CN113285762B - Modulation format identification method based on relative entropy calculation - Google Patents
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Abstract
The invention relates to a modulation format identification method based on relative entropy calculation, which solves the technical problems of high complexity and low robustness, and calculates a normalized power signal by performing signal normalization by adopting the step one; dividing the theoretical amplitude probability distribution of the four modulation formats into three intervals, and calculating the theoretical amplitude probability distribution of the four modulation formats; step three, respectively counting the normalized power signals in the step one in three intervals, and calculating the amplitude probability distribution of the signals to be identified according to the counting result; calculating the relative entropy of the probability distribution of the current signal relative to the four modulation formats as KL of the signal to be identified; and step five, determining an optimal judgment KL value as a judgment threshold according to a function of the KL value changing along with the OSNR, and judging the current modulation format according to the judgment threshold and the KL value.
Description
Technical Field
The invention relates to the field of digital coherent reception in coherent optical communication, in particular to a modulation format identification method based on relative entropy calculation.
Background
With the rapid development of the internet of things, global communication is exponentially increased, and high requirements such as large transmission capacity, long distance, high spectrum utilization rate and the like are provided for an optical communication network. In order to increase the information transmission rate and improve the spectrum utilization rate, researchers have proposed various modulation formats including Phase Shift Keying (PSK), Quadrature Amplitude Modulation (QAM), Orthogonal Frequency Division Multiplexing (OFDM), etc., which can improve the transmission performance and the spectrum efficiency. However, in a coherent transmission system, the presence of such effects as dispersion (CD), nonlinear effect, self-phase modulation effect (SPM), cross-phase modulation effect (XPM), polarization film dispersion (PMD), Polarization Dependent Loss (PDL), frequency offset, laser phase noise, and the like seriously affect communication quality.
Currently, coherent detection technology combined with digital signal processing technology (DSP) is considered one of the most promising solutions in next generation optical networks. In DPS, various algorithms are able to compensate for signal impairments. These algorithms can be classified into modulation format independent algorithms (clock recovery algorithm, dispersion compensation algorithm, Constant Modulus Algorithm (CMA), etc.) and modulation format dependent algorithms (multimode algorithm (MMA), frequency offset compensation algorithm, carrier phase recovery algorithm, decoding, etc.). Therefore, without any a priori information, the present invention proposes that accurate identification of the modulation format is very important for a digital coherent reception system.
Disclosure of Invention
The technical problem to be solved by the invention is that the modulation format identification method in the prior art has high complexity and low robustness. The modulation format identification method based on the relative entropy calculation has the characteristics of low complexity and high robustness.
In order to solve the technical problems, the technical scheme is as follows:
a modulation format identification method based on relative entropy calculation is used for identifying four modulation formats, and comprises the following steps:
step one, carrying out signal normalization and calculating a normalized power signal A 1 ,A 2 ,…,A n ；
Step two, dividing the theoretical amplitude probability distribution of the four modulation formats into three intervals, and calculating the theoretical amplitude probability distribution of the four modulation formats as { q } QPSK_1 ,q QPSK_2 ,q QPSK_3 }，{q 16QAM_1 ,q 16QAM_2 ,q 16QAM_3 }，{q 32QAM_1 ,q 32QAM_2 ,q 32QAM_3 And q 64QAM_1 ,q 64QAM_2 ,q 64QAM_3 }；
Step three, normalizing the power signal A in the step one 1 ,A 2 ,…,A n Counting in the three intervals in the second step, and calculating the amplitude probability distribution of the signal to be identified according to the counting result;
step four, calculating the probability distribution { p) of the current signal 1 ,p 2 ,p 3 Relative entropy relative to four modulation formats is used as KL value KL of the signal to be identified QPSK 、KL 16QAM 、KL 32QAM And KL 64QAM ；
Changing the value of OSNR in the optical fiber link and KL values under different OSNR; and g, determining the optimal judgment KL value of each modulation format as a judgment threshold according to the variation function of the KL value along with the OSNR, and judging the current modulation format according to the judgment threshold and the KL value to finish the judgment of the modulation format of the signal to be identified.
In the foregoing solution, for optimization, further, the signal normalization includes:
defining the equalized reception as x 1 ,x 2 ,…,x n W is a weight value, and the calculation formula of the normalized weight value is as follows:
wherein the normalized power signal value is (x) 1 ·w) 2 ,(x 2 ·w) 2 ,…,(x n ·w) 2 。
Further, the calculating the amplitude probability distribution of the signal to be identified according to the statistical counting result comprises:
definition of N 1 +N 2 +N 3 N, then the signal A 1 ,A 2 ,…,A n The probability distribution in the three intervals is:
p 1 ＝P(0≤x i ≤0.85)＝N 1 /n
p 2 ＝P(0.85＜x i ≤1.23)＝N 2 /n
p 3 ＝P(1.23＜x i ≤2)＝N 3 n; i is a positive integer less than n.
Further, the relative entropy of the four modulation formats
Wherein i is 1,2, 3; p1, p2 and p3 are all actual probability distributions; q. q.s QPSK_i (x) For the theoretical distribution of the QPSK signal in the i-th interval, q QAM_i (x) For the theoretical distribution of QAM signals in the ith interval, q 64QAM_i (x) Representing the theoretical distribution of the 64QAM signal in the ith interval.
The invention has the beneficial effects that: the invention realizes the identification of the modulation format by utilizing the amplitude probability distribution information of the received signal and has tolerance to frequency deviation and phase noise. Compared with the similar algorithm (based on amplitude probability distribution), the method has the advantages of less required samples, less required calculation amount and capability of accurately identifying the modulation format under the condition of lower OSNR. Compared with a method based on machine learning, the method does not need to train a modulation format recognition system, not only reduces the complexity of the system, but also has the potential of being used in a real-time system.
Drawings
The invention is further illustrated with reference to the following figures and examples.
Fig. 1 is a schematic diagram of a digital coherent reception high-speed optical communication system in an embodiment.
FIG. 2 is a graph showing the variation of KL with OSNR in the example.
FIG. 3 is a diagram showing the result of step 7 in the example.
FIG. 4 is a diagram showing the result of step 8 in the example
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
The embodiment provides a modulation format identification method based on relative entropy calculation, which is used for identifying four modulation formats, and the method comprises the following steps:
step one, carrying out signal normalization and calculating a normalized power signal A 1 ,A 2 ,…,A n ；
Step two, dividing the theoretical amplitude probability distribution of the four modulation formats into three intervals, and calculating the theoretical amplitude probability distribution of the four modulation formats as { q } QPSK_1 ,q QPSK_2 ,q QPSK_3 }，{q 16QAM_1 ,q 16QAM_2 ,q 16QAM_3 }，{q 32QAM_1 ,q 32QAM_2 ,q 32QAM_3 And q 64QAM_1 ,q 64QAM_2 ,q 64QAM_3 }；
Step three, normalizing the power signal in the step oneA 1 ,A 2 ,…,A n Counting in the three intervals in the second step, and calculating the amplitude probability distribution of the signal to be identified according to the counting result;
step four, calculating the probability distribution { p) of the current signal 1 ,p 2 ,p 3 Relative entropy relative to four modulation formats is used as KL value KL of the signal to be identified QPSK 、KL 16QAM 、KL 32QAM And KL 64QAM ；
Changing the value of OSNR in the optical fiber link and KL values under different OSNR; and g, determining the optimal judgment KL value of each modulation format as a judgment threshold according to the variation function of the KL value along with the OSNR, and judging the current modulation format according to the judgment threshold and the KL value to finish the judgment of the modulation format of the signal to be identified.
Specifically, the method comprises the following steps:
step 1: constructing a coherent optical communication system: a digital coherent reception high-speed optical communication system as shown in fig. 1 was constructed. The sending end generates a pseudo-random code optical signal, the adopted modulation formats are PDM-QPSK, PDM-16QAM, PDM-32QAM and PDM-64QAM, and the transmitter power is 0-5 dB. The optical signal is transmitted through an optical fiber link, the optical fiber link is composed of a circulator, an optical amplifier, an OSNR device and a single mode fiber, and one cycle comprises 100km of single mode fiber and an EDFA with the gain of 20 dB. The receiving end is composed of a coherent receiver, ADC and DSP. The coherent receiver mixes the received optical signal with the local oscillator light, converts the optical signal into a corresponding electric signal to be processed, and converts the electric signal into a digital communication signal to be processed by the ADC module, wherein the algorithm provided by the invention is applied to a modulation format identification module in the DSP.
Step 2: received signal normalization: the signal after CMA equalization assumes that the received signal is x 1 ,x 2 ,…,x n Assuming a weight value w, the calculation formula of the normalized weight value is as follows:
And step 3: calculating theoretical amplitude probability distributions of the four modulation formats: dividing the theoretical amplitude probability distribution of four modulation formats into three intervals of [0, 0.85%],(0.85,1.23],(1.23,2]. The theoretical amplitude probability distributions of the four modulation formats are respectively marked as q QPSK_1 ,q QPSK_2 ,q QPSK_3 }，{q 16QAM_1 ,q 16QAM_2 ,q 16QAM_3 }，{q 32QAM_1 ,q 32QAM_2 ,q 32QAM_3 And q 64QAM_1 ,q 64QAM_2 ,q 64QAM_3 }. Normalized amplitude probability distributions for QPSK,16QAM,32QAM, and 64QAM signals, as in table 1.
TABLE 1
And 4, step 4: calculating the amplitude probability distribution of the signal to be identified: normalizing the power signal A in the step 2 1 ,A 2 ,…,A n According to the interval [0,0.85 respectively],(0.85,1.23],(1.23,2]Counting the number, respectively recording as N 1 ,N 2 ,N 3 And has N 1 +N 2 +N 3 N. Then signal A 1 ,A 2 ,…,A n The probability distribution in the three intervals can be calculated by the following formula.
p 1 ＝P(0≤x i ≤0.85)＝N 1 /n
p 2 ＝P(0.85＜x i ≤1.23)＝N 2 /n
p 3 ＝P(1.23＜x i ≤2)＝N 3 /n
And 5: calculating KL value of the signal to be identified, calculating probability distribution { p) of the current signal 1 ,p 2 P3 relative entropy with respect to the four modulation formats can be calculated using the following equation:
wherein i is 1,2, 3; p1, p2 and p3 are all actual probability distributions; QPSK_i (x) For the theoretical distribution of the QPSK signal in the ith interval, QAM_i (x) For the theoretical distribution of QAM signals in the ith interval, q 64QAM_i (x) Representing the theoretical distribution of 64QAM signals in the ith interval;
step 6: determining the modulation format of the signal to be identified: and changing the value of the OSNR in the optical fiber link, thereby obtaining the KL values of the probability distribution of the unknown modulation format signal and the four theoretical values under different OSNR and determining the current threshold.
The modulation format identification module determines the optimal determination KL value of each modulation format according to the variation curve of the KL value along with the OSNR, and the determination KL value is obtained in the experiment. And (3) judging the current modulation format by combining the judgment threshold and the KL value, namely:
when the values of OSNR are the same, KL _ QPSK is the smallest of the four KL values and KL _ QPSK < th1, it is decided that the current modulation format is DP-QPSK.
When the values of the OSNRs are the same, KL _16QAM is the smallest of the four KL values and KL _16QAM < th2, it is determined that the current modulation format is DP-16 QAM.
When the values of the OSNR are the same, KL _32QAM is the smallest of the four KL values and KL _32QAM < th3, it is determined that the current modulation format is DP-32 QAM.
When the values of the OSNR are the same, KL _64QAM is the smallest of the four KL values and KL _64QAM < th4, it is determined that the current modulation format is DP-64 QAM.
And 7: at an OSNR of 7% FEC, the transmitter power is changed, verifying the tolerance of the algorithm to non-linearities: setting OSNR as a 7% FEC value of a corresponding modulation format, setting transmission distances of QPSK,16QAM,32QAM and 64QAM as 1700km, 1500km, 1500km and 1000km respectively, changing the power (0-5dB) of a transmitter, repeating the modulation format judging method in the step 6, and verifying the tolerance of the algorithm to the nonlinear effect through identification accuracy.
And 8: when the OSNR is 7% FEC, the number of samples is changed to 1000 and the 10000 interval value is 1000 respectively, and the modulation format determination method in step 6 is repeated to verify the number of samples required by the algorithm.
Experimental results and analysis of this example:
KL values vary with OSNR: the graph of KL values versus OSNR in step 6 is shown in fig. 2, where the vertical dashed line is the 7% FEC threshold value for various modulation formats. According to step 6, the threshold values for the four modulation formats are determined to be th1 ═ 0.33, th2 ═ 0.30, th3 ═ 0.084, and th4 ═ 0.084, respectively. As can be seen from fig. 2, according to step 6, it can be seen that the proposed algorithm can accurately identify the current modulation format when the OSNR is much lower than 7% FEC.
Non-linearity tolerance: the result of step 7 is shown in FIG. 3. We can see that the proposed algorithm can accurately identify the current modulation format when the transmit power is 0-5 dB. Therefore, the method has certain tolerance to nonlinearity.
Number of samples: the result of step 8 is shown in fig. 4, and we can see that the proposed algorithm can also accurately identify the current modulation format when the number of samples is 1000.
Although the illustrative embodiments of the present invention have been described above to enable those skilled in the art to understand the present invention, the present invention is not limited to the scope of the embodiments, and it is apparent to those skilled in the art that all the inventive concepts using the present invention are protected as long as they can be changed within the spirit and scope of the present invention as defined and defined by the appended claims.
Claims (3)
1. A modulation format identification method based on relative entropy calculation is characterized in that: the modulation format identification method based on the relative entropy calculation is used for identifying four modulation formats, and comprises the following steps:
step one, carrying out signal normalization and calculating a normalized power signal A 1 ,A 2 ,…,A n ；
Step two, dividing the theoretical amplitude probability distribution of the four modulation formats into three intervals, and defining the theoretical amplitude probability distribution of the four modulation formats as { q } QPSK_1 ,q QPSK_2 ,q QPSK_3 }，{q 16QAM_1 ,q 16QAM_2 ,q 16QAM_3 }，{q 32QAM_1 ,q 32QAM_2 ,q 32QAM_3 And q 64QAM_1 ,q 64QAM_2 ,q 64QAM_3 }；
Step three, normalizing the power signal A in the step one 1 ,A 2 ,…,A n Counting in the three intervals in the second step, and calculating the amplitude probability distribution of the signal to be identified according to the counting result;
step four, calculating the probability distribution { p) of the current signal 1 ,p 2 ,p 3 Relative entropy relative to four modulation formats is used as KL value KL of the signal to be identified QPSK 、KL 16QAM 、KL 32QAM And KL 64QAM ；
Changing the value of OSNR in the optical fiber link and KL values under different OSNR; determining the optimal judgment KL value of each modulation format as a judgment threshold according to the variation function of the KL value along with the OSNR, judging the current modulation format according to the judgment threshold and the KL value, and finishing the judgment of the modulation format of the signal to be identified;
relative entropy of four modulation formats
Wherein i is 1,2, 3; p1, p2 and p3 are all actual probability distributions; q. q.s QPSK_i (x) For the theoretical distribution of the QPSK signal in the i-th interval, q QAM_i (x) For theoretical distribution of QAM signals in the ith interval, q 64QAM_i (x) Representing the theoretical distribution of the 64QAM signal in the ith interval.
2. A modulation format identification method based on relative entropy calculation according to claim 1, characterized in that: the signal normalization includes:
defining the equalized reception as x 1 ,x 2 ,…,x n W is a weight value, and the calculation formula of the normalized weight value is as follows:
wherein the normalized power signal value is (x) 1 ·w) 2 ,(x 2 ·w) 2 ,…,(x n ·w) 2 。
3. A modulation format identification method based on relative entropy calculation according to claim 1, characterized in that: calculating the amplitude probability distribution of the signal to be identified according to the statistical counting result comprises the following steps:
definition of N 1 +N 2 +N 3 N, then the signal A 1 ,A 2 ,…,A n The probability distribution in the three intervals is：
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