CN107135176A - Figure field communication signal modulate method based on fractional lower-order Cyclic Spectrum - Google Patents
Figure field communication signal modulate method based on fractional lower-order Cyclic Spectrum Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L27/00—Modulated-carrier systems
- H04L27/0012—Modulated-carrier systems arrangements for identifying the type of modulation
Abstract
The invention discloses a kind of figure field communication signal modulate method based on fractional lower-order Cyclic Spectrum.Utilize the three-dimensional fractional lower-order Cyclic Spectrum for receiving signal, it will be transformed into by the modulated signal of α Stable distritation noise jammings on figure domain, then effective characteristic parameters line index arrangement set is extracted in the sparse adjacency matrix that can be represented from figure as the feature of modulation type, according to training signal and the line index arrangement set Hamming distance for receiving signal, to realize under α Stable distritation noise jammings, the identification of more stable more effective signal of communication modulation type.
Description
Technical field
The invention belongs to signal processing technology field, more specifically, it is related to a kind of based on fractional lower-order Cyclic Spectrum
Figure field communication signal modulate method.
Background technology
Automatic Modulation is classified (Automatic Modulation Classification, abbreviation AMC), also referred to as communication letter
Number Modulation Identification can recognize the modulation type for receiving signal in the case of little or no priori, be signal detection and
An essential important step between demodulation, and it is widely used in many military and civilian communications fields.
Classical automatic Modulation classification (AMC) method, is commonly divided into two classes：(i) (LB) decision theory side based on likelihood
(FB) pattern-recognition (PR) method of method and (ii) feature based.However, LB method is inevitable that some shortcomings, example
Such as lack closed solutions, it is difficult to the high computation complexity stood, probabilistic model is mismatched.The performance of FB methods be not it is optimal, so
And they can effectively be realized, therefore, the different feature of many research and utilizations and different sorting algorithms are to pursue FB side
The robust performance of method.
It is worth noting that, LB methods and FB methods are applied in the hypothesis of Gaussian noise channels, however, various each
The research of sample shows, in actual radio communication mid band, is typically the multi-access inference as caused by obvious pulse, low frequency air
Noise, electromagnetic interference etc..These physical noises show sharp pulse characteristic and with heavy-tailed probability density distribution.According to
Central-limit theorem, it is steady that the non-gaussian distribution noise of these main source of error in a wireless communication system can be modeled as α
Determine partition noise.In the channel that α Stable distritations noise occurs, obvious deterioration occurs in conventional AMC method performance.
Automatic Modulation classification (AMC based on figure domainG) AMC technologies are introduced into graphic field for the first time, and have been realized in
Than existing PR and the more excellent performance of the decision theory algorithm based on LB.But the Second Order Cyclic Spectrum that this method is the docking collection of letters number enters
Row figure domain mapping extracts figure characteristic of field.But the statistic of second order and higher order is not present in α Stable distritation noises, so,
Existing AMCGMethod is also failed in α Stable distritation noises, therefore, and the new more stable α stabilizations that are more effectively applied to are divided
The AMC technologies of cloth noise are urgently found.
The content of the invention
It is an object of the invention to overcome the deficiencies in the prior art, propose that a kind of figure domain based on fractional lower-order Cyclic Spectrum leads to
Believe signal modulate method, to adapt to α Stable distritation noises, realize more stable more effective signal of communication modulation type
Identification.
For achieving the above object, the figure field communication signal modulate side of the invention based on fractional lower-order Cyclic Spectrum
Method, it is characterised in that comprise the following steps：
(1), the feature extraction of modulation type training signal
1.1) the figure domain mapping, based on fraction low price Cyclic Spectrum
For the training signal x of muting kth class modulation typek(t), k=1,2 ..., K, K are the class of modulation type
Type quantity；Its sample sequence is divided into L sections, each section carries out once figure domain mapping：
Using FAM algorithms ((Fast Fourier transform) Accumulation Method)：FFT accumulation algorithms,
For calculating Cyclic spectrum density) calculate FLOCS (the Fractional Low-Order Cyclic of l sections of training signals
Spectrum, fractional lower-order Cyclic Spectrum), obtain the set of figure domain：
Wherein,H=1,2...H, represent the cycle frequency that the l sections of the training signal of kth class modulation type are remained
εhThe smooth cycle diagram of corresponding time domain, extracts H cycle frequency εhThe neighbour of the smooth cycle diagram of corresponding time domain
Matrix is connect, adjacency matrix set is obtained：
Wherein, the smooth cycle diagram of time domain is obtained according in the following manner：
A1), the FLOCS i.e. fractional lower-order Cyclic Spectrum calculated is normalized and quantification treatment, it is 1 to obtain maximum
And discrete fractional lower-order Cyclic SpectrumWherein, ε is cycle frequency, and f is frequency；
In FAM algorithms, FLOCS frequency resolution is Δ f=fsThe Δ t=of/N ', cycle frequency resolution ax α=1/
fs/ N, wherein, fsFor sample frequency, N ' is the points of data used in complex demodulation, and N is the data points of input in the Δ t times, this
Sample uses the FLOCS that FAM algorithms are calculated for (N '+1) × (2N+1) matrix；
A2), because FLOCS has symmetry, circulated based on discrete fractional lower-orderA quarter quadrant build
Found corresponding figure domain mapping：
Define stable cycle frequency εp, p=1,2...N, εpMeet condition：
By stable cycle frequency εp, p=1, the corresponding frequency values of 2...N obtain vertex set as summit：
Using the Magnitude Difference between two summits as side, line set is obtained：
Wherein：
So, in each stable cycle frequency εpUnder obtain corresponding figure domain mapping, the i.e. smooth cycle period of time domain
Figure is：
Fractional lower-order Cyclic Spectrum is deleted for 0 cycle frequency, the H cycle frequency ε remained are obtainedhCorresponding
The smooth cycle diagram of time domain：
1.2), the extraction of line index sequence
For each adjacency matrixL=1,2 ..., L extracts the non-zero entry of the minor diagonal directly over leading diagonal
(element), extracts the line index sequence corresponding to these non-zero entries (element)The principle of line index sequential extraction procedures is as follows：
B1 the nonzero value of minor diagonal), is checked, the line index corresponding to these nonzero values is listed, and according to these non-zeros
The absolute value of value carries out descending arrangement to these line index, then, extracts line index successively in descending order；
B2) if, two or more non-zero entries there is identical absolute value, extract the row rope extracted before distance
Draw closest line index, others are abandoned；
B3) if, two or more non-zero entries there is identical absolute value, it is and maximum, then select the line index of maximum,
Others are abandoned；
So obtain cycle frequency εhCorresponding obtains L line index sequence, is chosen in L line index sequence and occurs
Probability is more than 95% line index and constitutes a stable line index sequence
For the training signal of kth class modulation type, H cycle frequency ε is extractedhStable line index sequence, is constituted
Stable line index arrangement set：And it is used as the feature of kth class modulation type；
(2), the identification of signal of communication modulation type
For receiving signal, the feature of its modulation type, line index arrangement set are obtained according to the method for step (1)Wherein, V is the cycle frequency number remained；
Calculate line index arrangement setWith the feature of kth class modulation type
Hamming distance, obtain K Hamming distanceK=1,2 ..., K, then look for the Hamming distance of minimum, its is corresponding wherein
Modulation type is the modulation type for receiving signal of communication.
The object of the present invention is achieved like this.
For reply α Stable distritation noises, the figure field communication signal modulate side of the invention based on fractional lower-order Cyclic Spectrum
Method.Using the three-dimensional fractional lower-order Cyclic Spectrum for receiving signal, figure domain will be transformed into by the modulated signal of α Stable distritation noise jammings
On, extract effective characteristic parameters line index arrangement set in the sparse adjacency matrix that then can be represented from figure and be used as modulation type
Feature, according to training signal with receive signal line index arrangement set Hamming distance, to realize α Stable distritation noise jammings
Under, the identification of more stable more effective signal of communication modulation type.
Brief description of the drawings
Fig. 1 is a kind of embodiment theory diagram that the present invention is applied.
Embodiment
The embodiment to the present invention is described below in conjunction with the accompanying drawings, so as to those skilled in the art preferably
Understand the present invention.Requiring particular attention is that, in the following description, when known function and design detailed description perhaps
When can desalinate the main contents of the present invention, these descriptions will be ignored herein.
Describe, the relevant speciality term occurred in embodiment is illustrated for convenience first：
AMC(automatic modulation classification)：Automatic Modulation is classified；
FB(feature-based)：Based on statistical nature
PR(pattern recognition)：Pattern-recognition
LB(Likelihood-based influence)：Based on likelihood function
AMCG(graph-based automatic modulation classification)：Figure domain automatic Modulation point
Class；
PDF(probability density function)：Probability density function；
CF(characteristic function)：Characteristic function；
FLOCS(fractional low-order cyclic spectrum)：Fractional lower-order Cyclic Spectrum；
FLOC(fractional low-order correlation)：Fractional lower-order auto-correlation function；
FLOCC(fractional low-order cyclic correlation)：Fractional lower-order circulates auto-correlation；
FAM(FFT(fast Fourier transform)accumulation method)：FFT accumulation algorithms, are used for
Calculate Cyclic spectrum density；
BPSK(binary phase-shift keying)：Binary phase shift keying；
QPSK(quadrature phase-shift keying)：QPSK；
OQPSK(offset quadrature phase-shift keying)：Offset quadraphase shift keying；
2FSK(binary frequency-shift keying)：Binary Frequency Shift Keying；
4FSK(quadrature frequency-shift keying)：Quaternary frequency shift keying；
MSK(minimum shift keying)：MSK；
1st, α Stable distritations
α Stable distritations are also known as non-gaussian Stable distritation, heavytailed distribution, are a kind of Gaussian Profile of broad sense, this distribution
Model can be in actual wireless communications environment, the statistical property of analogue noise exactly.
The model of α Stable distritations is unique model for meeting stability and broad sense central-limit theorem, and α Stable distritations are not
In the presence of the probability density function (PDF) of unified closing, but there is unified characteristic function (CF) in it, and its characteristic function can be with table
It is shown as：
ψ (u)=exp jau- γ | u |α[1+jβsgn(u)ω(u,α)]} (9)；
Wherein, sgn () is sign function.α (0 ＜ α≤2) is characterized index, and it determines the distribution pulse characteristic degree,
α values are smaller, and the hangover of corresponding distribution is thicker, therefore pulse characteristic is more notable；β (- 1≤β≤1) is deflection parameter, for true
Surely the symmetrical degree being distributed；γ (γ ＞ 0) is the coefficient of dispersion, also known as scale parameter, and it is point for deviateing its average on sample
The measurement for the degree of dissipating, similar to the variance in Gaussian Profile；α (- ∞ ＜ a ＜+∞) is location parameter, corresponding to Stable distritation
Average or intermediate value, u are characterized the stochastic variable of function.
As α=2, α Stable distritations deteriorate to Gaussian Profile；
As α=1 and β=0, α Stable distritations are Cauchy's distribution；
As β=0, α Stable distritations are that, on the symmetrical of average α, it is steady that we claim such distribution to be designated as symmetrical α
Fixed (S α S) distribution.
2nd, fractional lower-order Cyclic Spectrum (FLOCS) is analyzed
By modulated signal s (t) is polluted by the noise n (t) for obeying α Stable distritations, therefore reception signal x (t) can be with
It is modeled as：
X (t)=s (t)+n (t) (11)；
Wherein, n (t) is obeys the noise of S α S distributions, because the noise of α Stable distritations has significant spike special
Property, without second order or second order above statistic, traditional AMC algorithms based on second order or Higher-Order Cyclic Statistics are stable in α
The noise of distribution can fail, and the fractional lower-order Cyclic Spectrum (FLOCS) that the docking collection of letters number progress nonlinear transformation is obtained can be effective
Suppression α Stable distritations noise, therefore, for AMC technologies, corresponding information can be extracted from the FLOCS for receiving signal
It is modulated the identification of signal.
Fig. 1 is a kind of embodiment theory diagram that the present invention is applied.
In the present embodiment, input data obtains modulated signal s (t), then in transmitters after modulators modulate
The α Stable distritation noise n (t) being mixed into the channel, the reception signal x (t) as receiver.
First, in automatic Modulation grader, docking collection of letters x (t) is with sample frequency Fs=1/TsCarry out uniform sampling,
The fractional lower-order auto-correlation function (FLOC) of discrete signal x (n) after sampling can be represented as：
FLOC (n, m)=E { [x (n+m)]{b}[x*(n)]{b}} (12)；
x(n){b}=| x (n) |b-1x*(n) (13)；
Wherein, formula (12) is the b rank nonlinear transformations to scattered signal x (n), 0 ＜ b ＜ α/2；E () is expects, x*(n)
It is x (n) conjugation.So, the fractional lower-order circulation auto-correlation (FLOCC) of signal is：
Wherein,<·>The expression time is averaged, it is notable that b ranks nonlinear transformation only changes the amplitude of signal, does not have
There is change cycle information, so the related undefined similarly suitable fractional lower-order circulation of cycle frequency of second-order cyclic is related；If b=
1, then FLOCC deteriorate to second-order cyclic auto-correlation.FLOCS is FLOCC Fourier transformation, can be represented as：
In fact,It can utilize that time domain smoothing algorithm --- FAM algorithms are estimated, be given for one
Fixed frequency f and cycle frequency ε, the smooth cycle diagram of time domain can be expressed from the next：
Wherein g (n) is that width is NTsThe unified weighting function of second, f1And f2It is the centre frequency of FAM algorithm median filters,
TsIt is the sampling period, wherein, f1=f+ α/2, f2=f- α/2, XT(r,f1) and XT(r,f2) be x (n) complex demodulation, can be under
Formula is calculated.
It is T=N ' T the duration that wherein a (r), which is,sThe cone data window of second, its width is FLOCS frequency discrimination
Rate Δ f, if a (r) is normalized, FLOCS can realize unbiased esti-mator, such as following formula by time domain smoothness period figure：
3rd, figure domain mapping
Calculated using FAM algorithmsThe amplitude of graphics is non-negative, and to calculating
FLOCS is normalized and quantification treatment, obtains maximum for 1 and discrete fractional lower-order Cyclic SpectrumIn FAM algorithms
In, FLOCS frequency resolution is Δ f=fsThe Δ t=f of/N ', cycle frequency resolution ax α=1/s/ N, wherein, fsFor sampling
Interval, N ' is the points of data used in complex demodulation, and N is the data points of input in the Δ t times.Calculated using FAM algorithms
FLOCS matrixes (N '+1) × (2N+1) matrix.
Because FLOCS has symmetry, therefore to discrete spectrumA quarter quadrant set up corresponding figure domain and reflect
Penetrate.Define stable cycle frequency εp, p=1,2...N, εpMeet condition：
Using the stable corresponding frequency values of cycle frequency as summit, it is set to：By two
Magnitude Difference between individual summit is set to as side：q1,q2=0,1 ..., N '/2, wherein：
So far, corresponding figure domain mapping can be obtained under each stable cycle frequencyP=0,
1 ..., N, it is clear that the figure under each cycle frequency has cyclicity, therefore can extract the adjacency matrix of corresponding figureAs
The differentiation feature of unlike signal.
4th, feature is extracted
If modulation type collection is combined intoWherein,Expression kth class modulation type, k=1,
2,...,K.In the present embodiment, 6 class modulation type signals can be identified, i.e. BPSK, 2FSK, 4FSK, QPSK,
OQPSK, MSK, for muting kth class modulation type training signal, can calculate its FLOCS, according to the method for third portion
Build figure domain collection.
The sample sequence of training signal is divided into L sections, L figure domain mapping can be set up, for figure domain mapping each time,
H figure can be obtained, for the l times figure domain mapping, the set in figure domain can be expressed asWhereinH=1,2...H, represent the cycle frequency ε that the training signal of kth kind modulation type is remainedhCorresponding figure, its is right
The adjacency matrix set expression that the figure answered is extracted is
Because FLOCS represents the directed loop of a weighting, any adjacency matrix in figure domainIt is the sparse matrix of following property
Wherein,For adjacency matrix(u, v) individual entry, for each adjacency matrixExtract adjacent square
The non-zero entry of minor diagonal directly over battle array leading diagonal, extracts the line index sequence corresponding to these non-zero entriesOK
The principle that index sequence is extracted is as follows：
B1 the nonzero value of minor diagonal), is checked, the line index corresponding to these nonzero values is listed, and according to these non-zeros
The absolute value of value carries out descending arrangement to these line index, then, extracts line index successively in descending order；
B2) if, two or more non-zero entries there is identical absolute value, extract the row rope extracted before distance
Draw closest line index, others are abandoned；
B3) if, two or more non-zero entries there is identical absolute value, it is and maximum, then select the line index of maximum,
Others are abandoned；
So obtain cycle frequency εhCorresponding obtains L line index sequence, is chosen in L line index sequence and occurs
Probability is more than 95% line index and constitutes a stable line index sequence
For the training signal of kth class modulation type, H cycle frequencys ε is extractedhStable line index sequence, is constituted steady
Determine line index arrangement set：And it is used as the feature of kth class modulation type.
Note, these line index sequencesThere need not be the element of same number, because the length of each sequence is by relative
The adjacency matrix answeredNonzero element determine.
5th, the identification of signal of communication modulation type
For receiving signal, the feature of its modulation type, line index arrangement set are obtained according to the method for the 3rd, 4 partsWherein, V is the cycle frequency number remained；
Calculate line index arrangement setWith the feature of kth class modulation type
Hamming distance, obtain K Hamming distanceK=1,2 ..., K, then look for the Hamming distance of minimum, its is corresponding wherein
Modulation type is the modulation type for receiving signal of communication.
In the present embodiment, sent into as shown in figure 1, receiving after signal x (t) is pre-processed in grader according to foregoing the
The method of 5 parts carries out Modulation Recognition of Communication Signal, and modulation type is sent into demodulator, according to corresponding modulation type pair
Pretreated reception signal is demodulated, and obtains output data.
As shown in figure 1, the present invention is by calculating fractional lower-order Cyclic Spectrum FLOCS by by the tune of α Stable distritation noise jammings
Signal processed is transformed on figure domain, then by figure domain mapping and feature extraction, obtains the feature of modulation type training signal, then
Figure domain classification is carried out according to feature, realized under α Stable distritation noise jammings, more stable more effective signal of communication modulation type
Identification.
Although illustrative embodiment of the invention is described above, in order to the technology of the art
Personnel understand the present invention, it should be apparent that the invention is not restricted to the scope of embodiment, to the common skill of the art
For art personnel, as long as various change is in the spirit and scope of the present invention that appended claim is limited and is determined, these
Change is it will be apparent that all utilize the innovation and creation of present inventive concept in the row of protection.
Claims (1)
1. a kind of figure field communication signal modulate method based on fractional lower-order Cyclic Spectrum, it is characterised in that including following step
Suddenly：
(1), the feature extraction of modulation type training signal
1.1), the figure domain mapping based on fractional lower-order Cyclic Spectrum
For the training signal x of muting kth class modulation typek(t), k=1,2 ..., K, K are the number of types of modulation type
Amount；Its sample sequence is divided into L sections, each section carries out once figure domain mapping：
Using FAM algorithms ((Fast Fourier transform) Accumulation Method)：FFT accumulation algorithms, are used for
Calculate Cyclic spectrum density) calculate l sections of training signals FLOCS (Fractional Low-Order Cyclic Spectrum,
Fractional lower-order Cyclic Spectrum), obtain the set of figure domain：
Wherein,Represent the cycle frequency ε that the l sections of the training signal of kth class modulation type are remainedhInstitute
The corresponding smooth cycle diagram of time domain, extracts H cycle frequency εhThe adjoining of the smooth cycle diagram of corresponding time domain
Matrix, obtains adjacency matrix set：
Wherein, the smooth cycle diagram of time domain is obtained according in the following manner：
A1), the FLOCS i.e. fractional lower-order Cyclic Spectrum calculated is normalized and quantification treatment, obtain maximum for 1 and from
Scattered fractional lower-order Cyclic Spectrum
In FAM algorithms, FLOCS frequency resolution is Δ f=fsThe Δ t=f of/N ', cycle frequency resolution ax α=1/s/ N,
Wherein, fsFor sample frequency, N ' is the points of data used in complex demodulation, and N is the data points of input in the Δ t times, is so adopted
The FLOCS calculated with FAM algorithms is (N '+1) × (2N+1) matrix；
A2), because FLOCS has symmetry, circulated based on discrete fractional lower-orderA quarter quadrant set up phase
The figure domain mapping answered：
Define stable cycle frequency εp, p=1,2...N, εpMeet condition：
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So, in each stable cycle frequency εpUnder obtain corresponding figure domain mapping, i.e. the smooth cycle diagram of time domain is：
Fractional lower-order Cyclic Spectrum is deleted for 0 cycle frequency, the H cycle frequency ε remained are obtainedhCorresponding time domain
Smooth cycle diagram：
1.2), the extraction of line index sequence
For each adjacency matrixExtract the non-zero entry (member of the minor diagonal directly over leading diagonal
Element), extract the line index sequence corresponding to these non-zero entries (element)The principle of line index sequential extraction procedures is as follows：
B1 the nonzero value of minor diagonal), is checked, the line index corresponding to these nonzero values is listed, and according to these nonzero values
Absolute value carries out descending arrangement to these line index, then, extracts line index successively in descending order；
B2) if, two or more non-zero entries there is identical absolute value, extract the line index extracted before distance away from
From nearest line index, others are abandoned；
B3) if, two or more non-zero entries there is identical absolute value, it is and maximum, then select the line index of maximum, other
Discarding；
So obtain cycle frequency εhCorresponding obtains L line index sequence, is chosen at probability of occurrence in L line index sequence
A stable line index sequence is constituted more than 95% line index
For the training signal of kth class modulation type, H cycle frequency ε is extractedhStable line index sequence, constitutes stable row
Index sequence set：And it is used as the feature of kth class modulation type；
(2), the identification of signal of communication modulation type
For receiving signal, the feature of its modulation type, line index arrangement set are obtained according to the method for step (1)Wherein, V is the cycle frequency number remained；
Calculate line index arrangement setWith the feature of kth class modulation type's
Hamming distance, obtains K Hamming distanceThen the Hamming distance of minimum is looked for wherein, its corresponding tune
Type processed is the modulation type for receiving signal of communication.
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| CN201710546645.9A CN107135176B (en) | 2017-07-06 | 2017-07-06 | Image domain communication signal modulation identification method based on fractional low-order cyclic spectrum |
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