CN108712353A - Soft iterative channel estimation method - Google Patents
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- CN108712353A CN108712353A CN201810268994.3A CN201810268994A CN108712353A CN 108712353 A CN108712353 A CN 108712353A CN 201810268994 A CN201810268994 A CN 201810268994A CN 108712353 A CN108712353 A CN 108712353A
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- H—ELECTRICITY
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
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- H04L25/0202—Channel estimation
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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
- H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
- H04L25/03006—Arrangements for removing intersymbol interference
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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
- H04L25/03—Shaping networks in transmitter or receiver, e.g. adaptive shaping networks
- H04L25/03006—Arrangements for removing intersymbol interference
- H04L25/03159—Arrangements for removing intersymbol interference operating in the frequency domain
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Abstract
The present invention provides a kind of soft iterative channel estimation method, including：Receiving terminal data symbol is estimated in initial channel estimation part by transform domain to obtain the channel response initial estimate of frequency domainFrequency domain equalizer in Turbo balanced devices utilizes the valueIt compensates to obtain the frequency domain estimation of data symbol to receiving dataThe likelihood ratio Soft Inform ation of data symbol is obtained by the channel decoder in Turbo balanced devices after treatmentThen the Soft Inform ation is mapped as specific data symbolAnd by the valueIt is sent to iterative estimate part and is responded for channel estimation and updated, while the frequency domain equalizer being sent in Turbo balanced devices is used for the adaptive updates of receiving terminal data symbol, such loop iteration judgement output coding sequence after all output Soft Inform ations tend towards stability.This method can greatly reduce error rate of system in the case that system signal noise ratio is lower.
Description
Technical field
The present invention relates to wireless communication fields, especially a kind of soft iterative channel estimation side suitable for wireless communication system
Method can be applied to wireless communication field to receiving the higher occasion of error rate of system performance requirement, especially wireless Ad Hoc
Network system.
Background technology
The performance of wireless communication system is largely influenced by wireless channel, such as shadow fading and frequency selectivity
Decline etc. so that the propagation path between transmitter and receiver is extremely complex.Wireless channel is fixed unlike wire channel
And it is foreseeable that but have prodigious randomness, this just proposes prodigious challenge to the design of receiver.In order to receive
The transmission signal of transmitting terminal is accurately restored at end, and people resist influence of the multipath effect to transmission signal using various measures,
The realization of channel estimation technique needs to know the information of wireless channel, such as the exponent number of channel, Doppler frequency shift and multidiameter delay or
The parameters such as the impulse response of person's channel.Therefore, channel parameter estimation is to realize a key technology of wireless communication system.
Detailed channel information can be obtained, is to weigh a nothing to correctly demodulate transmitting signal in receiving terminal
The important indicator of line communication system performance and a major issue in wireless communication system.
Existing blind Channel Estimation is not by additional information, the related system only by receiving client information to sending client information
Meter characteristic estimating goes out the channel parameter of wireless channel.Such algorithm does not need additional auxiliary information, and it is additional not need system
Spectral overhead, therefore channel utilization is higher.
Due to sending the non-intellectual of client information, Blind channel estimation algorithm need in force complicated signal processing technology and
A large amount of calculate causes convergence rate slow, and bit error rate performance promotion is smaller, and it is gradual or constant to be only applicable to channel circumstance
Channel.
The existing channel estimation scheme based on auxiliary information be by by reference signal auxiliary carry out channel estimation,
Least square (Least Square, LS) algorithm can be divided into according to the criterion difference used in estimation procedure, lowest mean square misses
Difference algorithm (Minimum Mean Square Error, MMSE) algorithm, transform domain (Discrete Fourier Transform,
DFT) algorithm.These three algorithms need the reference signal that dose known amounts are inserted into transmitting terminal data frame, and receiving terminal is using locally
Reference signal first estimates the channel parameter values at reference signal position, then obtains the ginseng of the channel at data symbol by interpolation
Numerical value.
In channel estimation scheme based on auxiliary information, least square (Least Square, LS) algorithm realizes simple, fortune
It is low to calculate complexity, but it has ignored the interference of noise, when noise is smaller, accuracy of estimation substantially reduces；Lowest mean square misses
Poor (Minimum Mean Square Error, MMSE) algorithm estimation performance improves a lot compared to LS algorithms, but needs
Channel prior information and computational complexity is too high；Transform domain (Discrete Fourier Transform, DFT) algorithm for estimating master
The characteristic that system channel impulse response length is less than cyclic prefix (Cyclic Prefix, CP) length is utilized, channel is joined
Exceed the time-domain information zero setting of CP length in number, and then obtain more accurate channel estimation value, but the algorithm can not inhibit
Noise in CP length.
Invention content
The purpose of the present invention is being to overcome the deficiencies in the prior art, a kind of soft iterative channel estimation side is provided
Method can greatly reduce error rate of system in the case that system signal noise ratio is lower, can preferably ensure wireless communication system
System receptivity.The technical solution adopted by the present invention is：
A kind of soft iterative channel estimation method, mainly thes improvement is that,
The channel response that receiving terminal data symbol by transform domain estimates to obtain frequency domain in initial channel estimation part is initial
Estimated valueFrequency domain equalizer in Turbo balanced devices utilizes the valueIt compensates to obtain data to receiving data
The frequency domain of symbol is estimatedThe likelihood of data symbol is obtained by the channel decoder in Turbo balanced devices after treatment
Compare Soft Inform ationThen the Soft Inform ation is mapped as specific data symbolAnd by the valueIt is sent to repeatedly
For estimating part for channel estimation response update, while the frequency domain equalizer being sent in Turbo balanced devices is used for receiving terminal number
According to the adaptive updates of symbol, such loop iteration is until all output Soft Inform ations adjudicate output coding sequence c after tending towards stability
(n)。
Further, initial channel estimation includes：
First frequency domain LS channel estimation is carried out to receiving data, it is assumed that reception auxiliary information frequency domain representation is Yrs(k),
Local auxiliary information frequency domain representation is Xrs(k), then the output of LS channel estimations is expressed as at auxiliary information
It willIt is transformed into time domain and obtains channel time domain responseTime domain denoising is carried out, then to filtering out noise
Channel time domain respondsThe channel response initial estimate of frequency domain is obtained as DFT transformFor next Turbo
Equilibrium treatment.
Further, when time domain denoising, set channel energy decision threshold asE indicates mean value meter
It calculates, by less than the channel response sample value zero setting of the decision threshold, to filter out noise.
Further, Turbo balanced devices include MMSE balanced devices and Turbo decoders, carry out Turbo equilibrium treatments：
MMSE balanced devices are worth to obtain the frequency domain estimated value of data symbol using channel response initial estimation：
Y (k)=FFT[y(n)], n=0N-1 (3)
Wherein,It is the channel response initial estimate of frequency domain, Y (k) is the frequency domain table of receiving data sequence y (n)
Show,It is data symbol x (n) desired valuesFrequency domain representation,By the output Soft Inform ation mapping of Turbo decoders
At in first iteration, zero can be initialized as；To receive data variance mean value,For channel noise power；
Then by the likelihood ratio Soft Inform ation of the Turbo decoder output data symbols in Turbo balanced devicesAnd it is mapped as specific data symbol, such as following formula：
The valueAdaptive updates for channel estimation response update and data symbol.
Further, channel estimation response update uses LMS filtering algorithms；
LMS filtering algorithm median filter input signals areThe Soft Inform ation fed back by Turbo decoders maps,It is y ' (n) by exporting response after filter, is expressed as
hL(n) it is filter coefficient；
It enables receiving data sequence y (n) be responded for filter desired output, the two is compared to obtain error signal e
(n),
E (n)=y (n)-y ' (n) (8)
E (n) andFilter coefficient is adjusted by LMS filtering algorithms, works as |e(n)|2When minimum, filter system
Number hL(n) it is approximately channel response tap weight value to be estimatedhL(n) right value update is
Wherein μ is step factor；
Work as hL(n) it when converging on optimal solution, enablesAnd convert it to frequency domain
It is exactlyUpdate, while by the frequency domain valueMMSE balanced devices are fed back to for changing next time
Generation.
The advantage of the invention is that：
1) signal noise is filtered out than more thoroughly, channel parameter estimation is accurate, it can be achieved that accurate compensation to sending signal.
2) present invention in the case that signal-to-noise ratio it is lower solve the problems, such as conventional method bit error rate performance dissipate, be
System receiving terminal noise robustness is substantially improved.
3) present invention's is versatile, realizes that cost is low, has wide applicability.
Description of the drawings
Fig. 1 is the soft iterative channel estimation method block diagram of the present invention.
Fig. 2 is the initial channel estimation block diagram of the present invention.
Fig. 3 is the Turbo equilibrium treatment block diagrams of the present invention.
Fig. 4 is the LMS filtering method block diagrams of the present invention.
Specific implementation mode
With reference to specific drawings and examples, the invention will be further described.
Conventional wireless receives system and generally only does a channel parameter estimation, and number is exported after then equalised, channel decoding
According to bit stream, such processing system bit error rate is larger, and with the development of the communication technology, turbo channel decoder is obviously improved
Error rate of system performance, but since the channel parameter estimation of single is inaccurate, as a result, the present invention propose it is a kind of it is soft repeatedly
For channel parameter estimation method, wireless communication system can be made to have preferable error code in the case of low signal-to-noise ratio using this method
Rate performance, while having lower implementation complexity.
The data bit flow that channel decoder exports mainly is fed back to channel estimator and balanced device by the present invention program
It completes to update the iteration of channel parameter, to achieve the effect that eliminate noise and increasing bit error rate performance.Implement block diagram such as
Shown in Fig. 1.
The invention mainly comprises channel estimators and Turbo balanced device two parts, wherein channel estimator includes initial again
Channel estimation part and iterative estimate part, Turbo balanced devices include frequency domain equalizer and channel decoder first, receiving terminal number
Frequency is obtained by transform domain (Discrete Fourier Transform, DFT) estimation in initial channel estimation part according to symbol
The channel response initial estimate in domainFrequency domain equalizer in Turbo balanced devices utilizes the valueTo receiving data
It compensates to obtain the frequency domain estimation of data symbolIt is translated by the channel in Turbo balanced devices after a series of processing
Code device obtains the likelihood ratio Soft Inform ation of data symbolThen the Soft Inform ation is mapped as specific data symbolAnd by the valueIt is sent to iterative estimate part and is responded for channel estimation and updated, while being sent in Turbo balanced devices
Frequency domain equalizer be used for receiving terminal data symbol adaptive updates, such loop iteration until all output Soft Inform ations tend to
Judgement output coding sequence c (n) after stabilization.
Wireless communication system carries out initial channel estimation to signal first, and initial channel estimation uses conventional transformation domain
(Discrete Fourier Transform, DFT) method of estimation, basic principle are first to carry out frequency domain minimum to receiving data
Two multiply (Least Square, LS) channel estimation, it is assumed that reception auxiliary information frequency domain representation is Yrs(k), local auxiliary information frequency
Domain representation is Xrs(k), then the output of LS channel estimations is expressed as at auxiliary information
It willIt is transformed into time domain and obtains channel time domain responseCarry out time domain denoising, it is contemplated that channel is possible to
Non- sampling interval channel, i.e. channel energy does not concentrate on all in CP length, so set channel energy decision threshold asE indicates mean value computation, by less than the channel response sample value zero setting of the decision threshold, to filter out noise；So
The channel time domain for filtering out noise is responded afterwardsThe channel response initial estimate of frequency domain is obtained as DFT transformFor
Next Turbo equilibrium treatments.Basic procedure is as shown in Figure 2.
It is Turbo equilibrium treatments after initial channel estimation, balanced purpose is eliminated caused by Multipath Transmission
Intersymbol interference is generally performed separately in receiving terminal balance module and channel decoding module, and Turbo balancing techniques will be equal
The output information of weighing apparatus and channel decoder carries out successive ignition interaction, reduces decoder hard decision band when the two is individually performed
The symbolic information loss come, ensure that the preferable bit error rate performance of system.The present invention uses MMSE balanced devices and Turbo decoders
The Turbo balanced devices of composition, as shown in Figure 3.
MMSE balanced devices are worth to obtain the frequency domain estimated value of data symbol using channel response initial estimation：
Y (k)=FFT[y(n)], n=0N-1 (3)
Wherein,It is the channel response initial estimate of frequency domain, Y (k) is the frequency domain table of receiving data sequence y (n)
Show,It is data symbol x (n) desired valuesFrequency domain representation,By the output Soft Inform ation mapping of Turbo decoders
At in first iteration, zero can be initialized as；To receive data variance mean value,For channel noise power.
Then by the likelihood ratio Soft Inform ation of the Turbo decoder output data symbols in Turbo balanced devicesAnd it is mapped as specific data symbol, such as following formula：
The valueAdaptive updates for channel estimation response update and data symbol.
It is finally channel estimation response update processing, which uses lowest mean square (LMS) filtering algorithm, LMS filtering to calculate
Method is one kind in stochastic gradient algorithm, it can realize the adaptive updates of filter coefficient, makes its output response and it is expected to ring
Answer deviation minimum, meanwhile, the algorithm computation complexity is low, and fast convergence rate can make filter coefficient converge to optimal value.Fig. 4
For LMS filtering method block diagrams.
In Fig. 4For LMS filtering algorithm median filter input signals, mapped by the Soft Inform ation that Turbo decoders are fed back
Form, can approximate substitution transmitting terminal data symbol x (n),It is y ' (n) by exporting response after filter, is expressed as
hL(n) it is filter coefficient；
It enables system receiving data sequence y (n) be responded for filter desired output, the two is compared to obtain error signal
E (n),
E (n)=y (n)-y ' (n) (8)
E (n) andFilter coefficient is adjusted by LMS filtering algorithms, works as |e(n)|2When minimum, filter system
Number hL(n) it is approximately channel response tap weight value to be estimatedhL(n) right value update is
Wherein μ is step factor, for adjusting iterative convergence speed；
Work as hL(n) it when converging on optimal solution, enablesAnd convert it to frequency domain
Just yesUpdate, while by the frequency domain valueMMSE balanced devices are fed back to for changing next time
In generation, so moves in circles until entire communication system performance is restrained.
Method proposed by the present invention includes initial channel estimation, Turbo equilibriums, LMS iterative channel estimation three parts, first
It proposes to filter out signal noise by setting decision threshold in beginning channel estimation, can more accurately obtain channel parameter values.
This method proposes after channel decoding, utilizes the specific data symbol adaptive updates of output bit flow generation
Channel parameter, by less iterations, can significant increase receive error rate of system performance, it is anti-interference to enhance receiver
Property, there is general applicability.
It should be noted last that the above specific implementation mode is merely illustrative of the technical solution of the present invention and unrestricted,
Although being described the invention in detail with reference to example, it will be understood by those of ordinary skill in the art that, it can be to the present invention
Technical solution be modified or replaced equivalently, without departing from the spirit of the technical scheme of the invention and range, should all cover
In the scope of the claims of the present invention.
Claims (5)
1. a kind of soft iterative channel estimation method, which is characterized in that
Receiving terminal data symbol is estimated in initial channel estimation part by transform domain to obtain the channel response initial estimation of frequency domain
ValueFrequency domain equalizer in Turbo balanced devices utilizes the valueIt compensates to obtain data symbol to receiving data
Frequency domain estimationThe likelihood ratio for obtaining data symbol by the channel decoder in Turbo balanced devices after treatment is soft
InformationThen the Soft Inform ation is mapped as specific data symbolAnd by the valueIteration is sent to estimate
Meter part is for channel estimation response update, while the frequency domain equalizer being sent in Turbo balanced devices is used for receiving terminal data symbols
Number adaptive updates, such loop iteration until all output Soft Inform ations tend towards stability after judgement export coding sequence c (n).
2. soft iterative channel estimation method as described in claim 1, which is characterized in that
Initial channel estimation includes：
First frequency domain LS channel estimation is carried out to receiving data, it is assumed that reception auxiliary information frequency domain representation is Yrs(k), local
Auxiliary information frequency domain representation is Xrs(k), then the output of LS channel estimations is expressed as at auxiliary information
It willIt is transformed into time domain and obtains channel time domain responseTime domain denoising is carried out, then when channel to filtering out noise
Domain responseThe channel response initial estimate of frequency domain is obtained as DFT transformAt next Turbo equilibriums
Reason.
3. soft iterative channel estimation method as claimed in claim 2, which is characterized in that
When time domain denoising, set channel energy decision threshold asE indicates mean value computation, will be less than the judgement threshold
The channel response sample value zero setting of value, to filter out noise.
4. soft iterative channel estimation method as claimed in claim 2, which is characterized in that
Turbo balanced devices include MMSE balanced devices and Turbo decoders, carry out Turbo equilibrium treatments：
MMSE balanced devices are worth to obtain the frequency domain estimated value of data symbol using channel response initial estimation：
Y (k)=FFT[y(n)], n=0 ... N-1 (3)
Wherein,It is the channel response initial estimate of frequency domain, Y (k) is the frequency domain representation of receiving data sequence y (n),It is data symbol x (n) desired valuesFrequency domain representation,It is mapped by Turbo decoders output Soft Inform ation,
In first iteration, it is initialized as zero；To receive data variance mean value,For channel noise power；
Then by the likelihood ratio Soft Inform ation of the Turbo decoder output data symbols in Turbo balanced devicesAnd it will
It is mapped as specific data symbol, such as following formula：
The valueAdaptive updates for channel estimation response update and data symbol.
5. the soft iterative channel estimation method as described in claim 1,2,3 or 4, which is characterized in that
Channel estimation response update uses LMS filtering algorithms；
LMS filtering algorithm median filter input signals areThe Soft Inform ation fed back by Turbo decoders maps,It is logical
Output response is y ' (n) after wave filter, is expressed as
hL(n) it is filter coefficient；
It enables receiving data sequence y (n) be responded for filter desired output, the two is compared to obtain error signal e (n),
E (n)=y (n)-y ' (n) (8)
E (n) andFilter coefficient is adjusted by LMS filtering algorithms, works as |e(n)|2When minimum, filter coefficient hL
(n) it is approximately channel response tap weight value to be estimatedhL(n) right value update is
Wherein μ is step factor；
Work as hL(n) it when converging on optimal solution, enablesAnd convert it to frequency domain
It is exactlyUpdate, while by the frequency domain valueMMSE balanced devices are fed back to for next iteration.
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| CN111901262A (en) * | 2020-07-30 | 2020-11-06 | 西安烽火电子科技有限责任公司 | High-order modulation Turbo time domain equalization algorithm suitable for short-wave communication |
| CN112039809A (en) * | 2020-08-20 | 2020-12-04 | 重庆邮电大学 | Block iterative equalizer based on mixed soft information and bidirectional block iterative equalizer |
| WO2021082690A1 (en) * | 2019-10-31 | 2021-05-06 | 华为技术有限公司 | Channel estimation method and apparatus |
| CN112866151A (en) * | 2021-01-18 | 2021-05-28 | 中国人民解放军战略支援部队信息工程大学 | Underwater sound MPSK signal blind Turbo equalization method based on channel blind estimation |
| CN113242189A (en) * | 2021-04-13 | 2021-08-10 | 华南理工大学 | Adaptive equalization soft information iteration receiving method combined with channel estimation |
| CN113748626A (en) * | 2019-04-29 | 2021-12-03 | 诺基亚技术有限公司 | Iterative detection in a communication system |
| WO2022052933A1 (en) * | 2020-09-10 | 2022-03-17 | 华为技术有限公司 | Signal processing method and related device |
| CN114244670A (en) * | 2021-12-08 | 2022-03-25 | 北京理工大学 | Blind channel estimation method and system based on channel coding assistance |
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| CN113748626A (en) * | 2019-04-29 | 2021-12-03 | 诺基亚技术有限公司 | Iterative detection in a communication system |
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| CN112866151A (en) * | 2021-01-18 | 2021-05-28 | 中国人民解放军战略支援部队信息工程大学 | Underwater sound MPSK signal blind Turbo equalization method based on channel blind estimation |
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| CN114244670A (en) * | 2021-12-08 | 2022-03-25 | 北京理工大学 | Blind channel estimation method and system based on channel coding assistance |
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