Method and device for estimating signal-to-noise ratio

Abstract

The present application provides a method and device for estimating a signal-to-noise ratio, the method comprising: using a pilot signal to perform channel estimation to determine an initial value of the signal-to-noise ratio; determining a target value of the signal-to-noise ratio according to the initial value of the signal-to-noise ratio and the time domain / frequency domain correlation of the pilot signal. The embodiment of the present application finds through analysis that the estimated value of the signal-to-noise ratio is associated with the time domain / frequency domain correlation of the pilot signal, and estimates the signal-to-noise ratio based on the association relationship, thereby reducing the number of associated parameters in the signal-to-noise ratio estimation process and helping to reduce the complexity of the signal-to-noise ratio estimation.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and more specifically, to a method and device for estimating a signal-to-noise ratio. Background Art

[0002] The channel estimation result based on the pilot signal can determine the initial value of the signal-to-noise ratio (SNR), but the initial value of the signal-to-noise ratio often has a certain error. In order to solve this problem, the relevant technology compensates the estimation result of the signal-to-noise ratio according to the mapping relationship between the various associated parameters affecting the signal-to-noise ratio, the signal-to-noise ratio estimation result and the offset. However, the acquisition of the mapping relationship is very complicated, which makes the determination process of the signal-to-noise ratio estimation value also complicated. Summary of the invention

[0003] The present application provides a method and device for estimating a signal-to-noise ratio. The following introduces various aspects involved in the embodiments of the present application.

[0004] In a first aspect, a method for estimating a signal-to-noise ratio is provided, the method comprising: performing channel estimation using a pilot signal to determine an initial value of the signal-to-noise ratio; and determining a target value of the signal-to-noise ratio based on the initial value of the signal-to-noise ratio and a time domain / frequency domain correlation of the pilot signal.

[0005] In a second aspect, a device for estimating a signal-to-noise ratio is provided, the device comprising: a first determination module, configured to perform channel estimation using a pilot signal to determine an initial value of the signal-to-noise ratio; and a second determination module, configured to determine a target value of the signal-to-noise ratio based on the initial value of the signal-to-noise ratio and a time domain / frequency domain correlation of the pilot signal.

[0006] According to a third aspect, a device for estimating a signal-to-noise ratio is provided. The device includes a memory and a processor. The processor is used to call a program from the memory to execute the method according to the first aspect.

[0007] Through analysis, the embodiments of the present application find that the estimated value of the signal-to-noise ratio is associated with the time domain / frequency domain correlation of the pilot signal, and the signal-to-noise ratio is estimated based on the association relationship, which reduces the number of associated parameters in the signal-to-noise ratio estimation process and helps to reduce the complexity of the signal-to-noise ratio estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A schematic diagram of a CRS pilot structure provided in an embodiment of the present application.

[0009] Figure 2 An EPA model provided in the embodiment of the present application is Figure 1 Simulation diagram under pilot configuration.

[0010] Figure 3 An EVA model provided in the embodiment of the present application is Figure 1 Simulation diagram under pilot configuration.

[0011] Figure 4 An ETU model provided in the embodiment of the present application is Figure 1 Simulation diagram under pilot configuration.

[0012] Figure 5 A flowchart of a method for estimating a signal-to-noise ratio provided in an embodiment of the present application is provided.

[0013] Figure 6 A flowchart of another method for estimating a signal-to-noise ratio provided in an embodiment of the present application.

[0014] Figure 7 A schematic diagram of a DMRS pilot structure provided in an embodiment of the present application.

[0015] Figure 8 A schematic diagram of the structure of a device for estimating a signal-to-noise ratio provided in an embodiment of the present application.

[0016] Fig. 9 A schematic diagram of the structure of another device for estimating a signal-to-noise ratio provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0018] At present, in the field of wireless communication, channel-related parameters are usually estimated to improve the quality of wireless communication. The signal-to-noise ratio is the ratio of signal power to noise power, which is a type of channel parameter estimation. In actual use, the signal-to-noise ratio is usually used as a parameter to measure channel transmission quality.

[0019] In some embodiments, the signal-to-noise ratio is usually estimated based on the result of the noise estimation. However, the signal-to-noise ratio estimated by this method is often too low.

[0020] Combined with the following Figure 1 , taking the pilot signal of a cell reference signal (CRS) as an example, a method for estimating a signal-to-noise ratio in the related art is introduced.

[0021] See also Figure 1 , the channel estimation results corresponding to multiple pilot signals under this resource block are: H LS00 , H LS01 , H LS20 , HLS21 , H LS10 , H LS11 , H LS30 , H LS31 , which is a channel estimation result obtained based on the least squares (LS) algorithm. The above channel estimation results can be divided into two groups, where the channel estimation results in the solid line frame are one group, and the channel estimation results in the dotted line frame are one group.

[0022] In practical applications, the estimation is usually performed in groups, and then the estimation results of all groups are statistically averaged to obtain the estimated result of the signal-to-noise ratio. Taking the group in the solid line frame as an example, before estimating the signal-to-noise ratio, a four-point cross-subtraction algorithm can be used to estimate the noise power according to Formula 1.

[0023]

[0024] in, is an estimate of the noise power.

[0025] Based on the above noise power estimation result, the signal-to-noise ratio estimation result can be obtained. However, the error of the above noise power estimation result may be affected by factors such as delay spread, Doppler, pilot structure, etc., so the signal-to-noise ratio estimation result also has errors under the influence of the above factors.

[0026] Figures 2 to 4 For Figure 1 Simulation diagram of the signal-to-noise ratio estimation method mentioned above under different channel models under the pilot configuration. Figure 2 Simulation results of SNR for the extended pedestrian A (EPA) channel model at different Doppler frequencies. Figure 3 The simulation results of SNR of the extended vehicular A (EVA) channel model at different Doppler frequencies. Figure 4 The simulation results of SNR of the extended typical urban (ETU) channel model at different Doppler frequencies.

[0027] from Figures 2 to 4It can be seen that the signal-to-noise ratio estimation results obtained by the above method are lower than the configured value. In order to solve this problem, in the related art, a certain bias (biasSnr) is usually added to the SNR estimation value (i.e., the initial value of SNR) obtained by the above method. In some embodiments, the mapping relationship between the factors affecting the signal-to-noise ratio error, the initial value of SNR, and the bias mentioned above is obtained according to the empirical value and stored in the form of a table. Based on the initial value of SNR, the bias can be obtained by looking up the table to obtain the compensated SNR estimation value. However, since there are many factors that affect the error of the initial value of SNR, it is very complicated to obtain the mapping relationship mentioned above. In addition, because the initial value of SNR itself has an error, the biasSnr obtained by looking up the table based on the initial value of SNR will also be erroneous.

[0028] In view of the above problems, the embodiment of the present application first analyzes the noise power estimation method and finds that the factor affecting the SNR estimation error is the time domain correlation / frequency domain correlation of the pilot signal. The following uses Formula 1 as an example to introduce the analysis process.

[0029] First, H LS00 =h 00 +n 00 , H LS01 =h 01 +n 01 , H LS20 =h 20 +n 20 , H LS21 =h 21 +n 21 Substituting into Formula 1, we can obtain Formula 2, where h is the ideal channel corresponding to the pilot position, and n is the noise corresponding to the pilot position.

[0030]

[0031] Secondly, assuming that the power of the signal is P, Formula 2 can be converted into the following Formula 3.

[0032]

[0033] Among them, σ 2 is the noise, r f is the correlation between the two pilot points in the frequency domain, that is, h 20 and h 21 The correlation, or h 00 and h 01 The maximum correlation is 1. t is the correlation between the two pilot points in the time direction, that is, h 00 and h 20 The correlation, or h 01 and h21 The maximum value is 1. real means taking the real part.

[0034] According to the above analysis results, the error term of noise estimation is P(1―real(r f ))(1―real(r t )), that is, the time domain correlation / frequency domain correlation of the pilot signal affects the error of noise estimation, and further affects the error of SNR estimation.

[0035] At the same time, according to the above analysis results, the relationship between the change of time domain correlation / frequency domain correlation and the error can be obtained. When the frequency domain correlation is high or the time domain correlation is high (that is, close to 1), the noise estimation value is close to the ideal value. However, when the frequency domain correlation is weak and the time domain correlation is weak, the error term at this time cannot be ignored. Especially when the SNR is large (the noise power is small and the signal power is large), the error term may dominate, resulting in a larger noise estimate and a smaller SNR value calculated in the end.

[0036] The above findings proposed in the embodiment of the present application are verified based on the simulation results. Different channel types and different pilot distances will have different frequency domain correlations, and different Doppler Hz and different pilot symbol spacing will bring different time domain correlations. For example, under the same pilot configuration mode and the same pilot point spacing: the frequency domain correlation of the EPA channel> the frequency domain correlation of the EVA channel> the frequency domain correlation of the ETU channel. For another example, the larger the Doppler frequency, the weaker the time domain correlation, that is, r t The smaller.

[0037] See again Figures 2 to 4 When the frequency domain correlation is large or the time domain correlation is large, the SNR estimation value is close to the configuration value. However, when the frequency domain correlation is small and the Doppler is large, such as the ETU channel model when the Doppler is greater than 120Hz and the SNR is high, the SNR estimation value will have a large error relative to the configuration value. For example, when the SNR configuration value is 30dB, the SNR estimation value is about 17dB, and the error reaches about 13dB.

[0038] In order to solve the problem of complex and large error in the method of estimating the signal-to-noise ratio in the related art, the embodiments of the present application can estimate the signal-to-noise ratio based on the time domain / frequency domain correlation of the pilot signal according to the above findings, which helps to reduce the complexity of the signal-to-noise ratio estimation and reduce the estimation error of the signal-to-noise ratio.

[0039] Combined with the following Figure 5 , the method for estimating the signal-to-noise ratio provided in the embodiment of the present application is introduced in detail.

[0040] See also Figure 5, method 500 includes step S510 and step S520.

[0041] In step S510, channel estimation is performed using the pilot signal to determine an initial value of the signal-to-noise ratio.

[0042] The pilot signal may be a pilot signal included in a plurality of reference signals. For example, the pilot signal may be a pilot signal in a CRS, or a pilot signal in a demodulation reference signal (DMRS) in a new radio (NR).

[0043] In actual use, the pilot signal may include multiple pilot symbols. The pilot signal may include multiple distribution modes, that is, the multiple pilot symbols in the pilot signal may include multiple position distribution modes. For example, the distribution mode of the pilot signal may be different depending on the reference signal. For another example, the distribution mode of the pilot signal may be different depending on the pilot configuration. The distribution mode of the pilot signal may be, for example, a comb distribution or a block distribution.

[0044] In some embodiments, channel estimation using pilot signals may be based on different algorithms, such as LS algorithm or minimum mean square error algorithm.

[0045] As an implementation method, the noise estimation result can be determined according to the channel estimation result of the pilot signal. Different algorithms can be used to estimate the noise for different position distributions of the pilot signal or different pilot structures. For example, a four-point cross-subtraction algorithm can be used to estimate the noise, or a six-point cross-subtraction algorithm can be used to estimate the noise.

[0046] In step S520, a target value of the signal-to-noise ratio is determined according to the initial value of the signal-to-noise ratio and the time-domain / frequency-domain correlation of the pilot signal.

[0047] According to the analysis in the previous article, the time domain / frequency domain correlation of the pilot signal is related to factors such as channel type, Doppler, and pilot symbol spacing. In different scenarios, different methods can be used to obtain the time domain and frequency domain correlation of the pilot signal. As a common method, the time domain / frequency domain correlation of the pilot signal can be obtained based on the estimation results of the channel parameters.

[0048] As mentioned above, the embodiment of the present application found that the time domain correlation / frequency domain correlation of the pilot signal is a factor that affects the error in the signal-to-noise ratio estimation. Based on this discovery, according to the initial value of the signal-to-noise ratio and the time domain / frequency domain correlation of the pilot signal, the target value of the signal-to-noise ratio can be determined. The target value of the signal-to-noise ratio may refer to the estimated value of the signal-to-noise ratio obtained by the method provided in the embodiment of the present application.

[0049] As an implementation method, the method for determining the target value of the signal-to-noise ratio is different based on different noise estimation algorithms. If a four-point cross-subtraction algorithm is used for noise estimation, taking the noise estimation result in Formula 3 as an example, it can be deduced that the target value of the signal-to-noise ratio satisfies Formula 4.

[0050]

[0051] If the six-point cross-subtraction algorithm is used for noise estimation, the signal-to-noise ratio target value satisfies Formula 5.

[0052]

[0053] In Formula 4 and Formula 5, SNR is the target value of the signal-to-noise ratio, is the initial value of the signal-to-noise ratio, r f is the frequency domain correlation of the pilot signal, r t is the time domain correlation of the pilot signal, and real represents the real part.

[0054] The embodiment of the present application finds through analysis that the estimated value of the signal-to-noise ratio is associated with the time domain / frequency domain correlation of the pilot signal, and estimates the signal-to-noise ratio based on the correlation relationship, thereby reducing the number of associated parameters in the signal-to-noise ratio estimation process, and helping to reduce the complexity of the signal-to-noise ratio estimation. In addition, the embodiment of the present application provides a calculation formula for the estimated value of the signal-to-noise ratio, avoiding the complex process of obtaining the mapping relationship between multiple associated parameters and offsets, and further simplifying the method for estimating the signal-to-noise ratio.

[0055] As an implementation method, the error parameter of the signal-to-noise ratio can be first determined based on the time domain / frequency domain correlation of the pilot signal, and then the target value of the signal-to-noise ratio can be determined based on the initial value of the signal-to-noise ratio and the error parameter. The error parameter can be associated with the difference between the initial value and the target value of the signal-to-noise ratio (also referred to as an offset). For example, the error parameter can be the aforementioned (1-real(r f ))(1―real(r t )), the error parameter can be recorded as ΔR.

[0056] If the estimated frequency domain correlation and time domain correlation are both ideal, and the estimated signal-to-noise ratio is calculated for each group separately, then the ideal target value of the signal-to-noise ratio can be obtained according to the above content. However, for the parameter estimation in channel estimation, it is generally the result of long-term statistical averaging, not the most ideal value of the correlation between every two points, and the initial value of the signal-to-noise ratio is also the statistical average result within the entire effective bandwidth.

[0057] Therefore, in some cases, due to the errors in the estimated frequency domain correlation and time domain correlation, the denominator of the target value of the signal-to-noise ratio may be negative, for example, in this case, the denominator in Formula 4 may be negative. This phenomenon is not in line with actual usage. In order to avoid this situation, the process of determining the target value of the signal-to-noise ratio needs to be optimized.

[0058] As an implementation, the signal-to-noise ratio target value acquisition process can be processed differently according to the intermediate parameter. The intermediate parameter can be determined by a variety of methods. For example, the intermediate parameter can be determined according to the reciprocal of the initial value of the signal-to-noise ratio and the difference of the error parameter. As an example, the intermediate parameter can be the denominator term in Formula 4 or the denominator term in Formula 5.

[0059] First, the case where the value of the intermediate parameter is less than or equal to zero is analyzed.

[0060] In some cases, the error of the initial estimation result of the signal-to-noise ratio is small, and the impact of the error on the system is within the allowable range. At this time, the offset between the target value of the signal-to-noise ratio and the initial value of the signal-to-noise ratio can be set to 0, that is, the target value of the signal-to-noise ratio is equal to the initial value, and no compensation is performed.

[0061] The error of the initial estimation result of the signal-to-noise ratio is small, which can be determined based on experience or simulation results, or based on error parameter analysis. Figures 2 to 4 , when the initial value of the signal-to-noise ratio is less than 12dB, the error of the initial estimate of the signal-to-noise ratio is very small. Even in the ETU model with the largest error, when the Doppler is 240Hz, the error of the initial estimate of the signal-to-noise ratio is only about 2dB. For another example, referring to Formula 3, combined with the previous analysis, when the error parameter ΔR is very small, the closer the noise estimate is to the ideal value, that is, the smaller the estimated error of the initial value of the signal-to-noise ratio. As an example, when the error parameter ΔR≤0.01, the error of the initial estimate of the signal-to-noise ratio can be ignored.

[0062] Therefore, in some embodiments, if the value of the intermediate parameter is less than or equal to zero, when the initial estimated value of the signal-to-noise ratio is ≤12 dB or ΔR ≤0.01, the offset between the target signal-to-noise ratio value and the initial signal-to-noise ratio value may be set to 0.

[0063] In other cases, the error of the initial estimation result of the signal-to-noise ratio cannot be ignored, but the error of the parameter estimation value used to calculate the target value of the signal-to-noise ratio is large, and the offset can be set to a fixed value. In some embodiments, when the initial estimation value of the signal-to-noise ratio is greater than 12 dB and ΔR>0.01, the offset between the target value of the signal-to-noise ratio and the initial value of the signal-to-noise ratio can be set to a fixed value. As an example, the fixed value can be 8 dB.

[0064] When the value of the intermediate parameter is greater than zero, in order to prevent the fluctuation of the channel estimation result and the estimation result of the related parameters from causing a large fluctuation of the signal-to-noise ratio target value, the offset may be limited. For example, the offset may be limited to 12 dB.

[0065] Figure 6 A flowchart of another method for estimating a signal-to-noise ratio provided in an embodiment of the present application is shown in FIG.

[0066] In step S610, LS channel estimation.

[0067] Based on the pilot signal, the LS algorithm is used to perform channel estimation. In some embodiments, relevant parameters of the channel are also estimated, such as frequency domain correlation and time domain correlation. Therefore, in the embodiments of the present application, the frequency domain correlation and time domain correlation can be used as known quantities.

[0068] In step S621, the noise power is calculated.

[0069] Based on the channel estimation results, the noise power can be estimated using a four-point cross-subtraction algorithm.

[0070] In step S622, the signal power is calculated.

[0071] In step S630, an initial value of the signal-to-noise ratio is calculated.

[0072] As an implementation manner, the initial value of the signal-to-noise ratio is the ratio of the above signal power to the noise power.

[0073] In step S640, an error parameter is calculated.

[0074] The error parameter may be a parameter indicating the error caused by correlation, for example, the above-mentioned ΔR=(1-real(r f ))(1―real(r t )).

[0075] In step S650, it is determined whether the intermediate parameter is less than or equal to zero.

[0076] As an implementation method, the intermediate parameter can be determined based on the initial value of the error parameter and the signal-to-noise ratio. For example, the intermediate parameter can be Where SNR0 is the initial value of the signal-to-noise ratio mentioned above.

[0077] If the intermediate parameter is less than or equal to 0, jump to step S660.

[0078] If the intermediate parameter is greater than 0, jump to step S670.

[0079] In step S660, it is determined whether ΔR>0.01&SNR0_dB>12dB is true, where SNR0_dB is the dB value of SNR0 (linear value).

[0080] If the above judgment is true, jump to step S661; if the above judgment is false, jump to step S662.

[0081] In step S661, the offset is set to a fixed value, for example, 8 dB.

[0082] In step S662, the offset amount is set to 0 dB.

[0083] In step S670, the dB value of SNR1 is calculated, where SNR1 is the target value of the signal-to-noise ratio.

[0084] As a way to achieve this,

[0085] In step S680, the offset is limited to 12 dB.

[0086] As an implementation method, the calculation formula of the offset can be obtained according to Formula 4:

[0087] The offset is calculated according to the above formula. If the offset is greater than 12dB, the offset is set to 12dB. If the offset is less than or equal to 12dB, no processing is done on the offset.

[0088] In step S690, a target value of the signal-to-noise ratio is determined.

[0089] The target value of the signal-to-noise ratio may be determined according to the offset and the initial value of the signal-to-noise ratio. For example, the target value of the signal-to-noise ratio may be the sum of the initial value of the signal-to-noise ratio and the offset.

[0090] The embodiment of the present application helps to reduce the error of the target value of the signal-to-noise ratio by performing certain processing on the offset according to different situations.

[0091] Tables 1 and 2 are simulation results obtained by using the signal-to-noise ratio estimation method provided in the embodiment of the present application. setSnr is the configuration value of the signal-to-noise ratio, estSNR is the initial value of the signal-to-noise ratio (i.e. Figure 6 SNR0_dB in the signal-to-noise ratio), the signal-to-noise ratio value after bias compensation (target value of the signal-to-noise ratio) SNRwithBias, where SNRwithBias=estSNR+bias.

[0092] Table 1 shows the initial and target SNR values ​​corresponding to different SNR configuration values ​​for the three channel models when two pilot columns of DRMS ​​(located at the 2nd symbol and the 11th symbol respectively) and Doppler is 200 Hz.

[0093] Table 1 Simulation results under one scenario

[0094]

[0095] Table 2 shows the initial and target SNR values ​​corresponding to different SNR configuration values ​​for the three channel models when there are four pilot columns of DRMS ​​(located at the 2nd symbol, the 5th symbol, the 8th symbol and the 11th symbol respectively) and the Doppler is 200 Hz.

[0096] Table 2 Simulation results under another scenario

[0097]

[0098] It can be seen from the simulation results in the above table that compared with the configured value of the signal-to-noise ratio, the initial value of the signal-to-noise ratio is low. When the signal-to-noise ratio is large, the initial value of the signal-to-noise ratio is lower. In particular, the larger the delay spread channel (the delay spread of the three models ETU>EVA>EPA), the greater the estimation deviation. The target value of the signal-to-noise ratio obtained by the method provided in the embodiment of the present application is greatly improved relative to the initial value of the signal-to-noise ratio, and the error is significantly reduced.

[0099] In order to make the solution provided by the embodiment of the present application clearer, the derivation process of Formula 4 is supplemented below.

[0100] Based on formula 3, formula 4 can be derived. The specific derivation process is as follows.

[0101]

[0102] Based on formula 3, the following calculation formula for the initial value of the signal-to-noise ratio can be obtained.

[0103]

[0104] Furthermore, a calculation formula for the target value of the signal-to-noise ratio (ie, Formula 4) can be derived.

[0105]

[0106] As mentioned above, the method provided in the embodiment of the present application can also be based on a six-point cross subtraction algorithm. If different algorithms are used for noise estimation, the frequency domain correlation / time domain correlation has different effects on the target value of the signal-to-noise ratio.

[0107] The following uses the DMRS pilot in NR (see Figure 7) as an example, a method for determining the target value of the signal-to-noise ratio based on the six-point cross-subtraction algorithm is introduced in detail.

[0108] As an implementation manner, the noise power may be calculated based on the channel estimation result.

[0109]

[0110] Where h is the ideal channel at the corresponding pilot position, and n is the noise at the corresponding position. f is the correlation between two pilot points in the frequency domain, that is, the correlation between Ls0 and Ls2, and the maximum value is 1. t is the correlation between two pilot points in the time direction, that is, the correlation between Ls0 and Ls0′, and the maximum value is 1.

[0111] It can be seen from the above formula that the noise estimation error is associated with the frequency domain correlation and the time domain correlation, so it can be deduced that the target value of the signal-to-noise ratio is associated with the frequency domain correlation and the time domain correlation. The derivation process is as follows.

[0112]

[0113] but:

[0114]

[0115] Combination of the above Figures 1 to 7 , describes the method embodiment of the present application in detail, and the following is combined with Figures 8 to 9 , describes the device embodiment of the present application in detail. It should be understood that the description of the method embodiment corresponds to the description of the device embodiment, so the parts not described in detail can refer to the previous method embodiment.

[0116] Figure 8 A schematic diagram of the structure of a device for estimating a signal-to-noise ratio provided in an embodiment of the present application.

[0117] See also Figure 8 , the device 800 may include a first determination module 810 and a second determination module 820.

[0118] The first determination module 810 may be configured to perform channel estimation using a pilot signal to determine an initial value of a signal-to-noise ratio.

[0119] The second determination module 820 may be configured to determine a target value of the signal-to-noise ratio according to an initial value of the signal-to-noise ratio and a time-domain / frequency-domain correlation of the pilot signal.

[0120] Optionally, an error parameter of the signal-to-noise ratio is determined according to a time-domain / frequency-domain correlation of the pilot signal; and a target value of the signal-to-noise ratio is determined according to an initial value of the signal-to-noise ratio and the error parameter.

[0121] Optionally, determining the target value of the signal-to-noise ratio according to the initial value of the signal-to-noise ratio and the error parameter includes: determining an intermediate parameter according to the reciprocal of the initial value of the signal-to-noise ratio and a difference value of the error parameter; if the value of the intermediate parameter is less than or equal to zero, and according to the initial value of the signal-to-noise ratio and / or the value of the error parameter, setting an offset between the target value and the initial value of the signal-to-noise ratio to 0 or a fixed value; and determining the target value of the signal-to-noise ratio according to the initial value of the signal-to-noise ratio and the offset.

[0122] Optionally, the estimation of noise in the signal-to-noise ratio is based on a four-point cross-subtraction algorithm, and the second determination module is used to: determine a target value of the signal-to-noise ratio according to an initial value of the signal-to-noise ratio and a time domain / frequency domain correlation of the pilot signal, wherein the target value of the signal-to-noise ratio satisfies the following formula:

[0123]

[0124] Wherein, SNR is the target value of the signal-to-noise ratio, is the initial value of the signal-to-noise ratio, r f is the frequency domain correlation of the pilot signal, r t is the time domain correlation of the pilot signal, and real represents the real part.

[0125] Optionally, the estimation of noise in the signal-to-noise ratio is based on a six-point cross-subtraction algorithm, and the second determination module is used to: determine a target value of the signal-to-noise ratio according to an initial value of the signal-to-noise ratio and a time domain / frequency domain correlation of the pilot signal, wherein the target value of the signal-to-noise ratio satisfies the following formula:

[0126]

[0127] Wherein, SNR is the target value of the signal-to-noise ratio, is the initial value of the signal-to-noise ratio, r f is the frequency domain correlation of the pilot signal, r t is the time domain correlation of the pilot signal, and real represents the real part.

[0128] Fig. 9 A schematic diagram of the structure of another device for estimating a signal-to-noise ratio provided in an embodiment of the present application. Fig. 9 , the device 900 may include a memory 910 and a processor 920 .

[0129] The memory 910 may be used to store instructions. The memory 910 may be independent of the processor 920 or may be integrated in the processor 920.

[0130] The processor 920 may be configured to call instructions stored in the memory so that the processor 920 executes the method described in the foregoing method embodiment.

[0131] It should be understood that in the embodiment of the present application, "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0132] It should be understood that the term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0133] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0134] In the several embodiments provided in the present application, it should be understood that the disclosed systems and devices can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0135] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0136] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0137] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that can be read by a computer or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0138] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for estimating a signal-to-noise ratio, characterized in that include: Using the pilot signal to perform channel estimation to determine an initial value of the signal-to-noise ratio; Determining a target value of the signal-to-noise ratio according to the initial value of the signal-to-noise ratio and the time domain and frequency domain correlation of the pilot signal; Determining a target value of the signal-to-noise ratio according to the initial value of the signal-to-noise ratio and the time domain and frequency domain correlation of the pilot signal includes: Determining an error parameter of the signal-to-noise ratio according to time domain and frequency domain correlation of the pilot signal; Determining a target value of the signal-to-noise ratio according to the initial value of the signal-to-noise ratio and the error parameter; The step of determining a target value of the signal-to-noise ratio according to the initial value of the signal-to-noise ratio and the error parameter includes: Determining an intermediate parameter according to the reciprocal of the initial value of the signal-to-noise ratio and the difference of the error parameter; If the value of the intermediate parameter is less than or equal to zero, and according to the initial value of the signal-to-noise ratio and / or the value of the error parameter, the offset between the target value and the initial value of the signal-to-noise ratio is set to 0 or a fixed value; A target value of the signal-to-noise ratio is determined according to the initial value of the signal-to-noise ratio and the offset.

2. The method according to claim 1, characterized in that The estimation of the noise in the signal-to-noise ratio is based on a four-point cross-subtraction algorithm, Determining a target value of the signal-to-noise ratio according to the initial value of the signal-to-noise ratio and the time domain and frequency domain correlation of the pilot signal includes: The target value of the signal-to-noise ratio is determined according to the initial value of the signal-to-noise ratio and the time domain and frequency domain correlation of the pilot signal, wherein the target value of the signal-to-noise ratio satisfies the following formula: Wherein, SNR is the target value of the signal-to-noise ratio, is the initial value of the signal-to-noise ratio, r f is the frequency domain correlation of the pilot signal, r t is the time domain correlation of the pilot signal, and real represents the real part.

3. The method according to claim 1, characterized in that The estimation of the noise in the signal-to-noise ratio is based on a six-point cross-subtraction algorithm, Determining a target value of the signal-to-noise ratio according to the initial value of the signal-to-noise ratio and the time domain and frequency domain correlation of the pilot signal includes: The target value of the signal-to-noise ratio is determined according to the initial value of the signal-to-noise ratio and the time domain and frequency domain correlation of the pilot signal, wherein the target value of the signal-to-noise ratio satisfies the following formula: Wherein, SNR is the target value of the signal-to-noise ratio, is the initial value of the signal-to-noise ratio, r f is the frequency domain correlation of the pilot signal, r t is the time domain correlation of the pilot signal, and real represents the real part.

4. A device for estimating a signal-to-noise ratio, characterized in that include: A first determination module, configured to perform channel estimation using a pilot signal to determine an initial value of a signal-to-noise ratio; A second determination module, configured to determine a target value of the signal-to-noise ratio according to the initial value of the signal-to-noise ratio and the time domain and frequency domain correlation of the pilot signal; The second determining module is used for: Determining an error parameter of the signal-to-noise ratio according to time domain and frequency domain correlation of the pilot signal; Determining a target value of the signal-to-noise ratio according to the initial value of the signal-to-noise ratio and the error parameter; The step of determining a target value of the signal-to-noise ratio according to the initial value of the signal-to-noise ratio and the error parameter includes: Determining an intermediate parameter according to the reciprocal of the initial value of the signal-to-noise ratio and the difference of the error parameter; If the value of the intermediate parameter is less than or equal to zero, and according to the initial value of the signal-to-noise ratio and / or the value of the error parameter, the offset between the target value and the initial value of the signal-to-noise ratio is set to 0 or a fixed value; A target value of the signal-to-noise ratio is determined according to the initial value of the signal-to-noise ratio and the offset.

5. The device according to claim 4, characterized in that The estimation of the noise in the signal-to-noise ratio is based on a four-point cross-subtraction algorithm, and the second determination module is used for: The target value of the signal-to-noise ratio is determined according to the initial value of the signal-to-noise ratio and the time domain and frequency domain correlation of the pilot signal, wherein the target value of the signal-to-noise ratio satisfies the following formula: Wherein, SNR is the target value of the signal-to-noise ratio, is the initial value of the signal-to-noise ratio, r f is the frequency domain correlation of the pilot signal, r t is the time domain correlation of the pilot signal, and real represents the real part.

6. The device according to claim 4, characterized in that The estimation of the noise in the signal-to-noise ratio is based on a six-point cross-subtraction algorithm, and the second determination module is used for: The target value of the signal-to-noise ratio is determined according to the initial value of the signal-to-noise ratio and the time domain and frequency domain correlation of the pilot signal, wherein the target value of the signal-to-noise ratio satisfies the following formula: Wherein, SNR is the target value of the signal-to-noise ratio, is the initial value of the signal-to-noise ratio, r f is the frequency domain correlation of the pilot signal, r t is the time domain correlation of the pilot signal, and real represents the real part.

7. A device for estimating a signal-to-noise ratio, characterized in that The device includes a memory and a processor, and the processor is used to call the code in the memory to execute the method according to any one of claims 1-3.

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