A method, device and storage medium for measuring channel signal-to-noise ratio

Abstract

The application relates to the field of communication, in particular to a method for measuring channel signal-to-noise ratio, equipment and storage medium, wherein the method for measuring channel signal-to-noise ratio comprises the following steps: a preamble sequence is added before a service data block to form a transmitting signal; a receiving signal corresponding to the transmitting signal is sampled at a receiving end to obtain a sampling signal; the sampling signal is synchronized; the sampling signal is demodulated to obtain a demodulated signal, and superposition of the sampling signal is completed, the demodulated signal comprises a preamble sequence demodulated signal and a service data block demodulated signal; SNR_headline is obtained, the SNR_headline is the SNR of the preamble sequence demodulated signal; SNR_trline is obtained, the SNR_trline is the SNR of the service data block demodulated signal; and the SNR of the channel is determined according to the SNR_headline, the SNR_trline and a preset SNR calculation rule. The application has the effect of improving the calculation accuracy of the signal-to-noise ratio.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of communication, in particular to a method for measuring channel signal-to-noise ratio, a computer device and a computer readable storage medium. BACKGROUND

[0002] The quality estimation of a channel is crucial for a wireless communication system, and can be used to select the most suitable channel by measuring the real-time change of channel characteristics, so that the system can change with the environment and maintain good communication quality throughout. Real-time channel estimation technology (RTCE) is a core technology for developing wireless communication systems, which tests some appropriate parameters of a group of communication channels in real time, and then uses the obtained numbers to accurately describe the state of the group of channels and the ability to communicate certain communication services, that is, to study which frequencies best meet the user's usage requirements, while also considering the values of channel parameters such as multipath spread, received information energy, Doppler spread, signal-to-noise ratio, and different quality requirements of communication.

[0003] The purpose of channel estimation research is to enable a digital receiver to seek the "best" estimation method with the least cost. Generally, the results of signal-to-noise ratio estimation are obtained by using a certain number of observed values of symbols and then performing mean value calculation. As can be seen from the standard for proposing a signal-to-noise ratio estimation algorithm, some traditional classic signal-to-noise ratio estimation algorithms, such as singular value decomposition of autocorrelation matrix, data fitting estimation method, and high-order cumulant algorithm, do not meet the requirements. The construction and decomposition of a matrix and the calculation of high-order statistics of a received signal not only require the use of many hardware resources, but also cannot ensure the calculation speed, and the available conditions of the data fitting algorithm are very narrow. Moreover, the following problems exist: (1) the data collection period is too long and the real-time performance is insufficient; (2) the calculation time is long and the time complexity of the algorithm is high; (3) there is a large estimation error; (4) it is not suitable for cases where the number of available symbols is not large; and (5) the frequency of measurement failure is high. SUMMARY

[0004] In order to at least solve the above problems, the present application provides a method for measuring channel signal-to-noise ratio, which uses all the signal-to-noise ratios estimated based on a preamble sequence and the signal-to-noise ratios estimated based on a service data block to calculate the overall signal-to-noise ratio of the channel, increases the number of samples of available data, and thus makes the error of the final estimation result smaller. At the same time, all the received signals can be used to estimate the signal-to-noise ratio, so there is no need to wait for the arrival of available data, thereby shortening the data collection period and making the real-time performance stronger.

[0005] The present application achieves the above-mentioned purposes by adopting the following technical solutions:

[0006] In a first aspect, the present application provides a method for measuring channel SNR, comprising the following steps: adding a preamble sequence before a service data block to form a transmission signal; sampling a receiving signal corresponding to the transmission signal at a receiving end to obtain a sampling signal; synchronizing the sampling signal; demodulating the sampling signal to obtain a demodulated signal and complete superposition of the sampling signal, wherein the demodulated signal comprises a preamble sequence demodulated signal and a service data block demodulated signal; obtaining SNR_headline, wherein the SNR_headline is SNR of the preamble sequence demodulated signal; obtaining SNR_trline, wherein the SNR_trline is SNR of the service data block demodulated signal; and determining SNR of the channel according to the SNR_headline, the SNR_trline and a preset SNR calculation rule.

[0007] By using the above technical solution, the preamble sequence and the service data block can be used to calculate the SNR, while in the prior art, only one of the preamble sequence and the service data block can be selected to calculate the SNR, so that the SNR cannot be calculated until the corresponding data arrives, and there is a time interval in between, so that the period of collecting data is too long, the real-time performance is insufficient, and the time complexity of the algorithm is high. However, the above technical solution does not need to wait, but directly uses the received data to calculate, so that the calculation time is short, the real-time performance is strong, and the time complexity of the algorithm is low. At the same time, since the preamble sequence and the service data block are used together to calculate the SNR, more signals can be smoothed to estimate the SNR, and the error can be effectively reduced. In addition, when the number of available signal symbols is not large, for example, only the preamble sequence sequence is available without the service data block, or the preamble sequence sequence is disturbed, there is still available signal to calculate the SNR. At the same time, compared with using only the preamble sequence or the service data block, using the preamble sequence and the service data block together to measure the SNR can increase the correct measurement results in a fixed time period, so that the frequency of measurement failure can be reduced.

[0008] Optionally, the method further comprises: performing channel estimation on the sampling signal; and performing equalization processing on the sampling signal.

[0009] By using the above technical solution, the case of processing the multipath signal as noise can be effectively avoided, so that the SNR can be effectively and accurately estimated in the case of multipath communication.

[0010] Optionally, the demodulating the sampling signal comprises: performing constant envelope demodulation or phase demodulation on the sampling signal.

[0011] Optionally, the obtaining the SNR_headline comprises: taking the preamble sequence sequence as x(n), and obtaining the SNR_headline according to the following formula: calculating power of the preamble sequence demodulation signal; according to the formula: calculating total power of the preamble sequence demodulation signal and preamble sequence noise, wherein P ALL is the total power of the preamble sequence demodulation signal and preamble sequence noise; according to the formula: P N = 2 (P ALL -P sx ) calculating power of the preamble sequence noise, wherein P N is the power of the preamble sequence noise; according to the formula: calculating signal-to-noise ratio of the preamble sequence demodulation signal.

[0012] By adopting the technical scheme, under the condition of low signal-to-noise ratio, the accurate SNR can still be measured through the preamble sequence demodulation signal.

[0013] Optionally, the obtaining of the SNR_trline comprises: taking the service data block demodulation signal as y (n) ; according to the formula: calculating power signal P S , wherein P S is the power of the service data block demodulation signal; according to the formula: calculating total power of the service data block demodulation signal and service data noise, wherein P ALL is the total power of the service data block demodulation signal and service data noise; according to the formula: P N = 2 (P ALL -P S ) calculating power of the service data noise, wherein P N is the power of the service data noise; according to the formula: calculating signal-to-noise ratio of the service data block demodulation signal.

[0014] Optionally, the preset SNR calculation rule comprises: in response to the SNR_headline being greater than SNR_TH, calculating the SNR according to the formula: SNR = SNR_headline * a + SNR_trline * (1-a), wherein a is a proportion value calculated through a preset proportion calculation rule; in response to the SNR_headline being less than or equal to SNR_TH, then SNR = SNR_headline; and SNR_TH is a preset SNR threshold.

[0015] By adopting the technical scheme, the proportion of the preamble sequence and the service data block in the calculation of the SNR can be adjusted according to the historical accuracy of the preamble sequence and the service data block in the calculation of the SNR, so that the calculation result of the SNR is more accurate.

[0016] Optionally, the preset proportion calculation rule is: in response to the SNR_headline being greater than SNR_TH and less than 2*SNR_TH, then wherein M is a preset proportion coefficient, length(head_train) is the length of the preamble sequence, and length(data) is the length of the service data block; in response to the SNR_headline being greater than or equal to 2*SNR_TH, then a=0.5.

[0017] By using the above technical solution, the proportion of SNR_headline in SNR calculation can be determined according to the proportion of the preamble sequence in the transmitted signal, and the calculation result is more reasonable; and the SNR_headline is more accurate when it is in a certain value range, so the proportion of SNR_headline in SNR calculation can be increased by adjusting the value of M in the value range, so that the calculation result of SNR is more accurate.

[0018] Optionally, the range of SNR_TH is 2dB-10dB.

[0019] By using the above technical solution, when the signal-to-noise ratio is lower than 2dB-10dB, the value of SNR calculated by the service data block starts to be inaccurate and has a lower limit, while the SNR measured based on the preamble sequence sequence is still accurate, so by reasonably setting the value of SNR_TH, the accuracy of SNR can be improved.

[0020] In a second aspect, the present application provides a computer device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program.

[0021] In a third aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of any of the above methods when executed by a processor.

[0022] In summary, compared with the existing method of calculating the channel signal-to-noise ratio only by using the preamble sequence or the service data block, the technical solution of the present application calculates SNR by using the preamble sequence and the service data block together, without selecting data and waiting for the arrival of useful data, and directly using the received data for calculation, so as to save time and improve real-time performance, and the time complexity of the algorithm is low. In addition, since more sampling signals can be smoothed to estimate the signal-to-noise ratio, the estimation error can be effectively reduced. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 is a flowchart of the method for measuring the channel signal-to-noise ratio in one embodiment of the present application;

[0024] Figure 2 is a structure diagram of a transmitted signal in one embodiment of the present application;

[0025] Figure 3 is a simulation result diagram of a signal-to-noise ratio estimation mean in one embodiment of the present application; and

[0026] Figure 4 is a simulation result diagram of a signal-to-noise ratio estimation variance in one embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to make the objects, technical solutions and advantages of the present application clearer, the following further describes the present application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not used to limit the present application. Figure 1 - the accompanying drawings, and Figure 4 and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not used to limit the present application.

[0028] The main purpose of the RTCE is to investigate the frequency that is expected to be selected in real time. If this purpose is to be achieved, the method of channel estimation and the starting point of thinking should use a different path from that of long-term prediction and short-term prediction. The RTCE selects the frequency band and frequency used for public communication without considering the structure and detail changes of the ionosphere, selects a specific communication mode from the communication mode, and processes the signals of various frequencies received by the receiving end in real time, while also considering the values of channel parameters such as multipath spread, received information energy, Doppler spread, signal-to-noise ratio, and different quality requirements of communication. The real-time frequency prediction has the following advantages: 1. It can increase the utilization rate of high-quality communication lines; 2. It can ensure the high quality of the communication circuit and improve the correctness of the message transmission; 3. It can increase the number of users by using real-time frequency allocation and calling; 4. In frequency hopping communication, the frequency hopping repetition can realize the maximum ratio MRC combination based on SNR.

[0029] There are many signal-to-noise ratio calculation methods in the prior art, such as square signal-to-noise variance ratio estimation (SNV), which mainly analyzes BPSK signals in real AWGN channels. This method belongs to the received data aided method (RXDA). However, in fact, the SNV estimation method is a kind of ML estimation method, and the special feature of the SNV estimation method is that the ML estimation method needs to sample N times per symbol of information at the receiving end of the matched filter, but the SNV estimation only needs to perform optimal sampling at the receiving end of the matched filter. Similarly, a correction factor can be used to reduce the certain deviation existing in the original algorithm. Beaulieu Dol and Pauluzzi first gave the initial SNV RxDA estimation formula for BPSK signals, then gave how to reduce the difference in the estimation formula, and then applied the estimation method to high-order modulation signals in complex channels.

[0030] When the maximum likelihood estimation theory (MLE) is derived, it is shown that the maximum likelihood estimation of the signal-to-noise ratio (SNR) for BPSK signals in real AWGN channels is an in-service algorithm. They first analyze two parameters of the SNR, S and N, where S is the signal power and N is the noise power. By proper normalization approximation, the SNR calculation formula SNR = S / N is obtained. Then the maximum likelihood estimation of the parameters is derived to obtain their ratio, i.e., the SNR estimation. If the channel is a real channel, the estimation method has some deviation. Therefore, the researchers give a correction factor, and the deviation is reduced through the correction factor. Later, the researchers further applied the ML estimation method to high-order signal modulation mode MPSK signals in complex Gaussian white noise channels, and gave the modified solution.

[0031] The separation symbol matrix (SSME) estimation method is for BPSK signals in a wideband AWGN channel, which can only estimate the SNR of BPSK in the AWGN channel. The second-order and fourth-order moment estimation method uses the relationship between the second-order and fourth-order moments of the signal to estimate the SNR. It is an adaptive algorithm that does not require carrier phase recovery and does not require decision at the receiver. The signal variance ratio (SVR) estimation method is also based on the multi-order moment algorithm and is often used for channel quality control in multipath fading channels. This method is suitable for MPSK signals and is difficult to apply to other forms of modulation. The signal-to-interference ratio estimation is applied in narrowband time division multiple access (TDMA) cellular systems. The correlation matrix of the received signal is generated by a training sequence, and the power estimation of the signal and the noise is based on the signal subspace (SB) decomposition, so that the calculated real-time SNR is correct.

[0032] The above-mentioned methods are all based on the same model, respectively studying real channels and complex channels. Finally, the mean square error (MSE) is used to compare the performance of each algorithm. The performance of the simulation estimation method is compared with the Cramer-Rao bound (CRB) in real and complex Gaussian white noise channels. It can be seen that the M2M4 method, the SNV method and the ML method have better performance. However, they all cannot achieve high enough real-time performance and have large estimation error.

[0033] Figure 1 is a flowchart of a method for measuring the channel SNR in an embodiment of the present application; Figure 2 is a structure diagram of a transmitted signal in an embodiment of the present application. As Figure 1As shown, the method for measuring channel SNR disclosed by the embodiment of the present application comprises steps S101-S107. In step S101, a preamble sequence is added before a service data block to form a transmission signal, where the preamble sequence can be a set fixed sequence. In an embodiment, the structure of the transmission signal can be as shown in the following figure. Figure 2 As shown, a transmission signal can comprise 1286 symbols, where the preamble sequence (head_train in the figure) comprises 255 symbols, the service data block (data in the figure) comprises 1024 symbols, the start system block comprises 3 symbols, and the end system block comprises 4 symbols.

[0034] In step S102, a sampling signal is obtained by sampling a reception signal corresponding to the transmission signal at a receiving end. In actual application, the reception signal can be sampled at the best sampling time by an ADC circuit. After the sampling signal is obtained, in step S103, the sampling signal is synchronized. Since the preamble sequence is known, the synchronization can be performed by correlation operation of the preamble sequence, so as to find the preamble sequence, and finally the carrier synchronization and time synchronization of the sampling signal (including the preamble sequence) can be realized. In an embodiment, the sampling signal can also be subjected to channel estimation and equalization processing, so as to effectively eliminate the interference of multipath signals on the SNR estimation, and thus the accuracy of the SNR can be improved in the case of multipath communication.

[0035] In step S104, the sampling signal is demodulated to obtain a demodulation signal and complete the superposition of the sampling signal, such as the signal superposition of two symbol sampling points, and the vector superposition in two symbols can improve the anti-noise performance in a complex environment. The demodulation signal comprises a preamble sequence demodulation signal and a service data block demodulation signal. In an application scenario, the method for demodulating the sampling signal can be constant envelope demodulation (such as GMSK) or phase demodulation (such as QPSK, DPSK, PSK and 8PSK, etc.), and the above demodulation mode is more suitable for the technical solution of the present application.

[0036] In step S105, SNR_headline is calculated, which is the SNR of the preamble sequence demodulation signal. The method for calculating SNR_headline is referred to as preamble estimation algorithm. Specifically, it can comprise steps S1051-S1054.

[0037] In step S1051, the preamble sequence is denoted as x(n), where x(n) can be an M sequence and can be mapped to ±1, and the preamble sequence demodulation signal is denoted as y(n). According to the formula: Calculate the power of the demodulated signal (with the modulation information of the preamble sequence removed) of the preamble sequence. In step S1052, according to the formula: Calculate the total power P of the demodulated signal and the noise of the preamble sequence. ALL This represents the total power of the demodulated signal and noise of the preamble sequence. In step S1053, according to the formula: P N =2(P ALL -P sx Calculate the power of the preamble sequence noise, P. N The power of the preamble sequence noise. Those skilled in the art should understand that the SNR is the quotient of the discrete signal power and the discrete noise power input to the decision circuit at the optimal sampling time. Therefore, in step S1054, according to the formula: Calculate the signal-to-noise ratio of the demodulated signal from the preamble sequence.

[0038] In step S106, SNR_trline is calculated. SNR_trline is the SNR of the demodulated signal of the service data block. The method for calculating SNR_trline here is simply referred to as a blind estimation algorithm. Specifically, this may include steps S1061-S1064. In step S1061, the demodulated signal of the service data block is denoted as y(n), according to the formula: Calculate the power signal P S The P S The power of the demodulated signal for the service data block. In step S1062, according to the formula: Calculate the total power of the demodulated signal and the noise in the service data block, where P... ALL The total power of the demodulated signal and service data noise of the service data block. In step S1063, according to the formula: P N =2(P ALL -P S ) Calculate the power of the service data noise, wherein P S The power of the service data noise. At step S1064, according to the formula: Calculate the signal-to-noise ratio of the demodulated signal of the service data block.

[0039] At step S107, SNR of the channel is determined according to SNR_headline, SNR_trline and preset SNR calculation rule. In one embodiment, the preset SNR calculation rule includes: when SNR_headline is greater than SNR_TH, SNR is calculated according to the formula: SNR=SNR_headline*a+SNR_trline*(1-a); when SNR_headline is less than or equal to SNR_TH, SNR=SNR_headline, wherein SNR_TH is a preset SNR threshold, and SNR_TH ranges from 2dB to 10dB, for example, 5dB.

[0040] For parameter a, it is calculated according to preset proportion calculation rule, specifically, when SNR_headline is greater than SNR_TH and less than 2*SNR_TH, a=SNR_headline / 2*SNR_TH. wherein M is a preset proportion coefficient, and M ranges from 2 to 3, length(head_train) is the length of the preamble sequence, and length(data) is the length of the service data block. The proportion calculation rule can determine the proportion of SNR_headline in SNR calculation according to the proportion of the length of the preamble sequence in the length of the transmission signal (excluding the start system block and the end system block), so that the calculation result is more reasonable. Meanwhile, because SNR_headline is more accurate within a certain value range, the proportion of SNR_headline in SNR calculation can be increased by multiplying the coefficient M within the value range, so that the calculation result of SNR is more accurate; when SNR_headline is greater than or equal to 2*SNR_TH, a=0.5, that is, the proportions of SNR_headline and SNR_trline in SNR calculation are the same.

[0041] The reason for setting SNR threshold (SNR_TH) is as follows: the estimation performance of the preamble frequency estimation algorithm and the blind estimation algorithm is simulated, and the simulation results of the SNR estimation mean value are as follows: Figure 3 When the signal-to-noise ratio is less than 5dB, the mean value of the blind estimation algorithm starts to be inaccurate, and there is a lower limit, while the preamble frequency estimation algorithm based on the preamble sequence is still very accurate. When the signal-to-noise ratio is 5-15dB, the mean values of the two algorithms are very accurate. When the signal-to-noise ratio is greater than 15dB, the mean values of the two algorithms start to be inaccurate, and there is an upper limit. Because SNR is much higher than the signal demodulation threshold when SNR is greater than 15dB, the bit error rate is generally equal to zero at this time, so the accuracy of SNR measurement is not important at this time, and the MRC (Maximal Ratio Combining) based on SNR measurement is basically not affected.

[0042] On the other hand, the algorithm for SNR estimation based on the preamble sequence is accurate, but the preamble sequence of each transmitted signal data block is much shorter than the service data block, so the variance evaluated by the preamble estimation algorithm is larger, and the SNR estimation accuracy is lower. Figure 4 The SNR estimation variance simulation results shown in the following table can be seen. From the table, it can be seen that the variance of the blind estimation algorithm is basically unchanged, and the variance of the preamble sequence algorithm becomes smaller and smaller with the increase of the SNR. Figure 4 As can be seen from the table, the variance of the blind estimation algorithm is basically unchanged, and the variance of the preamble sequence algorithm becomes smaller and smaller with the increase of the SNR. Therefore, when the SNR_headline is greater than SNR_TH (SNR_TH = 5 dB), the service data block and the preamble sequence are both involved in the SNR measurement. When the SNR value is small, only the preamble sequence is involved in the SNR measurement and calculation, so that the accuracy of the SNR measurement is higher, the variance is small, the jitter of the SNR measurement is small, and the SNR value can be calculated quickly, so that the real-time performance of the SNR measurement is met. Moreover, the SNR measurement is performed after signal equalization, which eliminates the influence of multipath signals and makes the measurement more accurate.

[0043] It should be noted that in the above SNR calculation process, the sampling signal is changed into a real number, and the sampling signal collected by the ADC is a complex number, which is divided into a real part I path and an imaginary part Q path. However, when calculating the SNR, the I path signal and the Q path signal need to be changed into a signal. The Q path signal exists in the odd position, and the I path signal exists in the even position, so that the overall data length is doubled.

[0044] In the technical solution of the present application, the SNR_headline estimated based on the preamble sequence and the SNR_trline estimated based on the service data block are all used for calculating the SNR, which is equivalent to increasing the sample number of the reference data, so that the final estimation result is more accurate.

[0045] In addition, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program.

[0046] Meanwhile, the present application also provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of any of the above methods when executed by a processor.

[0047] The embodiments of the specific implementation are the preferred embodiments of the present application, but do not limit the protection scope of the present application, wherein the same parts are indicated by the same reference numerals. Therefore: any equivalent changes made according to the structure, shape and principle of the present application should be covered within the protection scope of the present application.

Claims

1. A method of measuring a channel signal-to-noise ratio, characterized by, The method comprises the following steps: adding a preamble sequence before a service data block to form a transmission signal; sampling a receiving signal corresponding to the transmission signal to obtain a sampling signal at a receiving end; synchronizing the sampling signal; demodulating the sampling signal to obtain a demodulation signal and complete superposition of the sampling signal, the demodulation signal comprising a preamble sequence demodulation signal and a service data block demodulation signal; obtaining SNR_headline, the SNR_headline being SNR of the preamble sequence demodulation signal; obtaining SNR_trline, the SNR_trline being SNR of the service data block demodulation signal; determining SNR of a channel according to the SNR_headline, the SNR_trline and a preset SNR calculation rule; the preset SNR calculation rule comprising: in response to the SNR_headline being greater than SNR_TH, calculating SNR according to a formula: SNR=SNR_headline*a+SNR_trline*(1-a), the a being a proportion value calculated through a preset proportion calculation rule; in response to the SNR_headline being less than or equal to SNR_TH, SNR=SNR_headline the SNR_TH being a preset SNR threshold value; the preset proportion calculation rule comprising: in response to the SNR_headline being greater than SNR_TH and less than 2*SNR_TH, then wherein M is a preset proportional coefficient, length (head train) is the length of the preamble sequence, and length (data) is the length of the service data block. in response to the SNR_headline being greater than or equal to 2*SNR_TH, a=0.

5.

2. The method for measuring channel SNR according to claim 1, characterized in that, The method further comprises: performing channel estimation on the sampling signal; and performing equalization processing on the sampling signal.

3. The method of measuring channel signal-to-noise ratio according to claim 1, wherein, The demodulating the sampling signal comprises performing constant envelope demodulation or phase demodulation on the sampling signal.

4. The method of measuring a channel signal-to-noise ratio according to any one of claims 1-3, wherein, The acquiring SNR_headline comprises: taking a preamble sequence as x(n), and calculating the SNR_headline according to the formula: calculating the power of the preamble sequence demodulation signal; The total power of the preamble sequence demodulation signal and the preamble sequence noise is calculated according to the formula: The total power of the preamble sequence demodulation signal and the preamble sequence noise is calculated according to the formula: The total power of the preamble sequence demodulation signal and the preamble sequence noise is calculated according to the formula: The power of the preamble sequence noise is calculated according to the formula: is the power of the preamble sequence noise.​ According to the formula: SNR_headline = 10 log10 (Pilot(n) / N0) The signal-to-noise ratio of the preamble sequence demodulation signal is calculated; and the service data block demodulation signal is denoted as y(n).

5. The method of measuring a channel signal-to-noise ratio according to any one of claims 1-3, wherein, The obtaining SNR_trline comprises: denoting the service data block demodulation signal as y(n); According to the formula: Calculate power signal The The power of the demodulated signal of the service data block; The total power of the service data block demodulation signal and the service data noise is calculated according to the formula: The total power of the service data block demodulation signal and the service data noise is calculated according to the formula:​ The power of the traffic data noise is calculated according to the formula: is the power of the traffic data noise.​ According to the formula: SNR_trline= Calculate the signal-to-noise ratio of the demodulated signal of the service data block.

6. The method for measuring a channel SNR according to claim 1, wherein, the SNR_TH having a value range of 2dB-10dB. 7.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-6. The processor executes the computer program to implement the steps of the method according to any one of claims 1-6.

8. A computer readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method according to any one of claims 1-6.

Images