CN114268525A - Adaptive blind equalization method and system based on WDTB burst signal - Google Patents
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Abstract
The invention discloses a self-adaptive blind equalization method and a self-adaptive blind equalization system based on WDTB burst signals. The adaptive blind equalization method comprises the following steps: s1: acquiring an intermediate frequency signal; s2: performing a plurality of filtering on the intermediate frequency signal; s3: performing timing synchronization and frequency offset correction on the data obtained after the multiple items of filtering; s4: aiming at data obtained after timing synchronization and frequency offset correction, sequentially utilizing an RLS method and an LMS method to carry out self-adaptive blind equalization; s5: and sequentially carrying out initial phase correction and symbol level processing on the data obtained after the adaptive blind equalization to obtain decoding output. The invention can effectively reduce and eliminate the intersymbol interference in the burst signal system and improve the communication quality in the satellite burst signal system by the combined design of the RLS algorithm and the LMS algorithm. The method has the advantages of good real-time tracking performance, high convergence rate and low steady-state error, and is suitable for WDTB burst signal scenes and various modulation mode scenes.
Description
Technical Field
The invention relates to the technical field of narrow-band satellite communication, in particular to a self-adaptive blind equalization method and a self-adaptive blind equalization system based on WDTB burst signals.
Background
Due to the influence of factors such as Doppler frequency offset, multipath effect and limited channel bandwidth generated by high-speed movement in a communication scene of a burst satellite, if a receiving end does not have an accurate measurement compensation mechanism, intersymbol interference exists, so that signal distortion is caused, and the transmission quality of a communication system is influenced. The equalization technique can effectively eliminate intersymbol interference. However, although the conventional adaptive equalization algorithm can adapt to the channel characteristics changing with time, it needs to acquire a priori information of the channel through a training sequence, thereby limiting the transmission efficiency and application scenarios of the communication system. The adaptive blind equalization algorithm does not need to receive a training sequence like the traditional equalization algorithm when the algorithm works, so that the efficiency of the algorithm is greatly improved, and the time-interval receiver can be suitable for more complex scenes. Therefore, considering the high-speed scene of narrow-band satellite communication and the time-varying characteristic of the channel, the adaptive blind equalization technology for adjusting the parameters in real time can greatly reduce the intersymbol interference, reduce the nonlinear influence of the channel and improve the communication quality.
The disclosure number CN110213184A provides a blind equalization method based on second-order statistical characteristics, which obtains two paths of received signals by using a dual-antenna receiving or oversampling technology, estimates channel transmission characteristics by using an lms (least Mean square) method, estimates an unknown additive noise variance on each transmission channel to correct a cost function, obtains an optimal solution for correcting the cost function by using an iteration method, and compensates for estimation deviation caused by noise, thereby constructing an equalizer according to an obtained unbiased estimation value of the transmission channel, realizing equalization of the channel, and recovering a transmitted signal. The method is mainly designed aiming at the scene of two paths of transmission antennas and has the characteristic of high convergence speed. However, for the current WDTB burst signal scenario, only single-antenna transmission needs to be supported, so the method may increase the amount of computation and is not suitable for the current WDTB burst signal scenario.
In view of this, the present application is specifically made.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the existing blind equalization method is large in operation amount and not suitable for WDTB burst signal scenes, and aims to provide an adaptive blind equalization method and an adaptive blind equalization system based on WDTB burst signals.
The invention is realized by the following technical scheme:
in one aspect, the present invention provides an adaptive blind equalization method based on WDTB burst signals, comprising the steps of:
s1: acquiring an intermediate frequency signal;
s2: performing a plurality of filtering on the intermediate frequency signal;
s3: performing timing synchronization and frequency offset correction on the data obtained after the multiple items of filtering;
s4: aiming at data obtained after timing synchronization and frequency offset correction, sequentially utilizing an RLS method and an LMS method to carry out self-adaptive blind equalization;
s5: and sequentially carrying out initial phase correction and symbol level processing on the data obtained after the adaptive blind equalization to obtain decoding output.
The invention adopts a mode of combining the RLS algorithm and the LMS algorithm to carry out self-adaptive blind equalization on input data, and utilizes the characteristics of high convergence speed of the RLS algorithm and simple calculation of the LMS algorithm to firstly carry out primary self-adaptive blind equalization by the RLS algorithm before the data equalization convergence and then further equalize by the LMS algorithm after the equalization is stable, thereby further realizing the simplest self-adaptive blind equalization on the basis of ensuring the quick convergence. And in the adaptive blind equalization process, an error threshold is set, the magnitude of the error value obtained in the equalization process of the RLS algorithm is compared with the error threshold, and the time for switching the RLS algorithm into the LMS algorithm is determined according to the comparison result. In addition, the invention carries out multinomial filtering on the output intermediate frequency signal at the beginning, and reduces the error through digital filtering and an adaptive algorithm.
As a further description of the present invention, before S3, the multiple filtered data are digitally down-converted and matched filtered to obtain a low-frequency SNR value.
As a further description of the present invention, the S3 includes:
s31: carrying out coarse timing synchronization on the low-frequency SNR value by using a synchronization head to obtain a data frame header;
s32: and carrying out frequency offset measurement and compensation on the data frame head by using the synchronous head pilot frequency to obtain a frequency offset value.
S33: carrying out fine timing synchronization on the frequency offset value to obtain a frequency offset value without sampling errors;
s34: and aiming at the frequency offset value without the sampling error, performing frequency tracking by using a PLL (phase locked loop) loop to obtain a fine frequency offset value.
As a further description of the present invention, the method of adaptive blind equalization is:
s41: presetting an error threshold;
s42: performing adaptive blind equalization on the fine frequency offset value by using an RLS method, and comparing an error value obtained in the adaptive blind equalization process with the error threshold; if the error value is less than the error threshold, the method used by the adaptive blind equalization is switched from the RLS method to the LMS method, and the adaptive blind equalization is continuously carried out by using the LMS method.
As a further description of the present invention, the symbol level processing method is: and sequentially performing descrambling, data despreading and LDPC decoding on the data obtained after the initial phase correction to finally obtain decoding output.
In another aspect, the present invention provides an adaptive blind equalization system based on WDTB burst signals, comprising:
the signal acquisition module is used for acquiring an intermediate frequency signal;
the adaptive filter is used for carrying out multi-term filtering on the intermediate frequency signal;
the timing synchronization module is used for carrying out timing synchronization on the data obtained after the multiple items of filtering;
the frequency offset correction module is used for carrying out frequency offset correction on the data after timing synchronization;
the adaptive equalization module is used for carrying out adaptive blind equalization on the data obtained after the frequency offset correction by sequentially utilizing an RLS method and an LMS method;
the initial phase correction module is used for sequentially performing initial phase correction on the data obtained after the self-adaptive blind equalization;
and the symbol level processing module is used for carrying out symbol level processing on the data obtained after the initial phase correction to obtain decoding output.
As a further description of the present invention, the adaptive blind equalization system based on WDTB burst signal further comprises:
the digital down-conversion module is used for performing digital down-conversion on the data obtained after the multiple filtering to obtain a low-frequency signal;
and the matched filter is used for performing matched filtering on the low-frequency signal to obtain a low-frequency SNR value.
As a further description of the present invention, the timing synchronization module includes:
the coarse timing synchronization unit is used for performing coarse timing synchronization on the data obtained after the frequency offset correction to obtain a data frame header and transmitting the data frame header to the frequency offset correction module;
and the fine timing synchronization unit is used for performing fine timing synchronization on the output data of the frequency offset correction module to obtain a frequency offset value without a sampling error.
As a further description of the present invention, the adaptive blind equalization system based on WDTB burst signal further comprises: and the frequency tracking module is used for carrying out frequency tracking on the frequency offset value without the sampling error by utilizing a PLL (phase locked loop) loop to obtain a fine frequency offset value.
As a further description of the present invention, the adaptive equalization module includes:
the RLS equalization unit is used for carrying out first self-adaptive blind equalization on the fine frequency offset value by using an RLS method;
the error comparison unit is used for comparing the error value obtained in the first adaptive blind equalization process with the error threshold and outputting a comparison result;
the switching unit is used for switching the method used by the adaptive blind equalization from the RLS method to the LMS method when the error value is less than the error threshold;
and the LMS equalization unit is used for carrying out second self-adaptive blind equalization on the data obtained after the first self-adaptive blind equalization by utilizing an LMS method.
As a further description of the present invention, the symbol level processing module comprises:
the data descrambling unit is used for descrambling the data obtained after the initial phase correction;
a data de-spreading unit for de-spreading the data obtained after de-scrambling;
and the LDPC decoding unit is used for performing LDPC decoding on the despread data to obtain decoded output.
Compared with the prior art, the invention has the following advantages and beneficial effects: the invention can effectively reduce and eliminate the intersymbol interference in the burst signal system and improve the communication quality in the satellite burst signal system by the combined design of the RLS algorithm and the LMS algorithm. The method has the advantages of good real-time tracking performance, high convergence rate and low steady-state error, and is suitable for WDTB burst signal scenes and various modulation mode scenes.
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In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and that for those skilled in the art, other related drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a schematic flowchart of an adaptive blind equalization method based on WDTB burst signals according to embodiment 1 of the present invention;
fig. 2 is a schematic diagram illustrating a structure and a principle of an adaptive filter according to embodiment 1 of the present invention;
fig. 3 is a schematic diagram of simulation results when SNR is 30dB and FO is 2KHz, which is provided in embodiment 2 of the present invention;
fig. 4 is a diagram illustrating simulation results when the SNR is 30dB and FO is 650KHz, which are provided in embodiment 2 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Example 1
Because the current WDTB burst signal scene only needs to support single-antenna transmission, while a blind equalization method based on second-order statistical characteristics in the prior art is mainly designed for the scene of two transmission antennas, the calculation amount can be increased, and the method is not suitable for the current WDTB burst signal scene. To this end, this embodiment provides an adaptive blind equalization method based on WDTB burst signal, whose flow is shown in fig. 1, and the method performs adaptive blind equalization by combining RLS algorithm and LMS algorithm without prior information, so as to eliminate inter-symbol interference, which specifically includes the following steps:
step 1: and acquiring an intermediate frequency signal.
Step 2: and performing multiple filtering on the intermediate frequency signal.
In the present embodiment, an adaptive filter is used to implement the polynomial filtering. The structure and the schematic block diagram of the adaptive filter are shown in fig. 2, and mainly include two parts, namely a digital filter h (z) and an adaptive algorithm. In the adaptive filter, most of H (z) selects FIR. The two functions of the adaptive algorithm are mainly learning and tracking. And H (z) according to a given initial value, the self-adaptive algorithm continuously updates the self-adaptive algorithm through continuous learning, and finally reaches or approaches the optimal solution. x (n) is the input signal, the data after the adaptive filter is y (n), d (n) is the desired signal, e (n) is the error signal. The adaptive algorithm adjusts the coefficients of the filter according to e (n), so that the mean square error of the error signal e (n) at any time tends to be minimum.
And step 3: and carrying out digital down-conversion and matched filtering on the data obtained after the multiple items of filtering to obtain a low-frequency SNR value.
And 4, step 4: and carrying out coarse timing synchronization on the low-frequency SNR value by using a synchronization head to obtain a data frame header.
And 5: and carrying out frequency offset measurement and compensation on the data frame head by using the synchronous head pilot frequency to obtain a frequency offset value.
Step 6: carrying out fine timing synchronization on the frequency offset value to obtain a frequency offset value without sampling errors;
and 7: and aiming at the frequency offset value without the sampling error, performing frequency tracking by using a PLL (phase locked loop) loop to obtain a fine frequency offset value.
And 8: and (4) performing adaptive blind equalization by using an RLS (recursive least squares) method and an LMS (least mean square) method in sequence aiming at the precise frequency offset data.
As shown in fig. 1, in the adaptive blind equalization process, according to the characteristic that the convergence speed of the RLS algorithm is high, the RLS algorithm is first adopted for primary equalization before data equalization convergence; and after the data are basically stable, further balancing is performed by adopting an LMS algorithm, the LMS algorithm is simple in calculation, and the calculation amount can be reduced in a maintaining stage after the balancing is stable, so that the system is guaranteed to be simplified to the greatest extent under the condition of guaranteeing the fastest convergence rate.
It should be noted that, in the adaptive blind equalization process, there is a precedence order between the RLS algorithm and the LMS algorithm, so there is a process of switching the adaptive blind equalization method from the RLS algorithm to the LMS algorithm. The method is characterized in that a preset error threshold table mode is adopted for setting, searching is carried out according to different SNR values and modulation modes, and when the error value calculated in the RLS algorithm equalization process is smaller than the preset value searched in the table, the adaptive blind equalization algorithm is switched to the LMS algorithm.
It is further to be noted that,
the RLS algorithm uses least squares, and the recursion means that the filter coefficient h (n) at time n is obtained using the filter coefficient h (n-1) at time n-1. RLS algorithm orderEpsilon of the formulaMTo the minimum, to find the filter coefficients h (n):
where ρ is a weighting factor, 0 < ρ < 1. The nature of the above equation using exponential weighting is that the error found for the new data is given the greatest weight, while the error found for the earlier data is given a lesser weight. The purpose is to obtain a new hM(n) the time-varying statistical characteristics of the input signal can be tracked as quickly as possible.
From the above formula, it follows: rM(n)hM(n)＝DM(n) wherein RM(n) is the signal weighted autocorrelation matrix estimated at time n, as follows:
thus, the filter coefficients at time n are:
thereby, R is obtainedM(n) and DMRecursive expression of (n), i.e.
DM(n)＝ρDM(n-1)+XM(n)d(n)。
Then obtaining the matrix according to the inverse lemma of the matrix
Namely:
further, define
And determining the kalman gain vector as:
thus, the following results were obtained:
then solve for hM(n)。
In the formula (I), the compound is shown in the specification,
the output of the adaptive filter at time n is noted as:
the calculation error is: e.g. of the typeM(n)＝d(n)-y(n)。
In order to reduce the time consumption of the table lookup comparison process, the preset value N (N) can be used>0) Determining the receiving process every N times to judge the error value of RLS algorithm, looking up table according to different SNR values and modulation modes, and calculating the error value of RLS algorithm according to the value of eMAnd (n) if the sum of the.
In addition, the LMS algorithm utilizes the instantaneous error energy e2(n) instead of the mean square error energy. As can be seen from the adaptive filter block diagram, the error sequence is:
wherein X (n) is a data vector.
The gradient vector of the instantaneous error energy is:
the filter coefficients at the (n +1) th iteration can be found by the following equation, h (n +1) ═ h (n) + μ e (n) x (n).
The above formula can also be written as: h isl(n+1)＝hl(n) + μ x (n-l) e (n), l being 0, 1,.., M-1, where M is the length of the filter, n is the iteration number, and l is the number of filter coefficients.
And step 9: and sequentially carrying out initial phase correction and symbol level processing on the data obtained after the adaptive blind equalization to obtain decoding output. The symbol level processing method comprises the following steps: and sequentially performing descrambling, data despreading and LDPC decoding on the data obtained after the initial phase correction to finally obtain decoding output.
Through the joint design of the RLS algorithm and the LMS algorithm, the intersymbol interference in the burst signal system is effectively reduced and eliminated, and the communication quality in the satellite burst signal system is improved. The method has the advantages of good real-time tracking performance, high convergence rate and low steady-state error, and is suitable for various modulation mode scenes.
Example 2
In this embodiment, the adaptive blind equalization method based on WDTB burst signal shown in embodiment 1 is used to perform simulation under the conditions of signal-to-noise ratio SNR of 30dB, fine frequency offset FO of 2KHz, SNR of 30dB, and fine frequency offset FO of 650KHz, so as to obtain an error sequence and constellation before and after equalization under different frequency offsets.
When the SNR is 30dB and FO is 2KHz, the error tends to stabilize up to 400 iterations according to the error sequence curve. When convergence is reached, the error sequence value is about 0.02, and the average value of the errors after stabilization is 0.00718. The simulation results are shown in fig. 3.
When the SNR is 30dB and FO is 650KHz, the error tends to stabilize up to 400 iterations according to the error sequence curve. When convergence is reached, the error sequence value is about 0.02, and the average value of the errors after stabilization is 0.00719. The simulation results are shown in fig. 4.
Example 3
The present embodiment provides an adaptive blind equalization system based on WDTB burst signal, including:
the signal acquisition module is used for acquiring an intermediate frequency signal;
the adaptive filter is used for carrying out multi-term filtering on the intermediate frequency signal;
the timing synchronization module is used for carrying out timing synchronization on the data obtained after the multiple items of filtering;
the frequency offset correction module is used for carrying out frequency offset correction on the data after timing synchronization;
the adaptive equalization module is used for carrying out adaptive blind equalization on the data obtained after the frequency offset correction by sequentially utilizing an RLS method and an LMS method;
the initial phase correction module is used for sequentially performing initial phase correction on the data obtained after the self-adaptive blind equalization;
and the symbol level processing module is used for carrying out symbol level processing on the data obtained after the initial phase correction to obtain decoding output.
Wherein the content of the first and second substances,
the timing synchronization module comprises:
the coarse timing synchronization unit is used for performing coarse timing synchronization on the data obtained after the frequency offset correction to obtain a data frame header and transmitting the data frame header to the frequency offset correction module;
and the fine timing synchronization unit is used for performing fine timing synchronization on the output data of the frequency offset correction module to obtain a frequency offset value without a sampling error.
The adaptive equalization module includes:
the RLS equalization unit is used for carrying out first self-adaptive blind equalization on the fine frequency offset value by using an RLS method;
the error comparison unit is used for comparing the error value obtained in the first adaptive blind equalization process with the error threshold and outputting a comparison result;
the switching unit is used for switching the method used by the adaptive blind equalization from the RLS method to the LMS method when the error value is less than the error threshold;
and the LMS equalization unit is used for carrying out second self-adaptive blind equalization on the data obtained after the first self-adaptive blind equalization by utilizing an LMS method.
The symbol level processing module comprises:
the data descrambling unit is used for descrambling the data obtained after the initial phase correction;
a data de-spreading unit for de-spreading the data obtained after de-scrambling;
an LDPC decoding unit for performing LDPC decoding on the despread data to obtain a decoded output
In addition, the adaptive blind equalization system based on WDTB burst signal further comprises:
the digital down-conversion module is used for performing digital down-conversion on the data obtained after the multiple filtering to obtain a low-frequency signal;
the matched filter is used for performing matched filtering on the low-frequency signal to obtain a low-frequency SNR value;
and the frequency tracking module is used for carrying out frequency tracking on the frequency offset value without the sampling error by utilizing a PLL (phase locked loop) loop to obtain a fine frequency offset value.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. An adaptive blind equalization method based on WDTB burst signals, comprising the steps of:
s1: acquiring an intermediate frequency signal;
s2: performing a plurality of filtering on the intermediate frequency signal;
s3: performing timing synchronization and frequency offset correction on the data obtained after the multiple items of filtering;
s4: aiming at data obtained after timing synchronization and frequency offset correction, sequentially utilizing an RLS method and an LMS method to carry out self-adaptive blind equalization;
s5: and sequentially carrying out initial phase correction and symbol level processing on the data obtained after the adaptive blind equalization to obtain decoding output.
2. The method of claim 1, wherein said plurality of filtered data are digitally downconverted and matched filtered prior to S3 to obtain a low frequency SNR value.
3. The method of claim 2, wherein said S3 comprises:
s31: carrying out coarse timing synchronization on the low-frequency SNR value by using a synchronization head to obtain a data frame header;
s32: and carrying out frequency offset measurement and compensation on the data frame head by using the synchronous head pilot frequency to obtain a frequency offset value.
S33: carrying out fine timing synchronization on the frequency offset value to obtain a frequency offset value without sampling errors;
s34: and aiming at the frequency offset value without the sampling error, performing frequency tracking by using a PLL (phase locked loop) loop to obtain a fine frequency offset value.
4. The method of claim 3, wherein the adaptive blind equalization is performed by:
s41: presetting an error threshold;
s42: performing adaptive blind equalization on the fine frequency offset value by using an RLS method, and comparing an error value obtained in the adaptive blind equalization process with the error threshold; if the error value is less than the error threshold, the method used by the adaptive blind equalization is switched from the RLS method to the LMS method, and the adaptive blind equalization is continuously carried out by using the LMS method.
5. The method of claim 1, wherein the symbol-level processing comprises: and sequentially performing descrambling, data despreading and LDPC decoding on the data obtained after the initial phase correction to finally obtain decoding output.
6. An adaptive blind equalization system based on WDTB burst signals, comprising:
the signal acquisition module is used for acquiring an intermediate frequency signal;
the adaptive filter is used for carrying out multi-term filtering on the intermediate frequency signal;
the timing synchronization module is used for carrying out timing synchronization on the data obtained after the multiple items of filtering;
the frequency offset correction module is used for carrying out frequency offset correction on the data after timing synchronization;
the adaptive equalization module is used for carrying out adaptive blind equalization on the data obtained after the frequency offset correction by sequentially utilizing an RLS method and an LMS method;
the initial phase correction module is used for sequentially performing initial phase correction on the data obtained after the self-adaptive blind equalization;
and the symbol level processing module is used for carrying out symbol level processing on the data obtained after the initial phase correction to obtain decoding output.
7. The adaptive blind equalization system based on WDTB burst signals of claim 6, comprising:
the digital down-conversion module is used for performing digital down-conversion on the data obtained after the multiple filtering to obtain a low-frequency signal;
and the matched filter is used for performing matched filtering on the low-frequency signal to obtain a low-frequency SNR value.
8. The system of claim 7, wherein the timing synchronization module comprises:
the coarse timing synchronization unit is used for performing coarse timing synchronization on the data obtained after the frequency offset correction to obtain a data frame header and transmitting the data frame header to the frequency offset correction module;
a fine timing synchronization unit, configured to perform fine timing synchronization on the output data of the frequency offset correction module to obtain a frequency offset value without a sampling error;
further comprising: and the frequency tracking module is used for carrying out frequency tracking on the frequency offset value without the sampling error by utilizing a PLL (phase locked loop) loop to obtain a fine frequency offset value.
9. The adaptive blind equalization system of claim 8 wherein said adaptive equalization module comprises:
the RLS equalization unit is used for carrying out first self-adaptive blind equalization on the fine frequency offset value by using an RLS method;
the error comparison unit is used for comparing the error value obtained in the first adaptive blind equalization process with the error threshold and outputting a comparison result;
the switching unit is used for switching the method used by the adaptive blind equalization from the RLS method to the LMS method when the error value is less than the error threshold;
and the LMS equalization unit is used for carrying out second self-adaptive blind equalization on the data obtained after the first self-adaptive blind equalization by utilizing an LMS method.
10. The system of claim 6, wherein the symbol-level processing module comprises:
the data descrambling unit is used for descrambling the data obtained after the initial phase correction;
a data de-spreading unit for de-spreading the data obtained after de-scrambling;
and the LDPC decoding unit is used for performing LDPC decoding on the despread data to obtain decoded output.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050281327A1 (en) * | 2004-06-18 | 2005-12-22 | Chien-Hsing Liao | Hierarchical adaptive equalizer and design method thereof |
| CN110213184A (en) * | 2019-06-03 | 2019-09-06 | 北京理工大学 | A kind of adaptive channel blind equalization method based on amendment cost function |
| CN112152663A (en) * | 2020-09-25 | 2020-12-29 | 中国电子科技集团公司第五十四研究所 | Anti-multipath anti-interference signal receiving method based on time division multiple access |
| CN112468419A (en) * | 2020-11-23 | 2021-03-09 | 中国科学院国家空间科学中心 | Self-adaptive dual-mode blind equalization method and system |
-
2021
- 2021-12-30 CN CN202111656644.2A patent/CN114268525A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050281327A1 (en) * | 2004-06-18 | 2005-12-22 | Chien-Hsing Liao | Hierarchical adaptive equalizer and design method thereof |
| CN110213184A (en) * | 2019-06-03 | 2019-09-06 | 北京理工大学 | A kind of adaptive channel blind equalization method based on amendment cost function |
| CN112152663A (en) * | 2020-09-25 | 2020-12-29 | 中国电子科技集团公司第五十四研究所 | Anti-multipath anti-interference signal receiving method based on time division multiple access |
| CN112468419A (en) * | 2020-11-23 | 2021-03-09 | 中国科学院国家空间科学中心 | Self-adaptive dual-mode blind equalization method and system |
Non-Patent Citations (1)
| Title |
|---|
| KYUNG SEUNG AHN: "Blind Adaptive Channel Equalization Using Multichannel Linear Prediction-Based Cross-Correlation Vector Estimation", IEEE, vol. 50, no. 4, 30 November 2004 (2004-11-30), pages 1026 - 1031, XP001224736, DOI: 10.1109/TCE.2004.1362494 * |
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