CN111277526A - Modulation identification method of constellation diagram identical signals based on compressed sensing - Google Patents

Abstract

The invention provides a modulation identification method for the same signal in a constellation diagram based on compressed sensing. By performing non-uniform sampling and rough reconstruction on the signal, and then using the spectral line quantity feature of the high-power spectrum of the signal, the second order of the signal is realized. quist frequency sampling, while realizing the signal identification in the signal set {QPSK, OQPSK, π/4‑DQPSK, 8PSK}. Due to the adoption of the technical means of compressing the signal quadratic spectrum, the present invention not only solves the technical problems of identifying between QPSK signals and OQPSK signals, and identifying between 8PSK signals and π/4-DQPSK signals, but also can effectively reduce the Sampling rate and number of sampling points.

Description

A Modulation Identification Method of Constellation Identical Signals Based on Compressed Sensing

technical field

The invention relates to the field of signal processing, in particular to a modulation identification method of a constellation diagram, which is suitable for signal identification in a signal set {QPSK, OQPSK, π/4-DQPSK, 8PSK}.

Background technique

With the increase of communication equipment and the increase of communication types, in order to realize the intercommunication of multi-system signals, the modulation mode of the signal is indispensable. Modulation identification is a key technology between signal detection and signal demodulation. In order to achieve reliable, efficient and intelligent communication in the crowded electromagnetic spectrum environment, in software radio and cognitive radio systems, the receiving end It is necessary to estimate the modulation mode, modulation parameters and other information of the received signal, so as to realize the intelligent reception and demodulation of various modulated signals.

The current modulation identification methods are mainly based on extraction features. The extraction features mainly include the following: instantaneous features including amplitude, frequency and phase, statistical features and transform domain features. Digital Modulated Signal Recognition Algorithms (DMRAs), the most effective feature identification method at present, adopts key feature parameters such as instantaneous envelope, phase and frequency as the features of modulation identification. The algorithm has fast calculation speed, obvious classification effect, and is easy in engineering. However, there is no corresponding feature extraction algorithm between signals with the same constellation diagram, such as the identification between QPSK signals and OQPSK signals, and the identification between 8PSK signals and π/4-DQPSK signals. At the same time, this method relies on a large amount of data. Under the high-speed signal, it also brings huge pressure on the sampling frequency of the digital-to-analog converter and the storage capacity of the system.

SUMMARY OF THE INVENTION

In order to overcome the deficiencies of the prior art, the present invention provides a modulation identification method for the same signal in a constellation diagram based on compressed sensing. In order to solve the problem that some signals cannot be identified and the sampling frequency of ADC is high, the present invention realizes the sub-Nyquis of the signal by performing non-uniform sampling and rough reconstruction on the signal, and then using the number of spectral lines of the high-power spectrum of the signal. At the same time, it realizes the signal identification in the signal set {QPSK, OQPSK, π/4-DQPSK, 8PSK}.

The technical scheme adopted by the present invention to solve its technical problem comprises the following steps:

Step 1: Non-uniform sampling of the signal;

非均匀采样信号与奈奎斯特采样信号的关系表示为y＝Ψr，其中y为M×1维压缩采样信号，r为N×1维奈奎斯特采样信号，Ψ为M×N维观测矩阵，Ψ表示为

其中k_j∈[1,ω],ψ_ij∈Ψ，得到y[j]＝r[ω(j-1)+k_j]；Assuming that the required Nyquist sampling frequency of the signal is f _nyq , and the sampling frequency of the digital-to-analog converter is f _ADC , the sampling compression ratio obtained is

The relationship between the non-uniform sampling signal and the Nyquist sampling signal is expressed as y=Ψr, where y is the M×1-dimensional compressed sampling signal, r is the N×1-dimensional Nyquist sampling signal, and Ψ is the M×N-dimensional observation matrix, Ψ is expressed as

where k _j ∈[1,ω], ψ _ij ∈Ψ, get y[j]=r[ω(j-1)+k _j ];

Step 2: Since the QPSK signal, OQPSK signal and π/4-DQPSK are four-phase shift modulation, the non-uniform sampling signal is subjected to fourth-order nonlinear transformation to obtain y ₄ , y ₄ [j]=(y[j]) ⁴ , j=0,1,...,M-1;

其中

表示傅Step 3: Perform coarse reconstruction on the fourth-order nonlinear transformation of the non-uniform sampling signal to obtain the quartic spectrum u, and the coarse reconstruction method is u=ΦΨ ^H y ₄ , where u is the fourth power of N×1-dimensional reconstruction spectrum, Φ is the N×N-dimensional Fourier transform basis, that is, the Fourier transform is performed on Ψ ^H y ₄ ; the fourth power spectrum of the signal is obtained by the Fourier transform of the fourth-order nonlinear transformed signal, that

in

means Fu

represents the Fourier transform;

Step 4: Find the positions of the 3 peaks with the largest 4th power spectrum u of the signal, denoted as F _max ={f _m1 , f _m2 , f _m3 }, where |u(f _m1 )|≥|u(f _m2 )|≥|u(f _m3 )|;

Step 5: Divide y ₄ into two segments of M/2×1-dimensional vectors y _4,1 and y _4,2 equally, and construct a new N/2×N/2-dimensional discrete Fourier basis Φ _N/2 , At the same time, the M×N-dimensional observation matrix Ψ is divided into four parts of M/2×N/2 dimensions as follows:

Step 6: Obtain the quadratic spectrum of the two slices u ₁ and u ₂ as: u ₁ =Φ _N/2 Ψ ₁ ^H y _4,1 , u ₂ =Φ _N/2 Ψ ₄ ^H y _4,2 ;

Step 7: Find the positions F _max,1 ={f _m1,1 ,f _m2,1 ,f _{m3,1 }} and F _{max,2 =} {f _{m1, 2} ,f _m2,2 ,f _m3,2 }, where u ₁ (f _m1,1 )≥u ₁ (f _m2,1 )≥u ₁ (f _m3,1 ), u ₂ (f _m1,2 )≥ u ₂ (f _m2,2 )≥u ₂ (f _m3,2 );

Step 8: Find the union F _max,o =F _max,1 ∪F _max,2 ;

Step 9: Find the intersection F _max,a =F _max ∩F _max,o ;

Step 10: Obtain the number of elements N _r in F _max,a ;

If N _r is 0, the modulation mode of the output identification result is 8PSK; if N _r is 1, the modulation mode of the output identification result is OQPSK; if N _r is 3, the modulation mode of the output identification result is QPSK; if N _r is 2, go to step 11 ;

是否大于门限η，若大于η，输出识别结果调制方式为QPSK；若小于等于η,输出识别结果调制方式为π/4-DQPSK。Step 11: If N _r is 2, judge

Whether it is greater than the threshold η, if it is greater than η, the modulation mode of the output identification result is QPSK; if it is less than or equal to η, the modulation mode of the output identification result is π/4-DQPSK.

The beneficial effect of the present invention is that because the technical means of compressing the quartet spectrum of the signal is adopted, not only the identification between the QPSK signal and the OQPSK signal, but also the identification between the 8PSK signal and the π/4-DQPSK signal is solved. Moreover, the sampling rate and the number of sampling points can be effectively reduced.

Description of drawings

Fig. 1 is the flow chart of the spectral peak detection algorithm under the compression framework of the present invention.

FIG. 2 is a block diagram of the principle of non-uniform sampling according to the present invention.

FIG. 3 is a timing diagram of the sample and hold module of the present invention.

FIG. 4 is a timing diagram of the non-uniform sampling system of the present invention.

FIG. 5 is a rough reconstruction quartogram of different signals of the present invention.

FIG. 6 is the identification probability diagram of different signals when the compression ratio is 4 according to the present invention.

Detailed ways

The present invention is further described below in conjunction with the accompanying drawings and embodiments, the present invention includes but is not limited to the following embodiments, and specifically includes the following steps:

Step 1: Non-uniform sampling of the signal;

非均匀采样信号与奈奎斯特采样信号的关系表示为y＝Ψr，其中y为M×1维压缩采样信号，r为N×1维奈奎斯特采样信号，Ψ为M×N维观测矩阵，Ψ表示为

其中k_j∈[1,ω],ψ_ij∈Ψ，得到y[j]＝r[ω(j-1)+k_j]；Assuming that the required Nyquist sampling frequency of the signal is f _nyq , and the sampling frequency of the digital-to-analog converter is f _ADC , the sampling compression ratio obtained is

The relationship between the non-uniform sampling signal and the Nyquist sampling signal is expressed as y=Ψr, where y is the M×1-dimensional compressed sampling signal, r is the N×1-dimensional Nyquist sampling signal, and Ψ is the M×N-dimensional observation matrix, Ψ is expressed as

where k _j ∈[1,ω], ψ _ij ∈Ψ, get y[j]=r[ω(j-1)+k _j ];

Step 2: Since the QPSK signal, OQPSK signal and π/4-DQPSK are four-phase shift modulation, the non-uniform sampling signal is subjected to fourth-order nonlinear transformation to obtain y ₄ , y ₄ [j]=(y[j]) ⁴ , j=0,1,...,M-1;

其中

表示傅里叶变换;Step 3: Perform coarse reconstruction on the fourth-order nonlinear transformation of the non-uniform sampling signal to obtain the quartic spectrum u, and the coarse reconstruction method is u=ΦΨ ^H y ₄ , where u is the fourth power of N×1-dimensional reconstruction spectrum, Φ is the N×N-dimensional Fourier transform basis, that is, the Fourier transform is performed on Ψ ^H y ₄ ; the fourth power spectrum of the signal is obtained by the Fourier transform of the fourth-order nonlinear transformed signal, that

in

represents the Fourier transform;

Step 4: Find the positions of the three peaks with the largest 4th power spectrum u of the signal, denoted as F _max ={f _m1 ,f _m2 ,f _m3 }, where |u(f _m1 )|≥u|(f _m2 )|≥|u(f _m3 )|;

Step 5: Divide y ₄ into two segments of M/2×1-dimensional vectors y _4,1 and y _4,2 equally, and construct a new N/2×N/2-dimensional discrete Fourier basis Φ _N/2 , At the same time, the M×N-dimensional observation matrix Ψ is divided into four parts of M/2×N/2 dimensions as follows:

Step 6: Obtain the quadratic spectrum of the two slices u ₁ and u ₂ as: u ₁ =Φ _N/2 Ψ ₁ ^H y _4,1 , u ₂ =Φ _N/2 Ψ ₄ ^H y _4,2 ;

Step 7: Find the positions F _max,1 ={f _m1,1 ,f _m2,1 ,f _{m3,1 }} and F _{max,2 =} {f _{m1, 2} ,f _m2,2 ,f _m3,2 }, where |u ₁ (f _m1,1 )|≥|u ₁ (f _m2,1 )|≥|u ₁ (f _m3,1 )|, |u ₂ (f _m1,2 )|≥|u ₂ (f _m2,2 )|≥|u ₂ (f _m3,2 )|;

Step 8: Find the union F _max,o =F _max,1 ∪F _max,2 ;

Step 9: Find the intersection F _max,a =F _max ∩F _max,o ;

Step 10: Obtain the number of elements N _r in F _max,a ;

If N _r is 0, the modulation mode of the output identification result is 8PSK; if N _r is 1, the modulation mode of the output identification result is OQPSK; if N _r is 3, the modulation mode of the output identification result is QPSK; if N _r is 2, go to step 11 ;

是否大于门限η，若大于η，输出识别结果调制方式为QPSK；若小于等于η,输出识别结果调制方式为π/4-DQPSK。Step 11: If N _r is 2, judge

Whether it is greater than the threshold η, if it is greater than η, the modulation mode of the output identification result is QPSK; if it is less than or equal to η, the modulation mode of the output identification result is π/4-DQPSK.

采样符号数N_symbol＝4096,由图1所示，本发明提供了一种基于压缩感知的调制识别，具体实施例如下：The present invention takes an unknown modulated signal with residual carrier frequency f _c =0.5GHz and symbol rate f _b =1GHz after filtering and down-conversion as an example. Assuming that the equivalent Nyquist sampling frequency f _nyq = 4GHz at the receiving end, the sampling compression ratio ω = 4 The actual sampling frequency of the analog-to-digital converter

The number of sampling symbols N _symbol = 4096, as shown in FIG. 1 , the present invention provides a modulation recognition based on compressed sensing, and the specific embodiment is as follows:

其中k_j∈[1,4],ψ_ij∈Ψ。Step 1: The signal is sampled by non-uniform sampling, and the 4096×1-dimensional sampling value y is obtained. The schematic diagram of the system is shown in Figure 2. The whole system is composed of clock generation module, sample and hold module and data acquisition module. The clock generation module is responsible for generating two clocks, one is the ADC sampling clock and the other is a non-uniform clock. The non-uniform clock is generated by a series of pseudo-random binary codes that satisfy the Nyquist frequency. When the pseudo-random code is 1, the output clock is high, and when the pseudo-random code is 0, the output clock is low. In an ADC There is always only one falling edge for non-uniformity in the sampling clock period, and the pseudo-random code is also set to 1 when the next rising edge of the ADC sampling clock arrives, that is, the pseudo-random code is in the form of '...11100...'. The function of the sample and hold module is to collect the signal at this time when the falling edge of the non-uniform clock arrives and keep it until the rising edge of the next non-uniform clock, and when the clock signal is high, the analog signal can pass freely, as shown in Figure 3. Show. The data acquisition module samples the signal from the sample and hold module according to the ADC sampling clock. The description of the whole process is shown in Figure 4. It can be seen that although the sampling clock of the ADC is much lower than the Nyquist sampling frequency, the actual collected signal is obtained by non-uniform sampling according to the Nyquist frequency through the sampling and holding module. , the equivalent observation matrix is Ψ of 4096×16384 dimensions, and Ψ can be expressed as

where k _j ∈ [1,4], ψ _ij ∈ Ψ.

Step 2: Calculate the fourth power of each value of the non-uniformly sampled signal to obtain y ₄ .

Step 3: The rough reconstruction quartic spectrum is obtained by the formula u=ΦΨ ^H y _4. The 16384×16384-dimensional Fourier transform basis Φ can be obtained by taking the Fourier transform of the identity matrix of the corresponding dimension. The reconstructed spectrum is shown in Figure 5.

Step 4: Find the positions of the 3 largest spectral peaks in the quartic spectrum of the signal, denoted as F _max ={f _m1 , f _m2 , f _m3 }, where |u(f _m1 )|≥|u(f _m2 )|≥|u(f _m3 )|.

Step 5: Divide y ₄ into two 2048×1-dimensional vectors y _4,1 and y _4,2 equally, construct a new 8192×8192-dimensional discrete Fourier basis Φ _N/2 , and divide 4096×16384-dimensional The observation matrix Ψ is divided into 4 parts of 2048×8192 dimensions as follows:

Step 6: The quadratic spectrum of the two slices u ₁ and u ₂ is obtained as: u ₁ =Φ _N/2 Ψ ₁ ^H y _4,1 , u ₂ =Φ _N/2 Ψ ₄ ^H y _4,2 .

Step 7: Find the positions F _max,1 ={f _m1,1 ,f _m2,1 ,f _{m3,1 }} and F _{max,2 =} {f _{m1, 2} ,f _m2,2 ,f _m3,2 }, where |u ₁ (f _m1,1 )|≥|u ₁ (f _m2,1 )|≥|u ₁ (f _m3,1 )|, |u ₂ (f _m1,2 )|≥|u ₂ (f _m2,2 )|≥|u ₂ (f _m3,2 )|.

Step 8: Find the union F _max,o =F _max,1 ∪F _max,2 .

Step 9: Find the intersection F _max,a =F _max ∩F _max,o .

Step 10: Obtain the number N _r of elements in F _max,a .

If N _r is 0, the modulation mode of the output identification result is 8PSK; if N _r is 1, the modulation mode of the output identification result is OQPSK; if N _r is 3, the modulation mode of the output identification result is QPSK. If N _r is 2, go to step eleven.

是否大于门限η＝1.8，若大于1.8，输出识别结果调制方式为QPSK；若小于等于1.8输出识别结果调制方式为π/4-DQPSK。从图5可以看出π/4-DQPSK信号高阶谱最大的2根谱线强度相近，而QPSK信号高阶谱最大的2根谱线强度相差较大，因此设置η＝1.8。在QPSK有1根较弱谱线可能无法检测出的时候，通过从2个谱线的情况中分离出QPSK信号能够提升识别率。Step 11: Judge if N _r is 2

Whether it is greater than the threshold η=1.8, if it is greater than 1.8, the modulation mode of the output identification result is QPSK; if it is less than or equal to 1.8, the modulation mode of the output identification result is π/4-DQPSK. It can be seen from Figure 5 that the intensity of the two largest spectral lines in the high-order spectrum of the π/4-DQPSK signal is similar, while the intensity of the two largest spectral lines in the high-order spectrum of the QPSK signal is quite different, so η=1.8 is set. When QPSK has a weak spectral line that may not be detected, the recognition rate can be improved by separating the QPSK signal from the case of two spectral lines.

Figure 6 shows the recognition probability diagram of different signals when the compression ratio is 4. It can be seen from Figure 6 that when the signal-to-noise ratio is 5dB, except for the QPSK signal, the recognition rate of 100% is achieved. When the signal-to-noise ratio is 10dB, The identification probability of all signals reaches 100%, which means that the signal set {QPSK,OQPSK,π/4-DQPSK,8PSK} can be achieved with only one quarter of the AD sampling rate of the Nyquist sampling frequency. The detection probability of the internal signal is 100%, and the amount of sampled data is a quarter of that of uniform sampling, which also reduces the pressure of storage.

Claims (1)

1. a modulation identification method based on the same signal of the constellation diagram of compressed sensing, is characterized in that comprising the following steps: Step 1: Non-uniform sampling of the signal; Assuming that the required Nyquist sampling frequency of the signal is f _nyq , and the sampling frequency of the digital-to-analog converter is f _ADC , the sampling compression ratio obtained is

The relationship between the non-uniform sampling signal and the Nyquist sampling signal is expressed as y=Ψr, where y is the M×1-dimensional compressed sampling signal, r is the N×1-dimensional Nyquist sampling signal, and Ψ is the M×N-dimensional observation matrix, Ψ is expressed as

where k _j ∈[1,ω], ψ _ij ∈Ψ, get y[j]=r[ω(j-1)+k _j ]; Step 2: Since the QPSK signal, OQPSK signal and π/4-DQPSK are four-phase shift modulation, the non-uniform sampling signal is subjected to fourth-order nonlinear transformation to obtain y ₄ , y ₄ [j]=(y[j]) ⁴ , j=0,1,...,M-1; Step 3: Perform coarse reconstruction on the fourth-order nonlinear transformation of the non-uniform sampling signal to obtain the quartic spectrum u, and the coarse reconstruction method is u=ΦΨ ^H y ₄ , where u is the fourth power of N×1-dimensional reconstruction spectrum, Φ is the N×N-dimensional Fourier transform basis, that is, the Fourier transform is performed on Ψ ^H y ₄ ; the fourth power spectrum of the signal is obtained by the Fourier transform of the fourth-order nonlinear transformed signal, that

in

represents the Fourier transform; Step 4: Find the positions of the 3 peaks with the largest 4th power spectrum u of the signal, denoted as F _max ={f _m1 , f _m2 , f _m3 }, where |u(f _m1 )|≥|u(f _m2 )|≥|u(f _m3 )|; Step 5: Divide y ₄ into two segments of M/2×1-dimensional vectors y _4,1 and y _4,2 equally, and construct a new N/2×N/2-dimensional discrete Fourier basis Φ _N/2 , At the same time, the M×N-dimensional observation matrix Ψ is divided into four parts of M/2×N/2 dimensions as follows:

Step 6: Obtain the quadratic spectrum of the two slices u ₁ and u ₂ as: u ₁ =Φ _N/2 Ψ ₁ ^H y _4,1 , u ₂ =Φ _N/2 Ψ ₄ ^H y _4,2 ; Step 7: Find the positions F _max,1 ={f _m1,1 ,f _m2,1 ,f _{m3,1 }} and F _{max,2 =} {f _{m1, 2} ,f _m2,2 ,f _m3,2 }, where |u ₁ (f _m1,1 )|≥|u ₁ (f _m2,1 )|≥|u ₁ (f _m3,1 )|, |u ₂ (f _m1,2 )|≥|u ₂ (f _m2,2 )|≥|u ₂ (f _m3,2 )|; Step 8: Find the union F _max,o =F _max,1 ∪F _max,2 ; Step 9: Find the intersection F _max,a =F _max ∩F _max,o ; Step 10: Obtain the number of elements N _r in F _max,a ; If N _r is 0, the modulation mode of the output identification result is 8PSK; if N _r is 1, the modulation mode of the output identification result is OQPSK; if N _r is 3, the modulation mode of the output identification result is QPSK; if N _r is 2, go to step 11 ; Step 11: If N _r is 2, judge

Whether it is greater than the threshold η, if it is greater than η, the modulation mode of the output identification result is QPSK; if it is less than or equal to η, the modulation mode of the output identification result is π/4-DQPSK.

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