CN116566516A - Method, device and storage medium for measuring channel signal-to-noise ratio - Google Patents
Method, device and storage medium for measuring channel signal-to-noise ratio Download PDFInfo
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- H04B17/309—Measuring or estimating channel quality parameters
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Abstract
The present application relates to the field of communications, and in particular, to a method, an apparatus, and a storage medium for measuring a signal-to-noise ratio of a channel, where the method for measuring the signal-to-noise ratio of the channel includes the following steps: adding a pre-pilot sequence in front of a service data block to form a transmitting signal; sampling a receiving signal corresponding to the transmitting signal at a receiving end to obtain a sampling signal; synchronizing the sampling signals; demodulating the sampling signal to obtain a demodulation signal, and finishing superposition of the sampling signal, wherein the demodulation signal comprises a front pilot sequence demodulation signal and a service data block demodulation signal; obtaining an SNR_header, wherein the SNR_header is the SNR of a demodulation signal of a front pilot sequence; obtaining an SNR_trline, wherein the SNR_trline is the SNR of a demodulation signal of a service data block; and determining the SNR of the channel according to the SNR_header line, the SNR_trline and a preset SNR calculation rule. The method and the device have the effect of improving the signal-to-noise ratio calculation accuracy.
Description
Technical Field
The present application relates to the field of communications, and in particular, to a method, a computer device, and a computer readable storage medium for measuring a signal-to-noise ratio of a channel.
Background
The quality estimation of the channel is crucial for the wireless communication system, and the best-fit channel frequency channel can be freely selected by measuring the channel characteristic change in real time, so that the system can change along with the change of the environment, and the good communication quality can be kept all the way. The real-time channel estimation technique RTCE (Real Time Channel Evaluation) is a core technique for developing a wireless communication system, and is characterized in that a set of appropriate parameters of a communication channel are tested in real time, and then the obtained numbers are used for accurately describing the state of the set of channels and the capability of conveying a certain communication service, namely, researching which frequencies are most suitable for users to use, and considering the values of the channel parameters such as multipath broadening, received information energy, doppler broadening, signal-to-noise ratio and the like and the different quality requirements of communication.
The purpose of the channel estimation study is to have the digital receiver seek the "best" estimation method with minimal cost. Generally, the signal-to-noise ratio estimation results are all obtained by averaging the observed values of a certain number of symbols. From the proposed standard of the snr estimation algorithm, it can be seen that some classical snr estimation algorithms, such as autocorrelation matrix singular value decomposition, data fitting estimation, and higher order cumulant algorithm, are not satisfactory. Constructing and decomposing the matrix and solving the high order statistics of the received signals not only requires the use of a lot of hardware resources, but also cannot ensure the calculation speed, and the available conditions of the data fitting algorithm are not very wide. And there are the following problems: the method comprises the following steps of (1) excessively long data collection period and insufficient real-time performance; (2) long calculation time and high time complexity of algorithm; (3) has larger estimation error; 4) Not applicable in cases where the number of available symbols is not large; (5) the frequency of measurement failure is high.
Disclosure of Invention
In order to solve at least the above problems, the present application provides a method for measuring a signal-to-noise ratio of a channel, wherein the signal-to-noise ratio estimated based on a previous pilot sequence and the signal-to-noise ratio estimated based on a service data block are all used for calculating the overall signal-to-noise ratio of the channel, and the number of samples of available data is increased, so that the error of the final estimation result is smaller; meanwhile, the received signals can be used for estimating the signal-to-noise ratio, so that the arrival of available data is not required to be waited, the period of collecting the data is shortened, and the instantaneity is higher.
The application adopts the following technical scheme to realize the purposes:
in a first aspect, the present application provides a method of measuring signal-to-noise ratio of a channel, comprising the steps of: adding a pre-pilot sequence in front of a service data block to form a transmitting signal; sampling a receiving signal corresponding to the transmitting signal at a receiving end to obtain a sampling signal; synchronizing the sampling signals; demodulating the sampling signal to obtain a demodulation signal and finishing superposition of the sampling signal, wherein the demodulation signal comprises a front pilot sequence demodulation signal and a service data block demodulation signal; obtaining an SNR_header, wherein the SNR_header is the SNR of the demodulation signal of the front pilot sequence; obtaining an SNR_trline, wherein the SNR_trline is the SNR of the demodulation signal of the service data block; and determining the SNR of the channel according to the SNR_header line, the SNR_trline and a preset SNR calculation rule.
By adopting the technical scheme, the method can be used for calculating the SNR no matter the front pilot sequence or the service data block, but only one of the front pilot sequence and the service data block is selected to calculate the SNR in the prior art, so that the SNR can be calculated only after corresponding data arrive, a time interval exists in the middle, the period for collecting the data is overlong, the instantaneity is insufficient, the time complexity of the algorithm is high, the technical scheme does not need to wait, the received data is directly utilized for calculation, and the method has the advantages of short calculation time, strong instantaneity and low time complexity of the algorithm. Meanwhile, as the signal-to-noise ratio is calculated by using the pre-pilot sequence and the service data block together, more signals can be smoothed to estimate the signal-to-noise ratio, and errors can be effectively reduced. In addition, when the number of available signal symbols is not large, for example, only the pre-pilot sequence has no traffic data block, or the pre-pilot sequence is interfered with, there is still a signal available to calculate SNR. Meanwhile, the co-measurement of SNR using the pre-pilot sequence and the traffic data block may increase the correct measurement result within a fixed period of time, compared to using only the pre-pilot sequence or the traffic data block, and thus may reduce the frequency of measurement failure.
Optionally, the method further comprises: performing channel estimation on the sampling signal; and carrying out equalization processing on the sampled received signals.
By adopting the technical scheme, the situation that the multipath signals are treated as noise can be effectively avoided, so that the signal-to-noise ratio can be effectively and accurately estimated under the condition of multipath communication.
Optionally, demodulating the sampled signal includes: and carrying out constant envelope demodulation or phase demodulation on the sampling signal.
Optionally, the acquiring snr_header includes: the pre-symbol pilot sequence is x (n) according to the formula:calculating the power of a demodulation signal of a pilot sequence before calculation; according to the formula:calculating the total power of the demodulation signal of the pilot sequence and the noise of the pilot sequence, wherein P is ALL Demodulating the total power of the signal and the noise of the front pilot sequence for the front pilot sequence; according to the formula: p (P) N ＝2(P ALL -P sx ) Calculating the power of the pre-pilot sequence noise, said P N Power for the noise of the front pilot sequence; according to the formula:The signal-to-noise ratio of the pre-pilot sequence demodulated signal is calculated.
By adopting the technical scheme, under the condition of low signal-to-noise ratio, the accurate SNR can still be measured by demodulating the signal through the pre-pilot sequence.
Optionally, the obtaining snr_line includes: the demodulation signal of the service data block is recorded as y (n); according to the formula:calculating the power signal P S The P is S Demodulating the power of the signal for the traffic data block; according to the formula:Calculating the total power of the demodulation signal of the service data block and the service data noise, wherein P is as follows ALL Demodulating the total power of the signal and the service data noise for the service data block; according to the formula: p (P) N ＝2(P ALL -P S ) Calculating the power of service data noise, wherein P is N Power for traffic data noise; according to the formula:And calculating the signal to noise ratio of the service data block demodulation signal.
Optionally, the preset SNR calculation rule includes: in response to the snr_header being greater than snr_th, according to the formula: snr=snr_header_a+snr_trline (1-a) calculating SNR, where a is a proportional value calculated by a preset proportional calculation rule; in response to the snr_header being less than or equal to snr_th, then snr=snr_header; the snr_th is a preset SNR threshold.
By adopting the technical scheme, the proportion of the front pilot sequence and the business data block in the calculation of the SNR can be adjusted according to the historical accuracy of the front pilot sequence and the business data block in the calculation of the SNR, so that the calculation result of the SNR is more accurate.
Optionally, the preset proportion calculation rule is: in response to the snr_header being greater than snr_th and less than 2 x snr_th, thenWherein M is a preset proportionality coefficient, length (head_train) is the length of a front pilot sequence, and length (data) is the length of a service data block; in response to the snr_header being greater than or equal to 2 x snr_th, a=0.5.
By adopting the technical scheme, the proportion of the SNR_header in the SNR calculation can be determined according to the proportion of the preamble sequence in the transmission signal, and the calculation result is more reasonable; further, since snr_header is more accurate when it is within a certain range, the specific weight of snr_header in SNR calculation can be increased by adjusting the value of M within the range, thereby making the SNR calculation result more accurate.
Optionally, the snr_th ranges from 2dB to 10dB.
By adopting the technical scheme, when the signal-to-noise ratio is lower than 2dB-10dB, the value of the SNR calculated by the service data block starts to be inaccurate, the lower limit exists, and the measured SNR based on the front pilot sequence is still accurate, so that the accuracy of the SNR can be improved by reasonably setting the value of the SNR_TH.
In a second aspect, the present application provides a computer device comprising a memory storing a computer program and a processor implementing the steps of any of the methods described above when the computer program is executed by the processor.
In a third aspect, the present application provides a computer readable storage medium storing a computer program which when executed by a processor performs the steps of any of the methods described above.
In summary, compared with the existing method for calculating the signal-to-noise ratio of the channel by only selecting the front pilot sequence or the service data block, the technical scheme of the application utilizes the front pilot sequence and the service data block to jointly calculate the SNR, does not need to select data, does not need to wait for the arrival of useful data, and directly utilizes the received data to calculate, thereby saving time, improving instantaneity and having low time complexity of an algorithm. In addition, as more sampling signals can be smoothed to estimate the signal-to-noise ratio, estimation errors can be effectively reduced.
Drawings
FIG. 1 is a flow chart of a method of measuring channel signal-to-noise ratio in one embodiment of the present application;
FIG. 2 is a schematic diagram of a structure of a transmission signal according to one embodiment of the present application;
FIG. 3 is a schematic diagram of simulation results of a mean value of SNR estimation according to one embodiment of the present application; and
fig. 4 is a schematic diagram of a snr estimation variance simulation result according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to fig. 1 to 4 and the embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The main purpose of RTCE is to examine the frequencies that one wishes to select in real time. If this is to be achieved, the method of channel estimation and the starting point for the thinking problem are to use a different path than long-term prediction. The frequency band and frequency used by the RTCE to select public communication are not considered by the construction of the ionosphere and the variation of details, a specific communication mode is selected from the communication modes, the signals of various frequencies received by the receiving end are processed in real time, and meanwhile, the values of channel parameters such as multipath broadening, information energy, doppler broadening, signal to noise ratio and the like and the different quality requirements of the communication are considered. The prediction of real-time frequency has the following advantages: 1. the utilization rate of the high-quality communication line can be increased; 2. high quality of the communication circuit can be ensured and accuracy of the transmitted message can be improved; 3. the number of users can be increased by employing real-time frequency allocation and invocation; 4. the maximum ratio MRC combining based on SNR can be realized in frequency hopping communication.
In the prior art, a multi-clock signal-to-noise ratio calculation method, such as square signal-to-noise variance ratio estimation (SNV), mainly analyzes a BPSK signal in a real AWGN channel, and the method belongs to a received data auxiliary method (RxDA). However, in reality, the SNV estimation method is one of ML estimation methods, and the SNV estimation method is characterized in that the ML estimation method needs to sample information N times per symbol 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. Also, certain deviations present in the original algorithm can be reduced by a correction factor. Beaulieuu Dol and Paul uzzi first give the initial SNV RxDA estimation for BPSK signals, then give how to reduce the difference in the estimation, and then apply the estimation method to higher order modulated signals in complex channels.
After the maximum likelihood estimation theory (maximum likelihood estimation, abbreviated as MLE) is derived, the research shows that in the real AWGN channel, the maximum likelihood estimation algorithm of the BPSK signal to noise ratio is a real-time (in-service) algorithm. They first analyze two parameters of the signal-to-noise ratio, S and N, where S is the signal power and N is the noise power, and by appropriate normalization approximation, can get the signal-to-noise ratio calculation formula snr=s/N, after which the ratio, i.e. SNR estimate, is obtained from the maximum likelihood estimates of the parameters. If the channel is a real channel, there is some deviation in the estimation method, so the researcher gives a correction factor and reduces the deviation by the correction factor. Later researchers further applied the ML estimation method in the higher order signal modulation mode MPSK signal in the complex gaussian white noise channel and provided a modified solution.
The Split Symbol Moment (SSME) estimation method is all for the case of BPSK signals in wideband AWGN channels, which can only estimate the signal-to-noise ratio of BPSK in AWGN channels. The second and fourth order moment estimation methods use the relationship between the second and fourth order moments of the signal to estimate the signal-to-noise ratio, which is an adaptive algorithm that does not use carrier phase recovery and does not require decisions at the receiver. The Signal Variance Ratio (SVR) estimation method is also based on a multi-moment algorithm, and is often used for controlling the channel quality of a multipath fading channel. The method is suitable for MPSK signals, and is difficult to apply to other forms of modulation modes. The signal-to-interference ratio estimation is applied in a narrow-band Time Division Multiple Access (TDMA) cellular system. The correlation matrix of the received signal is generated by a training sequence, and the power estimates of the signal and noise are based on a signal Subspace (SB) decomposition, which makes the calculated real-time signal-to-noise ratio more accurate.
The above mentioned methods, which are exemplified by the same model, study the real channel and the complex channel respectively, and finally, use the Mean Square Error (MSE) to make a careful comparison of the performance of each algorithm. The performance of the simulation estimation method is fully compared with that of the real and complex Gaussian white noise Channels (CRB) by comparing the obtained complex Channel (CRB) with the real Channel (CRB). It can be seen that the M2M4 method, the SNV method and the ML method have better performance. But they do not reach enough real-time, and have large estimation errors.
FIG. 1 is a flow chart of a method of measuring channel signal-to-noise ratio in one embodiment of the present application; fig. 2 is a schematic diagram of a structure of a transmission signal in one embodiment of the present application. As shown in fig. 1, a method for measuring a signal-to-noise ratio of a channel according to an embodiment of the present application includes steps S101 to S107. At step S101, a pre-pilot sequence is added before the traffic data block to form a transmission signal, where the pre-pilot sequence may be a set fixed sequence. In one embodiment, the structure of the transmission signal may be as shown in fig. 2, and one transmission signal may include 1286 symbols, where a front pilot sequence (head_train in the figure) includes 255 symbols, a service data block (data in the figure) includes 1024 symbols, a start system block includes 3 symbols, and an end system block includes 4 symbols.
At step S102, a receiving signal corresponding to the above-mentioned transmitting signal is sampled at the receiving end, thereby obtaining a sampled signal. In practice, the received signal may be sampled at an optimal sampling time by the ADC circuit. After the sampling signal is acquired, the sampling signal is synchronized at step S103, and since the pre-pilot sequence is known, synchronization can be performed here by the correlation operation of the pre-pilot sequence, so as to find the pre-pilot sequence, and finally carrier synchronization and time synchronization of the sampling signal (including the pre-pilot sequence) can be achieved. In one embodiment, channel estimation and equalization processing may also be performed on the sampled signal. Therefore, the interference of the multipath signals to the signal-to-noise ratio estimation is effectively eliminated, and the accuracy of the signal-to-noise ratio can be improved under the condition of multipath communication.
At step S104, the foregoing sampled signals are demodulated, so as to obtain demodulated signals, and superposition of the sampled signals is completed, for example, signal superposition of two symbol sampling points is completed, and the noise immunity of the complex environment can be improved by vector superposition in two symbols. The demodulation signals include a preamble sequence demodulation signal and a traffic data block demodulation signal. In one application scenario, the method for demodulating the sampled signal herein may be constant envelope demodulation (for example, GMSK) or phase demodulation (for example, QPSK, DPSK, PSK, 8PSK, etc.), where the above demodulation method is more suitable for the technical solution of the present application.
At step S105, an snr_header is calculated, where the snr_header is the SNR of the demodulated signal of the previous pilot sequence, and the method of calculating the snr_header is referred to herein as a previous pilot estimation algorithm. Specifically, step S1051 to step S1054 may be included.
At step S1051, the pre-pilot sequence is denoted as x (n), where x (n) may be an M sequence and may be mapped to ±1, and the pre-pilot sequence demodulation signal is denoted as y (n), according to the formula:the power of the pre-pilot sequence demodulation signal (the modulation information of the pre-pilot sequence has been removed) is calculated. At step S1052, according to the formula:calculating total power of demodulation signal of pilot sequence and noise of pilot sequence, P ALL The total power of the signal and the noise of the front pilot sequence is demodulated for the front pilot sequence. At step S1053, according to the formula: p (P) N ＝2(P ALL -P sx ) Calculating the power of pilot sequence noise before P N Is the power of the noise of the previous pilot sequence. It will be appreciated by those skilled in the art that SNR is the quotient of the discrete signal power and the discrete noise power of the input decider at the optimal sampling time, and therefore at step S1054, according to the formula:The signal-to-noise ratio of the pre-pilot sequence demodulated signal is calculated.
At step S106, an SNR_trline is calculated, which is a service data block solutionSNR of the tone signal, the method of calculating snr_line is referred to herein simply as a blind estimation algorithm. Specifically, step S1061 to step S1064 may be included. At step S1061, the traffic data block demodulation signal is noted as y (n), according to the formula:calculating the power signal P S The P is S Demodulating the power of the signal for the traffic data block. At step S1062, according to the formula:calculating the total power of the demodulation signal of the service data block and the service data noise, wherein P is as follows ALL And demodulating the total power of the signal and the service data noise for the service data block. At step S1063, according to the formula: p (P) N ＝2(P ALL -P S ) Calculating the power of service data noise, wherein P is S Is the power of the traffic data noise. At step S1064, according to the formula:and calculating the signal to noise ratio of the service data block demodulation signal.
At step S107, the SNR of the channel is determined according to the snr_header, the snr_trline, and a preset SNR calculation rule. In one embodiment, the preset SNR calculation rule includes: when snr_header is greater than snr_th, according to the formula: SNR = snr_header a+snr_trline (1-a) calculate SNR; when snr_header is less than or equal to snr_th, snr=snr_header, where snr_th is a preset SNR threshold, and the value range of snr_th is 2dB to 10dB, for example, 5dB.
For the parameter a, it is calculated according to a preset ratio calculation rule, specifically, when snr_header is greater than snr_th and less than 2×snr_th, thenWherein M is a preset proportionality coefficient, the value range of M can be 2-3, length (head_train) is the length of the front pilot sequence, length (data) is the length of the service data block,the proportion calculation rule can determine the proportion of the SNR_headline in the SNR calculation according to the proportion of the length of the preamble sequence to the length of the transmission signal (excluding the start system block and the end system block), so that the calculation result is more reasonable, and meanwhile, the proportion of the SNR_headline in the SNR calculation can be increased by multiplying the coefficient M in a certain value range, so that the calculation result of the SNR is more accurate; when snr_header is greater than or equal to 2×snr_th, a=0.5, that is, the snr_header and snr_trline occupy the same specific gravity in SNR calculation.
And the SNR threshold (snr_th) is set for the following reasons: by simulating the estimation performance of two different algorithms of the front pilot estimation algorithm and the blind estimation algorithm, as shown in the simulation result of the signal-to-noise ratio estimation mean value shown in fig. 3, when the signal-to-noise ratio is lower than 5dB, the mean value of the blind estimation algorithm starts to be inaccurate, and a lower limit exists, and the front pilot estimation algorithm based on the front pilot sequence is still accurate. When the signal-to-noise ratio is 5-15 dB, the average value of the two algorithms is very accurate. When the signal to noise ratio is greater than 15dB, the mean of the two algorithms starts to be inaccurate, and there is an upper limit. Since the SNR is far above the signal demodulation threshold when the bit error rate is typically equal to zero when the SNR exceeds 15dB, the accuracy of the SNR measurement is not important at this time, and the MRC (maximum ratio combining, maximal Ratio Combining) based on the SNR measurement is not substantially affected.
On the other hand, although the algorithm for SNR estimation based on the front pilot sequence is very accurate, the front pilot sequence of each transmitted signal data block is much shorter than the traffic data block, so the variance estimated by the front pilot estimation algorithm is larger, and the signal-to-noise ratio estimation variance simulation result shown in fig. 4 can be seen. As can be seen from fig. 4, the variance of the blind estimation algorithm is substantially constant, and as the signal-to-noise ratio increases, the variance of the pre-pilot sequence based algorithm becomes smaller. So when the pre-pilot sequence algorithm estimates snr_header > snr_th (snr_th=5 dB), both the traffic data block and the pre-pilot sequence participate in SNR measurements. When the SNR value is smaller, only the front pilot frequency sequence participates 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, the SNR value can be calculated rapidly, and the real-time performance of the SNR measurement is met. And the SNR measurement eliminates the influence of multipath signals after signal equalization, so that the measurement is more accurate.
In the SNR calculation process, the sampled signals are changed into real numbers, and the sampled signals collected by the ADC are complex numbers and are divided into a real part I path and an imaginary part Q path. However, in calculating SNR, the I-path signal and the Q-path signal need to be changed into one-path signal. The Q-way signal has an odd position and the I-way signal has an even position, so that the overall data length becomes 2 times.
According to the technical scheme, the SNR_header estimated based on the front pilot 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 number of samples of the reference data, so that the final estimation result is more accurate.
In addition, the application provides a computer device comprising a memory storing a computer program and a processor implementing the steps of any of the methods described above when the processor executes the computer program.
Also provided is a computer readable storage medium storing a computer program which when executed by a processor performs the steps of any of the methods described above.
The embodiments of this embodiment are all preferred embodiments of the present application, and are not intended to limit the scope of the present application, in which like parts are denoted by like reference numerals. Therefore: all equivalent changes in structure, shape and principle of this application should be covered in the protection scope of this application.
Claims (10)
1. A method of measuring signal to noise ratio of a channel, comprising the steps of:
adding a pre-pilot sequence in front of a service data block to form a transmitting signal;
sampling a receiving signal corresponding to the transmitting signal at a receiving end to obtain a sampling signal;
synchronizing the sampling signals;
demodulating the sampling signal to obtain a demodulation signal and finishing superposition of the sampling signal, wherein the demodulation signal comprises a front pilot sequence demodulation signal and a service data block demodulation signal;
obtaining an SNR_header, wherein the SNR_header is the SNR of the demodulation signal of the front pilot sequence;
obtaining an SNR_trline, wherein the SNR_trline is the SNR of the demodulation signal of the service data block;
and determining the SNR of the channel according to the SNR_header line, the SNR_trline and a preset SNR calculation rule.
2. The method of measuring channel signal-to-noise ratio of claim 1, further comprising: performing channel estimation on the sampling signal; and carrying out equalization processing on the sampling signals.
3. The method of measuring channel signal-to-noise ratio of claim 1, wherein demodulating the sampled signal comprises: and carrying out constant envelope demodulation or phase demodulation on the sampling signal.
4. A method of measuring channel signal to noise ratio according to any of claims 1-3, wherein said obtaining snr_header comprises: the pre-symbol pilot sequence is x (n) according to the formula:calculating the power of a demodulation signal of a pilot sequence before calculation;
according to the formula:calculating the total power of the demodulation signal of the pilot sequence and the noise of the pilot sequence, wherein P is ALL Demodulating the total power of the signal and the noise of the front pilot sequence for the front pilot sequence;
according to the formula: p (P) N ＝2(P ALL -P SX ) Calculating the power of the pre-pilot sequence noise, said P N Power for the noise of the front pilot sequence;
according to the formula:the signal-to-noise ratio of the pre-pilot sequence demodulated signal is calculated.
5. A method of measuring channel signal to noise ratio as defined in any one of claims 1-3, wherein said obtaining snr_line comprises:
the demodulation signal of the service data block is recorded as y (n);
according to the formula:calculating the power signal P S The P is S Demodulating the power of the signal for the traffic data block;
according to the formula:calculating the total power of the demodulation signal of the service data block and the service data noise, wherein P is as follows ALL Demodulating the total power of the signal and the service data noise for the service data block;
according to the formula: p (P) N ＝2(P ALL -P S ) Calculating the power of service data noise, wherein P is N Power for traffic data noise;
according to the formula:and calculating the signal to noise ratio of the service data block demodulation signal.
6. A method of measuring channel signal to noise ratio according to any of claims 1-3, wherein the pre-set SNR calculation rules comprise:
in response to the snr_header being greater than snr_th, according to the formula: snr=snr_header_a+snr_trline (1-a) calculating SNR, where a is a proportional value calculated by a preset proportional calculation rule;
in response to the snr_header being less than or equal to snr_th, snr=snr_header
The snr_th is a preset SNR threshold.
7. The method of claim 6, wherein the predetermined ratio calculation rule is:
in response to the snr_header being greater than snr_th and less than 2 x snr_th, thenWherein M is a preset proportionality coefficient, length (head_train) is the length of a front pilot sequence, and length (data) is the length of a service data block;
in response to the snr_header being greater than or equal to 2 x snr_th, a=0.5.
8. The method of measuring channel signal-to-noise ratio of claim 6, wherein the snr_th has a value in the range of 2dB to 10dB.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1-8 when the computer program is executed.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method of any one of claims 1-8.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202310455513.0A CN116566516A (en) | 2023-04-25 | 2023-04-25 | Method, device and storage medium for measuring channel signal-to-noise ratio |
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| US20060072656A1 (en) * | 2004-10-01 | 2006-04-06 | The Titan Corporation | Estimation of signal-to-noise ratio by processing measured error vector magnitude |
| EP1748610A1 (en) * | 2005-07-27 | 2007-01-31 | NTT DoCoMo, Inc. | Apparatus, method and computer program for estimating a channel for an OFDM transmission system |
| CN101106402A (en) * | 2006-06-30 | 2008-01-16 | 英特尔公司 | Signal-to-noise ratio (SNR) determination in the time domain |
| US20090141786A1 (en) * | 2007-11-29 | 2009-06-04 | Electronics And Telecommunications Research Institute | Apparatus and method of estimating signal-to-noise ratio |
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|---|---|---|---|---|
| US20060072656A1 (en) * | 2004-10-01 | 2006-04-06 | The Titan Corporation | Estimation of signal-to-noise ratio by processing measured error vector magnitude |
| EP1748610A1 (en) * | 2005-07-27 | 2007-01-31 | NTT DoCoMo, Inc. | Apparatus, method and computer program for estimating a channel for an OFDM transmission system |
| CN101106402A (en) * | 2006-06-30 | 2008-01-16 | 英特尔公司 | Signal-to-noise ratio (SNR) determination in the time domain |
| US20090141786A1 (en) * | 2007-11-29 | 2009-06-04 | Electronics And Telecommunications Research Institute | Apparatus and method of estimating signal-to-noise ratio |
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| Title |
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| 段鹏;何明一;薛敏彪;: "双衰落信道下OFDM信噪比估计算法", 计算机工程, no. 15, 5 August 2010 (2010-08-05) * |
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