresponding frequency band among the signal characteristic values ​​of all frequency bands, the signal characteristic coefficients of each sub-signal are determined. Combined with the time distance weights, the comprehensive characteristic value of each sub-signal is determined to accurately identify the signal with the most severe interference and the most attention required, providing more precise guidance for subsequent interference suppression or signal processing. The specific process is as follows:

[0070] In this embodiment, firstly, the time-domain signals of the current moment and all previous moments are divided into K observation windows according to time sequence to analyze the fluctuation of the time-domain signals in a short period of time. Then, all observation windows are numbered according to time sequence. In this embodiment, the value of K is 5. The implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0071] Furthermore, this embodiment determines the signal characteristic value of each frequency band by analyzing the average distribution of the frequency domain characteristic values ​​of all sub-signals corresponding to each frequency band within each observation window. Specifically:

[0072] In this embodiment, the signal characteristic value in the i-th frequency band The expression is In the formula, This represents the mean of the frequency domain eigenvalues ​​of all sub-signals corresponding to the i-th frequency band within the observation window k; k represents the number of the observation window k; and K represents the total number of observation windows.

[0073] Based on the signal characteristic values ​​under each frequency band, it can be understood that the signal characteristic values ​​reflect the overall estimate of the interference in a specific frequency band within the observation window. The larger the ratio of the observation window number k to the total number of observation windows for the time-domain signal, the larger the ratio of the time-domain signal within observation window k to the total number of observation windows for the time-domain signal. This indicates that the average interference level of the time-domain signal within observation window k contributes more to the signal characteristic values. Simultaneously, if the mean of the frequency domain characteristic values ​​of all sub-signals corresponding to the i-th frequency band within observation window k... The larger the value, the more severe the interference in the i-th frequency band, and the higher the overall interference estimate. Therefore, the larger the corresponding signal characteristic value.

[0074] Conversely, if the ratio of the observation window number k to the total number of observation windows for the time-domain signal is smaller, it indicates that the time-domain signal within observation window k is farther from the current time, and the average interference level of this time-domain signal contributes less to the signal characteristic values. At the same time, if the mean of the frequency domain characteristic values ​​of all sub-signals corresponding to the i-th frequency band within observation window k is... The smaller the value, the lighter the interference in the i-th frequency band, the lower the overall interference estimate, and therefore the smaller the corresponding signal characteristic value.

[0075] Furthermore, in this embodiment, the temporal distance weight of each sub-signal is determined based on the distance from the observation window of each sub-signal to the last observation window, specifically as follows:

[0076] In this embodiment, the ratio of the observation window number of each sub-signal to the corresponding observation window number of the time-domain signal at the current time is used as the time distance weight of each sub-signal.

[0077] Furthermore, in this embodiment, the signal characteristic coefficients of each sub-signal are determined based on the distribution of the signal characteristic values ​​of the corresponding frequency bands among the signal characteristic values ​​of all frequency bands, specifically as follows:

[0078] In this embodiment, the proportion of the signal characteristic value of each sub-signal corresponding to the frequency band among the maximum values ​​of signal characteristic values ​​of all frequency bands is used as the signal characteristic coefficient of each sub-signal.

[0079] Furthermore, in this embodiment, based on the signal characteristic coefficients of each sub-signal and in conjunction with the time distance weight, the comprehensive characteristic value of each sub-signal is determined, specifically as follows:

[0080] In one implementation method, in this embodiment, the product of the time distance weight of each sub-signal and the signal characteristic coefficient is used as the comprehensive characteristic value of each sub-signal.

[0081] Based on the temporal distance characteristic values ​​of each sub-signal, it can be understood that the comprehensive characteristic value reflects the reference degree of each sub-signal to the stability assessment of the current time-domain signal. If the temporal distance weight of the current sub-signal is larger, it means that for time-domain signals closer to the current time, the forgetting factor of the current adaptive filtering algorithm is larger, indicating that the sub-signal has greater reference significance for the stability assessment of the current time-domain signal. Therefore, the corresponding comprehensive characteristic value is also larger. At the same time, if the signal characteristic coefficient of the current sub-signal is larger, it means that the interference estimation of the detected sub-signal is very serious. The corresponding comprehensive characteristic value is larger. Therefore, we should pay more attention to the fluctuation degree of the current sub-signal to more effectively suppress the interference it carries.

[0082] Conversely, the smaller the time distance weight of the current sub-signal, the farther away the sub-signal is from the current time, and the less reference significance it has for the current time domain signal stability assessment. Therefore, the corresponding comprehensive characteristic value is also smaller. At the same time, if the signal characteristic coefficient of the current sub-signal is smaller, it means that the interference estimate of the detected sub-signal is relatively light, and the corresponding comprehensive characteristic value is also smaller. Therefore, the attention to the fluctuation degree of the sub-signal can be appropriately reduced, and the intensity of interference suppression can also be weakened accordingly.

[0083] Thus, this embodiment analyzes the frequency domain characteristic values ​​within the observation window, combines time distance and interference severity, and calculates the comprehensive characteristic values ​​of the sub-signals, thereby more accurately identifying the signal with the most severe interference. This provides a basis for subsequent adaptive interference suppression and effectively improves the problem of insufficient interference suppression in different frequency bands by low-noise amplifiers.

[0084] Step S4: Based on the comprehensive feature value, an adaptive filtering algorithm is used to suppress interference signals in the time domain signal at the current time.

[0085] Furthermore, based on the comprehensive feature value obtained in step S3, an adaptive filtering algorithm is used to suppress interference signals in the time-domain signal at the current moment, specifically as follows:

[0086] In this embodiment, in the frequency domain signal of the time domain signal at the current time, the corresponding part of each sub-signal in the time domain signal is used as the input of the adaptive filtering algorithm. The forgetting factor of the adaptive filtering algorithm is set as the normalized value of the inverse of the comprehensive feature value of the corresponding sub-signal. The corresponding part of each sub-signal in the time domain signal after filtering is output, and the corresponding parts of all sub-signals in the time domain signal are recombined to obtain the filtered time domain signal.

[0087] It should be noted that a larger comprehensive eigenvalue means that the sub-signal is subject to more severe interference, requiring a faster response to suppress it. Therefore, the forgetting factor should be smaller, as a smaller forgetting factor allows the adaptive filtering algorithm to forget old information more quickly and adjust its parameters more rapidly to adapt to current changes. Conversely, a smaller comprehensive eigenvalue means that the sub-signal is subject to less interference or is far from the sub-signal, requiring less rapid response. Therefore, the forgetting factor should be larger, as a larger forgetting factor allows the filtering algorithm to forget old information more slowly, retaining more historical data influence. This helps maintain the stability of the filter and smooth tracking of signal characteristics when interference is not severe, avoiding over-adjustment due to small fluctuations that could lead to signal distortion.

[0088] It should be noted that there are many commonly used adaptive filtering algorithms. In this embodiment, the recursive least squares (RLS) algorithm is used to filter the time domain signal. In practical applications, as other implementation methods, implementers can also set their own methods according to specific circumstances. This embodiment does not impose any special restrictions.

[0089] The principle of the Recursive Least Squares (RLS) algorithm is a well-known technique that utilizes the individual process of dialing a signal.

[0090] Thus, by analyzing the frequency domain characteristics, structural uniformity, and time weight of the sub-signals, the comprehensive characteristic value is calculated and mapped inversely to the forgetting factor of the adaptive filtering algorithm. This enables the filter to dynamically adjust its response speed according to the interference risk of the signal, responding quickly to strong interference and remaining stable to weak interference, thereby effectively suppressing the interference of the low-noise amplifier to signals in different frequency bands and improving signal quality.

[0091] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0092] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0093] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; modifications to the technical solutions described in the foregoing embodiments, or equivalent substitutions of some of the technical features, do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for suppressing interference signals in a high-bandwidth, low-noise amplifier chip, characterized in that, The method includes the following steps: The time-domain signal output by the high-bandwidth low-noise amplifier chip within a preset time period before each time moment is obtained and denoted as the time-domain signal at each time moment. The time-domain signal of the preset number of time moments before the current time moment is obtained. The frequency domain signal of the time-domain signal at each time moment is divided into sub-signals of multiple frequency bands. The time-domain signal at the current time moment and all time moments before it is traversed to obtain all sub-signals under each frequency band. All sub-signals at each time point are numbered in ascending order of frequency. Based on the numbering of each sub-signal in each frequency band, the feature weights of each sub-signal in each frequency band are determined. By analyzing the average distribution of the frequency intervals between all adjacent peaks in each sub-signal in each frequency band, the structural uniformity of each sub-signal in each frequency band is determined. Combined with the feature weights, the disorder level of each sub-signal in each frequency band is determined. Based on the distribution of the disorder level of each sub-signal in each frequency band among the disorder levels of all sub-signals in the corresponding frequency band and the disorder level itself, the frequency domain feature values ​​of each sub-signal in each frequency band are determined. The time-domain signals of the current moment and all previous moments are divided into multiple observation windows according to time sequence. By analyzing the average distribution of the frequency domain characteristic values ​​of all sub-signals corresponding to each frequency band in each observation window, the signal characteristic value of each frequency band is determined. Based on the distance from the observation window of each sub-signal to the current moment, the time distance weight of each sub-signal is determined. Based on the distribution of the signal characteristic values ​​of the corresponding frequency band of each sub-signal among the signal characteristic values ​​of all frequency bands, the signal characteristic coefficient of each sub-signal is determined. Combined with the time distance weight, the comprehensive characteristic value of each sub-signal is determined. Based on the comprehensive feature values, an adaptive filtering algorithm is used to suppress interference signals in the time-domain signal at the current moment; The expression for the feature weights of each sub-signal in each frequency band is as follows: In the formula, This represents the feature weight of sub-signal j in the i-th frequency band; This represents the number of all sub-signals in the sub-segment containing sub-signal j in the i-th frequency band; This represents the number of all sub-signals at time j in the i-th frequency band; , These represent the preset multiplicative factor and the preset additive factor, respectively. The method for determining the signal characteristic values ​​under each frequency band is as follows: Number all observation windows sequentially, and the signal characteristic value in the i-th frequency band. The expression is In the formula, This represents the mean of the frequency domain eigenvalues ​​of all sub-signals corresponding to the i-th frequency band within the observation window k; k represents the number of the observation window k; and K represents the total number of observation windows.

2. The interference signal suppression method for a high-bandwidth, low-noise amplifier chip as described in claim 1, characterized in that, The structural uniformity of each sub-signal in each frequency band is the average frequency interval between all adjacent peaks in each sub-signal of each frequency band.

3. The interference signal suppression method for a high-bandwidth, low-noise amplifier chip as described in claim 1, characterized in that, The disorder level of each sub-signal in each frequency band is the ratio of the feature weight to the structural uniformity of each sub-signal in each frequency band.

4. The interference signal suppression method for a high-bandwidth, low-noise amplifier chip as described in claim 1, characterized in that, The frequency domain feature value of each sub-signal in each frequency band is the result of positive fusion of the proportion of the disorder level of each sub-signal in each frequency band to the maximum disorder level of all sub-signals in the corresponding frequency band and the disorder level.

5. The interference signal suppression method for a high-bandwidth, low-noise amplifier chip as described in claim 1, characterized in that, The time distance weight of each sub-signal is the ratio of the observation window number of each sub-signal to the corresponding observation window number of the time-domain signal at the current time.

6. The interference signal suppression method for a high-bandwidth, low-noise amplifier chip as described in claim 1, characterized in that, The signal characteristic coefficient of each sub-signal is the proportion of the signal characteristic value of the corresponding frequency band of each sub-signal to the maximum value of the signal characteristic value of all frequency bands.

7. The interference signal suppression method for a high-bandwidth, low-noise amplifier chip as described in claim 1, characterized in that, The comprehensive characteristic value of each sub-signal is the product of the time distance weight of each sub-signal and the signal characteristic coefficient.

8. The interference signal suppression method for a high-bandwidth, low-noise amplifier chip as described in claim 1, characterized in that, The method of using an adaptive filtering algorithm to suppress interference signals in the time-domain signal at the current moment includes: In the frequency domain signal of the time domain signal at the current moment, the corresponding parts of each sub-signal in the time domain signal are used as the input of the adaptive filtering algorithm. The forgetting factor of the adaptive filtering algorithm is set as the normalized value of the inverse of the comprehensive feature value of the corresponding sub-signal. The corresponding parts of each sub-signal in the time domain signal after filtering are output, and the corresponding parts of all sub-signals in the time domain signal are recombined to obtain the filtered time domain signal.

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