Low-complexity signal-to-noise ratio estimation method and device

Abstract

The invention discloses a low-complexity signal-to-noise ratio estimation method and device, and relates to the technical field of coherent optical communication, and the method comprises the steps: employing a modulation mode to map a signal to a constellation diagram in a communication link, and extracting a to-be-estimated real number signal from a received signal according to a preset signal-to-noise ratio estimation mode; judging the real number signal to be estimated according to an estimation mode to obtain a positive signal and a negative signal; carrying out addition and subtraction operation on the positive and negative signals respectively and carrying out modulus averaging to obtain a noise modulus value of linear representation; and inputting the noise module value into a preset lookup table, and combining a log value of a known signal module value to obtain a signal-to-noise ratio estimation value based on logarithm representation. According to the method, multiplication is completely removed, the complexity is greatly reduced, iteration is not involved in the table look-up process, so that the complexity is very low, the estimation precision can be freely set according to the size of the table, and the flexibility is high.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of coherent optical communication, in particular to a low-complexity SNR estimation method and device. BACKGROUND

[0002] As an important technological innovation in the field of optical fiber communication, coherent optical communication significantly improves system performance through multi-dimensional modulation and coherent detection mechanism. Among them, the optical wave is decomposed into polarization, amplitude, phase and other orthogonal dimensions for joint modulation, and the spectral efficiency is close to 4 times that of traditional direct modulation and direct detection systems. Signal to noise ratio (SNR) estimation is a core technology of coherent optical communication, and with the continuous development of coherent optical communication technology, it plays an increasingly important role.

[0003] Signal to noise ratio is a core parameter for measuring channel quality, defined as the ratio of the power of the effective signal in the received signal to the power of the noise. On the one hand, the SNR estimate can be used for system performance monitoring, and on the other hand, the SNR estimate can be used to optimize the performance of other modules of the system, such as feeding back the SNR estimate to the adaptive equalization module of the digital signal processing (DSP) for coefficient iterative update.

[0004] The existing SNR estimation methods include maximum likelihood estimation, high-order statistical moment method, and frequency domain analysis method. Among them, the maximum likelihood estimation method uses iterative algorithm to approach the optimal solution, and uses the data after hard decision to estimate the noise power. When the signal to noise ratio is low, the SNR estimation will also have low precision due to the large decision error, and the complexity is very high due to the use of iterative algorithm. The high-order statistical moment method directly calculates based on the statistical characteristics of the signal modulation format, separates the signal and noise by calculating the sixth moment and the eighth moment of the signal, and has natural robustness to Gaussian noise, but the complexity of high-order moment operation is very high, which is difficult to realize in hardware. The frequency domain analysis method uses Fourier transform to analyze the frequency spectrum energy of the signal and the noise, calculates the signal frequency spectrum energy and the noise frequency spectrum energy in the frequency domain, and obtains the SNR estimate value. Since a large number of fast Fourier transforms are required, the complexity is also high.

[0005] In summary, the existing SNR estimation techniques generally have high computational complexity, are sensitive to low signal to noise ratio, and have inaccurate estimation, therefore, there is an urgent need to design a low-complexity, high-precision SNR estimation method. SUMMARY

[0006] The present application provides a low-complexity SNR estimation method and device, which can solve the technical problems of high computational complexity and low precision in the existing SNR estimation method.

[0007] In a first aspect, the embodiments of the present application provide a low-complexity SNR estimation method, which comprises: The signal is mapped to a constellation diagram by using a modulation mode in a communication link, and a real number signal to be estimated is extracted from a received signal according to a preset SNR estimation mode; The real number signal to be estimated is judged according to the estimation mode, and positive and negative signals are obtained; The positive and negative signals are respectively subjected to addition and subtraction operations and are averaged after being taken as modulus, and a linearly represented noise modulus value is obtained; The noise modulus value is input into a preset lookup table, and a logarithm value of a known signal modulus value is combined to obtain an SNR estimation value based on logarithmic representation.

[0008] In combination with the first aspect, in an implementation mode, the real number signal to be estimated is extracted from the received signal according to the preset SNR estimation mode, which comprises: The SNR estimation mode is divided into a blind estimation mode and a pilot-assisted mode, In the blind estimation mode, a complex number signal to be estimated is obtained from the received signal according to a preset extraction rule; In the pilot-assisted mode, the complex number signal to be estimated is obtained from the received signal according to a pilot signal insertion rule specified by a protocol; The real number signal to be estimated is extracted from the complex number signal to be estimated.

[0009] In combination with the first aspect, in an implementation mode, in the blind estimation mode, the preset extraction rule is an N extraction 1 mode, where N is a positive integer; In the pilot-assisted mode, the pilot signal insertion rule is to insert a pilot signal every M data to be transmitted.

[0010] In combination with the first aspect, in an implementation mode, the real number signal to be estimated is extracted from the complex number signal to be estimated, which comprises: The complex number signal to be estimated is obtained, the real part of an odd-numbered sequence number is taken as the real number signal to be estimated, and the imaginary part of an even-numbered sequence number is taken as the real number signal to be estimated.

[0011] In combination with the first aspect, in an implementation mode, the real number signal to be estimated is judged according to the estimation mode, and positive and negative signals are obtained, which comprises: When the estimation mode is the blind estimation mode, the real number signal to be estimated is judged according to the judgment position of different modulation formats on a standard constellation diagram, and the positive and negative signals in the outermost circle are obtained; When the estimation mode is the pilot-assisted mode, the real number signal to be estimated is judged after judging the symbol consistency with a reference pilot signal, and the positive and negative signals in the outermost circle are obtained.

[0012] In combination with the first aspect, in an implementation, when the estimation mode is the blind estimation mode, the decision positions of different modulation formats on the standard constellation diagram include: the decision position of the QPSK modulation format is 0; the decision position of the QAM modulation format is: two constellation points with the same vertical coordinate in the outermost circle and the second outermost circle, and the modulus value of the horizontal coordinate of the middle position; the decision position of the 16QAM modulation format is 2.

[0013] In combination with the first aspect, in an implementation, when the estimation mode is the blind estimation mode, if the real signal to be estimated is greater than the decision position, the real signal is decided into the positive signal; if the real signal to be estimated is less than or equal to the decision position, the real signal is decided into the negative signal.

[0014] In combination with the first aspect, in an implementation, the estimation mode is the pilot-assisted mode, when the real signal to be estimated is consistent with the symbol of the reference pilot signal, if the real signal to be estimated is greater than 0, the real signal is decided into the positive signal; if the real signal to be estimated is less than or equal to 0, the real signal is decided into the negative signal. The estimation mode is the pilot-assisted mode, when the real signal to be estimated is inconsistent with the symbol of the reference pilot signal, if the real signal to be estimated is greater than 0, the real signal is decided into the negative signal; if the real signal to be estimated is less than or equal to 0, the real signal is decided into the positive signal.

[0015] In combination with the first aspect, in an implementation, the modulus average of the addition and subtraction operations on the positive and negative signals is used to obtain the linear representation of the noise modulus value, including: According to the modulation format, the modulus of the positive signal in the positive and negative signals is subtracted by the addition factor, and the modulus of the negative signal is added by the addition factor; the modulus results are averaged to obtain the linear representation of the noise modulus value.

[0016] In combination with the first aspect, in an implementation, the noise modulus value is input into a preset lookup table, and the logarithmic value of the known signal modulus value is obtained based on the logarithmic representation to obtain the signal-to-noise ratio estimation value based on the logarithmic representation, including: According to the lookup table, the noise power value based on the logarithmic representation corresponding to the noise modulus value is found; The signal power value based on the logarithmic representation of the local known transmitted signal is obtained; The signal power value is subtracted from the noise power value to obtain the signal-to-noise ratio estimation value based on the logarithmic representation.

[0017] In combination with the first aspect, in an implementation, the lookup table is a 20-fold logarithm table with base 10.

[0018] In combination with the first aspect, in an implementation form, when the SNR estimation mode is the blind estimation mode, the low-complexity SNR estimation method is used before or after frame synchronization in a DSP processing flow of the digital coherent receiver. When the SNR estimation mode is the pilot-assisted mode, the low-complexity SNR estimation method is used after frame synchronization in the DSP processing flow of the digital coherent receiver.

[0019] In a second aspect, the embodiments of the present application provide a SNR estimation device based on any of the above low-complexity SNR estimation methods, and the SNR estimation device comprises: a signal extraction module configured to map signals to a constellation diagram in a modulation manner in a communication link, and extract to-be-estimated real signals from received signals according to a preset SNR estimation mode; a signal decision module configured to decide the to-be-estimated real signals according to the estimation mode, and obtain positive and negative signals; a noise module value calculation module configured to perform addition and subtraction operations on the positive and negative signals respectively and take a module average, and obtain a linearly represented noise module value; a SNR calculation module configured to input the noise module value into a preset lookup table, combine a logarithmic value of a known signal module value, and obtain a logarithmically represented SNR estimation value.

[0020] The technical scheme provided by the embodiments of the present application has the following beneficial effects: The addition and subtraction operations are performed on the positive and negative signals respectively, and the module average is taken, so that the linearly represented noise module value is obtained, the multiplication operation is completely removed by using the addition and subtraction operations, the complexity is greatly reduced, and the performance can be kept consistent.

[0021] The noise module value is input into the preset lookup table, the logarithmic value of the known signal module value is combined, and the logarithmically represented SNR estimation value is obtained. The known signal energy can be pre-configured, the calculation process of the signal energy in the SNR estimation process is optimized, the flexibility is high, and the calculation resources are also saved; the process of inputting the noise module value into the preset lookup table does not involve iteration, so the complexity is very low, and the estimation accuracy can be freely set according to the size of the table, and the flexibility is high. Therefore, the technical problems of high calculation complexity and low accuracy in the prior art are solved.

[0022] The present application can be applied to various modulation formats such as QPSK, 16QAM, 64QAM, and technologies such as Probability Constellation Shaping (PCS), and has a wide range of applications. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1Flow chart of low-complexity SNR estimation method of embodiments of the present application; Figure 2 Constellation diagram of complex signals to be estimated under QPSK modulation format provided by embodiments of the present application; Figure 3 Constellation diagram of real signals to be estimated under QPSK modulation format provided by embodiments of the present application; Figure 4 Schematic diagram of LUT with horizontal coordinate of 1-4096 provided by embodiments of the present application; Figure 5 Principle schematic diagram of calculation process of embodiments of the present application; Figure 6 SNR estimation effect diagram under QPSK modulation format based on blind estimation mode provided by embodiments of the present application; Figure 7 SNR estimation effect diagram under QPSK modulation format based on pilot-assisted mode provided by embodiments of the present application; Figure 8 SNR estimation effect diagram under 16QAM modulation format based on blind estimation mode provided by embodiments of the present application; Figure 9 SNR estimation effect diagram under 16QAM modulation format based on pilot-assisted mode provided by embodiments of the present application; Figure 10 SNR estimation effect diagram under PCS-16QAM modulation format based on blind estimation mode provided by embodiments of the present application; Figure 11 SNR estimation effect diagram under PCS-16QAM modulation format based on pilot-assisted mode provided by embodiments of the present application; Figure 12 Schematic diagram of embodiments of the present application used in DSP processing flow of digital coherent receiver before frame synchronization; Figure 13 Schematic diagram of embodiments of the present application used in DSP processing flow of digital coherent receiver after frame synchronization; Figure 14 Schematic diagram of low-complexity SNR estimation device of embodiments of the present application provided by embodiments of the present application. DETAILED DESCRIPTION

[0024] In order to make the person skilled in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by the person skilled in the art without creative labor fall within the scope of protection of the present application.

[0025] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be described in further detail below in conjunction with the drawings.

[0026] In a first aspect, the embodiments of the present application provide a low-complexity signal-to-noise ratio estimation method.

[0027] In an embodiment, as shown in FIG. 1, it is a flowchart of the low-complexity signal-to-noise ratio estimation method of the present application. The above signal-to-noise ratio estimation method comprises the following steps: Figure 1 S1: a signal is mapped to a constellation diagram by using a modulation mode in a communication link, and a real signal to be estimated is extracted from a received signal according to a preset signal-to-noise ratio estimation mode. S2: the real signal to be estimated is judged according to the estimation mode, and positive and negative signals are obtained.

[0028] S3: the positive and negative signals are respectively subjected to addition and subtraction operations and are averaged after taking modulus, and a linearly represented noise modulus value is obtained.

[0029] S4: the noise modulus value is input into a preset lookup table, and a logarithmic value of a known signal modulus value is combined to obtain a logarithmically represented signal-to-noise ratio estimation value.

[0030] In the embodiment, the positive and negative signals are respectively subjected to addition and subtraction operations and are averaged after taking modulus, and a linearly represented noise modulus value is obtained. Only addition or subtraction operation is used, and the multiplication operation is completely removed, which greatly reduces the complexity and solves the technical problem of high calculation complexity in the related art. The preset lookup table is used to input the noise modulus value for lookup, and the process of lookup does not involve iteration, so the complexity is very low. The estimation accuracy can be freely set according to the size of the table, and the flexibility is high. The technical problem of high calculation complexity and low accuracy in the prior art is solved. In addition, the logarithmic value of the known signal modulus value is combined to obtain the logarithmically represented signal-to-noise ratio estimation value. The known signal energy can be pre-configured, the calculation process of the signal energy in the signal-to-noise ratio estimation process is optimized, the flexibility is improved, and the calculation resources are also saved.

[0031]

[0032] ​In the step S1, the modulation mode used in the communication link is Quadrature Phase Shift Keying (QPSK) or Quadrature Amplitude Modulation (QAM), and the signal is modulated to a constellation diagram by QPSK or QAM.

[0033] The modulation format of the optical communication link can support multiple modulation formats such as QPSK, 16QAM, 64QAM, and the like, and technologies such as probabilistic constellation shaping. At the sending side, the signal is mapped to a standard constellation diagram by modulation; at the receiving side, after complex DSP processing, a constellation diagram carrying Gaussian white noise and other residual impairments can be obtained. Among them, the standard constellation diagram is identified as the minimum circle with a radius of sqrt(2), and the constellation point is ±1±1j.

[0034] It is worth noting that the received signal is a complex number. In the above noise ratio estimation method, the preset signal-to-noise ratio estimation mode is divided into a blind estimation mode and a pilot-assisted mode. In the step S1, in the blind estimation mode, according to a preset extraction rule, the complex signal to be estimated is obtained from the received signal; in the pilot-assisted mode, according to a protocol-specified pilot signal insertion rule, the complex signal to be estimated is obtained from the received signal; and then, the real signal to be estimated is extracted from the complex signal to be estimated.

[0035] Further, in an embodiment, the blind estimation mode is used, and the complex signal to be estimated is obtained from the received signal according to a preset extraction rule. The preset extraction rule is N extraction 1, and N is a positive integer. When N is set to 1, each received complex signal is a complex signal to be estimated; when N is set to 2, the 1st, 3rd, 5th, 7th, and the like are complex signals to be estimated; when N is set to 3, the 1st, 4th, 7th, 10th, and the like are complex signals to be estimated; and so on. The larger N is, the slower the SNR estimation speed is, and the lower the implementation power consumption is; the smaller N is, the faster the SNR estimation speed is, and the higher the implementation power consumption is.

[0036] Further, in an embodiment, the pilot-assisted mode is used, and the complex signal to be estimated is obtained from the received signal according to a protocol-specified (or known) pilot signal insertion rule. The pilot signal insertion rule is that one pilot is inserted every M data to be sent. The pilot is sequentially inserted, and when the number of pilots is P, that is, every P data to be sent, one pilot is inserted. M pieces of data to be sent, insert P pilots. Taking OpenZR+ MSA protocol as an example, the length of each subframe is 3712 symbols, wherein the number of pilots is 116, after the first pilot is sent, then 31 original signals are sent, then the second pilot is sent, and so on. The pilot is generally inserted in the outermost circle of the standard constellation diagram. Taking 16QAM as an example, the signals in the outermost circle of the standard constellation diagram are ±3±3j, and the pilot signal is the arrangement combination of the above four complex signals. After frame synchronization, the position of the pilot is determined, and the received pilot signal can be obtained according to the insertion rule of the pilot. Taking OpenZR+ MSA protocol as an example, the pilot position is taken as the starting point, and the received pilot signal is extracted every 32 data.

[0037] Due to the influence of channel noise and incomplete equalization, the complex signal to be estimated deviates from the corresponding standard constellation point on the constellation diagram. Figure 2 The constellation diagram of the complex signal to be estimated under QPSK modulation format.

[0038] In the above step S1, after obtaining the complex signal to be estimated, the real part of the first, third, fifth, …, odd sequence number is taken according to the odd-even arrangement, and the imaginary part of the second, fourth, sixth, …, even sequence number is taken, thereby obtaining the real signal to be estimated. Figure 3 The constellation diagram of the real signal to be estimated under QPSK modulation format can be seen that there is only data on the horizontal axis, and the vertical axis is 0.

[0039] Further, in the above step S2, the estimated mode is used to judge the real signal to be estimated, and positive and negative two-way signals are obtained, including: when the estimation mode is a blind estimation mode, the real signal to be estimated is judged according to the judgment position of different modulation formats on the standard constellation diagram, and positive and negative two-way signals in the outermost circle are obtained. When the estimation mode is a pilot assisted mode, the sign consistency of the real signal to be estimated and the reference pilot signal is judged (i.e. whether the sign of the pilot signal to be estimated is consistent with that of the reference pilot signal), and positive and negative two-way signals in the outermost circle are obtained.

[0040] In an embodiment, the estimation mode is a blind estimation mode. First, different judgment positions are obtained according to different signal modulation formats. On the standard constellation diagram, the judgment position of QPSK modulation format is the middle position of two constellation points, QPSK modulation has only one circle and is central symmetric, and the sum of two random constellation points is 0, so the judgment position is 0. For QAM modulation format, the judgment position is the modulus value of the horizontal coordinate of the middle position of two constellation points with the same vertical coordinate in the outermost circle and the second outermost circle. For 16QAM modulation, the two constellation points with the same vertical coordinate in the third quadrant outermost circle and the second outermost circle are -3-3j and -1-3j, the middle position is -2-3j, the horizontal coordinate is -2, and the judgment position is 2. The same is true for 64QAM and other modulation formats.

[0041] When the real number signal to be estimated is greater than the decision position, the real number signal is decided into the positive signal Sig_pos, and when the real number signal to be estimated is less than or equal to the decision position, the real number signal is decided into the negative signal Sig_neg, thereby obtaining the positive and negative signals.

[0042] In the embodiment, only the outermost circle of the signal is used to calculate the noise modulus, which reduces the complexity in decision-making and makes the SNR estimation value obtained by hard decision more accurate due to the highest distinguishability of the outermost circle signal.

[0043] In another embodiment, the estimation mode is a pilot-assisted mode, which first needs to judge whether the sign of the real number signal to be estimated (i.e., the pilot signal to be estimated) is consistent with that of the reference pilot signal, and then decides the signal. The pilot is in the outermost circle of the constellation diagram, i.e., the pilot is a QPSK signal, and thus the decision position is 0.

[0044] When the sign of the real number signal to be estimated is consistent with that of the reference pilot signal: if the real number signal to be estimated is greater than 0, the real number signal is decided into the positive signal Sig_pos; and when the real number signal to be estimated is less than or equal to 0, the real number signal is decided into the negative signal Sig_neg, thereby obtaining the positive and negative signals.

[0045] When the sign of the real number signal to be estimated is inconsistent with that of the reference pilot signal, the decision method is opposite to the above: if the real number signal to be estimated is greater than 0, the real number signal is decided into the negative signal Sig_neg; and if the real number signal to be estimated is less than or equal to 0, the real number signal is decided into the positive signal Sig_pos.

[0046] In the pilot-assisted mode, only the sign needs to be judged due to the pilot signal in the outermost circle, and the complexity is very low, and the outermost circle signal has the highest distinguishability, thereby obtaining a more accurate SNR estimation value; and the pilot-assisted mode can effectively utilize the prior information of the pilot to assist decision-making, greatly improving the accuracy of SNR estimation.

[0047] In the step S3, the positive and negative signals are respectively subjected to addition and subtraction operations and the modulus is averaged to obtain the linearly represented noise modulus, which includes: according to the different modulation formats, different addition factors are determined, the positive signal in the positive and negative signals is subtracted by the addition factor, and the modulus is taken; the negative signal is added by the addition factor, and the modulus is taken; and the multiple modulus results are averaged to obtain the linearly represented noise modulus.

[0048] Further, the addition factor in the blind estimation mode and the pilot-assisted mode is consistent with the decision position in the blind estimation mode. For example, the addition factor is 0 for QPSK modulation, the addition factor is 2 for 16QAM modulation, and the addition factor is the same for 64QAM and other modulation formats.

[0049] The positive signal will be subtracted by the addition factor, and the negative signal will be added by the addition factor. The final signal noise is near the 0 of the coordinate axis, that is, the expectation is 0. The modulus value of the signal noise is obtained by taking the modulus value, that is, the modulus value of the noise noise_modu_temp is obtained. The modulus value of the noise noise_modu_temp is averaged multiple times to obtain the final required linear representation of the noise modulus value noise_modu, and the calculation formula is as follows: ; Wherein, N represents the number of average, the larger N is, the more the average times are, the more accurate the calculation result is, but the longer the calculation time is; the smaller N is, the less the average times are, the larger the calculation result error is, but the shorter the calculation time is.

[0050] In the above step S4, the obtained noise modulus value is searched in the pre-designed lookup table (LUT), and the final signal-to-noise ratio (SNR) estimation value is obtained in combination with the logarithmic value of the known signal modulus value. The calculation formula of the logarithmic SNR is as follows: ; Wherein, Sig_dB is the logarithmic representation of the local known signal power value of the transmitted signal, LUT is a 20 times 10-based logarithmic table, which is used to convert the linear noise modulus value noise_modu into a logarithmic representation of the noise power value; Specifically, the LUT is a plurality of rows and two columns, the first column is the linear value of the noise modulus value, and the second column is the logarithmic value of the noise modulus value. In this embodiment, the LUT is 4096 rows and two columns.

[0051] In hardware implementation, for example, the linear noise modulus value noise_modu takes a value between 1 and 4096, the generated LUT has a vertical coordinate = 20 log10(1:4096), and the horizontal coordinate is the original linear noise modulus value noise_modu. The maximum value 4096 can be changed according to the actual situation. The larger the value is, the more accurate the table lookup is, and the more resources are consumed. Figure 4 The horizontal coordinate of the LUT is 1~4096.

[0052] Sig_dB is a local known value, noise_modu is searched in the pre-designed LUT to obtain LUT(noise_modu), and the logarithmic representation of the signal-to-noise ratio (SNR) estimation value is obtained by subtracting Sig_dB and LUT(noise_modu).

[0053] Further, the specific technical principle involved in the above steps S2 to S4 is further explained as follows: The positive and negative signals obtained in step S2 can be expressed as: ; The noise model in the communication channel is a Gaussian white noise model, so Sig_s2pos satisfies a normal distribution with an expectation of x and a standard deviation of y; Sig_s2neg satisfies a normal distribution with an expectation of -x and a standard deviation of y, where x is the decision position described in step S3.

[0054] In step S2, the positive signal Sig_s2pos is subtracted by an addition factor, and the negative signal Sig_s2neg is added by an addition factor, which can be expressed as: ; Therefore, Sig_s3pos satisfies a normal distribution with an expectation of 0 and a standard deviation of y; Sig_s2neg satisfies a normal distribution with an expectation of 0 and a standard deviation of y. Both signals satisfy the same normal distribution and are near the 0 coordinate on the constellation diagram. The two signals can be expressed as a unified signal as follows: ; with an expectation of 0 and a standard deviation of y.

[0055] Taking the modulus of the signal and then taking the expectation, the expectation of the noise modulus can be obtained At this time, from the calculation formula of the mathematical expectation of the signal modulus, we have: ; where the modulus of X satisfies a normal distribution with an expectation of μ and a standard deviation of σ, denotes the probability that the random variable x~N(0,1) takes a value less than or equal to x. Taking μ=0 and σ=y, we can obtain the calculation formula of the mathematical expectation of the signal modulus as: ; For an IQ modulated signal, the Gaussian white noise of the I or Q channel satisfies a normal distribution with an expectation of 0 and a standard deviation of w, where w can be expressed as: ; Sig_modu_temp is a locally known signal modulus based on linear representation, and w is brought into the formula , which can be known as: ; The signal-to-noise ratio estimation value of the receiving end can be obtained as: ; Sig_modu is the signal modulus based on linear representation after the conversion of sqrt(pi). noise_modu is the noise modulus based on linear representation.

[0056] The calculation formula of the SNR based on logarithm is: wherein, Sig_dB is the signal power value of the signal based on logarithmic representation known locally, LUT is a 20 times table of logarithm with base 10, which is used to convert the linear noise modulus noise_modu into the noise power value based on logarithmic representation.

[0057] Through the above complete calculation and derivation process, it can be seen that the above technical solutions conceived by the present application have the following advantages: In the calculation process of the noise modulus, addition and subtraction operations are used, and the multiplication operation is completely removed, which greatly reduces the complexity while the performance can be kept consistent.

[0058] In the blind estimation mode, only the outermost circle of the signal is used to calculate the noise modulus, which reduces the complexity in the decision-making process, and the SNR estimation value obtained through hard decision is more accurate due to the highest distinguishability of the outermost circle signal.

[0059] In the pilot-assisted mode, since the pilot signal is in the outermost circle, only symbol judgment is needed, the complexity is very low, and the outermost circle signal has the highest distinguishability, so a more accurate SNR estimation value is obtained; and the pilot-assisted mode can effectively utilize the prior information of the pilot to assist in decision-making, greatly improving the accuracy of the SNR estimation.

[0060] The above two working modes can accurately estimate the SNR in high and low SNR, and have strong robustness.

[0061] The known signal energy can be pre-configured, which optimizes the calculation process of the signal energy in the SNR estimation process, has high flexibility, and also saves computing resources.

[0062] The logarithmic value of the noise is obtained by table lookup, and the table lookup process does not involve iteration, so the complexity is very low, and the estimation accuracy can be freely set according to the size of the table, which has high flexibility.

[0063] It can be applied to QPSK, 16QAM, 64QAM and other modulation formats, as well as probability constellation shaping technology, and has a wide range of applications.

[0064] As shown in Figure 5 the simplified principle diagram of the entire calculation process is shown, and the above steps will be further described in combination with two embodiments.

[0065] ​In one embodiment, a look-up table of 20 log10 is generated. For example, log10([1:4096]).

[0066] In the QPSK mode, the power of each bit of the standard constellation is 1, the signal modulus is sqrt(2), the converted signal modulus is sqrt(2 / pi), the decision position is 0, and the addition factor is 1. In the PCS-QAM mode or when the time domain pilot exists, each point of the standard constellation is non-uniformly distributed, but the signal modulus is prior information, and the calculation method thereof is not described herein. In the hardware implementation, the converted signal modulus is taken as an example with a precision of 13 bits, and then 20 log10 is taken, and finally the fixed-point number = 20 log10(floor(sqrt(2 / pi 2^13)) = 76.

[0067] The complex signal is extracted in the N-out-of-1 mode, and N can be selected as 1024 / 4096 / 16384, etc. It is assumed that the obtained complex signal is [1.2+1.1j -1-0.8j]. The real and imaginary parts of the extracted data are extracted in the odd-even order, and the real number signal [1.2 -0.8] is obtained.

[0068] The positive and negative real number signals after the decision are stored in x1=1.2 and x2=-0.8, respectively, by comparison with the decision position 0. The data in x1 are all subtracted by the addition factor 1.2-1=0.2, and the modulus value is obtained as 0.2. The data in x2 are all added by the addition factor -0.8+1=-0.2, and the modulus value is obtained as 0.2.

[0069] The two-way signals are averaged for M times, and it is assumed that the noise modulus value after the averaging is 0.2. In the hardware implementation, the fixed-point number = floor(0.2 2^13) = 1638 is finally obtained with a precision of 13 bits.

[0070] The fixed-point modulus value of the noise is the value at the index=1638 in the look-up table, which is 64. The SNR estimation value in the logarithmic representation is 76-64=12 dB.

[0071] In another embodiment, a look-up table of 20 log10 is generated. For example, log10([1:15000]).

[0072] In the PCS-16QAM mode, the signal modulus obtained by different probability shaping is different, and here, the outermost circle probability is taken as 1 / 8, the middle circle is taken as 3 / 8, and the inner circle is taken as 4 / 8, the signal modulus is 7, and when there is one pilot in every 32 symbols, the signal modulus is 7.0909, the signal modulus after the conversion of sqrt(pi) is sqrt(7.0909 / pi), the decision position is 0, and the addition factor is 3; in the PCS-QAM mode or when there is a time domain pilot, each point of the standard constellation diagram is not uniformly distributed, but the signal modulus is prior information, and the calculation method is not described herein again; in the PCS-16QAM hardware implementation, the signal modulus is taken as an example with 13-bit precision, and 20 log10, the fixed point number = 20 log10(floor(sqrt(7.0909 / pi) 2^13)) = 81.

[0073] The pilot signal is extracted according to the extraction rule, and the pilot extraction can be selected as 1024 / 4096 / 16384, etc.; the complex signal obtained at this time is [3.3+3.1j -3-3.5j]; the real and imaginary parts of the odd-even order extracted data are obtained, and the real number signal [3.3 -3.5] is obtained.

[0074] In the pilot assisted scenario, the decision position is 0, and the positive and negative real number signals after the decision are stored in x1=3.3 and x2=-3.5 respectively; the data in x1 are all subtracted by the addition factor 3.3-3=0.3, and the modulus value is obtained as 0.3; the data in x2 are all added by the addition factor -3.5+3=-0.5, and the modulus value is obtained as 0.5.

[0075] The two-way signals are averaged for M times, and it is assumed that the noise modulus value after the averaging is 0.4, and in the hardware implementation, the 13-bit precision is taken as an example, and finally the fixed point number f = floor(0.4 2^13) = 3276.

[0076] The fixed point modulus value of the noise is the value at the index = 1638 in the table, which is 70, and the SNR estimation value is 82-70=12dB.

[0077] To verify the accuracy of the low complexity SNR estimation method, a series of simulation researches are performed, Figure 6 is the SNR estimation effect diagram under the QPSK modulation format based on the blind estimation mode; Figure 7 is the SNR estimation effect diagram under the QPSK modulation format based on the pilot assisted mode; Figure 8 is the SNR estimation effect diagram under the 16QAM modulation format based on the blind estimation mode; Figure 9 is the SNR estimation effect diagram under the 16QAM modulation format based on the pilot assisted mode;Figure 10 is a SNR estimation effect diagram under PCS-16QAM modulation format based on a blind estimation mode; Figure 11 is a SNR estimation effect diagram under PCS-16QAM modulation format based on a pilot assisted mode. In the above, the horizontal axis is the SNR setting value in the simulation, and the vertical coordinate is the SNR estimation value or estimation error value obtained in the simulation.

[0078] The specific result analysis is as follows: According to different modulation formats, application scenarios are different, and SNR requirements are also different. Therefore, in the simulation, the SNR when QPSK modulation is set to 5-30 dB, and the SNR when the remaining modulation formats is set to 7-30 dB. From the simulation results, it can be seen that under all modulation formats, based on the blind estimation mode and the pilot assisted mode, accurate estimation of 5-30 dB or 7-30 dB can be achieved. Among them, in the low signal-to-noise ratio, the estimation error of the blind estimation mode is not more than 3 dB, and the estimation error of the pilot assisted mode is not more than 2 dB; in the high signal-to-noise ratio, the estimation error of the two modes is not more than 2 dB, and the typical error value is only 1 dB. In actual application, the lookup table (LUT) can be enlarged and more points can be used for estimation to reduce the estimation error.

[0079] As shown in Figure 12 , the DSP processing flow of a typical digital coherent receiver includes resampling, SNR estimation, clock recovery, polarization demultiplexing and the like, and the corresponding modules. Among them, the SNR estimation is the position where the above-mentioned signal-to-noise ratio (SNR) estimation method can be set. The SNR estimation is set before the frame synchronization, and the pilot is not involved in the SNR estimation process, so the signal-to-noise ratio estimation mode needs to be set to the blind estimation mode. As shown in Figure 13 , the SNR estimation is set after the frame synchronization, and the pilot can be involved in the SNR estimation process, so the signal-to-noise ratio estimation mode can be set to the pilot assisted mode, and it also supports setting to the blind estimation mode.

[0080] Therefore, when the signal-to-noise ratio estimation mode is the blind estimation mode, the SNR estimation is used before or after the frame synchronization in the DSP processing flow of the digital coherent receiver; when the signal-to-noise ratio estimation mode is the pilot assisted mode, the SNR estimation is used after the frame synchronization in the DSP processing flow of the digital coherent receiver.

[0081] In a second aspect, the embodiments of the present application also provide an embodiment of a low-complexity signal-to-noise ratio estimation device, which can be used to implement the above-mentioned low-complexity signal-to-noise ratio estimation method. As shown in Figure 14 , the signal-to-noise ratio estimation device includes a signal extraction module, a signal decision module, a noise module value calculation module, and a signal-to-noise ratio (SNR) calculation module.

[0082] The signal extraction module is configured to map a signal to a constellation diagram in a modulation mode in a communication link, and extract an estimated real signal from a received signal according to a preset signal-to-noise ratio estimation mode. When the signal is mapped to the constellation diagram, the signal extraction module can be configured to extract the estimated real signal from the received signal according to the preset signal-to-noise ratio estimation mode. The signal-to-noise ratio estimation mode includes a blind estimation mode and a pilot-assisted mode.

[0083] The signal decision module is configured to decide the estimated real signal according to the estimation mode to obtain positive and negative signals. Specifically, the estimated real signal is decided in different ways according to different signal-to-noise ratio estimation modes.

[0084] The noise module calculation module is configured to perform addition and subtraction operations on the positive and negative signals respectively and take a module average to obtain a linearly represented noise module value.

[0085] The signal-to-noise ratio calculation module is configured to input the noise module value into a preset lookup table, and obtain a logarithmically represented signal-to-noise ratio estimation value by combining a known signal module value with a logarithm value. Specifically, the lookup table is a 20-fold logarithm table with base 10, which is configured to convert the linear noise module value Noise_modu into a logarithmically represented noise power value.

[0086] The functions of each module in the low-complexity signal-to-noise ratio estimation device correspond to the steps in the low-complexity signal-to-noise ratio estimation method embodiment, and the functions and implementation processes will not be repeated here.

[0087] It should be noted that the above sequence numbers of the embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0088] The terms "include" and "have" and any variations thereof in the specification and claims of the present application and the above drawings are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but can optionally include other steps or units not listed or can optionally include other steps or units inherent to the process, method, product or device. The terms "first", "second" and "third" and the like descriptions are used to distinguish different objects, and do not represent the order or limit the types of "first", "second" and "third".

[0089] In the description of the embodiments of the present application, "exemplary", "for example", "e.g." or "for instance" are used on the basis that a person of ordinary skill in the art will be able to bring to mind many examples of a given implementation as the implementation described is exemplary. As the skilled person is able to find many such examples, no exhaustive list of examples is given. No inference should be drawn from the use of "exemplary" or "for instance" that a particular example is preferred or more advantageous than other examples.

[0090] In the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B; "and / or" in the text only means a relationship between associated objects, which means that there can be three relationships, for example, A and / or B, which means that there are three cases of A alone, A and B together, and B alone. In addition, in the description of the embodiments of the present application, "multiple" means two or more than two.

[0091] In some of the processes described in the embodiments of the present application, a plurality of operations or steps are included in a specific order, but it should be understood that these operations or steps can be executed or performed in parallel or in an order different from that in which they appear in the embodiments of the present application. The serial number of the operation is only used to distinguish different operations, and the serial number itself does not represent any execution order. In addition, these processes can include more or fewer operations, and these operations or steps can be executed in sequence or in parallel, and these operations or steps can be combined.

[0092] From the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment method can be realized by means of software and necessary general hardware platform, of course, it can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as a ROM / RAM, a magnetic disk, an optical disk) as described above, and includes a plurality of instructions for causing a terminal device to execute the methods described in the embodiments of the present application.

[0093] The above is only the preferred embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation using the content of the present application specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A low-complexity signal-to-noise ratio estimation method, characterized in that, The method includes: In the communication link, the signal is mapped to a constellation diagram using modulation, and the real signal to be estimated is extracted from the received signal according to the preset signal-to-noise ratio estimation mode. The real number signal to be estimated is determined according to the estimation mode to obtain two signals, positive and negative; The positive and negative signals are added or subtracted respectively, and the average of the results is taken to obtain the linearly represented noise magnitude. The noise magnitude is input into a preset lookup table, and combined with the known logarithmic value of the signal magnitude, a signal-to-noise ratio estimate based on logarithmic representation is obtained.

2. The low-complexity signal-to-noise ratio estimation method as described in claim 1, characterized in that, Extract the real signal to be estimated from the received signal according to the preset signal-to-noise ratio estimation mode, including: The signal-to-noise ratio estimation modes are divided into blind estimation mode and pilot-assisted mode. In blind estimation mode, the complex signal to be estimated is obtained from the received signal according to the preset extraction rules; In pilot-assisted mode, the complex signal to be estimated is obtained from the received signal according to the pilot signal insertion rules specified in the protocol. Extract the real signals to be estimated from the complex signals to be estimated.

3. The low-complexity signal-to-noise ratio estimation method as described in claim 2, characterized in that: In blind estimation mode, the preset sampling rule is 1 out of N, where N is a positive integer; In pilot-assisted mode, the pilot signal insertion rule is to insert one pilot signal for every M data to be transmitted.

4. The low-complexity signal-to-noise ratio estimation method as described in claim 2, characterized in that, The step of extracting the real numbers from the complex signals to be estimated includes: The complex signal to be estimated is obtained. According to the parity arrangement, the real part of the numbers in the odd sequence is taken and the imaginary part of the numbers in the even sequence is taken, thereby obtaining the real signal to be estimated.

5. The low-complexity signal-to-noise ratio estimation method as described in claim 2, characterized in that, The real-valued signal to be estimated is determined according to the estimation mode to obtain two signals, positive and negative, including: When the estimation mode is blind estimation mode, the real signal to be estimated is decided according to the decision position of different modulation formats on the standard constellation diagram, and the positive and negative signals of the outermost ring are obtained. When the estimation mode is pilot-assisted mode, the sign of the real number signal to be estimated is consistent with that of the reference pilot signal before a decision is made to obtain the positive and negative signals of the outermost ring.

6. The low-complexity signal-to-noise ratio estimation method as described in claim 5, characterized in that, When the estimation mode is blind estimation mode, the decision positions for different modulation formats on the standard constellation diagram include: The decision position for QPSK modulation format is 0; The position of the QAM modulation format is determined by the magnitude of the x-coordinate of the two constellation points with the same ordinate on the outermost and second outermost rings. The determination position for the 16QAM modulation format is 2.

7. The low-complexity signal-to-noise ratio estimation method as described in claim 5, characterized in that: When the estimation mode is blind estimation mode, if the real number signal to be estimated is greater than the decision position, the real number signal is decided as a positive signal; If the real signal to be estimated is less than or equal to the decision position, the real signal is decided as a negative signal.

8. The low-complexity signal-to-noise ratio estimation method as described in claim 5, characterized in that: The estimation mode is pilot-assisted mode. When the sign of the real signal to be estimated is the same as that of the reference pilot signal, if the real signal to be estimated is greater than 0, the real signal is determined to be a positive signal. If the real signal to be estimated is less than or equal to 0, the real signal is classified as a negative signal; The estimation mode is pilot-assisted mode. When the sign of the real signal to be estimated is inconsistent with that of the reference pilot signal, if the real signal to be estimated is greater than 0, the real signal is determined to be a negative signal. If the real number signal to be estimated is less than or equal to 0, the real number signal is classified as a positive signal.

9. The low-complexity signal-to-noise ratio estimation method as described in claim 5, characterized in that, The positive and negative signals are added or subtracted separately, and then averaged modulowise to obtain the linearly represented noise magnitude, including: The addition factor is determined according to the modulation format. The addition factor is subtracted from the positive signal and the modulus is taken from the negative signal. The addition factor is added to the negative signal and the modulus is taken from the positive signal. The results of multiple modulus taking are averaged to obtain the linearly represented noise modulus.

10. The low-complexity signal-to-noise ratio estimation method as described in claim 5, characterized in that, The noise magnitude is input into a preset lookup table, and combined with the known logarithmic values ​​of the signal magnitude, a signal-to-noise ratio estimate based on logarithmic representation is obtained, including: According to the lookup table, find the noise power value based on logarithmic representation corresponding to the noise modulus; Obtain the logarithmic representation of the signal power value of the locally known transmitted signal; Subtracting the signal power value from the noise power value yields a signal-to-noise ratio estimate based on logarithmic representation.

11. The low-complexity signal-to-noise ratio estimation method as described in claim 10, characterized in that: The lookup table is a base-10 logarithmic table multiplied by 20.

12. The low-complexity signal-to-noise ratio estimation method as described in any one of claims 2-11, characterized in that: When the signal-to-noise ratio (SNR) estimation mode is blind estimation mode, the low-complexity SNR estimation method is used before or after frame synchronization in the DSP processing flow of the digital coherent receiver. When the signal-to-noise ratio (SNR) estimation mode is pilot-assisted mode, the low-complexity SNR estimation method is used after frame synchronization in the DSP processing flow of a digital coherent receiver.

13. A signal-to-noise ratio (SNR) estimation apparatus based on the low-complexity SNR estimation method according to any one of claims 1-11, characterized in that, The signal-to-noise ratio estimation device includes: The signal extraction module is used to map the signal to a constellation diagram in the communication link using a modulation method, and extract the real signal to be estimated from the received signal according to a preset signal-to-noise ratio estimation mode. The signal decision module is used to make a decision on the real number signal to be estimated according to the estimation mode, and obtain positive and negative signals. The noise modulus calculation module is used to perform addition and subtraction operations on the positive and negative signals respectively and take the modulus average to obtain the linearly represented noise modulus. The signal-to-noise ratio (SNR) calculation module is used to input the noise magnitude into a preset lookup table and combine it with the known logarithmic value of the signal magnitude to obtain an estimated SNR value based on logarithmic representation.

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