IQ imbalance compensation method and system based on adaptive filtering algorithm

Abstract

The invention relates to the field of algorithms, and discloses an IQ imbalance compensation method and system based on an adaptive filtering algorithm, and the system comprises a data collection module, an IQ imbalance estimation module, a compensation filter design module, a compensation filter parameter updating module, and an IQ imbalance compensation module. The data acquisition module is responsible for acquiring original IQ data from receiver hardware, the IQ imbalance estimation module is responsible for designing a corresponding compensation filter according to the acquired IQ data, the compensation filter design module is responsible for designing a corresponding compensation filter, and the compensation filter parameter updating module is responsible for updating the compensation filter parameter according to a real-time IQ imbalance estimation result. The IQ imbalance compensation module is used for dynamically updating parameters of a compensation filter, the IQ imbalance compensation module is used for compensating acquired original IQ data, the output module is used for outputting the processed IQ data, and the method has the advantages that degree-of-freedom gain under an interference channel is improved, and system capacity higher than that of a traditional second-order circle Gaussian signaling scheme is obtained.

Description

Technical Field

[0001] The present invention relates to the field of algorithms, and in particular to an IQ imbalance compensation method and system based on an adaptive filtering algorithm. Background Art

[0002] In communication systems, I / Q imbalance refers to the mismatch in amplitude or phase of the orthogonal signals collected during signal acquisition and processing due to factors such as hardware design, temperature changes, and interference. This can cause problems such as distortion and inter-code interference in the processing of the signal at the receiving or transmitting end, which in turn affects the performance and reliability of the communication system. With the rapid development of civil mobile communications and personal communication technologies, low cost, small area, and low power consumption have become important goals in the hardware design of terminal equipment. Due to the limited terminal cost, low-cost circuit components are usually accompanied by many non-ideal characteristics of the RF front end, and I / Q imbalance has become an important factor affecting performance. When a similar method of second-order non-circular signals, such as binary phase shift keying (BPSK), is used as the desired signal or interference signal, the sensor array can usually obtain higher direction of arrival (DOA) accuracy. In Multiple Input Multiple Output (MIMO) wireless communications, using an improper Gaussian signal and a matching interference alignment scheme can improve the degree of freedom gain under interference channels and obtain a higher system capacity than the traditional second-order circular Gaussian signaling scheme.

[0003] As one of the non-ideal factors of the RF front end, I / Q imbalance will reduce the accuracy of signal modulation at the transmitter and cause errors in channel estimation and signal detection at the receiver, thus affecting the reliability and effectiveness of the entire communication system. Existing research methods either require special pilot symbols or special circuit designs or transmission mechanisms, so they are difficult to be widely used. Studies have shown that frequency-selective IQ imbalance signals exhibit non-circular characteristics with non-zero complementary covariance matrices, and this characteristic is stable and not affected by multipath fading channels. This design uses the non-circular characteristics of the IQ imbalance signal x(n) to equate the blind compensation problem of IQ imbalance to the optimization problem of the signal "non-circular→circular". Therefore, this design uses an adaptive blind compensation algorithm (ACLMS) based on augmented CLMS to further improve the estimation performance. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In view of the shortcomings of the prior art, the present invention provides an IQ imbalance compensation method and system based on an adaptive filtering algorithm, which has the advantages of improving the degree of freedom gain under interference channels and obtaining a higher system capacity than the traditional second-order Gaussian signaling scheme, thereby solving the problems in the above-mentioned background technology.

[0006] (II) Technical solution

[0007] In order to achieve the above-mentioned purpose of improving the degree of freedom gain under the interference channel and obtaining a higher system capacity than the traditional second-order circle Gaussian signaling scheme, the present invention provides the following technical solutions: an IQ imbalance compensation method and system based on an adaptive filtering algorithm, comprising a data acquisition module, an IQ imbalance estimation module, a compensation filter design module, a compensation filter parameter update module, and an IQ imbalance compensation module, wherein the data acquisition module is responsible for collecting original IQ data from the receiver hardware, the IQ imbalance estimation module estimates the IQ imbalance of the signal by using an adaptive filtering algorithm according to the collected IQ data, the compensation filter design module designs a corresponding compensation filter according to the output result of the IQ imbalance estimation module, the compensation filter parameter update module is responsible for dynamically updating the parameters of the compensation filter according to the real-time IQ imbalance estimation result, the IQ imbalance compensation module uses the designed compensation filter to perform compensation processing on the collected original IQ data to eliminate the IQ imbalance effect in the signal, and the output module is used to output the processed IQ data.

[0008] According to the above technical solution, the data acquisition module includes a data interface module and a data conversion module. The data interface module is responsible for communicating with the receiver hardware and receiving the original IQ data. The data conversion module is used to convert the analog signal into a digital signal and perform sampling and quantization.

[0009] According to the above technical solution, the IQ imbalance estimation module includes a signal analysis module and a parameter estimation module. The signal analysis module is used to analyze the collected IQ data to detect the imbalance phenomenon therein, and the parameter estimation module uses an adaptive filtering algorithm to estimate the imbalance parameters.

[0010] According to the above technical solution, the compensation filter design module includes a filter parameter estimation module and a filter implementation module. The filter parameter estimation module is responsible for estimating the weights or coefficients of the compensation filter according to the characteristics and requirements of the system, and the filter implementation module is responsible for converting the parameters into the structure and coefficients of the filter, and evaluating and verifying the performance and effect of the filter.

[0011] According to the above technical solution, the compensation filter parameter update module includes an adaptive algorithm module and a parameter update module, the adaptive algorithm module includes selecting an adaptive filtering algorithm, and the parameter update module adjusts the weight parameters of the filter according to the error signal and the algorithm update rule.

[0012] According to the above technical solution, the IQ imbalance compensation module includes a filtering processing module and a compensation effect evaluation module. The filtering processing module is used to input the collected IQ data into the compensation filter for compensation processing, and the compensation effect evaluation module is used to evaluate the quality of the compensated IQ data.

[0013] According to the above technical solution, the method of estimating the unbalanced parameters using an adaptive filtering algorithm further includes using an adaptive filter to estimate the parameters, defining an estimation error, which represents the difference between the filter output and the actual output, and continuously iterating and updating the parameters until the convergence condition is met or the maximum number of iterations is reached. The formula is expressed as:

[0014] y(n)=W*x(n)

[0015] e(n)=d(n)-y(n)

[0016] W(n+1)=W(n)+μe(n)*x(n)

[0017] Among them, x(n) is the input signal, y(n) is the filter output signal, d(n) is the desired output signal, e(n) is the estimation error, W is the weight vector of the filter, and u is the adaptive long parameter that controls the update speed of the weight.

[0018] According to the above technical solution, the IQ imbalance compensation method based on the adaptive filtering algorithm comprises the following steps:

[0019] S1: Obtain IQ data from the receiver, including real and imaginary parts, and pre-process the collected IQ data, including noise removal and equalization;

[0020] S2: Design an augmented adaptive filter to estimate and compensate for the distortion caused by IQ imbalance;

[0021] S3: Initialize the parameters of the adaptive filter, including the weight parameters of the main channel and the additional channel;

[0022] S4: According to the update rule of the ACLMS algorithm, the parameters of the adaptive filter are continuously updated according to the received IQ signal and the expected output signal;

[0023] S5: Using the signal processed by the adaptive filter as the compensated output.

[0024] According to the above technical solution, step S1 further includes that there may be correlated noise or multipath effect in the received IQ data. Decorrelation technology is used to reduce these effects to improve signal quality. Decorrelation algorithms generally include the calculation of autocorrelation functions, and data processing is performed based on the results to reduce the impact of correlated noise on the system. Channel estimation is performed on the received IQ data to understand the fading characteristics and time-varying properties of the channel. The channel estimation results can be used for subsequent signal compensation and equalization processing. The received IQ data is equalized to compensate for the distortion and dispersion effects caused by the channel. Equalization processing helps to improve signal quality and demodulation performance. Equalization processing techniques include linear equalizers, nonlinear equalizers, adaptive equalizers, etc. During signal transmission, there may be problems such as clock drift or frequency offset. Time-frequency synchronization processing is performed to correct these offsets to ensure accurate reception and demodulation of data. Data resampling processing is performed according to the sampling rate of the received IQ data and system requirements. This helps to adjust the data to the correct sampling rate and match it with subsequent processing steps. Data resampling can be achieved through technologies such as interpolation and downsampling. Frequency offset correction is performed on the received IQ data to eliminate the error caused by carrier frequency offset. Frequency offset correction helps improve demodulation performance and signal quality.

[0025] According to the above technical solution, step S2 further includes initializing a weight vector W of the adaptive filter, which includes weight parameters of the main channel and the additional channel. The weight vector W is divided into two parts, one for the main channel W main , and the other part is used for the additional channel W extra , if the adaptive filter has N weight parameters, the first M parameters can be assigned to the main channel, and the remaining NM parameters can be assigned to the additional channels, where M < N. Initialize the weight parameters of the main channel and the additional channels. The initial values ​​of these parameters can usually be randomly selected or set based on prior knowledge. For the weight parameter W of the main channel main , can be initialized to a larger random value to ensure that the filter can effectively filter the signal at the beginning. For the weight parameter W of the additional channel extra , can be initialized to a small random value.

[0026] According to the above technical solution, step S4 further includes receiving an IQ signal of r=I+jQ, the desired output signal of d, initializing the weight vector w and augmentation vector a of the adaptive filter, and for the kth moment, the input signal r k The output y of the filter k The error is e k =d k -y k , update the weight vector w:

[0027]

[0028] Where μ is the step size parameter, is the conjugate transpose of the augmented vector, repeat the previous step until the stopping criterion is met.

[0029] (III) Beneficial effects

[0030] Compared with the prior art, the present invention provides an IQ imbalance compensation method and system based on an adaptive filtering algorithm, which has the following beneficial effects:

[0031] The IQ imbalance compensation method and system based on the adaptive filtering algorithm use the adaptive filtering algorithm to estimate the I / Q imbalance parameters by iteratively inputting a signal with a gradually changing circularity in real time. According to the updating rule of the ACLMS algorithm, the parameters of the adaptive filter are continuously updated according to the received IQ signal and the expected output signal. The non-circular characteristics of the IQ imbalance signal x(n) are utilized to equate the blind compensation problem of the IQ imbalance to the optimization problem of the signal "non-circular→circular", and the adaptive blind compensation algorithm (ACLMS) based on augmented CLMS is used to further improve the estimation performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] See also Figure 1The present invention provides a technical solution: an IQ imbalance compensation system based on an adaptive filtering algorithm, comprising a data acquisition module, an IQ imbalance estimation module, a compensation filter design module, a compensation filter parameter update module, and an IQ imbalance compensation module, characterized in that the data acquisition module is responsible for collecting original IQ data from receiver hardware, the IQ imbalance estimation module estimates the IQ imbalance of the signal by using an adaptive filtering algorithm according to the collected IQ data, the compensation filter design module designs a corresponding compensation filter according to the output result of the IQ imbalance estimation module, the compensation filter parameter update module is responsible for dynamically updating the parameters of the compensation filter according to the real-time IQ imbalance estimation result, the IQ imbalance compensation module uses the designed compensation filter to perform compensation processing on the collected original IQ data to eliminate the IQ imbalance effect in the signal, and the output module is used to output the processed IQ data.

[0035] The data acquisition module includes a data interface module and a data conversion module. The data interface module is responsible for communicating with the receiver hardware and receiving the original IQ data. The data conversion module is used to convert the analog signal into a digital signal and perform sampling and quantization.

[0036] The IQ imbalance estimation module includes a signal analysis module and a parameter estimation module. The signal analysis module is used to analyze the collected IQ data to detect the imbalance phenomenon therein, and the parameter estimation module uses an adaptive filtering algorithm to estimate the imbalance parameters.

[0037] The compensation filter design module includes a filter parameter estimation module and a filter implementation module. The filter parameter estimation module is responsible for estimating the weights or coefficients of the compensation filter according to the characteristics and requirements of the system, and the filter implementation module is responsible for converting the parameters into the structure and coefficients of the filter, and evaluating and verifying the performance and effect of the filter.

[0038] The compensation filter parameter updating module includes an adaptive algorithm module and a parameter updating module. The adaptive algorithm module includes selecting an adaptive filtering algorithm. The parameter updating module adjusts the weight parameters of the filter according to an error signal and an algorithm updating rule.

[0039] The IQ imbalance compensation module includes a filtering processing module and a compensation effect evaluation module. The filtering processing module is used to input the collected IQ data into the compensation filter for compensation processing, and the compensation effect evaluation module is used to evaluate the quality of the compensated IQ data.

[0040] An IQ imbalance compensation method based on an adaptive filtering algorithm comprises the following steps:

[0041] S1: Obtain IQ data from the receiver, including real and imaginary parts, and pre-process the collected IQ data, including noise removal and equalization;

[0042] S2: Design an augmented adaptive filter to estimate and compensate for the distortion caused by IQ imbalance;

[0043] S3: Initialize the parameters of the adaptive filter, including the weight parameters of the main channel and the additional channel;

[0044] S4: According to the update rule of the ACLMS algorithm, the parameters of the adaptive filter are continuously updated according to the received IQ signal and the expected output signal;

[0045] S5: Using the signal processed by the adaptive filter as the compensated output.

[0046] Step S1 further includes that there may be correlated noise or multipath effects in the received IQ data. Decorrelation technology is used to reduce these effects to improve signal quality. Decorrelation algorithms generally include the calculation of autocorrelation functions, and data processing is performed based on the results to reduce the impact of correlated noise on the system. Channel estimation is performed on the received IQ data to understand the fading characteristics and time-varying properties of the channel. The channel estimation results can be used for subsequent signal compensation and equalization processing. The received IQ data is equalized to compensate for the distortion and dispersion effects caused by the channel. Equalization processing helps to improve signal quality and demodulation performance. Equalization processing techniques include linear equalizers, nonlinear equalizers, adaptive equalizers, etc. During signal transmission, there may be problems such as clock drift or frequency offset. Time-frequency synchronization processing is performed to correct these offsets to ensure accurate reception and demodulation of data. Data resampling processing is performed according to the sampling rate of the received IQ data and system requirements. This helps to adjust the data to the correct sampling rate and match it with subsequent processing steps. Data resampling can be achieved through techniques such as interpolation and downsampling. Frequency offset correction is performed on the received IQ data to eliminate the error caused by carrier frequency offset. Frequency offset correction helps improve demodulation performance and signal quality.

[0047] Step S2 further includes initializing a weight vector W of the adaptive filter, which includes weight parameters of the main channel and the additional channel. The weight vector W is divided into two parts, one for the main channel W main , and the other part is used for the additional channel W extra If the adaptive filter has N weight parameters, the first M parameters can be assigned to the primary channel and the remaining NM parameters can be assigned to the additional channels, where M < N. Initialize the weight parameters of the primary channel and the additional channels. The initial values ​​of these parameters can usually be randomly selected or set based on prior knowledge. For the weight parameter W of the primary channelmain , can be initialized to a larger random value to ensure that the filter can effectively filter the signal at the beginning. For the weight parameter W of the additional channel extra , can be initialized to a small random value.

[0048] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" 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 other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0049] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An IQ imbalance compensation system based on an adaptive filtering algorithm, comprising a data acquisition module, an IQ imbalance estimation module, a compensation filter design module, a compensation filter parameter update module, and an IQ imbalance compensation module, characterized in that: The data acquisition module is responsible for collecting original IQ data from the receiver hardware. The IQ imbalance estimation module estimates the IQ imbalance of the signal using an adaptive filtering algorithm based on the collected IQ data. The compensation filter design module designs a corresponding compensation filter based on the output result of the IQ imbalance estimation module. The compensation filter parameter update module is responsible for dynamically updating the parameters of the compensation filter based on the real-time IQ imbalance estimation result. The IQ imbalance compensation module uses the designed compensation filter to perform compensation processing on the collected original IQ data to eliminate the IQ imbalance effect in the signal. The output module is used to output the processed IQ data.

2. The IQ imbalance compensation system based on the adaptive filtering algorithm according to claim 1, characterized in that: The data acquisition module includes a data interface module and a data conversion module. The data interface module is responsible for communicating with the receiver hardware and receiving the original IQ data. The data conversion module is used to convert the analog signal into a digital signal and perform sampling and quantization.

3. The IQ imbalance compensation system based on the adaptive filtering algorithm according to claim 1, characterized in that: The IQ imbalance estimation module includes a signal analysis module and a parameter estimation module. The signal analysis module is used to analyze the collected IQ data to detect the imbalance phenomenon therein, and the parameter estimation module uses an adaptive filtering algorithm to estimate the imbalance parameters.

4. The IQ imbalance compensation system based on the adaptive filtering algorithm according to claim 1, characterized in that: The compensation filter design module includes a filter parameter estimation module and a filter implementation module. The filter parameter estimation module is responsible for estimating the weights or coefficients of the compensation filter according to the characteristics and requirements of the system, and the filter implementation module is responsible for converting the parameters into the structure and coefficients of the filter, and evaluating and verifying the performance and effect of the filter.

5. The IQ imbalance compensation system based on the adaptive filtering algorithm according to claim 1, characterized in that: The compensation filter parameter updating module includes an adaptive algorithm module and a parameter updating module. The adaptive algorithm module includes selecting an adaptive filtering algorithm. The parameter updating module adjusts the weight parameters of the filter according to an error signal and an algorithm updating rule.

6. The IQ imbalance compensation system based on the adaptive filtering algorithm according to claim 1, characterized in that: The IQ imbalance compensation module includes a filtering processing module and a compensation effect evaluation module. The filtering processing module is used to input the collected IQ data into a compensation filter for compensation processing, and the compensation effect evaluation module is used to evaluate the quality of the compensated IQ data.

7. The IQ imbalance compensation system based on the adaptive filtering algorithm according to claim 1, characterized in that: The method of using an adaptive filter algorithm to adjust the weight of the filter according to the feedback of the error signal further includes compensating the weight of the filter to be W=[W0, W1, ...W N-1 ], where N is the length of the filter, x(N) is the input signal sample vector, d(n) is the desired output, y(n) is the output of the filter, and e(n) is the error signal. The formula is: Where W(n) is the filter weight vector at step n, μ is the adaptive step size parameter that controls the update speed of the weights, and J is the loss function that measures the difference between the expected output d[n] and the actual output y[n]. is the gradient of the loss function J with respect to the weight vector W.

8. The IQ imbalance compensation system based on adaptive filtering algorithm according to claim 3, characterized in that: The method of estimating the imbalanced parameters using an adaptive filtering algorithm further includes estimating the parameters using an adaptive filter, defining an estimation error representing the difference between the filter output and the actual output, and continuously iterating and updating the parameters until a convergence condition is met or a maximum number of iterations is reached, as expressed by the formula: y(n)=W*x(n) e(n)=d(n)-y(n) W(n+1)=W(n)+μe(n)*x(n) Among them, x(n) is the input signal, y(n) is the filter output signal, d(n) is the desired output signal, e(n) is the estimation error, W is the weight vector of the filter, and μ is an adaptive long parameter that controls the update speed of the weight.

9. An IQ imbalance compensation method based on an adaptive filtering algorithm, applied to the imbalance compensation system according to claims 1-6, characterized in that: The following steps are involved: S1: Obtain IQ data from the receiver, including real and imaginary parts, and pre-process the collected IQ data, including noise removal and equalization; S2: Design an augmented adaptive filter to estimate and compensate for the distortion caused by IQ imbalance; S3: Initialize the parameters of the adaptive filter, including the weight parameters of the main channel and the additional channel; S4: According to the update rule of the ACLMS algorithm, the parameters of the adaptive filter are continuously updated according to the received IQ signal and the expected output signal; S5: Using the signal processed by the adaptive filter as the compensated output.

10. The IQ imbalance compensation method based on adaptive filtering algorithm according to claim 9, characterized in that: The S4 further includes the received IQ signal, the expected output signal is d, the weight vector w and the augmentation vector a of the initialization adaptive filter, and for the kth moment, the input signal r k The output y of the filter k The error is e k =d k -y k , update the weight vector w: Where μ is the step size parameter, is the conjugate transpose of the augmented vector, e k is the error, repeat the previous step until the stopping criterion is met.

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