A receiver data recovery method based on channel equalization and iteration

Abstract

A receiving-end data recovery method based on channel equalization and iteration belongs to the field of wireless communication technology. The present invention solves the problem that existing methods cannot achieve a balance between reliability and efficiency in the communication process. The present invention uses the channel estimation results to find data points with large errors, completes channel equalization, and then performs inverse transformation and judgment. After the judgment is completed, the symbol data is re-processed and transformed inversely, and then the data obtained by the inverse process is used to replace the data at the high error point in the data after equalization and before inverse transformation. The iterative process is then repeated on the replaced data. The final result can reduce the error between the actual waveform and the ideal waveform after channel equalization without adding additional redundant information, thereby improving the channel equalization performance and reducing the symbol error rate after judgment. The method of the present invention can be applied to the field of wireless communication technology.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and in particular relates to a receiving end data recovery method based on channel equalization and iteration. Background Art

[0002] Reliability and effectiveness are important metrics for measuring communication system performance. Generally, these two qualities are contradictory: improving reliability often comes at the expense of effectiveness, and vice versa. However, reliability and effectiveness are deeply connected. Reducing the bit error rate during communication ensures both stable and reliable communication and improved efficiency.

[0003] Traditional communication systems aim to reduce bit error rates. One approach is to use error-correcting codes to detect and correct errors that may occur during data transmission. However, error-correcting codes themselves are not valid information but rather redundant. To achieve high error correction performance, very long error-correcting codes must be encoded, which significantly reduces communication efficiency. Another approach is to use higher-performance equalization methods, but this often results in high computational complexity. Furthermore, for some lightweight communication systems, channel estimation accuracy is low, and deep fade points may be impossible to estimate, resulting in performance degradation. Alternatively, in environments with malicious interference, such as when the system is subject to narrowband interference, the system may lose all energy at some sampling points, severely degrading performance.

[0004] In summary, it is very necessary to propose a method that can balance communication reliability and communication efficiency under the conditions of low complexity and when deep fading and malicious interference can be suppressed. Summary of the Invention

[0005] The purpose of the present invention is to propose a receiving end data recovery method based on channel equalization and iteration in order to balance the reliability and efficiency of the communication process. The proposed method has the characteristics of low complexity and the ability to suppress deep fading and malicious interference.

[0006] The technical solution adopted by the present invention to solve the above technical problems is:

[0007] According to one aspect of the present invention, a receiving-end data recovery method based on channel equalization and iteration comprises the following steps:

[0008] Step 1: The receiver down-converts the data received from the channel to obtain a time-domain baseband data sequence r, and transforms the time-domain baseband data sequence r to obtain a transformed data sequence y;

[0009] Step 2: Estimation matrix based on frequency domain channel The diagonal elements of determine whether there are abnormal points in the data sequence y;

[0010] If there are abnormal points in the data sequence y, replace the abnormal points in the data sequence y with 0 to obtain the data sequence y';

[0011] If there are no abnormal points in the data sequence y, there is no need to process the data sequence y, and y is directly used as the data sequence y';

[0012] Step 3: Estimation matrix based on frequency domain channel Equalize the data sequence y' and record the equalized result as x (0) ;

[0013] Step 4: Initialize the number of iterations i = 0;

[0014] Step 5: Use the frequency domain channel estimation matrix The diagonal elements of find the high error point location;

[0015] Step 6: x (i) Perform inverse transformation, and then judge the inverse transformed data to obtain the i-th judgment result, that is, the information sequence judgment value s obtained in the i-th iteration (i) ;

[0016] Step 7: Determine whether the iteration stop condition is met;

[0017] If the iteration stop condition is not met, execute step eight;

[0018] If the iteration stop condition is met, the information sequence decision value s (i) This is the final decision value. The final decision value is constellation demapped to recover the 0 and 1 bit data transmitted by the transmitter.

[0019] Step 8: (i) Perform the transformation and record the transformed result as x ( ' i+1) ;

[0020] Step 9: x (i) The data in is replaced, that is, the high error point position is used in x ( ' i+1) Replace the high error point position in x with the corresponding data (i) The corresponding data in the replacement result is recorded as x (i+1) ;

[0021] And let i=i+1, and return to execute step five.

[0022] According to another aspect of the present invention, a receiving end data recovery method based on channel equalization and iteration specifically comprises the following steps:

[0023] Step 1: The receiver down-converts the data received from the channel to obtain a time domain baseband data sequence r;

[0024] Step 2: Estimation matrix based on time domain channel Determine whether there are abnormal points in the data sequence r;

[0025] If there are abnormal points in the data sequence r, replace the abnormal points in the data sequence r with 0 to obtain the data sequence r';

[0026] If there are no abnormal points in the data sequence r, there is no need to process the data sequence r, and r is directly used as the data sequence r';

[0027] Step 3: Estimation matrix based on frequency domain channel The diagonal elements of the data sequence r' are balanced, and the balanced result is recorded as x (0) ;

[0028] Step 4: Initialize the number of iterations i = 0;

[0029] Step 5: Use the frequency domain channel estimation matrix The diagonal elements of find the high error point location;

[0030] Step 6: x (i) Perform the transformation, and then judge the transformed data to obtain the i-th judgment result, that is, the information sequence judgment value s obtained in the i-th iteration (i) ;

[0031] Step 7: Determine whether the iteration stop condition is met;

[0032] If the iteration stop condition is not met, execute step eight;

[0033] If the iteration stop condition is met, the information sequence decision value s (i) This is the final decision value. The final decision value is constellation demapped to recover the 0 and 1 bit data transmitted by the transmitter.

[0034] Step 8: (i) Perform the inverse transformation and record the result of the inverse transformation as x ( ' i+1) ;

[0035] Step 9: x (i) The data in is replaced, that is, the high error point position is used in x ( ' i+1) Replace the high error point position in x with the corresponding data (i) The corresponding data in the replacement result is recorded as x (i+1) ;

[0036] And let i=i+1, and return to execute step five.

[0037] The beneficial effects of the present invention are:

[0038] The present invention uses the channel estimation results to find data points with large errors. After completing channel equalization, it performs inverse transformation and judgment. After the judgment is completed, the symbol data is re-processed and transformed inversely. The data obtained by the inverse process is then used to replace the data at the high error points in the data after equalization and before inverse transformation. The iterative process is then repeated on the replaced data. The final result can reduce the error between the actual waveform and the ideal waveform after channel equalization without adding additional redundant information, thereby improving the channel equalization performance and reducing the symbol error rate after judgment.

[0039] For situations where the channel estimation accuracy is low and deep fade points cannot be estimated, or the system is subject to narrowband interference, the method of the present invention determines the abnormal points and replaces the values ​​at the abnormal points with 0 values. Then, channel equalization is performed on the data replaced with 0, and the 0 values ​​are replaced with the generated corresponding point data values ​​during the iterative process. In this way, the effects of deep fades and malicious interference environments are offset, while achieving improved communication performance and increased communication efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a flowchart of a receiving-end data recovery method based on channel equalization and iteration according to a first embodiment of the present invention;

[0041] Where: FFT stands for Fast Fourier Transform, IFFT stands for Inverse Fast Fourier Transform;

[0042] Figure 2 This is a flowchart of a receiving end data recovery method based on channel equalization and iteration according to a fifth specific embodiment of the present invention;

[0043] Figure 3 Schematic diagram of the data replacement process in the method of the present invention. DETAILED DESCRIPTION

[0044] Specific implementation method 1: Combination Figure 1 This embodiment describes a receiving-end data recovery method based on channel equalization and iteration, and the method specifically includes the following steps:

[0045] Step 1: The receiver down-converts the data received from the channel to obtain a time-domain baseband data sequence r, and transforms the time-domain baseband data sequence r to obtain a transformed data sequence y;

[0046] Step 2: Estimation matrix based on frequency domain channel (For broadband systems, the subcarrier spacing Δf is usually much larger than the maximum Doppler shift f d When the channel estimation process is performed in the frequency domain, the channel estimation matrix The diagonal elements of the data (data with larger amplitudes are distributed on the diagonal) determine whether there are abnormal points in the data sequence y;

[0047] If there are abnormal points in the data sequence y, replace the abnormal points in the data sequence y with 0 to obtain the data sequence y';

[0048] If there are no abnormal points in the data sequence y, there is no need to process the data sequence y, and y is directly used as the data sequence y';

[0049] Step 3: Estimation matrix based on frequency domain channel Equalize the data sequence y' (the present invention takes the minimum mean square error equalization as an example), and record the equalized result as x (0) ;

[0050] Step 4: Initialize the number of iterations i = 0;

[0051] Step 5: Use the frequency domain channel estimation matrix The diagonal elements of find the high error point location;

[0052] Step 6: x (i) Perform inverse transformation, and then judge the inverse transformed data to obtain the i-th judgment result, that is, the information sequence judgment value s obtained in the i-th iteration (i) ;

[0053] Step 7: Determine whether the iteration stop condition is met;

[0054] If the iteration stop condition is not met, execute step eight;

[0055] If the iteration stop condition is met, the information sequence decision value s (i) This is the final decision value. The final decision value is constellation demapped to recover the 0 and 1 bit data transmitted by the transmitter.

[0056] Step 8: (i) Perform the transformation and record the transformed result as x ( ' i+1) ;

[0057] Step 9: x (i) The data in is replaced, that is, the high error point position is used in x ( ' i+1) Replace the high error point position in x with the corresponding data (i) The corresponding data in Figure 3 As shown, the replacement result is recorded as x(i+1) (including the original x (i) the data that was not replaced and the data that was replaced);

[0058] And let i=i+1, and return to execute step five.

[0059] The transformation methods that can be used in the present invention include but are not limited to fast Fourier transform, symplectic Fourier transform of orthogonal time-frequency-space technology, and extended weighted fractional Fourier transform of extended weighted fractional Fourier transform system. The inverse transform corresponds to the previous transformation method, so it has wide applicability.

[0060] The core of the method of the present invention to achieve performance improvement is to use the data x after judgment and inverse transformation (i) , its error is compared with the initial value x (0) The error is smaller, especially for data at high-error points. By replacing the data at the corresponding position in the previous iteration with the data at the high-error point after judgment and inverse transformation (post-processing inverse transformation), the signal-to-interference-and-noise ratio can be reduced, thereby improving the bit error rate performance. Because the equalization operation is performed only once, the overall complexity is low. Furthermore, the method of the present invention converges quickly, often requiring only 1 to 2 iterations. Therefore, the present invention can achieve the goal of reducing the symbol error rate with very low complexity.

[0061] Specific embodiment 2: This embodiment differs from the specific embodiment 1 in that the frequency domain channel estimation matrix The diagonal elements of the time domain baseband data sequence y determine whether there are abnormal points, specifically: determine the frequency domain channel estimation matrix Whether there are values ​​less than the threshold α1 or greater than the threshold α2 in the diagonal elements of ;

[0062] If it exists, the sampling point data corresponding to the diagonal element value less than the threshold α1 or greater than the threshold α2 in the data sequence y is an abnormal point;

[0063] If it does not exist, there are no abnormal points in the data sequence y.

[0064] Other steps and parameters are the same as those in the first embodiment.

[0065] Since the channel estimation value corresponding to deep fading is 0 or very small, and the channel estimation value corresponding to narrowband interference has an abnormally large value, in this embodiment, α1 is a small value close to 0, and α2 is a value much larger than the average power.

[0066] Specific implementation method three: This implementation method is different from specific implementation methods one or two in that the specific process of step five is as follows:

[0067] Calculate the matrix Then, the matrix The elements on the diagonal of are sorted from small to large, and the positions of the high error points in the i-th iteration are the positions corresponding to the elements ranked from ig+1th to (i+1)gth.

[0068] Other steps and parameters are the same as those in the first or second embodiment.

[0069] Calculate the error e after equalization (0) :

[0070] e (0) =My′-x (0)

[0071] Where M is the equilibrium matrix;

[0072] Then the expectation of the norm of the error energy is:

[0073]

[0074] in,[] H represents the conjugate transposed matrix, E represents the mathematical expectation, tr represents the trace of the matrix, σ 2 represents the noise energy, I represents the identity matrix, and the superscript -1 represents the inverse of the matrix.

[0075] The equalization error corresponding to the high error point position is large. By finding a reasonable high error point position, the performance can be improved and the complexity can be reduced to a certain extent.

[0076] Specific embodiment 4: This embodiment differs from any one of specific embodiments 1 to 3 in that the iteration stopping condition is that the set maximum number of iterations d is reached.

[0077] The other steps and parameters are the same as those in the first to third embodiments.

[0078] Specific implementation method five: Combination Figure 2 This embodiment describes a receiving-end data recovery method based on channel equalization and iteration, and the method specifically includes the following steps:

[0079] Step 1: The receiver down-converts the data received from the channel to obtain a time domain baseband data sequence r;

[0080] Step 2: Estimation matrix based on time domain channel Determine whether there are abnormal points in the data sequence r;

[0081] If there are abnormal points in the data sequence r, replace the abnormal points in the data sequence r with 0 to obtain the data sequence r';

[0082] If there are no abnormal points in the data sequence r, there is no need to process the data sequence r, and r is directly used as the data sequence r';

[0083] Step 3: Estimation matrix based on frequency domain channel The diagonal elements of the data sequence r' are equalized (the present invention takes the minimum mean square error equalization as an example), and the equalized result is recorded as x (0) ;

[0084] Step 4: Initialize the number of iterations i = 0;

[0085] Step 5: Use the frequency domain channel estimation matrix The diagonal elements of find the high error point location;

[0086] Step 6: x (i) Perform the transformation, and then judge the transformed data to obtain the i-th judgment result, that is, the information sequence judgment value s obtained in the i-th iteration (i) ;

[0087] Step 7: Determine whether the iteration stop condition is met;

[0088] If the iteration stop condition is not met, execute step eight;

[0089] If the iteration stop condition is met, the information sequence decision value s (i) This is the final decision value. The final decision value is constellation demapped to recover the 0 and 1 bit data transmitted by the transmitter.

[0090] Step 8: (i) Perform the inverse transformation and record the result of the inverse transformation as x ( ' i+1) ;

[0091] Step 9: x (i) The data in is replaced, that is, the high error point position is used in x ( ' i+1) Replace the high error point position in x with the corresponding data (i) The corresponding data in Figure 3 As shown, the replacement result is recorded as x (i+1) (including the original x (i) the data that was not replaced and the data that was replaced);

[0092] And let i=i+1, and return to execute step five.

[0093] Specific embodiment 6: This embodiment differs from the specific embodiment 5 in that the frequency domain channel estimation matrix The diagonal elements of the time domain baseband data sequence r determine whether there are abnormal points, specifically: determine the frequency domain channel estimation matrix Whether there are values ​​less than the threshold α1 or greater than the threshold α2 in the diagonal elements of ;

[0094] If it exists, the sampling point data corresponding to the diagonal element value less than the threshold α1 or greater than the threshold α2 in the data sequence r is an abnormal point;

[0095] If it does not exist, then there are no outliers in the data sequence r.

[0096] Other steps and parameters are the same as those in the fifth embodiment.

[0097] In this embodiment, α1 is a small value close to 0, and α2 is a value much larger than the average power.

[0098] Specific embodiment seven: This embodiment differs from specific embodiment five or six in that the time domain channel estimation matrix The diagonal elements of are calculated as follows:

[0099] h(k)=y(k) / x(k)

[0100] Where y(k) is the received pilot data, x(k) is the known actual pilot data, k is the kth sampling point, and h(k) is the channel response of the kth sampling point;

[0101] h(k), k=1,2,…is the time domain channel estimation matrix The kth diagonal element in .

[0102] Other steps and parameters are the same as those in the fifth or sixth embodiment.

[0103] The time domain channel estimation matrix can be obtained by the single-tap channel estimation method The diagonal matrix elements in the , make the method of the present invention can be used in combination with time domain equalization, and can achieve a significant improvement in equalization performance while ensuring low channel estimation complexity.

[0104] Specific embodiment eight: This embodiment differs from any one of specific embodiments five to seven in that the specific process of step five is as follows:

[0105] Calculate the matrix Then, the matrix The elements on the diagonal of are sorted from small to large, and the positions of the high error points in the i-th iteration are the positions corresponding to the elements ranked from ig+1th to (i+1)gth.

[0106] The other steps and parameters are the same as those in any one of the fifth to seventh embodiments.

[0107] Specific embodiment 9: This embodiment differs from any one of specific embodiments 5 to 8 in that the iteration stopping condition is that the set maximum number of iterations d is reached.

[0108] The other steps and parameters are the same as those in any one of the fifth to eighth embodiments.

[0109] The method of the present invention can be applied before and after a post-processing module at the receiving end (such as a fast Fourier transform module of a single-carrier frequency domain equalization system or a fast Fourier transform module of an orthogonal frequency division multiplexing time domain equalization system or corresponding modules of other systems).

[0110] The above examples are merely illustrative of the calculation model and process of the present invention and are not intended to limit the embodiments of the present invention. Persons skilled in the art will readily appreciate that other variations or modifications based on the above description are possible. This list of embodiments is not exhaustive; however, any obvious variations or modifications derived from the technical solution of the present invention remain within the scope of protection of the present invention.

Claims

1. A receiving end data recovery method based on channel equalization and iteration, characterized in that: The method specifically comprises the following steps: Step 1: The receiver down-converts the data received from the channel to obtain a time-domain baseband data sequence r, and transforms the time-domain baseband data sequence r to obtain a transformed data sequence y; Step 2: Estimation matrix based on frequency domain channel The diagonal elements of determine whether there are abnormal points in the data sequence y; If there are abnormal points in the data sequence y, replace the abnormal points in the data sequence y with 0 to obtain the data sequence y'; If there are no abnormal points in the data sequence y, there is no need to process the data sequence y, and y is directly used as the data sequence y'; Step 3: Estimation matrix based on frequency domain channel Equalize the data sequence y' and record the equalized result as x (0) ; Step 4: Initialize the number of iterations i = 0; Step 5: Use the frequency domain channel estimation matrix The diagonal elements of find the high error point location; Step 6: x (i) Perform inverse transformation, and then judge the inverse transformed data to obtain the i-th judgment result, that is, the information sequence judgment value s obtained in the i-th iteration (i) ; Step 7: Determine whether the iteration stop condition is met; If the iteration stop condition is not met, execute step eight; If the iteration stop condition is met, the information sequence decision value s (i) This is the final decision value. The final decision value is constellation demapped to recover the 0 and 1 bit data transmitted by the transmitter. Step 8: (i) Perform the transformation and record the transformed result as x ( ' i+1) ; Step 9: x (i) The data in is replaced, that is, the high error point position is used in x ( ' i+1) Replace the high error point position in x with the corresponding data (i) The corresponding data in the replacement result is recorded as x (i+1) ; And let i=i+1, and return to execute step five.

2. The receiving end data recovery method based on channel equalization and iteration according to claim 1, characterized in that: The frequency domain channel estimation matrix The diagonal elements of the time domain baseband data sequence y determine whether there are abnormal points, specifically: determine the frequency domain channel estimation matrix Whether there are values ​​less than the threshold α1 or greater than the threshold α2 in the diagonal elements of ; If it exists, the sampling point data corresponding to the diagonal element value less than the threshold α1 or greater than the threshold α2 in the data sequence y is an abnormal point; If it does not exist, there are no abnormal points in the data sequence y.

3. The receiving end data recovery method based on channel equalization and iteration according to claim 2, characterized in that: The specific process of step five is: Calculate the matrix Then, the matrix The elements on the diagonal of are sorted from small to large, and the positions of the high error points in the i-th iteration are the positions corresponding to the elements ranked from ig+1th to (i+1)gth.

4. The receiving end data recovery method based on channel equalization and iteration according to claim 3, characterized in that: The iteration stopping condition is that the set maximum number of iterations d is reached.

5. A receiving end data recovery method based on channel equalization and iteration, characterized in that: The method specifically comprises the following steps: Step 1: The receiver down-converts the data received from the channel to obtain a time domain baseband data sequence r; Step 2: Estimation matrix based on time domain channel Determine whether there are abnormal points in the data sequence r; If there are abnormal points in the data sequence r, replace the abnormal points in the data sequence r with 0 to obtain the data sequence r'; If there are no abnormal points in the data sequence r, there is no need to process the data sequence r, and r is directly used as the data sequence r'; Step 3: Estimation matrix based on frequency domain channel The diagonal elements of the data sequence r' are balanced, and the balanced result is recorded as x (0) ; Step 4: Initialize the number of iterations i = 0; Step 5: Use the frequency domain channel estimation matrix The diagonal elements of find the high error point location; Step 6: x (i) Perform the transformation, and then judge the transformed data to obtain the i-th judgment result, that is, the information sequence judgment value s obtained in the i-th iteration (i) ; Step 7: Determine whether the iteration stop condition is met; If the iteration stop condition is not met, execute step eight; If the iteration stop condition is met, the information sequence decision value s (i) This is the final decision value. The final decision value is constellation demapped to recover the 0 and 1 bit data transmitted by the transmitter. Step 8: (i) Perform the inverse transformation and record the result of the inverse transformation as x ( ' i+1) ; Step 9: x (i) The data in is replaced, that is, the high error point position is used in x ( ' i+1) Replace the high error point position in x with the corresponding data (i) The corresponding data in the replacement result is recorded as x (i+1) ; And let i=i+1, and return to execute step five.

6. The receiving end data recovery method based on channel equalization and iteration according to claim 5, characterized in that: The frequency domain channel estimation matrix The diagonal elements of the time domain baseband data sequence r determine whether there are abnormal points, specifically: determine the frequency domain channel estimation matrix Whether there are values ​​less than the threshold α1 or greater than the threshold α2 in the diagonal elements of ; If it exists, the sampling point data corresponding to the diagonal element value less than the threshold α1 or greater than the threshold α2 in the data sequence r is an abnormal point; If it does not exist, then there are no outliers in the data sequence r.

7. The receiving end data recovery method based on channel equalization and iteration according to claim 6, characterized in that: The time domain channel estimation matrix The diagonal elements of are calculated as follows: h(k)=y(k) / x(k) Where y(k) is the received pilot data, x(k) is the known actual pilot data, k is the kth sampling point, and h(k) is the channel response of the kth sampling point; h(k), k=1,2,…is the time domain channel estimation matrix The kth diagonal element in .

8. The receiving end data recovery method based on channel equalization and iteration according to claim 7, characterized in that: The specific process of step five is: Calculate the matrix Then, the matrix The elements on the diagonal of are sorted from small to large, and the positions of the high error points in the i-th iteration are the positions corresponding to the elements ranked from ig+1th to (i+1)gth.

9. The receiving end data recovery method based on channel equalization and iteration according to claim 8, characterized in that: The iteration stopping condition is that the set maximum number of iterations d is reached.

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