A joint equalization method for signals in fading channels

Abstract

This invention provides a joint equalization method for signals in fading channels, comprising: obtaining the cost functions of a CMA equalizer and a DD equalizer based on the IQ data before equalization; obtaining the cost function of a joint equalizer based on the cost functions of the CMA equalizer and the DD equalizer; and performing joint equalization processing on the IQ data before equalization to obtain the equalized IQ data. This application has strong practicality and robustness; even with strong channel fading in the signal, it can compensate for the loss, enabling the signal to be demodulated.

Description

Technical Field

[0001] This application relates to the field of signal processing technology, and in particular to a joint equalization method for signals in fading channels. Background Technology

[0002] Since its invention, radio communication has played a vital role in numerous fields and is now inextricably linked to human society, with an increasing number of scientists and engineers dedicating themselves to the research and optimization of radio communication technology. In digital communication, data passing through the channel is often affected by external factors such as white noise, group delay, and multipath effects, resulting in a complex and variable channel characteristic that causes signal attenuation, distortion, and aberration. According to the Nyquist intersymbol interference-free criterion, this interference needs to be equalized to compensate for it, facilitating subsequent demodulation processing.

[0003] In recent years, equalization technology has become increasingly sophisticated, and commonly used single equalizers have begun to be widely used in signal demodulation equipment. However, facing complex and ever-changing channel conditions, single equalizers cannot fully meet the requirements. Therefore, the need for updating and iterating equalization technology is urgent, and combining various equalizers has become a trend. In radio signal technology, channel equalization plays a crucial role in demodulation. How to reduce convergence errors and how to accurately compensate channel parameters under multipath channel conditions are key issues. Summary of the Invention

[0004] In view of this, this application provides a joint equalization method for signals under fading channels, which uses a joint equalizer algorithm to recover signals based on the severe channel fading conditions in radio propagation.

[0005] This application discloses a joint equalization method for signals in fading channels, which includes:

[0006] Step 1: Based on the IQ data before equalization, obtain the cost function of the Constant Modulus Algorithm (CMA) equalizer and the cost function of the Decision Guided (DD) equalizer;

[0007] Step 2: Obtain the cost function of the joint equalizer based on the cost functions of the CMA equalizer and the DD equalizer;

[0008] Step 3: Perform joint equalization processing on the IQ data before equalization to obtain the equalized IQ data.

[0009] Further, step 1 includes:

[0010] Input the IQ data before balancing and sort them by first and last to obtain the first data;

[0011] The first data is input into the CMA equalizer to obtain the equalization error function of the CMA equalizer. y CMA (n) and These are the squared statistical magnitude of the CMA equalizer output signal and the squared magnitude of the modulation type, respectively.

[0012] Based on the equalization error function of the CMA equalizer, calculate the cost function of the CMA equalizer, where the cost function is the error function e. CMA Mean square error of (n)

[0013] Further, step 1 includes:

[0014] Input the IQ data before balancing and sort them by first and last to obtain the first data;

[0015] The first data is input into the DD equalizer to obtain the equalization error function of the DD equalizer. y DD (n) and These are the standard constellation mapping symbol points for the equalized output signal and modulation type of the DD equalizer, respectively.

[0016] Based on the equalization error function of the DD equalizer, calculate the cost function of the DD equalizer, where the cost function is the error function e. DD Mean square error of (n)

[0017] Further, step 2 includes:

[0018] The cost functions of the CMA equalizer and the DD equalizer are nonlinearly cascaded and coupled into the cost function of the joint equalizer.

[0019] The key parameters of the joint equalizer are set, namely the number of filter taps, the iteration step size, and the number of loops.

[0020] Furthermore, the method for obtaining the cost function of the joint equalizer includes:

[0021] Based on the cost function JDD of the DD equalizer, the coupling coefficient is obtained using the hyperbolic tangent function:

[0022]

[0023] Based on the coupling coefficient λ, and the cost functions of the DD equalizer and the CMA equalizer, the cost function of the joint equalizer is obtained:

[0024] J = (1-λ)*J DD +λ*J CMA

[0025] Among them, J DD J is the cost function of the DD equalizer. CMA Let J be the cost function of the CMA equalizer, and J be the cost function of the joint equalizer.

[0026] Furthermore, the cost function of the CMA equalizer is:

[0027]

[0028] Among them, y CMA (n) and These are the squared statistical magnitude of the CMA equalizer output signal and the squared magnitude of the modulation type, respectively; J CMA is the cost function of the CMA equalizer; E is the mean square error function.

[0029] Furthermore, the cost function of the DD equalizer is:

[0030]

[0031] Among them, y DD (n) and These represent the standard constellation mapping symbol points for the DD equalizer output signal and the modulation type, respectively; J DD Let E be the cost function of the DD equalizer; E is the mean square error function.

[0032] Further, step 3 includes:

[0033] The IQ data before equilibration is subjected to joint equilibration processing to determine whether the IQ data after equilibration meets the convergence condition. If the convergence condition is not met, equilibration continues; otherwise, a converged constellation diagram is obtained, which is the IQ data after equilibration.

[0034] Furthermore, if the vector error between the equalized constellation diagram and the standard constellation diagram shape of the corresponding modulation scheme is less than 10 within a signal-to-noise ratio of 20dB, then the equalized IQ data is determined to meet the convergence condition.

[0035] Furthermore, the formula for calculating the vector error is:

[0036]

[0037] Among them, EVM RMS Let Ik be the vector error, Qk be the equalized I data, and Qk be the equalized Q data. For standard constellation reference I data, The standard constellation diagram reference Q data is used, and N is the length of the IQ data.

[0038] Due to the adoption of the above technical solution, this application has the following advantages:

[0039] 1. The joint equalization method of this application has high accuracy. Commonly used CMA equalizers suffer from slow convergence speed and false convergence. Furthermore, when using the CMA algorithm to equalize a signal, the cost function is only related to the amplitude information of the received signal and not its phase information, resulting in a phase shift in the final equalized output signal. Similarly, DD equalization also has certain problems, such as poor restart capability, causing constellation diagram divergence and failure of eye diagram to open. The joint equalization method of this invention can avoid the problems of both and also ensure signal accuracy.

[0040] 2. In the process of joint signal equalization, this application can still correctly converge the signal even under deep multipath fading channel interference. The longer the duration of the signal, the more symbols can be used, and the smaller the steady-state error after joint equalization. This method has strong robustness.

[0041] 3. Compared with the prior art, this application has strong practicality and robustness. Even if there is strong channel fading in the signal, the loss can be compensated, and the signal can be demodulated.

[0042] 4. This application nonlinearly cascades two commonly used equalizers, enabling more accurate recovery of distorted signals and exhibiting strong robustness. Even under adverse channel conditions, it can perform the equalization task excellently.

[0043] 5. The joint equalization method for signals in fading channels designed in this application can effectively meet the convergence conditions. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating a joint equalization method for signals in fading channels according to an embodiment of this application.

[0045] Figure 2 This is a schematic diagram of the adaptive linear equalization principle of CMA equalization and DD equalization in an embodiment of this application.

[0046] Figure 3 This is a schematic diagram illustrating the process of using a nonlinear cascader to couple the CMA equilibrium cost function and the DD equilibrium cost function into a joint equilibrium cost function, as described in an embodiment of this application.

[0047] Figure 4 This is a diagram illustrating the effect of using a separate CMA equalization signal to recover the constellation in an embodiment of this application.

[0048] Figure 5 This is a diagram illustrating the constellation recovery effect using a separate DD equalization signal, as shown in an embodiment of this application.

[0049] Figure 6This is a diagram illustrating the constellation effect of joint equalization signal recovery according to an embodiment of this application. Detailed Implementation

[0050] The present application will be further described in conjunction with the accompanying drawings and embodiments. The described embodiments are only some, not all, of the embodiments of the present application. All other embodiments obtained by those skilled in the art should fall within the protection scope of the embodiments of the present application.

[0051] See Figure 1 This application provides an embodiment of a joint equalization method for signals in fading channels, comprising:

[0052] Step 1: Based on the IQ data before equalization, obtain the cost function of the Constant Modulus Algorithm (CMA) equalizer and the cost function of the Decision Guided (DD) equalizer;

[0053] Step 2: Obtain the cost function of the joint equalizer based on the cost functions of the CMA equalizer and the DD equalizer;

[0054] Step 3: Perform joint equalization processing on the IQ data before equalization to obtain the equalized IQ data.

[0055] This application nonlinearly cascades two commonly used equalizers, the CMA equalizer and the DD equalizer, enabling more accurate recovery of distorted signals and exhibiting strong robustness. Even under adverse channel conditions, it can perform the equalization task excellently.

[0056] This application nonlinearly cascades two commonly used equalizers, enabling more accurate recovery of distorted signals and exhibiting strong robustness. Even under adverse channel conditions, it can perform the equalization task excellently.

[0057] Optionally, step 1 includes:

[0058] like Figure 2 As shown, x(n) is the input data, and y(n) is the output data. Let n be the expected data and e(n) be the error data. The training data is T, where T is the signal period, and w0, w1, w2, ..., w M-1 For filter tap data, use subscripts to distinguish between CMA and DD;

[0059] Input the IQ data before balancing and sort them by first and last to obtain the first data;

[0060] The first data is input into the CMA equalizer to obtain the equalization error function of the CMA equalizer. y CMA (n) and These are the squared statistical magnitude of the CMA equalizer output signal and the squared magnitude of the modulation type, respectively.

[0061] Based on the equalization error function of the CMA equalizer, calculate the cost function of the CMA equalizer, where the cost function is the error function e. CMA Mean square error of (n)

[0062] Optionally, step 1 includes:

[0063] Input the IQ data before balancing and sort them by first and last to obtain the first data;

[0064] The first data is input into the DD equalizer to obtain the equalization error function of the DD equalizer. y DD (n) and These are the standard constellation mapping symbol points for the equalized output signal and modulation type of the DD equalizer, respectively.

[0065] Based on the equalization error function of the DD equalizer, calculate the cost function of the DD equalizer, where the cost function is the error function e. DD Mean square error of (n)

[0066] Optionally, step 2 includes:

[0067] The cost functions of the CMA equalizer and the DD equalizer are nonlinearly cascaded and coupled into the cost function of the joint equalizer.

[0068] The key parameters of the joint equalizer are set, namely the number of filter taps, the iteration step size, and the number of loops.

[0069] Optionally, the method for obtaining the cost function of the joint equalizer includes:

[0070] Based on the cost function JDD of the DD equalizer, the coupling coefficient is obtained using the hyperbolic tangent function:

[0071]

[0072] Based on the coupling coefficient λ, and the cost functions of the DD equalizer and the CMA equalizer, the cost function of the joint equalizer is obtained:

[0073] J = (1-λ)*J DD +λ*J CMA

[0074] Among them, J DD J is the cost function of the DD equalizer. CMALet J be the cost function of the CMA equalizer, and J be the cost function of the joint equalizer.

[0075] Optionally, the cost function of the CMA equalizer is:

[0076]

[0077] Among them, y CMA (n) and These are the squared statistical magnitude of the CMA equalizer output signal and the squared magnitude of the modulation type, respectively; J CMA is the cost function of the CMA equalizer; E is the mean square error function.

[0078] Optionally, the cost function of the DD equalizer is:

[0079]

[0080] Among them, y DD (n) and These represent the standard constellation mapping symbol points for the DD equalizer output signal and the modulation type, respectively; J DD Let E be the cost function of the DD equalizer; E is the mean square error function.

[0081] Optionally, step 3 includes:

[0082] The IQ data before equilibration is subjected to joint equilibration processing to determine whether the IQ data after equilibration meets the convergence condition. If the convergence condition is not met, equilibration continues; otherwise, a converged constellation diagram is obtained, which is the IQ data after equilibration.

[0083] Optionally, if the vector error between the equalized constellation diagram and the standard constellation diagram shape of the corresponding modulation method is less than 10 within a signal-to-noise ratio of 20dB, then the equalized IQ data is determined to meet the convergence condition.

[0084] Optionally, the formula for calculating the vector error is:

[0085]

[0086] Among them, EVM RMS Let Ik be the vector error, Qk be the equalized I data, and Qk be the equalized Q data. For standard constellation reference I data, The standard constellation diagram reference Q data is used, and N is the length of the IQ data.

[0087] In this specific embodiment, such as Figure 1 As shown, this application first inputs the IQ data before equilibrium and sorts them by first and last; then it obtains the CMA equilibrium cost function, which is not effective when used alone, such as... Figure 4As shown; secondly, the DD equilibrium cost function is obtained, but its effect is not good when used alone, such as... Figure 5 As shown; then a nonlinear cascade is used to couple the two equilibrium cost functions, as follows: Figure 3 As shown; next, determine the number of equalization filter taps, iteration step size, and number of iterations; finally, perform joint equalization recovery processing on the distorted signal to obtain a converged constellation diagram, as shown. Figure 6 As shown.

[0088] The cost function calculation structures for CMA equilibrium and DD equilibrium are as follows: Figure 2 As shown, the two have the same feedback link structure, the difference lies in the error calculation method of the cost function.

[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and not to limit them. Although this application has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation methods of this application. Any modifications or equivalent substitutions that do not depart from the spirit and scope of this application should be covered within the protection scope of the claims of this application.

Claims

1. A joint equalization method for signals in fading channels, characterized in that, include: Step 1: Based on the IQ data before equalization, obtain the cost function of the CMA equalizer and the cost function of the DD equalizer; Step 2: Obtain the cost function of the joint equalizer based on the cost function of the CMA equalizer and the cost function of the DD equalizer; Step 3: Perform joint equalization processing on the IQ data before equalization to obtain the equalized IQ data; Step 2 includes: The cost functions of the CMA equalizer and the DD equalizer are nonlinearly cascaded and coupled into the cost function of the joint equalizer. Set the key parameters of the joint equalizer, which are the number of filter taps, the iteration step size, and the number of loops; The method for obtaining the cost function of the joint equalizer includes: Based on the cost function J of the DD equalizer DD The coupling coefficients are obtained using the hyperbolic tangent function: Based on the coupling coefficient λ, and the cost functions of the DD equalizer and the CMA equalizer, the cost function of the joint equalizer is obtained: J=(1-λ)*J DD +λ*J CMA Among them, J DD J is the cost function of the DD equalizer. CMA Let J be the cost function of the CMA equalizer, and J be the cost function of the joint equalizer.

2. The joint equalization method for signals in fading channels according to claim 1, characterized in that, Step 1 includes: Input the IQ data before balancing and sort them by first and last to obtain the first data; The first data is input into the CMA equalizer to obtain the equalization error function of the CMA equalizer. These are the squared statistical magnitude and the squared magnitude of the modulation type of the equalized output signal of the CMA equalizer, respectively. Based on the equalization error function of the CMA equalizer, calculate the cost function of the CMA equalizer, where the cost function is the error function e. CMA Mean square error of (n) 3. The joint equalization method for signals in fading channels according to claim 1, characterized in that, Step 1 includes: Input the IQ data before balancing and sort them by first and last to obtain the first data; The first data is input into the DD equalizer to obtain the equalization error function of the DD equalizer. These are the standard constellation mapping symbol points for the equalized output signal and modulation type of the DD equalizer, respectively. Based on the equalization error function of the DD equalizer, calculate the cost function of the DD equalizer, where the cost function is the error function e. DD Mean square error of (n) 4. The joint equalization method for signals in fading channels according to claim 1, characterized in that, The cost function of the CMA equalizer is: Among them, y CMA (n) and These are the squared statistical magnitude of the CMA equalizer output signal and the squared magnitude of the modulation type, respectively; J CMA is the cost function of the CMA equalizer; E is the mean square error function.

5. The joint equalization method for signals in fading channels according to claim 1, characterized in that, The cost function of the DD equalizer is: Among them, y DD (n) and These represent the standard constellation mapping symbol points for the DD equalizer output signal and the modulation type, respectively; J DD Let E be the cost function of the DD equalizer; E is the mean square error function.

6. The joint equalization method for signals in fading channels according to claim 1, characterized in that, Step 3 includes: The IQ data before balancing is subjected to joint balancing processing, and it is determined whether the balanced IQ data meets the convergence condition. If the convergence condition is not met, balancing continues; otherwise, a converged constellation diagram is obtained, which is the balanced IQ data.

7. The joint equalization method for signals in fading channels according to claim 6, characterized in that, If the vector error between the equalized constellation diagram and the standard constellation diagram of the corresponding modulation scheme is less than 10 within a signal-to-noise ratio of 20dB, then the equalized IQ data is determined to meet the convergence condition.

8. The joint equalization method for signals in fading channels according to claim 7, characterized in that, The formula for calculating the vector error is: Among them, EVM RMS For vector error, I k For the balanced I data, Q k The Q data has been balanced. For standard constellation reference I data, The standard constellation diagram reference Q data is used, and N is the length of the IQ data.

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