CN114268525B - An adaptive blind equalization method and system based on WDTB burst signal - Google Patents
An adaptive blind equalization method and system based on WDTB burst signal Download PDFInfo
- Publication number
- CN114268525B CN114268525B CN202111656644.2A CN202111656644A CN114268525B CN 114268525 B CN114268525 B CN 114268525B CN 202111656644 A CN202111656644 A CN 202111656644A CN 114268525 B CN114268525 B CN 114268525B
- Authority
- CN
- China
- Prior art keywords
- blind equalization
- frequency offset
- self
- adaptive blind
- adaptive
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 83
- 230000003044 adaptive effect Effects 0.000 title claims abstract description 44
- 238000012937 correction Methods 0.000 claims abstract description 44
- 238000001914 filtration Methods 0.000 claims abstract description 28
- 238000012545 processing Methods 0.000 claims abstract description 12
- 230000008569 process Effects 0.000 claims description 13
- 238000005070 sampling Methods 0.000 claims description 12
- 238000006243 chemical reaction Methods 0.000 claims description 9
- 238000005259 measurement Methods 0.000 claims description 4
- 238000003672 processing method Methods 0.000 claims description 3
- 230000001360 synchronised effect Effects 0.000 claims description 3
- 238000004422 calculation algorithm Methods 0.000 abstract description 46
- 238000004891 communication Methods 0.000 abstract description 9
- 238000013461 design Methods 0.000 abstract description 3
- 230000005540 biological transmission Effects 0.000 description 9
- 238000010586 diagram Methods 0.000 description 5
- 238000004088 simulation Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 3
- 230000006641 stabilisation Effects 0.000 description 3
- 238000011105 stabilization Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 239000000654 additive Substances 0.000 description 1
- 230000000996 additive effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Landscapes
- Cable Transmission Systems, Equalization Of Radio And Reduction Of Echo (AREA)
Abstract
本发明公开了一种基于WDTB突发信号的自适应盲均衡方法及系统。自适应盲均衡方包括以下步骤：S1：获取中频信号；S2：对所述中频信号进行多项滤波；S3：对多项滤波后得到的数据进行定时同步和频偏校正；S4：针对经过定时同步和频偏校正后得到的数据，依次利用RLS方法和LMS方法进行自适应盲均衡；S5：对经过自适应盲均衡后得到的数据依次进行初相校正和符号级处理，得到译码输出。本发明通过对RLS和LMS算法的联合设计，可有效减小和消除突发信号系统中的码间干扰，提高卫星突发信号系统中的通信质量。具有良好的实时跟踪性能，收敛速度快、稳态误差低，并且适用于WDTB突发信号场景及多种调制模式场景。
The present invention discloses an adaptive blind equalization method and system based on WDTB burst signal. The adaptive blind equalization method includes the following steps: S1: acquiring an intermediate frequency signal; S2: performing multiple filtering on the intermediate frequency signal; S3: performing timing synchronization and frequency offset correction on the data obtained after the multiple filtering; S4: for the data obtained after the timing synchronization and frequency offset correction, adaptive blind equalization is performed using the RLS method and the LMS method in sequence; S5: performing initial phase correction and symbol level processing on the data obtained after the adaptive blind equalization in sequence to obtain a decoded output. The present invention can effectively reduce and eliminate inter-code interference in the burst signal system and improve the communication quality in the satellite burst signal system through the joint design of the RLS and LMS algorithms. It has good real-time tracking performance, fast convergence speed, low steady-state error, and is suitable for WDTB burst signal scenarios and various modulation mode scenarios.
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 system based on WDTB burst signals.
Background
Because of the influence of factors such as Doppler frequency offset, multipath effect, limited channel bandwidth and the like generated by high-speed movement in a burst satellite communication scene, if a receiving end does not have an accurate measurement compensation mechanism, inter-code crosstalk 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, the conventional adaptive equalization algorithm can adapt to the channel characteristics continuously transformed with time, but needs to acquire a priori information of the channel through a training sequence, so that the transmission efficiency and the application scenario of the communication system are limited. The adaptive blind equalization algorithm means that the training sequence does not need to be received as the traditional equalization algorithm when the algorithm works, so that the efficiency of the algorithm is greatly improved, and the time period receiver can be suitable for more complex scenes. Therefore, the adaptive blind equalization technology for adjusting parameters in real time can greatly reduce intersymbol interference, reduce nonlinear influence of channels and improve communication quality by considering high-speed scenes and time-varying characteristics of channels of narrow-band satellite communication.
The disclosure number CN110213184A provides a blind equalization method based on second-order statistical characteristics, two paths of received signals are obtained through a double-antenna receiving or oversampling technology, the channel transmission characteristics are estimated by using an LMS (LEAST MEAN square) method, unknown additive noise variance on each transmission channel is estimated to correct a cost function, an optimal solution of the corrected cost function is obtained by using an iteration method, the estimation deviation caused by noise is compensated, and then an equalizer is constructed according to the obtained unbiased estimation value of the transmission channel, so that the equalization of the channel is realized, and the transmitted signal is recovered. The method is mainly designed aiming at the scene of two paths of transmission antennas, and has the characteristic of high convergence rate. However, for the current WDTB burst signal scene, only single antenna transmission needs to be supported, so that the method can increase the operand and is not suitable for the current WDTB burst signal scene.
In view of this, the present application has been made.
Disclosure of Invention
The invention aims to provide an adaptive blind equalization method and system based on WDTB burst signals, which are large in operation amount and not suitable for WDTB burst signal scenes, and the adaptive blind equalization method and system are used for carrying out adaptive blind equalization by combining an RLS algorithm and an LMS algorithm under the condition that priori information is not available, so that the residual error of the algorithm is reduced under the condition that the complexity of the equalization algorithm is basically unchanged, and the current WDTB burst signal scene of the method is ensured.
The invention is realized by the following technical scheme:
In one aspect, the invention provides a self-adaptive blind equalization method based on WDTB burst signals, which comprises the following steps:
S1, obtaining an intermediate frequency signal;
s2, performing multiple filtering on the intermediate frequency signal;
S3, carrying out timing synchronization and frequency offset correction on the data obtained after the multiple filtering;
s4, carrying out self-adaptive blind equalization by sequentially utilizing an RLS method and an LMS method according to the data obtained after timing synchronization and frequency offset correction;
And S5, sequentially performing initial phase correction and symbol level processing on the data obtained after the self-adaptive blind equalization to obtain decoding output.
The invention adopts a mode of combining an RLS algorithm and an 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 carry out primary self-adaptive blind equalization by adopting the RLS algorithm before data equalization convergence, and further carries out further equalization by utilizing the LMS algorithm after equalization is stable, thereby further realizing the simplest self-adaptive blind equalization on the basis of ensuring rapid convergence. And in the self-adaptive blind equalization process, an error threshold is set, the error value obtained in the RLS algorithm equalization process is compared with the error threshold, and the time for switching the RLS algorithm to the LMS algorithm is determined according to the comparison result. In addition, the invention carries out multiple filtering on the output intermediate frequency signal at the beginning, and reduces errors through digital filtering and self-adaptive algorithm.
As a further description of the present invention, before S3, digital down-conversion and matched filtering are performed on the data obtained after the multiple filtering, to obtain a low-frequency SNR value.
As a further description of the present invention, the S3 includes:
s31, performing coarse timing synchronization on the low-frequency SNR value by using a synchronization head to obtain a data frame head;
and S32, carrying out frequency offset measurement and compensation on the data frame header by utilizing the synchronous header pilot frequency to obtain a frequency offset value.
S33, performing fine timing synchronization on the frequency offset value to obtain a frequency offset value without sampling error;
s34, aiming at the frequency offset value without sampling error, utilizing a PLL loop to carry out frequency tracking to obtain a precise frequency offset value.
As a further description of the invention, the method of adaptive blind equalization is:
S41, presetting an error threshold;
S42, performing self-adaptive blind equalization on the fine frequency offset value by using an RLS method, comparing an error value obtained in the self-adaptive blind equalization process with the error threshold, and if the error value is smaller than the error threshold, switching a self-adaptive blind equalization using method from the RLS method to an LMS method, and continuing the self-adaptive blind equalization by using the LMS method.
As a further description of the invention, the symbol level processing method comprises the steps of sequentially descrambling, data despreading and LDPC decoding the data obtained after initial phase correction, and finally obtaining 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 intermediate frequency signals;
The adaptive filter is used for carrying out multiple filtering on the intermediate frequency signal;
the timing synchronization module is used for performing timing synchronization on the data obtained after the multiple filtering;
the frequency offset correction module is used for carrying out frequency offset correction on the data after timing synchronization;
the self-adaptive equalization module is used for carrying out self-adaptive blind equalization by sequentially utilizing an RLS method and an LMS method according to the data obtained after the frequency offset correction;
The initial phase correction module is used for sequentially carrying out initial phase correction on the data obtained after the self-adaptive blind equalization;
And the symbol level processing module is used for performing 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 signals further comprises:
The digital down-conversion module is used for carrying out digital down-conversion on the data obtained after the multiple filtering to obtain a low-frequency signal;
And the matched filter is used for carrying out 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 carrying out fine timing synchronization on the output data of the frequency offset correction module to obtain a frequency offset value without sampling errors.
As a further description of the invention, the self-adaptive blind equalization system based on WDTB burst signals also comprises a frequency tracking module, which is used for carrying out frequency tracking on the frequency offset value without sampling error by using a PLL loop to obtain a refined frequency offset value.
As a further description of the present invention, the adaptive equalization module includes:
The RLS equalization unit is used for performing first self-adaptive blind equalization on the fine frequency offset value by utilizing an RLS method;
the error comparison unit is used for comparing the error value obtained in the first self-adaptive blind equalization process with the error threshold and outputting a comparison result;
a switching unit for switching a method for adaptive blind equalization from the RLS method to the LMS method when the error value < 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 using an LMS method.
As a further description of the present invention, the symbol-level processing module includes:
The data descrambling unit is used for descrambling the data obtained after the initial phase correction;
The data despreading unit is used for despreading the data obtained after descrambling;
and the LDPC decoding unit is used for performing LDPC decoding on the despread data to obtain decoding output.
Compared with the prior art, the method has the advantages that through the joint design of the RLS algorithm and the LMS algorithm, intersymbol interference in a burst signal system can be effectively reduced and eliminated, and the communication quality in the satellite burst signal system is improved. The method has good real-time tracking performance, high convergence speed and low steady-state error, and is suitable for WDTB burst signal scenes and multiple modulation mode scenes.
Drawings
In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings that are needed in the examples will be briefly described below, it being understood that the following drawings only illustrate some examples of the present invention and therefore should not be considered as limiting the scope, and that other related drawings may be obtained from these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of an adaptive blind equalization method based on WDTB burst signals provided in embodiment 1 of the present invention;
Fig. 2 is a schematic diagram of the structure and principle of an adaptive filter according to embodiment 1 of the present invention;
Fig. 3 is a schematic diagram of simulation results when snr=30db and fo=2khz provided in embodiment 2 of the present invention;
Fig. 4 is a schematic diagram of simulation results when snr=30db and fo=650khz provided in example 2 of the present invention.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present invention and the descriptions thereof are for illustrating the present invention only and are not to be construed as limiting the present invention.
Example 1
Because the current WDTB burst signal scene only needs to support single antenna transmission, but the blind equalization method based on the second-order statistical characteristic in the prior art is mainly designed aiming at the scene of two paths of transmission antennas, the operation amount is increased, and the blind equalization method is not suitable for the current WDTB burst signal scene. In this regard, the present embodiment provides a self-adaptive blind equalization method based on WDTB burst signals, where the method is shown in fig. 1, and the method performs self-adaptive blind equalization by combining RLS algorithm with LMS algorithm under the condition that no priori information exists, so as to eliminate intersymbol interference, and specifically includes the following steps:
and step 1, obtaining an intermediate frequency signal.
And 2, performing multiple filtering on the intermediate frequency signal.
The adaptive filter is used in this embodiment to implement the polynomial filtering. The structure and schematic block diagram of the adaptive filter are shown in fig. 2, and mainly comprise two parts, namely a digital filter H (z) and an adaptive algorithm. In the adaptive filter, H (z) mostly selects FIR. Two functions of the adaptive algorithm are mainly learning and tracking. H (z) is updated continuously by the self-adaptive algorithm through continuous learning according to a given initial value, and finally the optimal solution is reached or approximated. x (n) is an input signal, the data after passing through the adaptive filter is y (n), d (n) is a desired signal, and e (n) is an error signal. The adaptive algorithm adjusts the coefficients of the filter according to e (n) such that the mean square error of the error signal e (n) at any instant tends to be minimal.
And 3, performing digital down conversion and matched filtering on the data obtained after the multiple filtering to obtain a low-frequency SNR value.
And 4, performing coarse timing synchronization on the low-frequency SNR value by using a synchronization head to obtain a data frame head.
And 5, carrying out frequency offset measurement and compensation on the data frame header by utilizing the synchronous header pilot frequency to obtain a frequency offset value.
Step 6, performing fine timing synchronization on the frequency offset value to obtain a frequency offset value without sampling error;
and 7, aiming at the frequency offset value without sampling error, utilizing a PLL loop to carry out frequency tracking to obtain a precise frequency offset value.
And 8, aiming at the precise frequency offset data, carrying out self-adaptive blind equalization by using an RLS method and an LMS method in sequence.
As shown in fig. 1, the adaptive blind equalization process is characterized in that according to the characteristic of high convergence rate of the RLS algorithm, the RLS algorithm is adopted to perform primary equalization before data equalization convergence, and after data is basically stable, the LMS algorithm is adopted to perform further equalization, the LMS algorithm is simple to calculate, and the calculated amount can be reduced in a maintenance stage after equalization stabilization, so that the system is ensured to be most simplified under the condition of ensuring the highest convergence rate.
In the adaptive blind equalization process, there is a sequence 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 by the RLS algorithm equalization process is smaller than the preset value found in the table, the self-adaptive blind equalization algorithm is switched to an LMS algorithm.
It should be further noted that,
The RLS algorithm uses least squares, and recursion means that the filter coefficient h (n) at time n is obtained using the filter coefficient h (n-1) at time n-1. The RLS algorithm minimizes epsilon M of the following equation to find the filter coefficient h (n):
Where ρ is a weighting factor, 0< ρ <1. The nature of the above formula using exponential weighting is that errors found on new data are given the greatest weight, while errors found on earlier data are given the lesser weight. The purpose is to enable the newly found h M (n) to track the time-varying statistical characteristics of the input signal as soon as possible.
From the above formula, R M(n)hM(n)＝DM (n), where R M (n) is the signal weighted autocorrelation matrix estimated at time n, as follows:
Where D M (n) is the weighted cross-correlation vector estimated at time n,
Therefore, the filter coefficients at time n are:
Thus, recursive expressions of R M (n) and D M (n) are obtained, i.e
DM(n)＝ρDM(n-1)+XM(n)d(n)。
Then according to matrix inverse theory to obtainNamely:
Further, define And determining a Kalman gain vector as follows:
Thus, it was obtained:
Next solve for h M (n).
In the formula,The output of the adaptive filter at time n is denoted as: the calculation error is e M (n) =d (n) -y (n).
In order to reduce time consumption in the table lookup comparison process, it may be determined that the RLS algorithm error value is determined according to a preset value N (N > 0) in N receiving processes at each interval, table lookup is performed according to different SNR values and modulation modes, and when e M (N) in the above formula is greater than a preset threshold, the adaptive blind equalization algorithm is switched to the LMS algorithm.
In addition, the LMS algorithm uses the instantaneous error energy e 2 (n) instead of the mean square error energy. As can be seen from the adaptive filter block diagram, the error sequence is: Where X (n) is a data vector.
The gradient vector of the instantaneous error energy is:
the filter coefficients at the n+1th iteration can be found by h (n+1) =h (n) +μe (n) X (n) as follows.
The above can also be written as: h l(n+1)＝hl (n) +μx (n-l) e (n), l=0, 1,..m-1, where M is the length of the filter, n is the iteration number, and l is the number of the filter coefficients.
And 9, sequentially performing initial phase correction and symbol level processing on the data obtained after the self-adaptive blind equalization to obtain decoding output. The symbol level processing method comprises the steps of sequentially descrambling, despreading and LDPC decoding the data obtained after initial phase correction, and finally obtaining decoded output.
Through the joint design of the RLS 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 good real-time tracking performance, high convergence speed and low steady-state error, and is suitable for various modulation mode scenes.
Example 2
In this embodiment, an adaptive blind equalization method based on WDTB burst signals as shown in embodiment 1 is used to simulate in the cases of signal-to-noise ratio snr=30 dB, fine frequency offset fo=2 KHz, and snr=30 dB, fine frequency offset fo=650 KHz, so as to obtain error sequences and constellations before and after equalization under different frequency offset conditions.
At snr=30 db, fo=2 KHz, the error tends to stabilize when iterated to 400 times according to the error sequence curve. When convergence is reached, the error sequence value is approximately 0.02, and the mean error value after stabilization is 0.00718. The simulation results are shown in fig. 3.
At snr=30db, fo=650 KHz, the error tends to stabilize when iterated to 400 times according to the error sequence curve. When convergence is reached, the error sequence value is approximately 0.02, and the mean error value after stabilization is 0.00719. The simulation results are shown in fig. 4.
Example 3
The embodiment provides an adaptive blind equalization system based on WDTB burst signals, which comprises:
the signal acquisition module is used for acquiring intermediate frequency signals;
The adaptive filter is used for carrying out multiple filtering on the intermediate frequency signal;
the timing synchronization module is used for performing timing synchronization on the data obtained after the multiple filtering;
the frequency offset correction module is used for carrying out frequency offset correction on the data after timing synchronization;
the self-adaptive equalization module is used for carrying out self-adaptive blind equalization by sequentially utilizing an RLS method and an LMS method according to the data obtained after the frequency offset correction;
The initial phase correction module is used for sequentially carrying out initial phase correction on the data obtained after the self-adaptive blind equalization;
And the symbol level processing module is used for performing symbol level processing on the data obtained after the initial phase correction to obtain decoding output.
Wherein,
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 carrying out fine timing synchronization on the output data of the frequency offset correction module to obtain a frequency offset value without sampling errors.
The adaptive equalization module includes:
The RLS equalization unit is used for performing first self-adaptive blind equalization on the fine frequency offset value by utilizing an RLS method;
the error comparison unit is used for comparing the error value obtained in the first self-adaptive blind equalization process with the error threshold and outputting a comparison result;
a switching unit for switching a method for adaptive blind equalization from the RLS method to the LMS method when the error value < 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 using an LMS method.
The symbol-level processing module includes:
The data descrambling unit is used for descrambling the data obtained after the initial phase correction;
The data despreading unit is used for despreading the data obtained after descrambling;
an LDPC decoding unit for performing LDPC decoding on the despread data to obtain decoded output
In addition, the adaptive blind equalization system based on WDTB burst signals further comprises:
The digital down-conversion module is used for carrying out digital down-conversion on the data obtained after the multiple filtering to obtain a low-frequency signal;
the matched filter is used for carrying out 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 deviation value without sampling error by utilizing the PLL loop to obtain a precise frequency deviation value.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (4)
1. The self-adaptive blind equalization method based on WDTB burst signals is characterized by comprising the following steps of:
S1, obtaining an intermediate frequency signal;
s2, performing multiple filtering on the intermediate frequency signal;
S3, carrying out timing synchronization and frequency offset correction on the data obtained after the multiple filtering;
s4, carrying out self-adaptive blind equalization by sequentially utilizing an RLS method and an LMS method according to the data obtained after timing synchronization and frequency offset correction;
S5, sequentially performing initial phase correction and symbol level processing on the data obtained after self-adaptive blind equalization to obtain decoding output;
the step S3 is preceded by the steps of carrying out digital down-conversion and matched filtering on the data obtained after the multiple filtering to obtain a low-frequency SNR value;
The step S3 comprises the following steps:
s31, performing coarse timing synchronization on the low-frequency SNR value by using a synchronization head to obtain a data frame head;
S32, carrying out frequency offset measurement and compensation on the data frame header by utilizing the synchronous header pilot frequency to obtain a frequency offset value;
s33, performing fine timing synchronization on the frequency offset value to obtain a frequency offset value without sampling error;
S34, aiming at the frequency offset value without sampling error, utilizing a PLL loop to carry out frequency tracking to obtain a precise frequency offset value;
The self-adaptive blind equalization method comprises the following steps:
S41, presetting an error threshold;
S42, performing self-adaptive blind equalization on the fine frequency offset value by using an RLS method, comparing an error value obtained in the self-adaptive blind equalization process with the error threshold, and if the error value is smaller than the error threshold, switching a self-adaptive blind equalization using method from the RLS method to an LMS method, and continuing the self-adaptive blind equalization by using the LMS method.
2. The adaptive blind equalization method based on WDTB burst signals as claimed in claim 1, wherein the symbol level processing method is that data obtained after initial phase correction is sequentially subjected to descrambling, data despreading and LDPC decoding, and finally decoded output is obtained.
3. An adaptive blind equalization system based on WDTB burst signals, comprising:
the signal acquisition module is used for acquiring intermediate frequency signals;
The adaptive filter is used for carrying out multiple filtering on the intermediate frequency signal;
the timing synchronization module is used for performing timing synchronization on the data obtained after the multiple filtering;
the frequency offset correction module is used for carrying out frequency offset correction on the data after timing synchronization;
the self-adaptive equalization module is used for carrying out self-adaptive blind equalization by sequentially utilizing an RLS method and an LMS method according to the data obtained after the frequency offset correction;
The system comprises a primary phase correction module, a symbol level processing module, a decoding output module and a secondary phase correction module, wherein the primary phase correction module is used for sequentially carrying out primary phase correction on data obtained after self-adaptive blind equalization;
The digital down-conversion module is used for carrying out digital down-conversion on the data obtained after the multiple filtering to obtain a low-frequency signal;
the matched filter is used for carrying out matched filtering on the low-frequency signal to obtain a low-frequency SNR value;
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;
the fine timing synchronization unit is used for carrying out fine timing synchronization on the output data of the frequency offset correction module to obtain a frequency offset value without sampling errors;
The frequency tracking module is used for carrying out frequency tracking on the frequency deviation value without sampling error by using a PLL loop to obtain a fine frequency deviation value;
The adaptive equalization module includes:
The RLS equalization unit is used for performing first self-adaptive blind equalization on the fine frequency offset value by utilizing an RLS method;
the error comparison unit is used for comparing the error value obtained in the first self-adaptive blind equalization process with an error threshold and outputting a comparison result;
a switching unit for switching a method for adaptive blind equalization from the RLS method to the LMS method when the error value < 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 using an LMS method.
4. An adaptive blind equalization system based on a WDTB burst signal as claimed in claim 3, wherein said symbol-level processing module comprises:
The data descrambling unit is used for descrambling the data obtained after the initial phase correction;
The data despreading unit is used for despreading the data obtained after descrambling;
and the LDPC decoding unit is used for performing LDPC decoding on the despread data to obtain decoding output.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111656644.2A CN114268525B (en) | 2021-12-30 | 2021-12-30 | An adaptive blind equalization method and system based on WDTB burst signal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111656644.2A CN114268525B (en) | 2021-12-30 | 2021-12-30 | An adaptive blind equalization method and system based on WDTB burst signal |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114268525A CN114268525A (en) | 2022-04-01 |
| CN114268525B true CN114268525B (en) | 2024-12-03 |
Family
ID=80831869
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111656644.2A Active CN114268525B (en) | 2021-12-30 | 2021-12-30 | An adaptive blind equalization method and system based on WDTB burst signal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114268525B (en) |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7421019B2 (en) * | 2004-06-18 | 2008-09-02 | Chung Shan Institute Of Science And Technology, Armaments Bureau, M.N.D. | Hierarchical adaptive equalizer and design method thereof |
| CN110213184B (en) * | 2019-06-03 | 2020-06-26 | 北京理工大学 | Adaptive channel blind equalization method based on modified cost function |
| CN112468419B (en) * | 2020-11-23 | 2021-08-31 | 中国科学院国家空间科学中心 | An adaptive dual-mode blind equalization method and system |
-
2021
- 2021-12-30 CN CN202111656644.2A patent/CN114268525B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
Non-Patent Citations (1)
| Title |
|---|
| Blind Adaptive Channel Equalization Using Multichannel Linear Prediction-Based Cross-Correlation Vector Estimation;Kyung Seung Ahn;IEEE;20041130;第50卷(第4期);第1026-1031页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114268525A (en) | 2022-04-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR101477482B1 (en) | Adaptive equalizer for communication channels | |
| KR100447201B1 (en) | Channel equalizer and digital TV receiver using for the same | |
| Malik et al. | Adaptive equalization algorithms: an overview | |
| US20060164270A1 (en) | Decision feed forward equalizer system and method | |
| IE922325A1 (en) | Method and apparatus for updating coefficients in a complex¹adaptive equalizer | |
| US20060239339A1 (en) | CIR estimating decision feedback equalizer with phase tracker | |
| JP6540295B2 (en) | Adaptive equalization circuit, digital coherent receiver and adaptive equalization method | |
| CN113300988A (en) | Inter-modal interference suppression method for low-frequency underwater acoustic communication | |
| EP0527190B1 (en) | A method of equalization in a receiver of signals having passed a transmission channel | |
| US20040042545A1 (en) | Equalizer of single carrier receiver for improving equalization speed and equalization method thereof | |
| KR20070117791A (en) | Equalizer Using Estimated Noise Power | |
| CN101997790B (en) | Channel estimation device and method based on time domain pilot frequency sequence | |
| KR100728257B1 (en) | Decision Feedback Equalizer and its Method Using Channel Characteristics Changes | |
| CN114268525B (en) | An adaptive blind equalization method and system based on WDTB burst signal | |
| KR100403067B1 (en) | Receiving device and method of generating replica signal | |
| JP2503715B2 (en) | Adaptive receiver | |
| JP3424816B2 (en) | Diversity receiver and diversity reception control method | |
| JPH10173573A (en) | Adaptive equalizer | |
| JP4425520B2 (en) | Blind equalizer for high-definition television signals | |
| US20050163209A1 (en) | Method and apparatus for the control of a decision feedback equalizer | |
| JP2001196978A (en) | Adaptive equalization system, diversity reception system, and adaptive equalizer | |
| KR100753502B1 (en) | Noise Canceling Device and Method and Channel Equalization System | |
| EP1745620A1 (en) | Decision feedback equalizer using decisions from long and short traceback trellis decoders | |
| JP2569904B2 (en) | Interference wave canceller | |
| JP2595282B2 (en) | Decision feedback equalizer |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |