CN104022984A - Channel equalization method based on bidirectional noise prediction decision feedback - Google Patents
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Abstract
The invention discloses a channel equalization method based on bidirectional noise prediction decision feedback and mainly aims to solve a problem of high complexity of a traditional bidirectional noise prediction decision feedback algorithm. The method comprises steps that: 1, FFT conversion for a time domain signal is carried out to acquire a frequency domain signal; 2, feedforward filtering for the frequency domain signal is carried out; 3, IFFT conversion for the data after a feedforward filter is carried out to acquire a time domain signal; 4, signal overturning for the time domain signal is carried out; 5, positive noise prediction filtering for the signal before overturning is carried out; 6, reverse noise prediction filtering for the data after overturning is carried out; 7, the data acquired after positive noise prediction filtering and the data after reverse noise prediction filtering are combined to acquire a signal to be determined; and 8, the signal to be determined is determined to acquire a channel equalization result. According to the channel equalization method, only one feedforward filter is used, channel equalization complexity is reduced, the channel equalization method has stronger practicality and is suitable for a time varying channel having obvious multipath effects.
Description
Technical Field
The invention belongs to the field of wireless communication, and relates to a single carrier channel frequency domain equalization method which is suitable for a time-varying multipath channel.
Background
In a wireless communication system, radio waves are influenced by channel multipath in wireless channel transmission, multipath fading is generated, and when a channel coherent bandwidth caused by multipath time delay expansion is larger than a signal bandwidth, frequency selective fading is generated on the signal. In order to compensate for frequency selective fading of a signal caused by a channel, a technique of performing frequency domain compensation on a received signal by using a channel estimation value so that signal transmission satisfies a condition without intersymbol interference is called channel frequency domain equalization FDE. Channel equalization techniques include linear and non-linear equalization.
In terms of combating multipath fading channels, the basic transmission techniques can be divided into two broad categories, single carrier and multi-carrier. In the multi-carrier modulation technology, the OFDM technology attracts attention because of its advantages such as high spectrum efficiency, flexible resource allocation, easy combination with the multi-antenna MIMO technology, and low complexity of the equalizer, however, the OFDM system has a too high PAPR and is not suitable for the satellite communication system, so most of the satellite communication at present still adopts the single carrier modulation technology.
H.sari and i.jeanclade first propose a single carrier frequency domain equalization SC-FDE technique, which can effectively overcome the disadvantages of the OFDM system, and compared with a time domain equalization system, the frequency domain equalization system is more effective and less complex, and especially under the condition of relatively large multipath delay spread, the time domain equalizer has many tap coefficients, while the frequency domain equalizer has relatively few and less complex. The frequency domain equalizer is therefore suitable for use in satellite communication systems.
Typical linear equalization algorithms FD-LE include zero forcing equalization ZF and minimum mean square error equalization MMSE, and the linear equalization algorithms are simple but cannot well eliminate inter-symbol interference caused by multipath effects. Therefore, the researchers provide the frequency domain feedforward and time domain feedback combined equalizer H-DFE, which mainly comprises a frequency domain decision feedback equalizer FD-DFE and a decision feedback algorithm FDE-NP based on noise prediction.
The FDE-NP algorithm consists of a feedforward part and a feedback part, wherein the feedforward part is a frequency domain linear data balancer, and the feedback part is a time domain noise predictor. The feedforward and feedback coefficients of the algorithm do not influence each other. Because the feedforward coefficient removes the influence of a feedback part, the complexity of coefficient calculation is reduced, and the performance of the algorithm is obviously superior to that of an FD-LE algorithm. To further improve the performance of the H-DFE equalization algorithm, the two-way decision feedback algorithm FD-BiDFE has been proposed by the scholars, which consists of a combination of a direct equalizer and a time-flipped equalizer. Since performing the FFT after time flipping a sequence is equivalent to performing the FFT after the FFT of the sequence, this method places the time flipping after the FFT, which reduces the computational complexity. A bidirectional noise prediction decision feedback equalization algorithm FDE-BiNP based on a similar structure is also provided, the algorithm consists of a direct frequency domain equalization noise prediction and a time reversal frequency domain equalization noise prediction, the coefficients of a frequency domain equalization noise prediction feedforward filter and a feedback filter are mutually independent, and compared with a frequency domain bidirectional decision feedback equalizer, the structure reduces the complexity of equalization detail calculation, but the complexity of the algorithm is relatively high, and the application is limited.
Disclosure of Invention
The invention aims to provide a decision feedback equalization method based on bidirectional noise prediction aiming at the defect of higher complexity in the prior art, so as to reduce the equalization complexity and expand the application on the premise of not losing the performance.
The technical idea of the invention is as follows: and directly overturning the time domain signal obtained by the feedforward filtering, filtering the two paths of signals after overturning through a noise prediction filter respectively, then combining, and judging and outputting. The method comprises the following implementation steps:
1) serial-to-parallel conversion is carried out on input serial data y (N), namely, input first data is converted into output data y (0), input second data is converted into output data y (1), and input Nth data is converted into output data y (N-1) to obtain N paths of parallel data y [ [ y (0), y (1) ], y (N-1) ], N (N-1) ])]T；
2) For the N paths of parallel data y obtained in the step 1, [ y (0), y (1),. ] y (N-1)]TPerforming FFT to obtain a parallel data form after FFT:
Y＝Fy，
where Y is input parallel data represented by a column vector, and Y represents a frequency domain signal Y obtained by FFT [ Y (0), Y (1), …, Y (N-1)]TAnd F is the non-normalized FFT matrix:
in the formula, represents WNTo the k power of (a), k ═ 0,1, 2. (N-1) × (N-1);
3) performing feedforward filtering on the frequency domain signal Y output by the FFT to obtain a frequency domain signal output Z after the feedforward filter is obtained;
4) IFFT conversion is carried out on the signal Z after feedforward filtering to obtain a corresponding time domain signal Z, and an IFFT conversion matrix is FH/N, wherein [. ]]HRepresenting the conjugate transpose of the matrix, F being the FFT matrix;
5) performing parallel-serial conversion on the data z obtained in the step 4, converting N paths of parallel data into serial data, and expressing the output result of the parallel-serial conversion by using z (N), wherein N belongs to [0, N-1 ];
6) using the signal z (n) before inversion as the input of forward noise prediction filter, and using the inverted signal zI(n) as an input to an inverse noise predictor;
7) for the signal z before the flipD(n) performing forward noise prediction filtering to obtain data after forward noise prediction filteringFor the inverted signal zI(n) performing inverse noise prediction filtering to obtain inverse noise prediction filtered data
8) Predicting the output signal of a filter for forward noiseWith output signal of inverse noise prediction filterCombining to obtain the signal to be judged
9) Signal to be judgedMaking a decision, according to the minimum Euclidean distance criterion, toComparing with each constellation point in the constellation diagram, and obtaining the judged signal by taking the point seat judgment output with the minimum Euclidean distance
The invention has the following advantages:
1) because two paths of signals are filtered through the noise prediction filter respectively, two degrees of freedom are provided, and the influence of part of noise can be counteracted when the signals are weighted, so that better performance is obtained;
2) the invention uses the same feedforward equalizer, so the complexity is lower and the practicability is higher. The complexity of the present invention compared to the FDE-BiNP algorithm is shown in Table 1:
TABLE 1 complexity comparison of equalizers
Drawings
FIG. 1 is a schematic diagram of an implementation of the present invention;
FIG. 2 is a block diagram of a feed forward filter according to the present invention;
fig. 3 is a simulation of the bit error rate curve of the present invention.
Detailed Description
The technical process of the present invention is further described below by way of the accompanying drawings and examples.
Referring to fig. 1, the implementation steps of the invention are as follows:
step 1: the input serial data y (n) is converted from serial to parallel.
Converting the first input data into output data y (0), converting the second input data into output data y (1), …, converting the Nth input data into output data y (N-1), and obtaining N paths of parallel data y ═ y (0), y (1),.. once.y (N-1)]T。
Step 2: and performing FFT (fast Fourier transform) on the N paths of parallel data y obtained in the step 1.
Performing FFT on input parallel data y to obtain a parallel data form after FFT:
Y＝Fy，
where Y is input parallel data represented by a column vector, and Y represents a frequency domain signal Y obtained after FFT [ Y (0), Y (1) ], Y (N-1)]TAnd F is the non-normalized FFT matrix:
in the formula, represents WNThe power k of (a), k ═ 0,1,2, (N-1) × (N-1).
And step 3: and (3) performing feedforward filtering on the parallel data Y subjected to the FFT in the step (2).
Referring to fig. 2, the specific implementation of this step is as follows:
3.1) calculating feedforward filter coefficients:
whereinAndrepresenting the power of the transmitted signal and the channel noise power, and H (n) is the channel frequency domain response;
3.2) for the N data Y output from step 2 ═ Y (0), Y (1), …, Y (N-1)]TMultiplying N corresponding filter coefficients W (N) respectively, namely multiplying Y (0) by W (0), multiplying Y (1) by W (1), and multiplying Y (N-1) by W (N-1) to obtainTo feed-forward filtered signal Z ═ Z (0), Z (1), Z (N-1)]T。
And 4, step 4: and performing IFFT transformation on the signal Z subjected to feedforward filtering in the step 3.
The filtered data Z obtained in step 3 is [ Z (0), Z (1) ], Z (N-1)]TPerforming IFFT to obtain a corresponding time domain parallel signal z ═ z (0), z (1),.. multidot.z (N-1)]TIFFT transform matrix is FH/N, wherein [. cndot.)]HRepresenting the conjugate transpose of the matrix, F is the FFT matrix.
And 5: and 4, performing parallel-to-serial conversion on the time domain parallel signal z obtained in the step 4.
Outputting N paths of parallel data z ═ z (0), z (1),.. z, z (N-1)]TConverting the data into serial data, z (0) as the first data output, z (1) as the second data output, …, z (N-1) as the Nth data output, using z (N), N ∈ [0, N-1]]Representing the output result of the parallel-to-serial conversion;
step 6: and (5) performing signal inversion on the output z (N) of the feedforward filter in the step 5, wherein N belongs to [0, N-1 ].
Assume that serial data is input in the order z (N) ═ z (0), z (1), …, z (N-2), z (N-1)]TAfter signal inversion, the obtained output serial data is the reverse order of input: z is a radical ofI(n)＝[z(N-1)，z(N-2)，…，z(1)，z(0)]T。
And 7: and (3) performing forward noise prediction filtering on the serial data signal z (n) before the inversion in the step 6.
7.1) definition of zD(n) z (n) as input to the forward noise prediction filter, and calculating the output θ of the forward noise predictorD：
Wherein I represents an NxN identity matrix, b is an NxN circulant matrix composed of forward noise predictor tap coefficients, the main diagonal elements are all 1, and the first row elements are [1,0.,. 0, -b (L), -b (L-1), …, -b (1)]Where b (i), i ═ 1,2, …, L denotes the coefficients of the forward noise prediction filter, eD＝[eD(0)，eD(1)，…，eD(N-1)]TRepresenting the input signal of the noise predictor,representing the decision output signal, zDIs output by a feedforward filter before turning;
7.2) assuming the feedback sign is correct, the output signal after forward noise pre-filtering is represented as:
step 8 for the inverted signal zIAnd (n) performing reverse noise prediction filtering.
8.1) calculating the output θ of the inverse noise predictorI：
Wherein I represents an NxN identity matrix, b is an NxN circulant matrix composed of inverse noise predictor tap coefficients, the main diagonal elements are all 1, and the first row elements are [1,0.,. 0, -b (L), -b (L-1), …, -b (1)]Where b (i), i ═ 1,2, …, L denotes the coefficients of the inverse noise prediction filter, eI＝[eI(0)，eI(1)，…，eI(N-1)]TRepresenting the input signal of the noise predictor,representing the decision output signal, zIIs the output of the feedforward filter;
8.2) assuming the feedback sign is correct, the pre-flipped noise pre-filtered signal is expressed as:
8.3) predictive filtering of the inverse noise before inversionPerforming signal inversion to obtain inverted signalI.e. the output of the inverse noise prediction filtering.
And step 9: predicting the output signal of a filter for forward noiseWith output signal of inverse noise prediction filterCombining to obtain the signal to be judged
Wherein, α is a weighting factor, α is more than or equal to 0 and less than or equal to 1, and the optimal weighting factor is found by using the minimum mean square error method to obtain α as 0.5.
Step 10: signal to be judgedAnd (6) making a decision.
According to the minimum Euclidean distance criterion, willComparing with each constellation point in the constellation diagram, taking the constellation point with the minimum Euclidean distance as decision output to obtain a signal after decisionI.e. the result of the channel equalization.
The effects of the present invention can be further illustrated by the following simulations:
1. simulation conditions are as follows:
the satellite channel model Profile5 is obtained by the european telecommunication standardization institute, and the channel has three terrestrial repeaters, has urban outdoor channel characteristics, includes 10 propagation paths, and has a plurality of paths with high power, so that the multipath fading is severe. The channel parameters of Profile5 are shown in table 2:
table 2profile5 channel parameters
2. Simulation content and results:
the method of the invention and the traditional FDE-BiNP method are used for simulating the performance of the bit error rate along with the signal-to-noise ratio transformation under the simulation conditions, and the simulation result is shown in figure 3.
As can be seen from fig. 3, the performance of the present invention is superior to the conventional ZF algorithm and MMSE algorithm, as well as the FDE-BiNP method, while the complexity of the proposed equalizer is superior to the FDE-BiNP method.
Claims (5)
1. The channel equalization method based on the bidirectional noise prediction decision feedback comprises the following steps:
1) serial-to-parallel conversion is carried out on input serial data y (N), namely, input first data is converted into output data y (0), input second data is converted into output data y (1), …, input Nth data is converted into output data y (N-1), and N paths of parallel data y is obtained, namely [ y (0), y (1),.., y (N-1) ], and the N paths of parallel data y are obtained]T；
2) For the N paths of parallel data y obtained in the step 1, [ y (0), y (1),. ] y (N-1)]TPerforming FFT to obtain the FFTParallel data form:
Y＝Fy，
where Y is input parallel data represented by a column vector, and Y represents a frequency domain signal Y obtained after FFT [ Y (0), Y (1) ], Y (N-1)]TAnd F is the non-normalized FFT matrix:
in the formula, represents WNTo the k power of (a), k ═ 0,1, 2. (N-1) × (N-1);
3) performing feedforward filtering on the frequency domain signal Y output by the FFT to obtain a frequency domain signal output Z after the feedforward filter is obtained;
4) IFFT conversion is carried out on the signal Z after feedforward filtering to obtain a corresponding time domain signal Z, and an IFFT conversion matrix is FHN, wherein [ ·]HRepresenting the conjugate transpose of the matrix, F being the FFT matrix;
5) performing parallel-serial conversion on the data z obtained in the step 4, converting N paths of parallel data into serial data, and expressing the output result of the parallel-serial conversion by using z (N), wherein N belongs to [0, N-1 ];
6) using the signal z (n) before inversion as the input of forward noise prediction filter, and using the inverted signal zI(n) as an input to an inverse noise predictor;
7) for the signal z before the flipD(n) performing forward noise prediction filtering to obtain data after forward noise prediction filteringFor the inverted signal zI(n) performing inverse noise prediction filtering to obtain inverse noise prediction filtered data
8) Predicting the output signal of a filter for forward noiseWith output signal of inverse noise prediction filterCombining to obtain the signal to be judged
9) Signal to be judgedMaking a decision, according to the minimum Euclidean distance criterion, toComparing with each constellation point in the constellation diagram, and obtaining the judged signal by taking the point seat judgment output with the minimum Euclidean distance
2. The method for channel equalization based on decision feedback of bi-directional noise prediction as claimed in claim 1, wherein said step 3) of performing feedforward filtering on the frequency domain signal Y outputted by FFT, is performed according to the following steps:
3a) calculating coefficients of the feedforward filter:
whereinAndrepresenting the power of the transmitted signal and the channel noise power, and H (n) is the channel frequency domain response;
3b) frequency domain signal Y ═ Y (0), Y (1),.., Y (N-1)]TMultiplying the N corresponding filter coefficients w (N) to obtain a feedforward-filtered signal Z ═ Z (0), Z (1),.., Z (N-1)]T。
3. The method for channel equalization based on decision feedback of bi-directional noise prediction as claimed in claim 1, step 7) said pair of signals z before flippingD(n) performing forward noise prediction filtering, and performing the following steps:
forward noise prediction of filtered data assuming that the feedback symbols are always correctExpressed as:
wherein z isD＝[zD(0),zD(1),...,zD(N-1)]TRepresenting the output of a forward feedforward filter, thetaD＝[θD(0),θD(1),...,θD(N-1)]TRepresents the output of the forward noise prediction filter,representing the decision output signal, I represents an N × N identity matrix, b is an N × N circulant matrix of forward noise predictor tap coefficients, the main diagonal elements are all 1, the first row elements are [1,0.., 0, -b (L), -b (L-1),. -, -b (1)]Where b (i), i ═ 1, 2., L denotes the coefficients of the forward noise prediction filter, and L denotes the order of the forward noise prediction filter.
4. The method for channel equalization based on decision feedback of bi-directional noise prediction as claimed in claim 1, step 7) said pair of signals z before flippingI(n) inverse noise predictive filtering, asThe following steps are carried out:
7a) assuming that the feedback symbols are always correct, the inverse noise prediction filtered signal before inversion is usedExpressed as:
wherein z isI＝[zI(0),zI(1),...,zI(N-1)]TRepresenting the output of an inverse feedforward filter, thetaI＝[θI(0),θI(1),...,θI(N-1)]TRepresents the output of the inverse noise prediction filter,representing the decision output signal, I represents an N × N identity matrix, b is an N × N circulant matrix of inverse noise predictor tap coefficients, the main diagonal elements are all 1, the first row elements are [1,0.., 0, -b (L), -b (L-1),. -, -b (1)]Where b (i), i ═ 1, 2., L denotes the coefficients of the inverse noise prediction filter, and L denotes the order of the inverse noise prediction filter.
7b) For reverse noise before turningMeasuring the filtered signalPerforming signal inversion to obtain inverted signalI.e. the output of the inverse noise prediction filter.
5. The channel equalization method based on decision feedback of bi-directional noise prediction as claimed in claim 1, wherein the signal to be decided in step 8)Is represented as follows:
wherein,representing the output signal of the forward noise prediction filter,representing reverse noiseThe output signal of the acoustic prediction filter defines a weighting factor 0 ≦ α ≦ 1, and here, the optimal weighting factor is found by using the minimum mean square error method, and α is found to be 0.5.
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| CN113497773B (en) * | 2021-06-18 | 2022-11-29 | 西安电子科技大学 | Equalization method and system of scattering communication system, computer equipment and processing terminal |
| CN114285715A (en) * | 2021-12-20 | 2022-04-05 | 北京理工大学 | Nonlinear equalization method based on bidirectional GRU-conditional random field |
| CN114285715B (en) * | 2021-12-20 | 2023-12-19 | 北京理工大学 | Nonlinear equalization method based on bidirectional GRU-conditional random field |
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