CN112235165A - Signal-based cyclic correlation entropy spectrum projection symbol rate estimation method - Google Patents
Signal-based cyclic correlation entropy spectrum projection symbol rate estimation method Download PDFInfo
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- CN112235165A CN112235165A CN202011307273.2A CN202011307273A CN112235165A CN 112235165 A CN112235165 A CN 112235165A CN 202011307273 A CN202011307273 A CN 202011307273A CN 112235165 A CN112235165 A CN 112235165A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L25/00—Baseband systems
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- H04L25/0262—Arrangements for detecting the data rate of an incoming signal
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Abstract
The invention provides a symbol rate estimation method based on signal cyclic correlation entropy spectrum projection, and belongs to the field of cyclostationary signal processing. The method mainly comprises the following steps: 1. and reducing the digital modulation signal to zero intermediate frequency, dividing the digital modulation signal into I, Q paths for output, and constructing an orthogonal complex signal. 2. And calculating the cyclic correlation entropy of the orthogonal complex signals. 3. And calculating a circular correlation entropy spectrum of the orthogonal complex signals. 4. And calculating the circular correlation entropy spectrum projection of the orthogonal complex signals. 5. The symbol rate of the digitally modulated signal is estimated by circular correlation entropy spectrum projection. Experiments show that the method can obtain accurate estimation results.
Description
Technical Field
The invention belongs to the field of cyclostationary signal processing, and relates to a symbol rate estimation method based on signal cyclic correlation entropy spectrum projection.
Background
In practical applications, the electromagnetic environment during radio signal propagation may be complex, and the coexistence of impulse noise and co-band interference is a typical representative of complex electromagnetic environments. Under the condition, the symbol rate estimation method aiming at the Gaussian noise generates the phenomenon of performance degradation due to the existence of impulse noise; the performance of such methods can be further degraded or even completely fail when co-band interference is present. In order to solve the problem of symbol rate estimation of digital modulation signals under complex electromagnetic environment, particularly under the conditions of impulsive noise and same frequency band interference, the invention provides a symbol rate estimation method based on cyclic correlation entropy spectrum projection of signals.
Disclosure of Invention
The invention provides a symbol rate estimation method based on cyclic correlation entropy spectrum projection of signals.
The technical scheme adopted by the invention is as follows: a method for symbol rate estimation based on cyclic correlation entropy spectral projection of a signal, comprising the steps of:
s1: reducing the digital modulation signal to zero intermediate frequency, dividing the digital modulation signal into I, Q paths for output, and then constructing an orthogonal complex signal;
s2: calculating the cyclic correlation entropy of the orthogonal complex signal;
s3: calculating a cyclic correlation entropy spectrum of the orthogonal complex signal;
s4: calculating the cyclic correlation entropy spectrum projection of the orthogonal complex signal;
s5: the symbol rate of the digitally modulated signal is estimated by circular correlation entropy spectrum projection.
Drawings
FIG. 1 is a general flow diagram of a symbol rate estimation method based on cyclic correlation entropy spectrum projection of a signal according to the present invention;
FIG. 2 is a diagram of the cyclic correlation entropy (R) of the present invention, for example, for a 2PSK signalB＝0.1fs)；
FIG. 3 is a diagram of a cyclic correlation entropy spectrum (R) of the present invention, using a 2PSK signal as an exampleB＝0.1fs)；
FIG. 4 is a circular correlation entropy spectrum projection of the present invention using a 2PSK signal as an example, (a) RB＝0.1fs，(b)RB＝0.125fs，(c)RB＝0.2fs，(d)RB＝0.25fs
Detailed Description
For convenience of understanding, the technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention.
As shown in fig. 1, a method for estimating a symbol rate based on cyclic correlation entropy spectrum projection of a signal mainly includes the following steps:
s1: reducing the digital modulation signal to zero intermediate frequency, dividing the digital modulation signal into I, Q paths for output, and then constructing an orthogonal complex signal:
firstly, reducing a digital modulation signal to an intermediate frequency, and then reducing the digital modulation signal to a zero intermediate frequency;
the zero IF output is then divided into I signals
And Q path signal
Wherein a (t) represents the envelope of a zero intermediate frequency signal;
finally, as shown in equation (7), I, Q two signals are used to construct an orthogonal complex signal x (t), where "orthogonal" means that the phases of the two signals are different by 90 degrees:
s2: calculating the cyclic correlation entropy of the orthogonal complex signals:
first, as shown in equation (8), the correlation entropy V of the orthogonal complex signal is calculatedx(t,τ)：
Vx(t,τ)＝E(κσ(x(t)-x(t+τ))) (8)
Wherein t represents a time independent variable, τ represents a time shift independent variable, and E represents a systemOperator expected, κσ(. cndot.) represents a Gaussian kernel function defined by the formula:
in the formula, σ represents the gaussian kernel length.
Then, as shown in equation (10), a cyclic correlation entropy U of the orthogonal complex signal is calculatedx(ξ,τ)：
As shown in FIG. 2, the cyclic correlation entropy is illustrated for a 2PSK signal, where the symbol rate RB＝0.1fs，fsRepresenting the sampling frequency.
In the formula, T0Representing the correlation entropy VxThe period of (t, τ), ξ represents the cycle frequency.
S3: calculating a circular correlation entropy spectrum of the orthogonal complex signal:
as shown in equation (11), a cyclic correlation entropy spectrum S of the orthogonal complex signal is calculatedx(ξ,f)：
As shown in FIG. 3, a circular correlation entropy spectrum is illustrated for a 2PSK signal, where RB＝0.1fs。
S4: calculating a cyclic correlation entropy spectrum projection of the orthogonal complex signal:
as shown in equation (12), a cyclic correlation entropy spectrum projection P of the orthogonal complex signal is calculatedx(ξ)：
Px(ξ)＝max(|Sx(ξ,f)|) (12)
Where, | - | represents an absolute value, and max (·) represents a maximum function.
S5: estimating the symbol rate of the digitally modulated signal by cyclic correlation entropy spectrum projection:
the cycle frequency corresponding to the cycle correlation entropy spectrum projection spectrum peak of PSK orthogonal complex signals meets xi-kRBAnd the symbol rate R of the digitally modulated signal can be estimated accordinglyB。
As shown in fig. 4, a circular correlation entropy spectrum projection is exemplified by a 2PSK signal. The parameters of the 2PSK signal are set to RB＝0.1fs，RB＝0.125fs，RB＝0.2fs，RB＝0.25fs。
As can be seen from fig. 4, the cycle frequency corresponding to the cycle correlation entropy spectrum projection spectrum peak satisfies ξ ═ kRB。
Claims (6)
1. A method for symbol rate estimation based on cyclic correlation entropy spectral projection of a signal, comprising the steps of:
s1: reducing the digital modulation signal to zero intermediate frequency, dividing the digital modulation signal into I, Q paths for output, and then constructing an orthogonal complex signal;
s2: calculating the cyclic correlation entropy of the orthogonal complex signal;
s3: calculating a cyclic correlation entropy spectrum of the orthogonal complex signal;
s4: calculating the cyclic correlation entropy spectrum projection of the orthogonal complex signal;
s5: the symbol rate of the digitally modulated signal is estimated by circular correlation entropy spectrum projection.
2. The method for estimating symbol rate based on cyclic correlation entropy spectrum projection of signal according to claim 1, wherein the step S1 specifically includes the steps of:
firstly, reducing a digital modulation signal to an intermediate frequency, and then reducing the digital modulation signal to a zero intermediate frequency; the zero IF output is then divided into I signals
And Q path signal
Wherein, A (t) tableDisplaying the envelope of the zero intermediate frequency signal; finally, as shown in formula (1), I, Q two signals are used to construct an orthogonal complex signal x (t), where "orthogonal" means that the phases of the two signals are different by 90 degrees:
3. the method for estimating symbol rate based on cyclic correlation entropy spectrum projection of signal according to claim 1, wherein the step S2 specifically includes the steps of:
first, as shown in equation (2), the correlation entropy V of the orthogonal complex signal is calculatedx(t,τ)：
Vx(t,τ)＝E(κσ(x(t)-x(t+τ))) (2)
Where t denotes a time argument, τ denotes a time shift argument, E denotes a statistical expectation operator, κσ(. cndot.) represents a Gaussian kernel function defined by the formula:
in the formula, σ represents the gaussian kernel length.
Then, as shown in equation (4), a cyclic correlation entropy U of the orthogonal complex signal is calculatedx(ξ,τ)：
In the formula, T0Representing the correlation entropy VxThe period of (t, τ), ξ represents the cycle frequency.
4. The method for estimating symbol rate based on cyclic correlation entropy spectrum projection of signal according to claim 1, wherein the step S3 specifically includes the steps of:
as shown in(5) Shown, a circular correlation entropy spectrum S of an orthogonal complex signal is calculatedx(ξ,f)：
5. The method for estimating symbol rate based on cyclic correlation entropy spectrum projection of signal according to claim 1, wherein the step S4 specifically includes the steps of:
as shown in equation (6), a cyclic correlation entropy spectrum projection P of the orthogonal complex signal is calculatedx(ξ)：
Px(ξ)＝max(|Sx(ξ,f)|) (6)
Where, | - | represents an absolute value, and max (·) represents a maximum function.
6. The method for estimating symbol rate based on cyclic correlation entropy spectrum projection of signal according to claim 1, wherein the step S5 specifically includes the steps of:
the cycle frequency corresponding to the cycle correlation entropy spectrum projection spectrum peak of PSK orthogonal complex signals meets xi-kRBAnd the symbol rate R of the digitally modulated signal can be estimated accordinglyB。
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105302940A (en) * | 2015-09-16 | 2016-02-03 | 大连理工大学 | Carrier frequency estimation method based on circular correlation entropy |
| KR101611534B1 (en) * | 2015-08-28 | 2016-04-11 | 한화탈레스 주식회사 | Method for symbol rate estimation |
| CN106027432A (en) * | 2016-05-19 | 2016-10-12 | 电子科技大学 | Bit rate estimation method of CPFSK (Continuous Phase Frequency Shift Keying) based on correlation function of signal instantaneous frequency section |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101611534B1 (en) * | 2015-08-28 | 2016-04-11 | 한화탈레스 주식회사 | Method for symbol rate estimation |
| CN105302940A (en) * | 2015-09-16 | 2016-02-03 | 大连理工大学 | Carrier frequency estimation method based on circular correlation entropy |
| CN106027432A (en) * | 2016-05-19 | 2016-10-12 | 电子科技大学 | Bit rate estimation method of CPFSK (Continuous Phase Frequency Shift Keying) based on correlation function of signal instantaneous frequency section |
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Application publication date: 20210115 |