CN108848044A - A kind of extracting method of channel fine feature - Google Patents
A kind of extracting method of channel fine feature Download PDFInfo
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- CN108848044A CN108848044A CN201810658989.3A CN201810658989A CN108848044A CN 108848044 A CN108848044 A CN 108848044A CN 201810658989 A CN201810658989 A CN 201810658989A CN 108848044 A CN108848044 A CN 108848044A
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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
- H04L25/0238—Channel estimation using blind estimation
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/309—Measuring or estimating channel quality parameters
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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
- H04L25/024—Channel estimation channel estimation algorithms
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Abstract
The invention belongs to signal sorting technical fields, particularly relate to a kind of extracting method of channel fine feature.Emit signal in transmission process by the effect of channel, then receives the implicit fine feature that can characterize channel information of signal.In the present invention, signal is emitted from recovery in signal is received using blind equalization algorithm, and peak extraction, as the fine feature of channel, description and differentiation for channel is carried out to blind equalization weight vector.Pass through emulation experiment, the feasibility of 1 authenticated channel fine feature extracting method of embodiment.
Description
Technical field
The invention belongs to signal sorting technical fields, particularly relate to a kind of extracting method of channel fine feature.
Background technique
In mobile communications, the electromagnetic path that signal is propagated is referred to as wireless channel.Wireless channel and the environment of surrounding are close
Cut phase is closed, and the wireless channel under varying environment has the feature of some differentiation.How to find and extracts the letter in wireless channel
Number correlated characteristic has great researching value, and a current research hotspot.Common radio channel characteristic parameter has
Multidiameter delay, Doppler frequency shift etc..Currently, the feature extracting method of wireless channel mainly has three classes:Power estimation is based on parameter
The estimation method in space, deterministic parameter Estimation.
Power estimation, it can be common that multiple signal classification method (Multiple Signal Classification, MUSIC).
It can provide progressive nothing to incidence wave wavefront number, arrival direction or the direction of the launch, the intensity of incident waveform and cross-correlation
Estimation partially.But the calculation amount and amount of storage that the algorithm searches for parameter space are larger, and when incoming signal is coherent signal,
MUSIC algorithm is invalid.
The Parameter Subspace estimation technique, the representative of this kind of algorithm are rotation invariant technology estimation signal parameter algorithms
(Estimating Signal Parameters via Rotational Invariance Techniques, ESPRIT).It
Using the invariable rotary characteristic of signal subspace, the accurate parameter that can be used for horizontal angle is extracted.ESPRIT avoids space
Search process, calculation amount is few, but requiring the correspondence array element of subarray must be identical dipole pair.
Deterministic parameter Estimation, such as desired maximum calculated method (Expectation Maximization, EM), which is
It is generated based on maximum- likelihood estimation (Maximum Likelihood Estimation, MLE).It may be implemented pair
The Combined estimator of time delay, horizontal angle, amplitude.The popularization of EM algorithm is space-alternating generalized expectation-maximization algorithm (Space-
Alternating Generalized Expectation Maximization, SAGE).SAGE algorithm can simultaneously clock synchronization
The multidigits parameter such as prolong, leave angle, angle of arrival, Doppler frequency shift, amplitude and carries out Combined estimator.
Channel is the necessary link between transmitting terminal and receiving end, emits signal in transmission process by channel background environment
Effect, then it includes channel information that receiving end, which receives signal,.When channel background environment has differences, this species diversity is receiving signal
In also embodied.Therefore, the fine feature for being able to reflect channel information can be extracted in signal from receiving.If x (t) is transmitting letter
Number, h (t) is equivalent base band impulse response, that is, contain transmitting terminal, channel and the radio frequency of receiving end, intermediate-frequency section it is total
Transmission characteristic is represented by then receiving signal:
Wherein,Represent convolution.
By the balanced thinking for restoring x (t), the information of h (t) not only can be obtained, but also can avoid directly asking using from y (t)
Take h (t).If the impulse response of balanced device is ω (t), the output of balanced device is:
G (t) is total equivalent impulse response of transmitting terminal, channel, the radio frequency of receiving end, intermediate-frequency section and balanced device in formula.
The idea output (i.e. desired output) of balanced device is transmitting signal x (t), must be expired to make z (t)=x (t) then
Foot:
The purpose of balanced device is exactly to realize above formula, and frequency-domain expression is:
H (f) Ω (f)=1
Above formula shows that balanced device is actually the inverse filter of transmission channel.Accordingly, it is considered to balanced device weight vector table
The information in reference road.
Summary of the invention
The present invention extracts problem for channel characteristics, provides a kind of extracting method of channel fine feature, can be from reception
Channel information is extracted in signal.Compared to existing channel characteristics extracting method, this method calculation amount is small, realizes simple.
For achieving the above object, the extraction process of channel fine feature of the present invention, includes the following steps:
S1:It is if receiving signalN is data length, is normalized, and μ is the mean value for receiving signal, and σ is to connect
The standard deviation of the collection of letters number, normalization formula are:
S2:Iteration step length μ and blind equalization weight vector f is seti(k) initial value.
S3:Blind equalization is carried out to data using MCMA algorithm is improved, is generated error signal e (k), formula is:
E (k)=eR(k)+jeI(k)
Wherein,
S4:Blind equalization weight vector the number of iterations takes k=N, and iteration more new formula is:
fi(k+1)=fi(k)+μe(k)x(k-i)
Wherein, e (k) is error signal, and μ is iteration step length, and i is tap number, and k is the number of iterations.
S5:Using blind equalization tap coefficient excess mean-square error rmskAs the evaluation index of equalization performance, formula is:
Wherein, fk,iFor blind equalization weight vector fi(k) i-th of element.
S6:To blind equalization weight vector fi(k) modulus value extracts local maximum, and all local maximums are formed peak set
Wherein | | F | | indicate the number of the included element of set F.For sequential peak point, meet:The neighbor distance sequence of definition of order peak point:
S7:Based on peak set F, following fine feature is extracted:
Peak-to-average force ratio:Fa=max (F)/Fharmmean, wherein
Compare in peak:Fb=max (F)/median (F)
Peak point distribution consistency degree:Wherein | | F | | indicate peak value number.
Beneficial effects of the present invention are that it is poor that channel background environment is utilized in the extracting method of channel fine feature of the present invention
It is different to receive the embodiment in signal;In fact, in the present invention, transmitting signal passes through the effect of different channels, receives signal and deposit
In difference.Therefore, the fine feature for characterizing channel information can be used for the description and differentiation of channel.
Detailed description of the invention
Fig. 1 is simulation block diagram of the present invention;
Fig. 2 is the flow chart of realization process of the present invention;
Fig. 3 is different reception signal blind equalization weight vectors in the embodiment of the present invention 1.
Specific embodiment
A specific embodiment of the invention is described with reference to the accompanying drawing, preferably so as to those skilled in the art
Understand the present invention.Requiring particular attention is that in the following description, when known function and the detailed description of design perhaps
When can desalinate main contents of the invention, these descriptions will be ignored herein.
With the feasibility of emulation experiment data verification channel fine feature extracting method, simulation block diagram is as shown in Figure 1.
Experimental Hardware platform includes that a processor is Pentium (R) Dual-Core 3.2G, the interior desk-top meter for saving as 6G
Calculation machine, software platform are WIN7 operating system, Matlab2015b.
Fig. 2 is execution step of the present invention when application is extracted with channel fine feature.As shown in Fig. 2, the subtle spy of channel
The extracting method of sign includes the following steps:
S1:It is if receiving signalN is data length, is normalized, and μ is the mean value for receiving signal, and σ is to connect
The standard deviation of the collection of letters number, normalization formula are:
S2:Iteration step length μ=0.001 and blind equalization weight vector f are seti(k) initial value centre cap is 1, remaining is complete
It is 0.S3:Blind equalization is carried out to data using MCMA algorithm is improved, is generated error signal e (k), formula is:
E (k)=eR(k)+jeI(k)
Wherein,
S4:Blind equalization weight vector the number of iterations takes k=N, and iteration more new formula is:
fi(k+1)=fi(k)+μe(k)x(k-i)
Wherein, e (k) is error signal, and μ is iteration step length, and i is tap number, and k is the number of iterations.
S5:Using blind equalization tap coefficient excess mean-square error rmskAs the evaluation index of equalization performance, formula is:
Wherein, fk,iFor blind equalization weight vector fi(k) i-th of element.
S6:To blind equalization weight vector fi(k) modulus value extracts local maximum, and all local maximums are formed peak set
Wherein | | F | | indicate the number of the included element of set F.For sequential peak point, meet:The neighbor distance sequence of definition of order peak point:
S7:Based on peak set F, following fine feature is extracted:
Peak-to-average force ratio:Fa=max (F)/Fharmmean, wherein
Compare in peak:Fb=max (F)/median (F)
Peak point distribution consistency degree:Wherein | | F | | indicate peak value number.
Embodiment 1
The present embodiment is with the feasibility of emulation experiment data verification channel fine feature extracting method.Signal is sent to use
QPSK modulation system, 100000 points of data length.In emulation, channel is emulation Rayleigh channel, and h1, h2, h3 order are different.Emulation
Parameter setting is as shown in table 1:
Simulation parameter facilities in 1 embodiment 1 of table
As shown in Figure 3.It is improved that h1, h2, h3 respectively correspond three channels, 100000 points of normalization reception data in table 1
The modulus value of the last one weight vector obtained by the iterative processing of MCMA algorithm blind equalization, and h1, h2, h3 are the equilibrium of 44 rank balanced devices
As a result.As seen from Figure 3, the blind equalization result that different channels receive signal has differences.
As shown in table 2:
The fine feature of different channels extracts result in 2 embodiment 1 of table
Fine feature | Fa | Fb | Fp |
h1 | 11.6350 | 7.6917 | 16.7225 |
h2 | 3.7839 | 2.5320 | 19.0195 |
h3 | 4.8850 | 3.5450 | 17.8421 |
Tri- channels of h1, h2, h3 are compared, in peak-to-average force ratio Fa, compare F in peakb, peak point distribution consistency degree FpThree subtle spies
Sign numerically has differences.So channel is different, the fine feature of channel also can be variant.Therefore, fine feature:Peak is equal
Compare Fa, compare F in peakb, peak point distribution consistency degree Fp, can be used for the description and differentiation of different channels.
Claims (1)
1. a kind of extracting method of channel fine feature, which is characterized in that include the following steps:
S1, set receive signal asN is data length, is normalized:
Wherein, μ is the mean value for receiving signal, and σ is the standard deviation for receiving signal;
S2, setting iteration step length μ and blind equalization weight vector fi(k) initial value;
S3, blind equalization is carried out to data using improvement MCMA algorithm, generated error signal e (k):
E (k)=eR(k)+jeI(k)
Wherein,
S4, blind equalization weight vector the number of iterations take k=N, and iteration more new formula is:
fi(k+1)=fi(k)+μe(k)x(k-i)
Wherein, e (k) is error signal, and i is tap number, and k is the number of iterations;
S5:Using blind equalization tap coefficient excess mean-square error rmskEvaluation index as equalization performance:
Wherein, fk,iFor blind equalization weight vector fi(k) i-th of element;
S6:To blind equalization weight vector fi(k) modulus value extracts local maximum, and all local maximums are formed peak set:
Wherein | | F | | indicate the number of the included element of set F,For sequential peak point, meet:The neighbor distance sequence of definition of order peak point:
S7, it is based on peak set F, extracts following fine feature:
Peak-to-average force ratio:Fa=max (F)/Fharmmean, wherein
Compare in peak:Fb=max (F)/median (F);
Peak point distribution consistency degree:Wherein | | F | | indicate peak value number.
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