CN107026804A - Channel estimation methods based on exponential smoothing in MIMO ofdm systems - Google Patents
Channel estimation methods based on exponential smoothing in MIMO ofdm systems Download PDFInfo
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- CN107026804A CN107026804A CN201710302854.9A CN201710302854A CN107026804A CN 107026804 A CN107026804 A CN 107026804A CN 201710302854 A CN201710302854 A CN 201710302854A CN 107026804 A CN107026804 A CN 107026804A
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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
- H04L25/0212—Channel estimation of impulse response
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/02—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas
- H04B7/04—Diversity systems; Multi-antenna system, i.e. transmission or reception using multiple antennas using two or more spaced independent antennas
- H04B7/0413—MIMO systems
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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
- H04L25/022—Channel estimation of frequency response
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/26—Systems using multi-frequency codes
- H04L27/2601—Multicarrier modulation systems
Abstract
Channel estimation methods based on exponential smoothing in MIMO ofdm systems, solve the problem of LS channel estimation arithmetic accuracy is not high, after the LS estimates of each symbol frequency response are obtained, the LS time domain estimates of each symbol impulse response are obtained by inverse discrete fourier transform, recycle exponential smoothing algorithm to do channel impulse response estimation value to optimize, the channel frequency response based on exponential smoothing is obtained finally by DFT.The present invention uses exponential smoothing algorithm, disclose the changing rule of channel itself, effectively filter out the random disturbances in channel estimation results, the corresponding channel impulse response of each symbol only need to be stored, on the premise of a small amount of computational complexity is increased, the estimated accuracy of LS channel estimation methods is effectively improved, the bit error rate of system data transmission is reduced, in the case where channel condition is excellent and poor, gratifying result can be obtained.
Description
Technical field
The invention belongs to wireless communication system channel estimation field, and in particular to based on finger in a kind of MIMO-OFDM systems
The smooth channel estimation methods of number.
Background technology
With the high speed development of wireless mobile communications, user welcomes unprecedentedly for the demand of reliable high-speed radiocommunication link
Growth.In this regard, needing more perfect wireless communication system to realize various communication forms of the user in various environment.The
Four Generation Mobile Communication Systems employ the designing axiom of innovation, from the mobile communications network liter based on conventional mobile phone business
Level is to cover high-speed mobile communications network on a large scale by main internet-oriented of mobile data, it would be preferable to support the movement of more horn of plenty
Communication service, any type of information service needed for making user anywhere can obtain.
Forth generation mobile radio system has given full play to MIMO skills using MIMO-OFDM technologies as core transmission scheme
The respective advantage of art and OFDM technology.However, because in OFDM technology, signal is related to time domain and frequency domain simultaneously, wireless channel
Frequency selectivity (being caused by the multidiameter delay of channel) and time selectivity (being caused by Doppler effect) can all influence OFDM to believe
Number transmission, thus in signal detection, accurate channel condition information (CSI, Channel State Information) is
Receiving terminal recovers the key of data using coherent demodulation.Meanwhile, to eliminate interfering with each other between multiple antennas, mimo system is used
Space-time coding/decoding be also required to CSI as accurate as possible.Further, since using multiple antennas, the channel of SISO-OFDM systems is estimated
Calculating method cannot be used directly for MIMO-OFDM systems.To solve the above problems, realizing high-quality high reliability radio communication, need
CSI as accurate as possible is obtained, therefore in MIMO-OFDM systems, has most important for the research of channel estimation problems
Effect.
For the channel estimation method in MIMO-OFDM wireless communication systems, experts and scholars carried out it is a series of probe into, mesh
Preceding main channel estimation method include least square (LS, LeastSquares) algorithm, least mean-square error (MMSE,
MinimumMeanSquareError) algorithm, DFT (DFT, Discrete Fourier Transform) are calculated
Method etc..LS algorithm operatings are simple, and computational complexity is low, and any channel information need not be used in channel estimation process, but LS is calculated
In method, noise factor is ignored, therefore the precision of estimation result of algorithm is not high；In MMSE algorithms, the correlation of channel itself with
And signal to noise ratio environment turns into the principal element of channel estimation, its estimated accuracy will be far above LS algorithms, but be related in MMSE algorithms
And the inversion process of autocorrelation matrix, in addition it is also necessary to the statistical property and signal to noise ratio size of channel, therefore MMSE algorithms are obtained in advance
In practice with less；DFT channel estimation methods are on the basis of LS estimations to the channel impulse beyond maximum delay length
Zero setting is responded, the path for only including noise is eliminated, theory analysis shows that the algorithm for estimating performance based on DFT is better than LS algorithms,
And computational complexity is far below MMSE algorithms, but DFT algorithms need to obtain the maximum delay length of channel in advance.
The content of the invention
The present invention is directed to the problem of LS channel estimation method estimated accuracies are not high in MIMO-OFDM systems in the prior art,
Channel estimation methods based on exponential smoothing in a kind of MIMO-OFDM systems are provided.
To achieve the above object, the present invention uses following technical scheme：
Channel estimation methods based on exponential smoothing in a kind of MIMO-OFDM systems, comprise the following steps：
Step one：The LS estimates of each symbol frequency response are obtained in MIMO-OFDM systems；
Step 2：The LS estimates of each symbol impulse response are obtained by inverse discrete fourier transform；
Step 3：The LS estimates of channel impulse response are optimized by exponential smoothing algorithm；
Step 4：Channel frequency response based on exponential smoothing is obtained by DFT.
For optimization above-mentioned technical proposal, the concrete measure taken also includes：
In the step one：
The quantity of each antenna insertion pilot tone is M, and OFDM is using K subcarrier, the interval sub-carrier number of Comb Pilot
P, meets M=[K/p]；
The frequency-domain received signal of pilot frequency locations is obtained first：
Wherein, P represents pilot tone position, and f represents frequency domain form, and i represents transmitting antenna, and j represents reception antenna, ji tables
Show from i-th of transmitting antenna to j-th of reception antenna, NTRepresent transmitting antenna number, XiThe signal matrix on transmitting antenna i is represented,
hjiAnd zjSignal matrix and noise are represented respectively；
Pilot signal on all transmitting antennas is arranged in such a way, Q is usedm(n) m-th of transmitting day is represented
N-th of pilot signal on line, pilot signal is grouped according to n value and arranged, the value ascending order put in order according to antenna serial number m
Arrangement, so as to obtain Q matrixes：
Then all transmitting antennas are to the channel frequency response between j-th of reception antenna：
Pilot reception signal is：
The solution of LS channel estimations is：
Wherein
WillSequence number i according to antenna is grouped and then enters row interpolation, obtains
Channel frequency response from all transmitting antennas on j-th of reception antenna.
In the step 2：
LS estimates to channel frequency response do IDFT computings, obtain the corresponding channel impulse response of each OFDM symbol and estimate
Value：
Wherein,For by the channel frequency response after pilot frequency locations channel interpolation arithmetic, q represents q-th of OFDM
Symbol.
In the step 3：
Exponential smoothing processing is done to channel impulse response, the noise on each footpath of channel impulse response is reduced：
Wherein,The channel impulse response after exponential smoothing is represented, α represents smoothing factor.
In the step 4：
Channel impulse response after exponential smoothing is done into K point DFT computings, the improved LS channels frequency of exponential smoothing is utilized
Rate is responded：
Smoothing factor α is met：
Wherein, MSEESAAnd MSEDFTThe estimation mean square error of LS algorithms and DFT algorithms based on exponential smoothing is represented respectively
Vector, e is the estimation performance gap between LS algorithms and DFT algorithms.
The beneficial effects of the invention are as follows：In the channel circumstance of slow time-varying, preferably estimation performance can be obtained, index is put down
Sliding algorithm discloses the changing rule of channel itself, effectively filters out the random disturbances in channel estimation results, reduces MIMO-
The bit error rate of ofdm system data transfer, algorithm need to only store the corresponding channel impulse response of each symbol, in a small amount of fortune of increase
On the premise of calculating complexity, the estimated accuracy of LS channel estimation methods is effectively improved.
Brief description of the drawings
Fig. 1 is the data transfer flow figure of the present invention.
Fig. 2 is mean square error curve map of the present invention under different α and signal to noise ratio.
Fig. 3 is the algorithm mean square error curve map of the present invention.
Fig. 4 is the algorithm bit error rate curve map of the present invention.
Embodiment
In conjunction with the accompanying drawings, the present invention is further explained in detail.
Data transfer flow figure as shown in Figure 1, each symbol (OFDM of an entirety is obtained in MIMO-OFDM systems
Symbol period) channel frequency response LS estimates after, each symbol impulse response is obtained by inverse discrete fourier transform
LS estimates, recycle exponential smoothing algorithm channel impulse response estimation value is optimized, finally by discrete Fourier
Conversion obtains the channel frequency response based on exponential smoothing.
In the LS channel estimation methods of MIMO-OFDM systems, it is assumed that the quantity of each antenna insertion pilot tone is M, OFDM
Using K subcarrier, the interval sub-carrier number of Comb Pilot is p, meets M=[K/p], and [] represents rounding operation, therefore pilot tone
The frequency-domain received signal of position can be expressed as：
Wherein, subscript P represents pilot tone position, and f represents frequency domain form, and i represents transmitting antenna, and j represents reception antenna,
Ji is represented from i-th of transmitting antenna to j-th of reception antenna, NTRepresent transmitting antenna number, XiRepresent the signal on transmitting antenna i
Matrix, hjiAnd zjSignal matrix and noise are represented respectively.
Pilot signal in above formula on all transmitting antennas is arranged in the following manner：Use Qm(n) represent m-th
N-th of pilot signal on transmitting antenna, pilot signal is grouped according to n value and arranged, is put in order according to antenna serial number m's
It is worth ascending order arrangement, so as to obtain Q matrixes：
It is corresponding, all transmitting antennas are written as to the channel frequency response between j-th of reception antenna：
Therefore, pilot reception signal can be expressed as：
It is hereby achieved that the solution of LS channel estimations is：
Wherein
Again willSequence number i according to antenna is grouped and then enters row interpolation, just
The channel frequency response from all transmitting antennas on j-th of reception antenna can be obtained.As can be seen that LS channel estimation methods
In, estimation procedure have ignored noise factor, thus estimated accuracy is not high.
Exponential smoothing algorithm can be used for reducing the random factors in time series, for slow fading channel, channel
Impulse response is constant in a symbol, slowly varying between adjacent-symbol, the channel impulse corresponding to each OFDM symbol
Response constitutes one group of time series, therefore making an uproar on each footpath in LS channel estimation values can be reduced with utilization index smoothing algorithm
Sound, lifts the precision of channel estimation.There is provided according to the LS estimated results present invention and exponential smoothing is based in a kind of MIMO-OFDM systems
Channel estimation method, comprise the following steps：
(1) obtained channel frequency response, which does IDFT computings, to be estimated to LS algorithms, obtains the corresponding channel of each OFDM symbol
Impulse response valuation：
Wherein,For by the channel frequency response after pilot frequency locations channel interpolation arithmetic, q represents q-th of OFDM
Symbol；
(2) exponential smoothing processing is done to the channel impulse response of formula in step (1), reduces each footpath of channel impulse response
On noise：
Wherein,Represent the channel impulse response after exponential smoothing
(3) channel impulse response after exponential smoothing is done into K point DFT computings, is utilized the improved LS letters of exponential smoothing
Road frequency response：
The performance of this method carries out analysis and evaluation by MATLAB emulation, completes and index is based in MIMO-OFDM systems
The emulation of smooth LS innovatory algorithms, and innovatory algorithm and classics tri- kinds of algorithms of LS, DFT, MMSE are done into performance comparison.Emulation
In, channel is tapped delay line model, and channel is that channel does not change in slow fading channel, an OFDM symbol, adjacent
Slowly varying in symbol, the inserted mode of pilot tone is Comb Pilot, and different antennae pilot frequency locations are orthogonal, and system receives day using 2 hairs 2
Line is configured, and OFDM sub-carrier numbers are 1024, and modulation system is QPSK, and circulating prefix-length is 64 sub- carrier wavelengths, and channel is 6
Footpath channel, pilot interval is 8.
When taking different value due to smoothing factor α, the performance that exponential smoothing algorithm filters out noise is different, it is therefore desirable to find one
Individual α optimum value so that optimization of the innovatory algorithm to LS algorithms is good enough.In this regard, under different α and different signal to noise ratio,
LS innovatory algorithms based on exponential smoothing are emulated, the result of emulation is as shown in fig. 2, it can be seen that remove when α takes 0
Special circumstances, during low signal-to-noise ratio, α values are smaller, and the performance of innovatory algorithm is better, however as the increase of signal to noise ratio, and α values are got over
Small, innovatory algorithm performance is deteriorated on the contrary, thus needs to take an optimal α value to cause under the conditions of all signal to noise ratio, the α values pair
The innovatory algorithm performance answered is superior to former algorithm, meanwhile, find that the mean square error curve for improving LS algorithms further connects in emulation
Near DFT channel estimation methods, therefore following formula can be combined choose optimum a-value：
Wherein, MSEESAAnd MSEDFTRepresent that the estimation of improvement LS algorithms and DFT algorithms based on exponential smoothing is square respectively
Element is the mean square error under different signal to noise ratio in error vector, vector,Square of vectorial 2 norm computing is represented, e is to change
The estimation performance gap entered between LS algorithms and DFT algorithms.According to optimum coefficient value criterion, the optimal of coefficent of exponential smoothing takes
It is worth for 0.5.
Obtain after postfitted orbit coefficient, by the improvement LS algorithms based on exponential smoothing and classics LS algorithms, DFT algorithms and
MMSE algorithm simulatings realize that simulation result is as shown in Figure 3-4.The characteristics of in order to embody exponential smoothing algorithm and optimal smoothing system
Several advantages, multiple smoothing factors are have chosen between 0 to 1 and are emulated, most 0.1 and 0.2 two smoothing factor at last it is imitative
True result and the simulation result of postfitted orbit coefficient 0.5 are contrasted.As seen from Figure 3, under low signal-to-noise ratio environment, index
Smooth coefficient is smaller, and the estimation mean square error of innovatory algorithm is smaller, but with the increase of signal to noise ratio, smoothing factor is smaller, letter
The error of road estimation is bigger on the contrary, and the performance of innovatory algorithm is also poorer than former algorithm.This is due to that exponential smoothing algorithm has low pass
Filtering characteristic, smoothing factor is smaller, and filter effect is better, and most of noise can be filtered out in low signal-to-noise ratio, channel is optimized
The result of estimation, and in high s/n ratio, efficient channel information occupies leading position, when smoothing factor is smaller, channel is with the time
Change will be erroneously interpreted as random disturbances, exponential smoothing algorithm has done the amendment of mistake, is degrading the result of channel estimation.Cause
This, with reference to above-mentioned postfitted orbit coefficient Criterion of Selecting, when smoothing factor is 0.5, innovatory algorithm is in low middle high s/n ratio environment
Under, evaluated error is respectively less than classical LS channel estimation methods.Meanwhile, it is flat based on index compared with classical DFT channel estimation methods
Sliding improvement LS algorithms are when obtaining postfitted orbit coefficient, and the mean square error of channel estimation is avoided closest to DFT algorithms
The work of maximum delay length is obtained in DFT algorithm for estimating.As seen from Figure 4, when choosing optimal smoothing coefficient 0.5, base
Recover the bit error rates of data between classical LS, MMSE algorithm in the improvement LS channel estimation methods of exponential smoothing, and property
Can be close to DFT channel estimation methods.
It the above is only the preferred embodiment of the present invention, protection scope of the present invention is not limited merely to above-described embodiment,
All technical schemes belonged under thinking of the present invention belong to protection scope of the present invention.It should be pointed out that for the art
For those of ordinary skill, some improvements and modifications without departing from the principles of the present invention should be regarded as the protection of the present invention
Scope.
Claims (6)
1. the channel estimation methods based on exponential smoothing in a kind of MIMO-OFDM systems, comprise the following steps：
Step one：The LS estimates of each symbol frequency response are obtained in MIMO-OFDM systems；
Step 2：The LS estimates of each symbol impulse response are obtained by inverse discrete fourier transform；
Step 3：The LS estimates of channel impulse response are optimized by exponential smoothing algorithm；
Step 4：Channel frequency response based on exponential smoothing is obtained by DFT.
2. the channel estimation methods based on exponential smoothing in a kind of MIMO-OFDM systems as claimed in claim 1, its feature exists
In in the step one：
The quantity of each antenna insertion pilot tone is M, and OFDM is using K subcarrier, and the interval sub-carrier number of Comb Pilot is p, full
Sufficient M=[K/p]；
The frequency-domain received signal of pilot frequency locations is obtained first：
Wherein, P represents pilot tone position, and f represents frequency domain form, and i represents transmitting antenna, and j represents reception antenna, ji represent from
I-th of transmitting antenna is to j-th of reception antenna, NTRepresent transmitting antenna number, XiRepresent the signal matrix on transmitting antenna i, hji
And zjSignal matrix and noise are represented respectively；
Pilot signal on all transmitting antennas is arranged in such a way, Q is usedm(n) represent on m-th of transmitting antenna
N-th of pilot signal, by pilot signal according to n value be grouped arrange, put in order according to antenna serial number m value ascending order row
Row, so as to obtain Q matrixes：
Then all transmitting antennas are to the channel frequency response between j-th of reception antenna：
Pilot reception signal is：
The solution of LS channel estimations is：
Wherein
WillRow interpolation is grouped and then entered according to the sequence number i of antenna, is obtained j-th
Channel frequency response from all transmitting antennas on reception antenna.
3. the channel estimation methods based on exponential smoothing in a kind of MIMO-OFDM systems as claimed in claim 2, its feature exists
In in the step 2：
LS estimates to channel frequency response do IDFT computings, obtain the corresponding channel impulse response valuation of each OFDM symbol：
Wherein,For by the channel frequency response after pilot frequency locations channel interpolation arithmetic, q represents q-th of OFDM symbol.
4. the channel estimation methods based on exponential smoothing in a kind of MIMO-OFDM systems as claimed in claim 3, its feature exists
In in the step 3：
Exponential smoothing processing is done to channel impulse response, the noise on each footpath of channel impulse response is reduced：
Wherein,The channel impulse response after exponential smoothing is represented, α represents smoothing factor.
5. the channel estimation methods based on exponential smoothing in a kind of MIMO-OFDM systems as claimed in claim 4, its feature exists
In in the step 4：
Channel impulse response after exponential smoothing is done into K point DFT computings, the improved LS channel frequencys of exponential smoothing is utilized and rings
Should：
6. the channel estimation methods based on exponential smoothing in a kind of MIMO-OFDM systems as claimed in claim 4, its feature exists
In smoothing factor α is met：
Wherein, MSEESAAnd MSEDFTThe estimation mean square error vector of LS algorithms and DFT algorithms based on exponential smoothing is represented respectively,
E is the estimation performance gap between LS algorithms and DFT algorithms.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109672641A (en) * | 2018-12-21 | 2019-04-23 | 成都唯创华盛科技有限公司 | A kind of LTE down channel estimation method suitable under complex environment |
| CN110808927A (en) * | 2019-10-31 | 2020-02-18 | 江苏软仪科技股份有限公司 | Method for estimating 802.11ax protocol 2x mode channel coefficient |
| CN112637093A (en) * | 2020-12-09 | 2021-04-09 | 齐鲁工业大学 | Signal detection method based on model-driven deep learning |
| CN113132278A (en) * | 2021-04-20 | 2021-07-16 | 上海矽昌微电子有限公司 | Channel smoothing method and system of wireless OFDM system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101527583A (en) * | 2009-04-17 | 2009-09-09 | 中山大学 | WCDMA downlink channel parameter estimation method |
| CN105636127A (en) * | 2014-11-24 | 2016-06-01 | 普天信息技术有限公司 | Cell switching method and network side device |
-
2017
- 2017-05-02 CN CN201710302854.9A patent/CN107026804A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101527583A (en) * | 2009-04-17 | 2009-09-09 | 中山大学 | WCDMA downlink channel parameter estimation method |
| CN105636127A (en) * | 2014-11-24 | 2016-06-01 | 普天信息技术有限公司 | Cell switching method and network side device |
Non-Patent Citations (3)
| Title |
|---|
| XINGXING LI: ""An Improved Reconstruction Method for"", 《2013 INTERNATIONAL CONFERENCE ON CONNECTED VEHICLES AND EXPO (ICCVE)》 * |
| 仲伟志等: ""基于循环前缀的MIMO-OFDM改进信道估计算法"", 《系统工程与电子技术》 * |
| 李星星: ""基于导频的OFDM稀疏信道估计技术的研究"", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109672641A (en) * | 2018-12-21 | 2019-04-23 | 成都唯创华盛科技有限公司 | A kind of LTE down channel estimation method suitable under complex environment |
| CN109672641B (en) * | 2018-12-21 | 2022-03-22 | 成都唯创华盛科技有限公司 | LTE downlink channel estimation method suitable for complex environment |
| CN110808927A (en) * | 2019-10-31 | 2020-02-18 | 江苏软仪科技股份有限公司 | Method for estimating 802.11ax protocol 2x mode channel coefficient |
| CN112637093A (en) * | 2020-12-09 | 2021-04-09 | 齐鲁工业大学 | Signal detection method based on model-driven deep learning |
| CN113132278A (en) * | 2021-04-20 | 2021-07-16 | 上海矽昌微电子有限公司 | Channel smoothing method and system of wireless OFDM system |
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