CN111555993B - Channel estimation method based on iterative preprocessing in FBMC system - Google Patents

Channel estimation method based on iterative preprocessing in FBMC system Download PDF

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CN111555993B
CN111555993B CN202010429354.3A CN202010429354A CN111555993B CN 111555993 B CN111555993 B CN 111555993B CN 202010429354 A CN202010429354 A CN 202010429354A CN 111555993 B CN111555993 B CN 111555993B
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channel estimation
channel
pilot frequency
estimation value
pilot
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CN111555993A (en
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李靖
武晨辉
葛建华
任德锋
高明
吕宝均
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Xidian University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L25/00—Baseband systems
    • H04L25/02—Details ; arrangements for supplying electrical power along data transmission lines
    • H04L25/0202—Channel estimation
    • H04L25/024—Channel estimation channel estimation algorithms

Abstract

The invention discloses a channel estimation method based on iterative preprocessing in an FBMC system, which mainly solves the problem of poor channel estimation performance caused by limitation of the existing average weighting preprocessing algorithm for processing the reduced correlation of gain factor carriers in the FBMC system. The implementation scheme is as follows: 1) calculating a channel estimation initial value by using a paired pilot frequency POP algorithm; 2) carrying out iterative preprocessing on the initial value of the channel estimation at the pilot frequency; 3) interpolating the channel estimation value at the pilot frequency position after the iterative preprocessing to obtain the channel estimation value of the data symbol position; 4) the channel estimates for the data symbol positions are combined into a complete channel estimate. The invention considers the simplicity of POP method channel estimation calculation and the high channel gain characteristic obtained by iterative preprocessing under the flat channel environment, improves the overall performance of channel estimation, and can be used for a filter bank multi-carrier FBMC system under a slow fading channel.

Description

Channel estimation method based on iterative preprocessing in FBMC system
Technical Field
The invention belongs to the technical field of communication, and particularly relates to a channel estimation method which can be used for a filter bank multi-carrier FBMC system under a slow fading channel.
Background
Compared with the traditional orthogonal frequency division multiplexing OFDM system, the filter bank multi-carrier FBMC has the advantages that higher frequency spectrum utilization rate and smaller out-of-band leakage are obtained by introducing a prototype filter with good time-frequency focusing characteristic, and more flexibility is realized. Therefore, FBMC technology is a more potential multi-carrier technology, and has been a research hotspot and widely applied in wireless communication. However, the offset quadrature amplitude modulation OQAM used by the FBMC system to guarantee the transmission rate relaxes the condition of subcarrier orthogonality, so that the system can generate inherent interference, thereby affecting the performance of system channel estimation. At present, a great deal of research results are already available for flat fading channels, but the existing channel estimation algorithm is difficult to balance in complexity, accuracy and spectrum resource utilization rate.
Aiming at the problems, the existing channel estimation algorithm based on the block pilot frequency mainly comprises a paired pilot frequency method POP and an interference approximation method IAM, the POP algorithm can eliminate the influence of interference through simple calculation, but the POP algorithm is sensitive to noise disturbance due to neglect of noise consideration. The main idea of the IAM algorithm is to estimate the interference at the pilot frequency as a part of the pilot frequency energy, so as to reduce the influence of noise, and although the algorithm has better performance and different variants in the flat fading channel, the algorithm has higher complexity and is reduced compared with the POP method in terms of spectrum utilization. The average weighting preprocessing algorithm is used for carrying out average weighting processing on the channel estimation value of the POP algorithm, although the channel estimation performance of the POP algorithm is improved, the correlation coefficient of the corresponding carrier wave is gradually reduced along with the increase of the distance between the subcarrier at two sides and the subcarrier at the center, and therefore the gain of the weighting preprocessing is limited.
In summary, the problems of the prior art are as follows:
(1) the POP algorithm has insufficient channel estimation accuracy and is sensitive to noise influence.
(2) The IAM algorithm has a low spectrum utilization rate and a relatively high algorithm complexity.
(3) The average weighting-based preprocessing algorithm is not too complex, but the attenuation of the correlation coefficient limits the gain of the channel estimation.
Disclosure of Invention
The present invention is directed to the problem of the existing channel estimation method, and provides a channel estimation method based on iterative preprocessing to reduce the limitation of the correlation coefficient attenuation on the channel estimation gain.
In order to achieve the purpose, the technical scheme of the invention comprises the following steps:
(1) calculating the pilot frequency position channel estimation value in the FBMC system by using a POP algorithm:
(2) carrying out iterative preprocessing on channel estimation values on three subcarriers adjacent to a pilot frequency position to obtain a processing gain of the channel estimation:
(2a) smoothing three adjacent subcarriers to obtain channel estimation after one iteration
Figure BDA0002499952740000021
For the channel estimation value after one iteration
Figure BDA0002499952740000022
Smoothing is carried out on three continuous sub-carriers to obtain a channel estimation value after iteration twice
Figure BDA0002499952740000023
And analogizing until the channel estimation value is converged, and finally obtaining the optimal channel estimation value of the pilot frequency position
Figure BDA0002499952740000024
(2b) Smoothing three adjacent subcarriers at the adjacent pilot frequency position at the position L away from the pilot frequency in the step (2a) to obtain the channel estimation after one iteration
Figure BDA0002499952740000025
For the channel estimation value after one iteration
Figure BDA0002499952740000026
Smoothing is carried out on three continuous sub-carriers to obtain a channel estimation value after iteration twice
Figure BDA0002499952740000027
And repeating the steps until the channel estimation value is converged, and finally obtaining the optimal channel estimation value of the adjacent pilot frequency position at the distance L from the pilot frequency in the step (2a)
Figure BDA0002499952740000028
(2c) Two estimated values obtained by using (2a) and (2b)
Figure BDA0002499952740000029
And
Figure BDA00024999527400000210
performing first-order linear interpolation to obtain a systemGlobal channel estimation
Figure BDA00024999527400000211
The invention calculates the channel estimation value of the pilot frequency position by using the POP method with lower complexity, then carries out smooth filtering on the channel estimation values on the three adjacent subcarriers to obtain processing gain, and finally obtains higher channel estimation processing gain through multiple times of iterative processing, thereby improving the system performance. Compared with the POP method, due to the fact that iterative preprocessing is carried out on the channel estimation values on the three subcarriers adjacent to the pilot frequency position, channel estimation processing gain is obtained, and meanwhile algorithm complexity is not deteriorated excessively. Compared with the traditional average weighting preprocessing algorithm, the method considers the factor of the descending of the correlation degree of the subcarriers far away from the center of the carrier, thereby reducing the limitation of the attenuation of the correlation coefficient on the channel estimation gain through iteration, obtaining higher channel estimation processing gain and improving the system performance.
Drawings
FIG. 1 is a flow chart of an implementation of the present invention;
fig. 2 is a schematic diagram of a pilot structure applied in the present invention;
FIG. 3 is a sub-flow diagram of the iterative preprocessing of the present invention;
FIG. 4 is a graph of a performance simulation 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 with reference to the following embodiments.
Referring to fig. 1, the implementation steps of the invention are as follows:
step 1, calculating a channel estimation initial value by using a paired pilot frequency POP algorithm.
(1a) Obtaining a system transmission equation of pilot positions:
as shown in FIG. 2, in the paired-pilot POP algorithm, two consecutive pilot symbols are respectively set as dk,nAnd dk,n+1The received signals of the two pilot positions are yk,nAnd yk,n+1Channel estimation of the two pilot positionsThe measured value is Hk,nAnd Hk,n+1Let Wk,n＝1/Hk,nM is the total number of sub-carriers, L is the interval length of two groups of pilot symbols, neglecting the influence of noise on the signal, then in the two pilot symbols dk,nAnd dk,n+1The system transmission equation set is as follows:
Figure BDA0002499952740000031
wherein u isk,nAnd uk,n+1Are respectively pilot frequencies dk,nAnd dk,n+1The natural interference of the position is pure imaginary number;
(1b) solving the pilot frequency position channel estimation value:
(1b1) and a real part of the system equation set at the pilot frequency position obtains the following equation set:
Figure BDA0002499952740000032
wherein
Figure BDA0002499952740000033
And
Figure BDA0002499952740000034
are respectively Wk,nReal and imaginary parts of (c).
(1b2) Based on the fact that in a slowly varying channel, the channel frequency responses of adjacent symbols are approximately equal, i.e., have Wk,n≈Wk,n+1The system equation at the pilot is expressed as:
Figure BDA0002499952740000035
obtained from the formula (1b2)
Figure BDA0002499952740000036
And
Figure BDA0002499952740000037
the solution of (a):
Figure BDA0002499952740000038
association
Figure BDA0002499952740000039
And
Figure BDA00024999527400000310
the solution of (a) can be:
Figure BDA0002499952740000041
from Wk,n＝1/Hk,nThe channel estimation values at the available pilot frequency are:
Figure BDA0002499952740000042
where j is an imaginary unit, z is an integer,
Figure BDA0002499952740000043
and
Figure BDA0002499952740000044
respectively represent yk,nAnd yk,n+1The complex number of the,
Figure BDA0002499952740000045
presentation pair
Figure BDA0002499952740000046
And taking an imaginary part.
And 2, carrying out iterative preprocessing on the channel estimation value at the pilot frequency position.
(2a) And performing iterative smooth filtering on the channel estimation value at the pilot frequency:
referring to fig. 3, the specific implementation of this step is as follows:
(2a1) are connected in parallelThe channel estimation values on the continuous three subcarriers are smoothed to obtain the channel estimation value after the central subcarrier is iterated once
Figure BDA0002499952740000047
Figure BDA0002499952740000048
(2a2) Channel estimation value after one iteration is subjected to equation (2a1)
Figure BDA0002499952740000049
Then smoothing is carried out to obtain the channel estimation value after iteration twice
Figure BDA00024999527400000410
And analogizing until the channel estimation value is converged, and finally obtaining the optimal channel estimation value of the pilot frequency position
Figure BDA00024999527400000411
(2b) Iteratively smoothing the next set of pilot channel estimates adjacent to the pilot in (2 a):
(2b1) smoothing the next group of pilot channel estimation values adjacent to the pilot in the step (2a) to obtain the channel estimation value after one iteration
Figure BDA00024999527400000412
Figure BDA00024999527400000413
(2b2) Channel estimation value after one iteration is subjected to equation (2b1)
Figure BDA0002499952740000051
Then smoothing is carried out to obtain the channel estimation value after iteration twice
Figure BDA0002499952740000052
And analogizing until the channel estimation value is converged, and finally obtaining the optimal channel estimation value of the pilot frequency position
Figure BDA0002499952740000053
And 3, obtaining channel estimation values at the positions of the two groups of pilot frequency symbols through interpolation:
two estimated values obtained by using (2a) and (2b)
Figure BDA0002499952740000054
And
Figure BDA0002499952740000055
first order linear interpolation is performed at the nth symbol position between the two sets of pilots, as follows:
Figure BDA0002499952740000056
wherein L is the interval between two adjacent pilots,
Figure BDA0002499952740000057
and
Figure BDA0002499952740000058
respectively, representing channel response estimates at two adjacent pilot locations.
And 4, combining the channel estimation values among the pilot symbols into a complete channel estimation value.
Combining the channel estimation values obtained by interpolation between two groups of pilot frequencies according to the data symbol sequence to obtain the final integral channel estimation value
Figure BDA0002499952740000059
Expressed as:
Figure BDA00024999527400000510
wherein the content of the first and second substances,
Figure BDA00024999527400000511
indicating the channel estimate starting from the pilot position to the p-th symbol.
The effects of the present invention can be illustrated by the following analysis:
theoretical analysis
1. The signal-to-noise ratio gain expression of the invention is constructed as follows:
let I be the number of iterations in the iterative convergence of the channel, Sin/NinRepresenting the signal-to-noise ratio, S, before iterative preprocessingout/NoutThe signal-to-noise ratio after the iterative preprocessing is represented, and the signal-to-noise ratio gain G obtained by the channel estimation method based on the iterative preprocessing in the FBMC system of the invention is represented as:
Figure BDA00024999527400000512
wherein:
Figure BDA00024999527400000513
Figure BDA0002499952740000061
ρn-I,nis the correlation coefficient, p, between the subcarrier n-I and the subcarrier nn,n+IIs the correlation coefficient, p, between subcarrier n and subcarrier n + In-I,n+IIs the correlation coefficient between sub-carrier n-I and sub-carrier n + I.
2. The gain expression of the existing average weighting-based preprocessing algorithm is as follows:
Figure BDA0002499952740000062
wherein:
Figure BDA0002499952740000063
Figure BDA0002499952740000064
Ω(p.q)∈[-(K-1)/2,(K-1)/2]and p ≠ q, p ≠ 0, q ≠ 0, K denotes the number of subcarriers ρ ≠ qn+p,n+qRepresenting the correlation coefficient, p, between the subcarrier n + p and the subcarrier n + qn+i,nRepresenting the correlation coefficient between subcarrier n + i and subcarrier n.
3. Comparing the gain G of the present invention with the gain G of the prior art based on the average weighted preprocessing algorithmave：
When the number of iterations I is 1,
Figure BDA0002499952740000065
B＝Bavewhen G is equal to Gave；
When the number of iterations I > 1, the A term decreases exponentially with the number of iterations I, and AaveThe term decreases linearly with the number of subcarriers, so A < Aave(ii) a The B term only contains the correlation coefficient between three closely spaced subcarriers, and BaveContains the correlation coefficient between K subcarriers, so the B term with less influence of the correlation coefficient is closer to 0.
In summary, G > G when the number of iterations I > 1aveAnd as the number of iterations increases, gains G and GaveThe difference is larger and larger, and the method obviously has higher processing gain compared with the existing average weighting-based preprocessing algorithm.
Second, description of simulation
2.1) simulation parameters
The simulation parameters of the FBMC system employed in this example include two parts: the first part is filter bank multi-carrier offset quadrature amplitude modulation (FBMC)/OQAM system simulation parameters such as table 1, and the second part is extended vehicle channel Ethylene Vinyl Acetate (EVA) channel parameters such as table 2.
TABLE 1 FBMC/OQAM System simulation parameter settings
Figure BDA0002499952740000071
TABLE 2 EVA channel parameters
Figure BDA0002499952740000072
2.2) simulation content
The bit error rate of the system is simulated by the invention and the existing paired pilot frequency POP algorithm and the average weighting pre-processing algorithm respectively, and the simulation result is as shown in figure 4. The iteration number I of the iterative convergence of the invention is 3, and the iterative preprocessing of each central subcarrier involves the participation of the channel estimation values of adjacent continuous 7 subcarriers, so that the subcarrier number K of the average weighted preprocessing is 7 as a comparison.
As can be seen from fig. 4, in the EVA channel, the bit error rate of the conventional POP algorithm is the largest, the channel estimation performance is the worst, the bit error rate of the present invention is the smallest, the channel estimation is the best, and the channel estimation performance of the average weighted pre-processing algorithm is between that of the present invention and the POP algorithm. The reason is that the processing gain of the iterative preprocessing algorithm is higher than that of the average weighted preprocessing algorithm because the processing gain G of the iterative preprocessing algorithm contains fewer correlation coefficients and exponential decay factor items, so that the channel estimation performance is better, and the channel estimation performance is matched with the simulation result.
In summary, under the same slow fading EVA channel environment, the scheme proposed by the present invention has the best performance, and as the signal-to-noise ratio increases, the performance advantage is more obvious, and this performance advantage is obtained by iteratively reducing the limitation on the processing gain due to the decrease in correlation between subcarriers.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (3)

1. A channel estimation method based on iterative preprocessing in an FBMC system is characterized by comprising the following steps:
(1) calculating the pilot frequency position channel estimation value in the FBMC system by using a POP algorithm:
(2) carrying out iterative preprocessing on channel estimation values on three subcarriers adjacent to a pilot frequency position to obtain a processing gain of the channel estimation:
(2a) smoothing three adjacent subcarriers to obtain channel estimation after one iteration
Figure FDA0003165312770000011
For the channel estimation value after one iteration
Figure FDA0003165312770000012
Smoothing is carried out on three continuous sub-carriers to obtain a channel estimation value after iteration twice
Figure FDA0003165312770000013
And analogizing until the channel estimation value is converged, and finally obtaining the optimal channel estimation value of the pilot frequency position
Figure FDA0003165312770000014
(2b) Smoothing three adjacent subcarriers at the adjacent pilot frequency position at the position L away from the pilot frequency in the step (2a) to obtain the channel estimation after one iteration
Figure FDA0003165312770000015
For the channel estimation value after one iteration
Figure FDA0003165312770000016
Smoothing is carried out on three continuous sub-carriers to obtain a channel estimation value after iteration twice
Figure FDA0003165312770000017
And repeating the steps until the channel estimation value is converged, and finally obtaining the optimal channel estimation value of the adjacent pilot frequency position at the distance L from the pilot frequency in the step (2a)
Figure FDA0003165312770000018
(2c) Two estimated values obtained by using (2a) and (2b)
Figure FDA0003165312770000019
And
Figure FDA00031653127700000110
performing first-order linear interpolation to obtain the channel estimation value of the whole system
Figure FDA00031653127700000111
2. The method of claim 1 wherein the calculating pilot location channel estimates in the FBMC system using the POP algorithm as described in (1) is performed as follows:
let d be the transmitted symbols of two consecutive pilot positionsk,nAnd dk,n+1Let yk,nAnd yk,n+1The received symbols at the two pilot positions, respectively;
at the pilot symbol d is calculated byk,nAnd dk,n+1At the initial value of channel estimation Hk,nAnd Hk,n+1：
Figure FDA00031653127700000112
Wherein j is a unit of an imaginary number,
Figure FDA00031653127700000113
and
Figure FDA00031653127700000114
respectively represent yk,nAnd yk,n+1The complex number of the,
Figure FDA00031653127700000115
presentation pair
Figure FDA00031653127700000116
And taking an imaginary part.
3. The method of claim 1, wherein the channel estimates of pilot locations are utilized in (2c)
Figure FDA0003165312770000021
And
Figure FDA0003165312770000022
performing first-order linear interpolation in the time domain direction, and realizing the following steps:
(2c1) interpolating at the p-th symbol position between the two groups of pilots to obtain two groups of channel estimation values between the pilots, which are expressed as follows:
Figure FDA0003165312770000023
wherein L is the interval between two adjacent pilots,
Figure FDA0003165312770000024
and
Figure FDA0003165312770000025
respectively representing the channel response estimated values at two adjacent pilot frequency positions;
(2c2) combining the channel estimation values obtained by interpolation between the two groups of pilot frequencies according to the data symbol sequence to obtain the final overall channel estimation value, which is expressed as:
Figure FDA0003165312770000026
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