Noise compensation method, device, electronic device and computer readable storage medium

Abstract

The embodiment of the present application discloses a noise compensation method, device, electronic device and computer-readable storage medium, the method comprising: performing channel estimation on a received signal to obtain a channel estimation result; filtering the channel estimation result according to an MMSE filtering algorithm to determine noise estimation data; calculating a target compensation factor according to a filter coefficient matrix corresponding to the MMSE filtering algorithm; performing noise compensation on the noise estimation data according to the target compensation factor to obtain a target noise result. The above-mentioned noise compensation method, device, electronic device and computer-readable storage medium can improve the accuracy of noise estimation for a received signal, and the scheme has low complexity and is easy to implement.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and in particular to a noise compensation method, device, electronic device and computer-readable storage medium. Background Art

[0002] In wireless communication systems, due to the influence of noise and other factors, the received signal at the receiving end deviates from the signal sent by the transmitting end. In order to effectively and accurately restore the signal sent by the transmitting end, it is necessary to accurately estimate the noise of the received signal. The current method of estimating the noise of the received signal is still not accurate enough. Summary of the invention

[0003] The embodiments of the present application disclose a noise compensation method, device, electronic device and computer-readable storage medium, which can improve the accuracy of noise estimation for received signals, and the scheme has low complexity and is easy to implement.

[0004] The embodiment of the present application discloses a noise compensation method, including:

[0005] Performing channel estimation on the received signal to obtain a channel estimation result;

[0006] Filtering the channel estimation result according to an MMSE filtering algorithm to determine noise estimation data;

[0007] Calculate the target compensation factor according to the filter coefficient matrix corresponding to the MMSE filter algorithm;

[0008] Noise compensation is performed on the noise estimation data according to the target compensation factor to obtain a target noise result.

[0009] The embodiment of the present application discloses a noise compensation device, including:

[0010] A channel estimation module is used to perform channel estimation on a received signal to obtain a channel estimation result;

[0011] A noise estimation module, used for filtering the channel estimation result according to an MMSE filtering algorithm to determine noise estimation data;

[0012] A compensation factor calculation module, used to calculate a target compensation factor according to a filter coefficient matrix corresponding to the MMSE filter algorithm;

[0013] The compensation module is used to perform noise compensation on the noise estimation data according to the target compensation factor to obtain a target noise result.

[0014] An embodiment of the present application discloses an electronic device, including a memory, a processor, and a transceiver unit. The memory stores a computer program. When the computer program is executed by the processor, the processor implements the method described above.

[0015] An embodiment of the present application discloses a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor in a computer device, the processor implements the method described above.

[0016] The noise compensation method, device, electronic device and computer-readable storage medium disclosed in the embodiments of the present application perform channel estimation on a received signal to obtain a channel estimation result, filter the channel estimation result according to an MMSE filtering algorithm to determine noise estimation data, calculate a target compensation factor according to a filter coefficient matrix corresponding to the MMSE filtering algorithm, and perform noise compensation on the noise estimation data according to the target compensation factor to obtain a target noise result; in the embodiments of the present application, the target compensation factor is calculated according to the filter coefficient matrix used when filtering with the MMSE filtering algorithm, and the noise estimation data obtained initially is corrected and compensated according to the target compensation factor to obtain a more accurate target noise result, which can improve the accuracy of noise estimation for the received signal, and the scheme has low complexity and is easy to implement. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 is a flow chart of a noise compensation method in one embodiment;

[0019] Figure 2A A schematic diagram of noise power estimated under different channels and at different SNR points in the related art;

[0020] Figure 2B A schematic diagram of the difference between the estimated noise power and the ideal noise power in the related art;

[0021] Figure 3 A flowchart of calculating a target compensation factor in one embodiment;

[0022] Figure 4A A schematic diagram of noise power after compensation of noise power estimated at different SNR points in different channels in one embodiment;

[0023] Figure 4BA schematic diagram of the difference between the compensated noise power and the ideal noise power in one embodiment;

[0024] Figure 5 is a block diagram of a noise compensation device in one embodiment;

[0025] Figure 6 FIG. 4 is a structural block diagram of an electronic device in an embodiment. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] It should be noted that the terms "including" and "having" and any variations thereof in the embodiments of the present application and the accompanying drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products or devices.

[0028] like Figure 1 As shown, in one embodiment, a noise compensation method is provided, which can be applied to electronic devices, including but not limited to mobile phones, tablet computers, vehicle-mounted terminals, laptop computers, PCs (Personal Computers), wearable devices (such as smart watches, smart glasses), etc. The method may include the following steps:

[0029] Step 110: perform channel estimation on the received signal to obtain a channel estimation result.

[0030] In wireless communication systems, signals are transmitted through channels. Due to various reasons such as channel attenuation and noise, the signals received by the receiver are distorted. In order to accurately restore the original signal sent by the transmitter, it is necessary to perform channel estimation on the received signal. Channel estimation refers to the process of estimating the model parameters of a hypothetical channel model from the received signal.

[0031] In some embodiments, the electronic device may perform channel estimation on the received signal based on the least squares (LS) method to obtain an LS channel estimation result.

[0032] Step 120: Filter the channel estimation result according to the MMSE filtering algorithm to determine noise estimation data.

[0033] The electronic device can use the MMSE (Minimum Mean Squared Error) filtering algorithm to filter the channel estimation result of each pilot point in the received signal, and determine the noise estimation data based on the channel estimation result after filtering and the channel estimation result before filtering. Among them, the pilot point in the received signal refers to the location point where the pilot symbol is located, and the pilot symbol can be set at different positions of the received signal at intervals to facilitate channel estimation.

[0034] As an implementation method, the noise estimation data may include noise power. The channel estimation result after filtering obtained using the MMSE filtering algorithm may be subtracted from the channel estimation result before filtering, and the difference between the two may be squared and the average value may be calculated to finally obtain the noise power of the current time slot.

[0035] Specifically, the noise power can be calculated by formula (1):

[0036]

[0037] in, represents the noise power, H LS can be the LS channel estimation result obtained based on the LS algorithm, WH LS represents the Wiener channel estimation based on the MMSE algorithm, W is the filter coefficient of the MMSE filter algorithm, θ is the cross-correlation matrix, σ 2 is the ideal noise power, is the autocorrelation matrix and is a conjugate symmetric matrix.

[0038] As a specific implementation, taking the calculation of the noise power at the xth pilot point on the received signal as an example, performing N-order filtering, it can be derived according to the above formula (1) to obtain formula (2):

[0039]

[0040]

[0041] The channel estimation error term E[|Δh in formula (2) can be omitted x | 2 ], the noise power calculation formula (3) can be obtained:

[0042]

[0043] Among them, w x It represents the xth element value in the filter coefficient matrix corresponding to the MMSE filter algorithm, n N-1represents the noise at the N-1th pilot point, h x is the value of the ideal channel at the xth pilot point, h ls，N-1 It represents the LS channel estimation result under the N-1th pilot point.

[0044] Step 130, calculating the target compensation factor according to the filter coefficient matrix corresponding to the MMSE filter algorithm.

[0045] In the related art, the noise estimation data calculated by the MMSE filtering algorithm has a large deviation problem. For example, when the noise power is calculated by the MMSE filtering algorithm, the correlation between the data points is weak in a large delay extension channel. The farther the distance between the two data points, the weaker the correlation. This will cause the element value corresponding to the calculated pilot point in the obtained filter coefficient matrix to be large, while the element values ​​corresponding to the other pilot points are small, resulting in the final estimated noise power being small. When the distance between the pilot points in the channel estimation result used for filtering is farther, the deviation of the final estimated noise power is greater. Taking the ETU (Extended Typical Urban model) channel, the pilot with a CSI-RS (Channel State Information-Reference Signal) density of 1, and the use of a 6-order filter coefficient as an example, the 6*6 filter coefficient (6 groups of filter coefficients) under SNR (Signal-to-noise ratio) = 30dB can be shown as follows:

[0046] Group 1: 0.9561-0.0000i, 0.0496+0.0476i, 0.0196-0.0696i, -0.0768+0.0401i, 0.0536+0.0301i, -0.0100-0.0254i;

[0047] Group 2: 0.0496-0.0476i, 0.8509+0.0000i, 0.1066+0.1716i, 0.0671-0.1842i, -0.1347+0.305i, 0.0536+0.0301i;

[0048] Group 3: 0.0198+0.0696i, 0.1066-0.1716i, 0.7181-0.0000i, 0.1694+0.2183i, 0.0671-0.1842i, -0.0768+0.0401i;

[0049] Group 4: -0.0768+0.041i, 0.0671+0.1842i, 0.1694-0.2183i, 0.7181+0.0000i, 0.1066+0.1716i, 0.0198-0.0896i;

[0050] Group 5: 0.0536-0.0301i, -0.1347-0.0305i, 0.0671+0.1842i, 0.1066-0.1716i, 0.8509-0.0000i, 0.0496+0.0476i;

[0051] Group 6: -0.0100+0.0254i, 0.0536-0.0301i, -0.0768-0.0401i, 0.0198+0.0896i, 0.0496-0.0476i, 0.9651+0.0000i.

[0052] Among them, the mth group of filter coefficients is the filter coefficient corresponding to the mth pilot point in the received signal, that is, the filter coefficient corresponding to the mth filter value to be calculated. When calculating the filter value of each pilot point, multiple channel estimation results are required. The filter coefficients contained in the mth group are used to characterize the proportion of different channel estimation results when calculating the mth filter value. It can be seen from the filter coefficients shown above that one of the filter coefficients in each group is very large (the filter coefficient in bold above), and the rest of the filter coefficients are very small. For example, if the first group of filter coefficients is substituted into the above formula (3), the noise power calculated by formula (3) will be reduced by about 15dB relative to the ideal noise power.

[0053] For example, Figure 2A Schematic diagram of noise power estimated under different channels and at different SNR points in the related technology. Figure 2B Schematic diagram of the difference between the estimated noise power and the ideal noise power in the related art. Among them, AWGN (Additive white Gaussian noise) channel is a channel with only noise and no fading, EPA (Extended Pedestrian A model) channel, EVA (Extended Pedestrian A model extended pedestrian channel model) channel. Figure 2A and Figure 2B It can be seen intuitively that as the delay spread increases, the deviation of the estimated noise power from the ideal noise power increases.

[0054] Based on this, in an embodiment of the present application, the target compensation factor can be calculated according to the filter coefficient matrix used by the MMSE filter algorithm when filtering the channel estimation result, and the noise estimation data can be noise compensated according to the target compensation factor, which can correct the situation where the estimated filter power is too small due to the presence of a large filter coefficient in each group of filter coefficients and other filter data is very small, so as to obtain a more accurate target noise result. The specific calculation method of calculating the target compensation factor is introduced in the following embodiment.

[0055] Step 140: noise compensation is performed on the noise estimation data according to a target compensation factor to obtain a target noise result.

[0056] In some embodiments, the noise estimation data includes the estimated noise power, and the target compensation factor can be multiplied by the noise power to obtain the target noise result. Taking the noise power at the xth pilot point as an example, the target compensation factor can be multiplied by the noise power at the xth pilot point calculated according to the above formula (3), so as to obtain the compensated noise power corresponding to the xth pilot point.

[0057] It should be noted that the electronic device may also use other algorithms besides the LS algorithm and the MMSE algorithm to perform channel estimation on the received signal. For example, a channel estimation algorithm based on a training sequence may be used to perform channel estimation on the received signal, or a channel estimation algorithm based on a training sequence may be used to perform channel estimation on the received signal. Among them, the blind / semi-blind channel estimation algorithm obtains CSI (channel state information) or equalizer coefficients from the structure and statistical information of the received signal, and no or little training sequence is required. For channel estimation performed by other algorithms, the target compensation factor can be calculated based on the filter coefficient matrix used by other algorithms in the channel estimation process, and the noise estimation data obtained in the channel estimation process can be noise compensated based on the calculated target compensation factor to improve the accuracy of the noise estimation. The embodiments of the present application do not limit the specific channel estimation algorithm used.

[0058] In an embodiment of the present application, a target compensation factor is calculated based on a filter coefficient matrix used in filtering using an MMSE filtering algorithm, and the noise estimation data initially obtained is corrected and compensated based on the target compensation factor to obtain a more accurate target noise result, thereby improving the accuracy of noise estimation for a received signal, and the scheme has low complexity and is easy to implement.

[0059] like Figure 3 As shown, in one embodiment, the step of calculating the target compensation factor according to the filter coefficient matrix corresponding to the MMSE filter algorithm may include the following steps:

[0060] Step 302: Obtain a filter coefficient matrix of the MMSE filter algorithm. The filter coefficient matrix includes M groups of filter coefficients. The M groups of filter coefficients correspond to M pilot points in the received signal one by one, and M is a positive integer.

[0061] Optionally, the MMSE filtering algorithm may use a Wiener filter or the like to filter the LS channel estimation result of the received signal, and the filter coefficient of the MMSE filtering algorithm may be a Wiener coefficient, but is not limited thereto. Each pilot point in the received signal may correspond to a set of filter coefficients, thereby forming a filter coefficient matrix, which is the filter coefficient matrix used when the signal estimation result of the received signal is filtered using the MMSE filtering algorithm. For example, the 6*6 filter coefficients mentioned in the above embodiment can form a 6*6 filter coefficient matrix.

[0062] Step 304: Calculate the noise compensation factor corresponding to each pilot point according to each group of filter coefficients corresponding to each pilot point, and determine the target compensation factor according to the noise compensation factor corresponding to each pilot point.

[0063] As an optional implementation, for a group of filter coefficients corresponding to each pilot point of the received signal, the filter coefficient in the group of filter coefficients with the same position as the corresponding pilot point can be determined as the noise compensation factor corresponding to the pilot point. For example, for the first pilot point, the filter coefficient arranged in the first position in the corresponding group of filter coefficients can be determined as the noise compensation factor corresponding to the first pilot point. After determining the noise compensation factor corresponding to each pilot point, the average value of the noise compensation factor corresponding to each pilot point can be calculated to obtain the target compensation factor.

[0064] As another implementation, calculating the target compensation factor according to the filter coefficient matrix corresponding to the MMSE filter algorithm may also include: adjusting the filter coefficients corresponding to the first pilot point and the second pilot point in the filter coefficient matrix corresponding to the MMSE filter algorithm based at least in part on the correlation between the first pilot point and the second pilot point in the received signal to obtain an updated filter coefficient matrix, and the updated filter coefficient matrix may be regarded as the product of the original filter coefficient matrix and the target compensation factor. Further, the target compensation factor may be determined according to the updated filter coefficient matrix and the original filter coefficient matrix.

[0065] As an example, for pilot point RS1 and pilot point RS2 which are close to each other, the correlation between the two is high, and the filter coefficient corresponding to (RS1-RS2) can be appropriately reduced; for pilot point RS1 and pilot point RS8 which are far away, the correlation between the two is low, and the filter coefficient corresponding to (RS1-RS8) can be appropriately increased. Thus, the problem that the corresponding filter coefficient in the filter coefficient matrix obtained by the traditional Wiener filtering algorithm is small due to the weak correlation between pilot points under a large delay spread channel can be effectively solved, and the problem that the corresponding filter coefficient in the filter coefficient matrix obtained by the traditional Wiener filtering algorithm is large due to the strong correlation between pilot points, which causes the noise power estimation to have a large deviation can be effectively solved.

[0066] As another implementation, the values ​​of each element on the main diagonal in the filter coefficient matrix may be directly accumulated, that is, the filter coefficients on the main diagonal in the filter coefficient matrix may be accumulated, and the target compensation factor may be determined according to the accumulation result and the filter order used in the filtering process. Specifically, the target compensation factor may be calculated using formula (4):

[0067]

[0068] Among them, w k，k Represents the element value of the kth row and kth column in the filter coefficient matrix, that is, the element value on the main diagonal of the filter coefficient matrix.

[0069] In some embodiments, the electronic device obtains a filter coefficient matrix of the MMSE filter algorithm, and may also construct a diagonal matrix according to the filter coefficient matrix and the unit matrix, and calculate the target compensation factor according to the diagonal matrix.

[0070] Specifically, the derivation process of the target compensation factor can be shown in the following formula (5), formula (6) and formula (7) to obtain the formula (10) for calculating the target compensation factor. It can be assumed that the correlation when the LS channel estimation result is subjected to MMSE filtering is ideal, then the autocorrelation matrix R = HH H , where H represents an ideal channel, then:

[0071]

[0072] Where n represents noise, σ 2 represents the ideal noise power and I is the identity matrix.

[0073] The autocorrelation matrix is ​​a Hermitian matrix, and the autocorrelation matrix can be subjected to singular value decomposition (SVD), and then R = U∑U H ,but:

[0074]

[0075] diag(-(R+σ 2 I)(R(R+σ 2 I) -1 -I))

[0076] =diag((UΣU H +σ 2 )(I-UΣU H (UΣU H +σ 2 I) -1 )

[0077] =diag(U(Σ+σ 2 I)(I-Σ((Σ+σ 2 I) -1 ) H )

[0078] =Σ i (λ i +σ 2 -λ i )|u ii | 2 =Σ i σ 2 |u ii | 2 Formula (7);

[0079] Among them, λ i is the eigenvalue, u ii Represents the elements in the U matrix.

[0080] Formula (6) and formula (7) can be substituted into formula (5) to obtain formula (8):

[0081]

[0082] because:

[0083]

[0084] Then, from formula (8) and formula (9), we can get formula (10) for calculating the target compensation factor:

[0085]

[0086] For example, Figure 4A FIG. 1 is a schematic diagram of noise power after compensating the noise power estimated at different SNR points in different channels in one embodiment. Figure 4BFIG. 1 is a schematic diagram showing the difference between the compensated noise power and the ideal noise power in an embodiment. Figure 2A and Figure 2B , the deviation between the compensated noise power and the ideal noise power is greatly improved, which improves the accuracy of noise estimation.

[0087] In an embodiment of the present application, by exploring the causes of noise estimation deviation in the noise estimation scheme in MMSE filtering and deriving the calculation formula of the target compensation factor, the noise estimation data is corrected and compensated according to the target compensation factor to obtain a more accurate target noise result, which can improve the accuracy of noise estimation for the received signal, and the scheme has low complexity and is easy to implement.

[0088] In some embodiments, the noise compensation method provided in the embodiments of the present application can also be applied to compensate for the noise covariance matrix based on the MMSE channel estimation algorithm. The above-mentioned noise estimation data includes a noise covariance matrix, and the step of calculating the target compensation factor according to the filter coefficient matrix corresponding to the MMSE filtering algorithm may include: according to the channel estimation result corresponding to each received received signal, the filter coefficient matrix of the MMSE filtering algorithm used when filtering is used, and calculating the target compensation factor corresponding to each received received signal;

[0089] The step of performing noise compensation on the noise estimation data according to the target compensation factor to obtain the target noise result may include: for the elements arranged in the i-th row and j-th column in the noise covariance matrix, performing noise compensation on the elements arranged in the i-th row and j-th column according to the target compensation factor corresponding to the i-th received signal and the target compensation factor of the j-th received signal to obtain the compensation result corresponding to the elements arranged in the i-th row and j-th column; and determining the target noise result according to the compensation results corresponding to each element in the noise covariance matrix.

[0090] The elements arranged in the i-th row and j-th column of the noise covariance matrix represent the noise correlation matrix between the i-th received signal and the j-th received signal, where i and j are both positive integers. Optionally, the noise covariance matrix can be expressed as R nn , then R nn (i, j) represents the element arranged in the i-th row and j-th column in the noise covariance matrix, that is, the noise correlation matrix between the i-th received signal and the j-th received signal. Different received signals may refer to received signals received by the electronic device through different antennas.

[0091] For each received signal, the target compensation factor corresponding to each received signal can be determined according to the filter coefficient matrix used when filtering the channel estimation result of each received signal. As a specific implementation, the noise compensation method of the elements on the main diagonal of the noise covariance matrix can be the same as the noise power compensation method provided in the above embodiments, and the elements R on the main diagonal can be nn The target compensation factor of the qth received signal corresponding to (q, q) is the same as the element R nn Multiply (q, q) to get element R nn The compensation result corresponding to (q, q).

[0092] For the off-diagonal elements in the noise covariance matrix, formula (11) can be used to compensate for the off-diagonal elements in the noise covariance matrix to obtain the compensation results corresponding to each element:

[0093]

[0094] Among them, R′ nn (i, j) represents the element R arranged in the i-th row and j-th column of the noise covariance matrix nn The compensation result corresponding to (i, j), ρ i represents the target compensation factor corresponding to the i-th received signal, ρ j represents the target compensation factor corresponding to the jth received signal. After the compensation results corresponding to the elements in the noise covariance matrix are obtained, the compensation results corresponding to the elements can be used as the target noise results.

[0095] In an embodiment of the present application, the noise covariance matrix based on the MMSE channel estimation can be corrected and compensated according to the target compensation factor to obtain a more accurate target noise result, which can improve the accuracy of noise estimation for the received signal, and the solution has low complexity and is easy to implement.

[0096] like Figure 5 As shown, in one embodiment, a noise compensation device 500 is provided, which can be applied to the above electronic equipment. The noise compensation device 500 may include

[0097] The channel estimation module 510 is used to perform channel estimation on the received signal to obtain a channel estimation result.

[0098] The noise estimation module 520 is used to filter the channel estimation result according to the MMSE filtering algorithm and determine the noise estimation data.

[0099] The compensation factor calculation module 530 is used to calculate the target compensation factor according to the filter coefficient matrix corresponding to the MMSE filter algorithm.

[0100] The compensation module 540 is used to perform noise compensation on the noise estimation data according to the target compensation factor to obtain a target noise result.

[0101] In one embodiment, the noise estimation data includes noise power. The compensation module 540 is further configured to multiply the target compensation factor by the noise power to obtain a target noise result.

[0102] In an embodiment of the present application, a target compensation factor is calculated based on a filter coefficient matrix used in filtering using an MMSE filtering algorithm, and the noise estimation data initially obtained is corrected and compensated based on the target compensation factor to obtain a more accurate target noise result, thereby improving the accuracy of noise estimation for a received signal, and the scheme has low complexity and is easy to implement.

[0103] In one embodiment, the compensation factor calculation module 530 is also used to adjust the filter coefficients corresponding to the first pilot point and the second pilot point in the filter coefficient matrix corresponding to the MMSE filtering algorithm based at least in part on the correlation between the first pilot point and the second pilot point in the received signal to obtain an updated filter coefficient matrix.

[0104] In one embodiment, the compensation factor calculation module 530 is also used to obtain a filter coefficient matrix of the MMSE filtering algorithm, where the filter coefficient matrix includes M groups of filter coefficients, and the M groups of filter coefficients correspond one-to-one to the M pilot points in the received signal, respectively, and M is a positive integer; and according to each group of filter coefficients corresponding to each pilot point, the noise compensation factor corresponding to each pilot point is calculated, and the target compensation factor is determined according to the noise compensation factor corresponding to each pilot point.

[0105] In one embodiment, the compensation factor calculation module 530 is further configured to calculate an average value of the noise compensation factor corresponding to each pilot point to obtain a target compensation factor.

[0106] In one embodiment, the compensation factor calculation module 530 is also used to obtain a filter coefficient matrix of the MMSE filtering algorithm, where the filter coefficient matrix includes M groups of filter coefficients, and the M groups of filter coefficients correspond one-to-one to the M pilot points in the received signal, respectively, and M is a positive integer; and to accumulate each filter coefficient on the main diagonal in the filter coefficient matrix, and determine the target compensation factor based on the accumulation result and the filter order used in the filtering process.

[0107] In one embodiment, the compensation factor calculation module 530 is also used to obtain a filter coefficient matrix of the MMSE filtering algorithm, where the filter coefficient matrix includes M groups of filter coefficients, and the M groups of filter coefficients correspond one-to-one to the M pilot points in the received signal, respectively, and M is a positive integer; a diagonal matrix is ​​constructed according to the filter coefficient matrix and the unit matrix, and the target compensation factor is calculated according to the diagonal matrix.

[0108] In an embodiment of the present application, by exploring the causes of noise estimation deviation in the noise estimation scheme in MMSE filtering and deriving the calculation formula of the target compensation factor, the noise estimation data is corrected and compensated according to the target compensation factor to obtain a more accurate target noise result, which can improve the accuracy of noise estimation for the received signal, and the scheme has low complexity and is easy to implement.

[0109] In one embodiment, the compensation factor calculation module 530 is further used to calculate the target compensation factor corresponding to each received signal according to the filter coefficient matrix of the MMSE filtering algorithm used when filtering the channel estimation result corresponding to each received signal.

[0110] The compensation module 540 is also used to perform noise compensation on the elements arranged in the i-th row and j-th column in the noise covariance matrix according to the target compensation factor corresponding to the i-th received signal and the target compensation factor of the j-th received signal, so as to obtain the compensation result corresponding to the elements arranged in the i-th row and j-th column; and determine the target noise result according to the compensation results corresponding to each element in the noise covariance matrix.

[0111] In an embodiment of the present application, the noise covariance matrix based on the MMSE channel estimation can be corrected and compensated according to the target compensation factor to obtain a more accurate target noise result, which can improve the accuracy of noise estimation for the received signal, and the solution has low complexity and is easy to implement.

[0112] Figure 6 FIG. 1 is a structural block diagram of an electronic device in an embodiment. Figure 6 As shown, the electronic device may include: a radio frequency module 610, a memory 620, an input unit 630, a display unit 640, a sensor 650, an audio circuit 660, a WiFi (Wireless Fidelity) module 670, a processor 680, and a power supply 690. Those skilled in the art will appreciate that Figure 6 The electronic device structure shown in the figure does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0113] The RF module 610 can be used for receiving and sending signals during the process of sending and receiving information or making calls. In particular, after receiving the downlink information of the base station, it is sent to the processor 680 for processing; in addition, the designed uplink data is sent to the base station. Generally, the RF module 610 includes but is not limited to an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier (LNA), a duplexer, etc. In addition, the RF module 610 can also communicate with the network and other devices through wireless communication. The above wireless communication can use any communication standard or protocol, including but not limited to the global system of mobile communication (GSM), general packet radio service (GPRS), code division multiple access (CDMA), wideband code division multiple access (WCDMA), long term evolution, email, short messaging service (SMS), etc.

[0114] The memory 620 can be used to store software programs and modules. The processor 680 executes various functional applications and data processing of the electronic device by running the software programs and modules stored in the memory 620. The memory 620 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device (such as audio data, a phone book, etc.), etc. In addition, the memory 620 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0115] The input unit 630 can be used to receive input digital or character information, and to generate key signal input related to user settings and function control of the electronic device. Specifically, the input unit 630 may include a touch panel 632 and other input devices 634. The touch panel 632, also known as a touch screen, can collect the user's touch operation on or near it (such as the user's operation on the touch panel 632 or near the touch panel 632 using any suitable object or accessory such as a finger, stylus, etc.), and drive the corresponding connection device according to a pre-set program. Optionally, the touch panel 632 may include a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into contact coordinates, and then sends it to the processor 680, and can receive and execute commands sent by the processor 680. In addition, the touch panel 632 can be implemented in various types such as resistive, capacitive, infrared, and surface acoustic waves. In addition to the touch panel 632, the input unit 630 may also include other input devices 634. Specifically, other input devices 634 may include but are not limited to one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, etc.

[0116] The display unit 640 can be used to display information input by the user or information provided to the user and various menus of the electronic device. The display unit 640 may include a display panel 642. Optionally, the display panel 642 may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc. Further, the touch panel 632 may cover the display panel 642. When the touch panel 632 detects a touch operation on or near it, it is transmitted to the processor 680 to determine the type of touch event. Subsequently, the processor 680 provides a corresponding visual output on the display panel 642 according to the type of touch event. Although in Figure 6 In the embodiment, the touch panel 632 and the display panel 642 are used as two independent components to implement the input and output functions of the electronic device, but in some embodiments, the touch panel 632 and the display panel 642 can be integrated to implement the input and output functions of the electronic device.

[0117] The electronic device may also include at least one sensor 650, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel 642 according to the brightness of the ambient light, and the proximity sensor may turn off the display panel 642 and / or the backlight when the electronic device is moved to the ear. As a type of motion sensor, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally three axes), and can detect the magnitude and direction of gravity when stationary. It can be used for applications that identify the posture of the electronic device (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; as for other sensors that the electronic device can also be configured with, such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc., they will not be repeated here.

[0118] The audio circuit 660, the speaker 662, and the microphone 664 can provide an audio interface between the user and the electronic device. The audio circuit 660 can transmit the received audio data to the speaker 662 after converting the electrical signal, which is converted into a sound signal for output; on the other hand, the microphone 664 converts the collected sound signal into an electrical signal, which is received by the audio circuit 660 and converted into audio data, and then the audio data is output to the processor 680 for processing, and then sent to another electronic device through the radio frequency module 610, or the audio data is output to the memory 620 for further processing.

[0119] WiFi is a short-range wireless transmission technology. Electronic devices can help users send and receive emails, browse web pages, and access streaming media through WiFi module 670, which provides users with wireless broadband Internet access.

[0120] The processor 680 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. It executes various functions of the electronic device and processes data by running or executing software programs and / or modules stored in the memory 620, and calling data stored in the memory 620, so as to monitor the electronic device as a whole. Optionally, the processor 680 may include one or more processing units; preferably, the processor 680 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 680.

[0121] The electronic device also includes a power supply 690 (such as a battery) for supplying power to each component. Preferably, the power supply can be logically connected to the processor 680 through a power management system, so that the power management system can manage charging, discharging, and power consumption management. Although not shown, the electronic device can also include a camera, a Bluetooth module, etc., which will not be repeated here.

[0122] In one embodiment, when the computer program stored in the memory 620 is executed by the processor 680, the processor 680 implements the methods described in the above embodiments.

[0123] An embodiment of the present application discloses a computer-readable storage medium storing a computer program, wherein the computer program implements the methods described in the above embodiments when executed by a processor.

[0124] An embodiment of the present application discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program can be executed by a processor to implement the methods described in the above embodiments.

[0125] A person skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM), etc.

[0126] As used herein, any reference to memory, storage, database, or other medium may include nonvolatile and / or volatile memory. Suitable nonvolatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which is used as an external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0127] It should be understood that the "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. Those skilled in the art should also be aware that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily required for the present application.

[0128] In the various embodiments of the present application, it should be understood that the size of the serial numbers of the above-mentioned processes does not necessarily 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.

[0129] The units described above 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.

[0130] 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. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0131] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible memory. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several requests for a computer device (which can be a personal computer, a server or a network device, etc., specifically a processor in a computer device) to perform some or all of the steps of the above-mentioned methods of various embodiments of the present application.

[0132] The above is a detailed introduction to a noise compensation method, device, electronic device and computer-readable storage medium disclosed in the embodiments of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core ideas of the present application. At the same time, for those of ordinary skill in the art, according to the ideas of the present application, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as limiting the present application.

Claims

1. A noise compensation method, characterized in that: include: Performing channel estimation on the received signal to obtain a channel estimation result; Filtering the channel estimation result according to the MMSE filtering algorithm, and determining noise estimation data according to a difference between the channel estimation result after filtering and the channel estimation result before filtering; Calculate the target compensation factor according to the filter coefficient matrix corresponding to the MMSE filter algorithm; Noise compensation is performed on the noise estimation data according to the target compensation factor to obtain a target noise result.

2. The method according to claim 1, characterized in that The calculating the target compensation factor according to the filter coefficient matrix corresponding to the MMSE filter algorithm includes: At least partially based on the correlation between the first pilot point and the second pilot point in the received signal, the filter coefficients corresponding to the first pilot point and the second pilot point in the filter coefficient matrix corresponding to the MMSE filtering algorithm are adjusted to obtain an updated filter coefficient matrix.

3. The method according to claim 1, characterized in that The calculating the target compensation factor according to the filter coefficient matrix corresponding to the MMSE filter algorithm includes: Acquire a filter coefficient matrix of the MMSE filter algorithm, wherein the filter coefficient matrix includes M groups of filter coefficients, the M groups of filter coefficients correspond one-to-one to M pilot points in the received signal, and M is a positive integer; According to each group of filter coefficients corresponding to each pilot point, a noise compensation factor corresponding to each pilot point is calculated, and a target compensation factor is determined according to the noise compensation factor corresponding to each pilot point.

4. The method according to claim 3, characterized in that The determining the target compensation factor according to the noise compensation factor corresponding to each of the pilot frequency points includes: An average value of the noise compensation factor corresponding to each of the pilot frequency points is calculated to obtain a target compensation factor.

5. The method according to claim 1, characterized in that The calculating the target compensation factor according to the filter coefficient matrix corresponding to the MMSE filter algorithm includes: Acquire a filter coefficient matrix of the MMSE filter algorithm, wherein the filter coefficient matrix includes M groups of filter coefficients, the M groups of filter coefficients correspond one-to-one to M pilot points in the received signal, and M is a positive integer; A diagonal matrix is ​​constructed according to the filter coefficient matrix and the unit matrix, and a target compensation factor is calculated according to the diagonal matrix.

6. The method according to claim 1, characterized in that The calculating the target compensation factor according to the filter coefficient matrix corresponding to the MMSE filter algorithm includes: Acquire a filter coefficient matrix of the MMSE filter algorithm, wherein the filter coefficient matrix includes M groups of filter coefficients, the M groups of filter coefficients correspond one-to-one to M pilot points in the received signal, and M is a positive integer; The filter coefficients on the main diagonal of the filter coefficient matrix are accumulated, and the target compensation factor is determined according to the accumulation result and the filter order used in the filtering process.

7. The method according to any one of claims 1 to 6, characterized in that: The noise estimation data includes noise power; and performing noise compensation on the noise estimation data according to the target compensation factor to obtain a target noise result, including: The target compensation factor is multiplied by the noise power to obtain a target noise result.

8. The method according to any one of claims 1 to 6, characterized in that: The noise estimation data includes a noise covariance matrix, wherein the elements arranged in the i-th row and the j-th column of the noise covariance matrix represent a noise correlation matrix between the i-th received signal and the j-th received signal, and i and j are both positive integers; The calculating the target compensation factor according to the filter coefficient matrix corresponding to the MMSE filter algorithm includes: Calculate the target compensation factor corresponding to each received signal according to the filter coefficient matrix of the MMSE filter algorithm used when filtering according to the channel estimation result corresponding to each received signal; The performing noise compensation on the noise estimation data according to the target compensation factor to obtain a target noise result includes: For the elements arranged in the i-th row and j-th column in the noise covariance matrix, noise compensation is performed on the elements arranged in the i-th row and j-th column according to the target compensation factor corresponding to the i-th received signal and the target compensation factor of the j-th received signal, so as to obtain a compensation result corresponding to the elements arranged in the i-th row and j-th column; The target noise result is determined according to the compensation result corresponding to each element in the noise covariance matrix.

9. The method according to claim 8, characterized in that The following formula is used to perform noise compensation on the elements arranged in the i-th row and j-th column in the noise covariance matrix: Among them, R nn represents the noise covariance matrix, R nn (i, j) represents the element arranged in the i-th row and j-th column in the noise covariance matrix, R′ nn (i, j) represents the compensation result corresponding to the element arranged in the i-th row and j-th column, ρ i represents the target compensation factor corresponding to the i-th received signal, ρ j represents the target compensation factor of the j-th received signal.

10. A noise compensation device, characterized in that: include: A channel estimation module is used to perform channel estimation on a received signal to obtain a channel estimation result; A noise estimation module, used for filtering the channel estimation result according to the MMSE filtering algorithm, and determining the noise estimation data according to the difference between the channel estimation result after filtering and the channel estimation result before filtering; A compensation factor calculation module, used to calculate a target compensation factor according to a filter coefficient matrix corresponding to the MMSE filter algorithm; The compensation module is used to perform noise compensation on the noise estimation data according to the target compensation factor to obtain a target noise result.

11. An electronic device, characterized in that: The method comprises a memory, a processor and a transceiver unit. The memory stores a computer program. When the computer program is executed by the processor, the processor implements the method according to any one of claims 1 to 9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor in a computer device, the processor is enabled to implement the method according to any one of claims 1 to 9.

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