Noise whitening method, apparatus, device, and storage medium

Abstract

The present application relates to the technical field of noise processing, and discloses a noise whitening method, device, equipment and storage medium, the method comprises the following steps: constructing an error function based on an equalization signal and a training sequence; calculating based on the error function to obtain gradient information; updating the tap coefficient of the noise whitening filter based on the gradient information to obtain a target tap coefficient; whitening the colored noise based on the target tap coefficient. The present application updates the tap coefficient of the noise whitening filter by formulating the error function and calculating the gradient, solves the problem that when the channel equalization technology is used to overcome the bandwidth limitation, the equalization enhances the colored noise, leads to uneven noise power spectrum, and thus reduces the IM-DD transmission rate, realizes the whitening of the colored noise, makes the power spectrum flat, and thus significantly improves the performance of the IM-DD system and increases the communication capacity.

Description

Technical Field

[0001] This invention relates to the field of noise treatment technology, and in particular to a noise whitening method, apparatus, device, and storage medium. Background Technology

[0002] Achieving 200Gbps data transmission per wave in IM-DD communication systems is a challenging task. However, the bandwidth limitations of communication devices inevitably affect the signal baud rate. While the receiver can use channel equalization techniques to overcome these bandwidth limitations, this also introduces equalization-enhanced colored noise, resulting in a non-uniform noise power spectrum and thus reducing the IM-DD transmission rate. Summary of the Invention

[0003] The main objective of this invention is to provide a noise whitening method, apparatus, device, and storage medium, which aims to solve the technical problem that when using channel equalization technology to overcome bandwidth limitations, the introduction of equalization-enhanced colored noise leads to uneven noise power spectrum, thereby reducing the IM-DD transmission rate.

[0004] To achieve the above objectives, the present invention provides a noise whitening method, the method comprising the following steps:

[0005] An error function is constructed based on the equalization signal and training sequence;

[0006] Gradient information is obtained by calculating based on the error function.

[0007] The tap coefficients of the noise whitening filter are updated based on the gradient information to obtain the target tap coefficients;

[0008] The colored noise is whitened based on the target tap coefficient.

[0009] Optionally, the construction of the error function based on the equalization signal and the training sequence includes:

[0010] Construct a noise whitening filter;

[0011] The equalization signal and the training sequence are input into the noise whitening filter for noise filtering to obtain a first filtered signal and a second filtered signal.

[0012] The error function is obtained by calculating based on the first and second filtered signals.

[0013] Optionally, before constructing the error function based on the equalization signal and training sequence, the method further includes:

[0014] Obtain an initial symbol sequence, wherein the initial symbol sequence is a symbol sequence with inter-symbol interference received by the communication receiver;

[0015] The initial symbol sequence is input into a linear equalizer for linear equalization to obtain an equalized signal.

[0016] Optionally, the step of inputting the initial symbol sequence into a linear equalizer for linear equalization to obtain an equalized signal includes:

[0017] The initial symbol sequence is linearly equalized by the linear equalizer to obtain a symbol sequence with inter-symbol interference removed and equalized and enhanced colored noise.

[0018] Based on the symbol sequence with inter-symbol interference removed and the colored noise with equalization enhancement, an equalized signal is obtained.

[0019] Optionally, after obtaining the initial symbol sequence, the method further includes:

[0020] The initial symbol sequence is input into a nonlinear equalizer for nonlinear equalization to obtain an equalized signal.

[0021] Optionally, updating the tap coefficients of the noise whitening filter based on the gradient information to obtain the target tap coefficients includes:

[0022] The gradient direction and gradient step size are determined based on the gradient information.

[0023] The tap coefficients of the noise whitening filter are adjusted along the gradient direction according to the gradient step size until convergence is achieved, thus obtaining the target tap coefficients.

[0024] Optionally, after whitening the colored noise based on the target tap coefficient, the method further includes:

[0025] The maximum likelihood sequence estimate is determined based on the target tap coefficient;

[0026] The target symbol sequence is obtained by decoding the controllable inter-symbol interference caused by the noise whitening filter using the maximum likelihood sequence estimation.

[0027] Furthermore, to achieve the above objectives, the present invention also proposes a noise whitening device, the noise whitening device comprising:

[0028] The building block is used to construct an error function based on the equalization signal and the training sequence;

[0029] The calculation module is used to perform calculations based on the error function to obtain gradient information;

[0030] The update module is used to update the tap coefficients of the noise whitening filter based on the gradient information to obtain the target tap coefficients;

[0031] The whitening module is used to whiten colored noise based on the target tap coefficient.

[0032] Furthermore, to achieve the above objectives, the present invention also proposes a noise whitening device, the noise whitening device comprising: a memory, a processor, and a noise whitening program stored in the memory and executable on the processor, the noise whitening program being configured to implement the steps of the noise whitening method as described above.

[0033] Furthermore, to achieve the above objectives, the present invention also proposes a storage medium storing a noise whitening program, which, when executed by a processor, implements the steps of the noise whitening method described above.

[0034] This invention constructs an error function based on the equalization signal and training sequence; calculates gradient information based on the error function; updates the tap coefficients of the noise whitening filter based on the gradient information to obtain the target tap coefficients; and whitens colored noise based on the target tap coefficients. By defining the error function and calculating the gradient to update the tap coefficients of the noise whitening filter, this invention solves the problem that introducing equalization-enhanced colored noise to overcome bandwidth limitations when using channel equalization techniques leads to uneven noise power spectrum, thus reducing the IM-DD transmission rate. It achieves whitening of colored noise, flattening its power spectrum, thereby significantly improving the performance of the IM-DD system and increasing communication capacity. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the structure of the noise whitening device for the hardware operating environment involved in the embodiments of the present invention;

[0036] Figure 2 This is a flowchart illustrating the first embodiment of the noise whitening method of the present invention;

[0037] Figure 3 This is a flowchart illustrating the second embodiment of the noise whitening method of the present invention;

[0038] Figure 4 This is a schematic diagram of a linear noise whitening algorithm based on gradient descent, according to an embodiment of the noise whitening method of the present invention.

[0039] Figure 5 This is a schematic diagram of a nonlinear noise whitening algorithm based on gradient descent, according to an embodiment of the noise whitening method of the present invention.

[0040] Figure 6 This is a structural block diagram of the first embodiment of the noise whitening device of the present invention.

[0041] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0042] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0043] Reference Figure 1 , Figure 1 This is a schematic diagram of the noise whitening device structure in the hardware operating environment involved in the embodiments of the present invention.

[0044] like Figure 1 As shown, the noise whitening device may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen or an input unit such as a keyboard; optionally, the user interface 1003 may also include a standard wired interface or a wireless interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be high-speed random access memory (RAM) or stable non-volatile memory (NVM), such as a disk storage device. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0045] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the noise whitening device and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0046] like Figure 1 As shown, the memory 1005, which serves as a storage medium, may include an operating system, a network communication module, a user interface module, and a noise whitening program.

[0047] exist Figure 1 In the noise whitening device shown, the network interface 1004 is mainly used for data communication with the network server; the user interface 1003 is mainly used for data interaction with the user; the processor 1001 and the memory 1005 in the noise whitening device of the present invention can be set in the noise whitening device, and the noise whitening device calls the noise whitening program stored in the memory 1005 through the processor 1001 and executes the noise whitening method provided in the embodiment of the present invention.

[0048] This invention provides a noise whitening method, referring to... Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the noise whitening method of the present invention.

[0049] In this embodiment, the noise whitening method includes the following steps:

[0050] Step S10: Construct an error function based on the equalization signal and training sequence.

[0051] It should be noted that the execution subject of this embodiment is a noise whitening device, but it can also be other devices with the same or similar functions. This embodiment does not make specific limitations on this. This embodiment uses a noise whitening device as an example for explanation.

[0052] It is understood that the equalization signal refers to the output signal of the equalizer, which can be the output signal of a linear equalizer or the output signal of a nonlinear equalizer. This embodiment does not impose any specific restrictions on this.

[0053] In the specific implementation, the equalization signal and training sequence are input into the noise whitening filter, and the error function is constructed based on the output of the noise whitening filter.

[0054] Step S20: Calculate the gradient information based on the error function.

[0055] It should be noted that the gradient of the error function relative to the tap of the noise whitening filter is calculated, and the gradient direction is obtained. Based on the gradient direction and the pre-set gradient step size, the gradient information is obtained.

[0056] Understandably, the gradient of the error function relative to the tap of the noise whitening filter is usually calculated by calculating the gradient of the mean square error function (MSE).

[0057] In the specific implementation, for each tap coefficient α, the partial derivative of the error function is calculated to form a gradient vector. The gradient of the error function with respect to the taps of the noise whitening filter at time n is calculated as follows:

[0058] Formula 1:

[0059]

[0060] In Equation 1, α M Let be the tap coefficients of the noise whitening filter, M be the number of taps, and e(n) be the number of taps. 2 Let O(n) be the error function, O(n) be the output of the equalization signal after passing through the noise whitening filter (i.e., the first filtered signal), and TS(n) be the output of the training sequence after passing through the noise whitening filter (i.e., the second filtered signal). Let n be the time interval. This represents colored noise that has been uniformly enhanced.

[0061] Step S30: Update the tap coefficients of the noise whitening filter based on the gradient information to obtain the target tap coefficients.

[0062] It should be noted that the weight values ​​of the noise whitening filter are adjusted in the negative direction of the gradient based on the gradient information in order to find the global optimum and obtain the optimal tap coefficient, i.e., the target tap coefficient.

[0063] Further, updating the tap coefficients of the noise whitening filter based on the gradient information to obtain the target tap coefficients includes: determining the gradient direction and gradient step size based on the gradient information; adjusting the tap coefficients of the noise whitening filter along the gradient direction according to the gradient step size until convergence is obtained to obtain the target tap coefficients.

[0064] It should be noted that gradient information is used to adjust the weight values ​​of the noise whitening filter in the negative direction of the gradient in order to find the global optimum. The step size of descent is controlled to ensure the stability and convergence of the algorithm. Generally, the gradient step size can be set to 0.005. This embodiment does not impose specific restrictions on this.

[0065] In the specific implementation, the tap update of the noise whitening filter is as follows: Equation 2:

[0066]

[0067] In Equation 2, α (n+1) For the updated tap coefficient, α (n) Here, μ represents the tap coefficients before the update, O(n) is the step size of the gradient descent, TS(n) is the output of the equalized signal after passing through the noise whitening filter (i.e., the first filtered signal), and TS(n) is the output of the training sequence after passing through the noise whitening filter (i.e., the second filtered signal). Here, n represents the time step. This represents the colored noise that has been equalized and enhanced, where M is the number of taps.

[0068] It is worth noting that the weights of the noise whitening filter are updated by minimizing the error function. In this embodiment, the mean square error is used as the error function. However, optimization algorithms such as least squares, conjugate gradient descent, and quasi-Newton optimization can also be used as the error function to update the filter taps; this embodiment does not impose specific limitations on this. For example, the filter tap coefficients are updated using the least squares method, as shown in equations 3 to 5 below:

[0069] α (n+1) =α (n) -G T ×(e(n)) * (Equation 3)

[0070] G = Δ × e(n) T / (λ+(e(n))* ×Δ×e(n) T (Equation 4)

[0071] Δ=1 / λ×(Δ-G×e(n)×Δ) (Formula 5)

[0072] In equations 3 to 5, α (n+1) For the updated tap coefficient, α (n) denoted as the tap coefficients before the update, TS(n) is the output of the training sequence after passing through the noise whitening filter, e(n) is the error, G is the gain vector, Δ is the correlation matrix, and λ is the forgetting factor.

[0073] Step S40: Whiten the colored noise based on the target tap coefficient.

[0074] It should be noted that colored noise refers to noise whose power spectral density is not uniform across the frequency range. Colored noise causes a non-uniform noise power spectrum, thereby reducing the IM-DD transmission rate.

[0075] It is understandable that by using a noise whitening filter with target tap coefficients to whiten colored noise and flatten its power spectrum, the performance of the IM-DD system can be significantly improved and the communication capacity increased.

[0076] Furthermore, after whitening the colored noise based on the target tap coefficient, the method further includes: determining the maximum likelihood sequence estimate based on the target tap coefficient; and decoding the controllable inter-symbol interference caused by the noise whitening filter using the maximum likelihood sequence estimate to obtain the target symbol sequence.

[0077] It should be noted that maximum likelihood sequence estimation is used to estimate the most likely transmitted sequence of the signal sequence observed at the receiver, especially in the presence of inter-symbol interference, in order to improve decoding performance.

[0078] Understandably, the whitened signal is decoded using maximum likelihood sequence estimation in order to reconstruct the symbol sequence sent by the transmitter.

[0079] This embodiment constructs an error function based on the equalization signal and training sequence; calculates gradient information based on the error function; updates the tap coefficients of the noise whitening filter based on the gradient information to obtain the target tap coefficients; and whitens the colored noise based on the target tap coefficients. By defining the error function and calculating the gradient to update the tap coefficients of the noise whitening filter, this method solves the problem of introducing equalization enhancement of colored noise when using channel equalization technology to overcome bandwidth limitations, resulting in uneven noise power spectrum and reduced IM-DD transmission rate. It achieves whitening of colored noise, flattening its power spectrum, thereby significantly improving the performance of the IM-DD system and increasing communication capacity.

[0080] refer to Figure 3 , Figure 3 This is a flowchart illustrating the second embodiment of the noise whitening method of the present invention.

[0081] Based on the first embodiment described above, step S10 in the noise whitening method of this embodiment includes:

[0082] Step S101: Construct a noise whitening filter.

[0083] It should be noted that noise whitening filters are used to reverse the color of colored noise, making the power spectral density of the output signal more uniform across the frequency range, similar to white noise, in order to improve the quality and reliability of the signal.

[0084] Understandably, in order to flatten the power spectrum of the colored noise EECN, a noise whitening filter is constructed, and its Z-transform effect is as follows: Equation 6:

[0085]

[0086] In Equation 6, H GD-NM For noise whitening filters, α N Let N be the tap coefficients of the filter, and N be the filter length, where N = 1, 2, ..., M, and M is the number of taps.

[0087] Step S102: Input the equalization signal and the training sequence into the noise whitening filter for noise filtering to obtain the first filtered signal and the second filtered signal.

[0088] It should be noted that the equalization signal is input to the noise whitening filter for noise filtering, and the output of the noise whitening filter is the first filtered signal.

[0089] It is understandable that when the equalization signal is the linearly equalized signal, the linearly equalized signal, after passing through the noise whitening filter, can be represented as the output of the noise whitening filter, i.e., the first filtered signal, as shown in Equation 7 below:

[0090]

[0091] In Equation 7, O(n) is the output of the equalized signal after passing through the noise whitening filter, i.e., the first filtered signal. The signal after equalization, i.e., the equalized signal. k represents the tap coefficients of the filter. L Let k be the filter length, where k is the filter length. L =1,2,…,M, where M is the number of taps.

[0092] It is worth noting that the training sequence refers to a training sequence with a small number of known symbols. The training sequence is input into the noise whitening filter for noise filtering, and the output of the noise whitening filter is the second filtered signal.

[0093] Understandably, a small number of training sequences with known symbols are used to bring the algorithm to convergence. The training sequences are also passed through a noise whitening filter, which can be represented as the output of the noise whitening filter, i.e., the second filtered signal, as shown in Equation 8 below:

[0094]

[0095] In Equation 8, TS(n) is the output of the training sequence after passing through the noise whitening filter, i.e., the second filtered signal, x(n) is a training sequence with a small number of known symbols, and α kL k represents the tap coefficients of the filter. L Let k be the filter length, where k is the filter length. L =1,2,…,M, where M is the number of taps.

[0096] Step S103: Calculate the error function based on the first filtered signal and the second filtered signal.

[0097] It should be noted that the error is defined as the difference between the equalization signal and the training sequence noise output after passing through the whitening filter, that is, the difference between the first filtered signal and the second filtered signal.

[0098] It is understandable that the error is calculated based on the first and second filtered signals, and the mean square error is obtained from the error to get the error function.

[0099] In the specific implementation, the difference between the equalization signal and the training sequence noise output after passing through the whitening filter is calculated to obtain the error, as shown in Equation 9 below:

[0100]

[0101] In Equation 9, e(n) represents the error, O(n) is the output of the equalized signal after passing through the noise whitening filter (i.e., the first filtered signal), and TS(n) is the output of the training sequence after passing through the noise whitening filter (i.e., the second filtered signal). For the equalization signal, x(n) is a training sequence with a small number of known symbols. k represents the tap coefficients of the filter. L Let k be the filter length, where k is the filter length. L =1,2,…,M, where M is the number of taps. This represents colored noise that has been uniformly enhanced.

[0102] It is worth noting that in this embodiment, the error function can be defined as the mean square error, as shown in Equation 10 below:

[0103] MSE = E{e(n)} 2 (Equation 10)

[0104] In Equation 10, MSE is the mean square error, i.e., the error function, and e(n) is the error.

[0105] Furthermore, before constructing the error function based on the equalization signal and the training sequence, the method further includes: obtaining an initial symbol sequence, wherein the initial symbol sequence is a symbol sequence with inter-symbol interference received by the communication receiver; and inputting the initial symbol sequence into a linear equalizer for linear equalization to obtain an equalization signal.

[0106] It should be noted that the initial symbol sequence r(n) is the symbol sequence with inter-symbol interference received by the communication receiver.

[0107] Inter-symbol interference (ISI) is understandably a problem in digital communication systems. It occurs between adjacent symbols, making it difficult for the receiver to correctly identify the symbols. This interference is primarily caused by signal propagation in the transmission channel, typically due to bandwidth limitations or multipath effects in the signal propagation path. In digital communication systems, transmitted symbols, after passing through the channel, are affected by the preceding and / or following symbols, leading to inter-symbol interference. This can cause the receiver to fail to accurately identify symbols during sampling, thus degrading the performance of the communication system.

[0108] like Figure 4 As shown, Figure 4 This is a schematic diagram of a gradient descent-based linear noise whitening algorithm. The linear noise whitening algorithm includes a linear equalizer, a linear noise whitening filter, and a decoder. The symbol sequence r(n) with intersymbol interference received by the communication receiver is linearly equalized by the linear equalizer, and the equalized signal is output. Linear noise whitening filter for equalized signals Noise filtering is performed to obtain the output O(n) of the equalized signal after passing through the noise whitening filter. The training sequence x(n) is then filtered by a linear noise whitening filter to obtain the output TS(n) of the training sequence after passing through the noise whitening filter. Finally, the signal is decoded by a decoder to obtain the symbol sequence.

[0109] Further, the step of inputting the initial symbol sequence into a linear equalizer for linear equalization to obtain an equalized signal includes: performing linear equalization on the initial symbol sequence through the linear equalizer to obtain a symbol sequence with inter-symbol interference removed and equalized enhanced colored noise; and obtaining an equalized signal based on the symbol sequence with inter-symbol interference removed and the equalized enhanced colored noise.

[0110] It should be noted that linear equalizers are a common tool in digital communication systems used to combat inter-symbol interference (ISI). They adjust the weighting coefficients of the received signal to cancel out inter-symbol interference caused during transmission, thereby recovering the original symbol sequence. The basic idea of ​​a linear equalizer is to linearly combine the received signals at the receiving end, minimizing the impact on the preceding and following symbols during sampling.

[0111] Understandably, after passing through a linear equalizer, intersymbol interference is completely eliminated, but the equalized signal... However, it is affected by colored noise EECN, as shown in Equation 11:

[0112]

[0113] In Equation 11, For the equalized signal, x(n) is the symbol sequence after removing inter-symbol interference. This represents colored noise that has been uniformly enhanced.

[0114] Furthermore, after obtaining the initial symbol sequence, the method further includes: inputting the initial symbol sequence into a nonlinear equalizer for nonlinear equalization to obtain an equalized signal.

[0115] It should be noted that, unlike linear equalizers, nonlinear equalizers introduce nonlinear elements to more flexibly adapt to complex channel characteristics and nonlinear distortion.

[0116] Understandably, nonlinear equalization through a nonlinear equalizer can introduce nonlinear taps to address nonlinear impairments and whiten colored noise, thus providing more comprehensive performance optimization by simultaneously handling nonlinear impairments and colored noise.

[0117] like Figure 5 As shown, Figure 5 This is a schematic diagram of a gradient descent-based nonlinear noise whitening algorithm. The nonlinear noise whitening algorithm includes a nonlinear equalizer, a nonlinear noise whitening filter, and a decoder. The symbol sequence r(n) with intersymbol interference received by the communication receiver is linearly equalized by the nonlinear equalizer, and the equalized signal is output. Nonlinear noise whitening filter for equalized signals Noise filtering is performed to obtain the output O(n) of the equalized signal after passing through a non-noise whitening filter. The training sequence x(n) is then filtered for noise by a non-linear noise whitening filter to obtain the output TS(n) of the training sequence after passing through a non-noise whitening filter. Finally, the signal is decoded by a decoder to obtain the symbol sequence.

[0118] This embodiment constructs a noise whitening filter; the equalization signal and the training sequence are input into the noise whitening filter for noise filtering to obtain a first filtered signal and a second filtered signal; an error function is calculated based on the first and second filtered signals. By using the noise whitening filter to filter the equalization signal and the training sequence, the accuracy of gradient information is improved, thereby enhancing the effect of colored noise whitening.

[0119] Reference Figure 6 , Figure 6 This is a structural block diagram of the first embodiment of the noise whitening device of the present invention.

[0120] like Figure 6 As shown, the noise whitening device proposed in this embodiment of the invention includes:

[0121] Module 10 is used to construct an error function based on the equalization signal and the training sequence.

[0122] The calculation module 20 is used to perform calculations based on the error function to obtain gradient information.

[0123] The update module 30 is used to update the tap coefficients of the noise whitening filter based on the gradient information to obtain the target tap coefficients.

[0124] Whitening module 40 is used to whiten colored noise based on the target tap coefficient.

[0125] This embodiment constructs an error function based on the equalization signal and training sequence; calculates gradient information based on the error function; updates the tap coefficients of the noise whitening filter based on the gradient information to obtain the target tap coefficients; and whitens the colored noise based on the target tap coefficients. By defining the error function and calculating the gradient to update the tap coefficients of the noise whitening filter, this method solves the problem of introducing equalization enhancement of colored noise when using channel equalization technology to overcome bandwidth limitations, resulting in uneven noise power spectrum and reduced IM-DD transmission rate. It achieves whitening of colored noise, flattening its power spectrum, thereby significantly improving the performance of the IM-DD system and increasing communication capacity.

[0126] In one embodiment, the construction module 10 is further configured to construct a noise whitening filter; input the equalization signal and the training sequence into the noise whitening filter for noise filtering to obtain a first filtered signal and a second filtered signal; and calculate an error function based on the first filtered signal and the second filtered signal.

[0127] In one embodiment, the construction module 10 is further configured to acquire an initial symbol sequence, wherein the initial symbol sequence is a symbol sequence with inter-symbol interference received by the communication receiver; and input the initial symbol sequence into a linear equalizer for linear equalization to obtain an equalized signal.

[0128] In one embodiment, the construction module 10 is further configured to perform linear equalization on the initial symbol sequence through the linear equalizer to obtain a symbol sequence with inter-symbol interference removed and equalized enhanced colored noise; and to obtain an equalized signal based on the symbol sequence with inter-symbol interference removed and the equalized enhanced colored noise.

[0129] In one embodiment, the construction module 10 is further configured to input the initial symbol sequence into a nonlinear equalizer for nonlinear equalization to obtain an equalized signal.

[0130] In one embodiment, the update module 30 is further configured to determine the gradient direction and gradient step size based on the gradient information; adjust the tap coefficients of the noise whitening filter along the gradient direction according to the gradient step size until convergence is achieved, thereby obtaining the target tap coefficients.

[0131] In one embodiment, the whitening module 40 is further configured to determine the maximum likelihood sequence estimate based on the target tap coefficient; and to decode the controllable inter-symbol interference caused by the noise whitening filter using the maximum likelihood sequence estimate to obtain the target symbol sequence.

[0132] Furthermore, to achieve the above objectives, the present invention also proposes a noise whitening device, the noise whitening device comprising: a memory, a processor, and a noise whitening program stored in the memory and executable on the processor, the noise whitening program being configured to implement the steps of the noise whitening method as described above.

[0133] Since this noise whitening device adopts all the technical solutions of all the above embodiments, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be repeated here.

[0134] Furthermore, embodiments of the present invention also propose a storage medium storing a noise whitening program, wherein when the noise whitening program is executed by a processor, it implements the steps of the noise whitening method described above.

[0135] Since this storage medium adopts all the technical solutions of all the above embodiments, it has at least all the beneficial effects brought about by the technical solutions of the above embodiments, which will not be repeated here.

[0136] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solutions of the present invention. In specific applications, those skilled in the art can make settings as needed, and the present invention does not impose any restrictions on this.

[0137] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this invention. In practical applications, those skilled in the art can select some or all of the workflow to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0138] In addition, for technical details not described in detail in this embodiment, please refer to the noise whitening method provided in any embodiment of the present invention, which will not be repeated here.

[0139] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0140] It should be understood that although the steps in the flowcharts of this application's embodiments are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0141] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0142] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0143] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A noise whitening method, characterized in that, The method includes: An error function is constructed based on the equalization signal and training sequence; Gradient information is obtained by calculating based on the error function. The tap coefficients of the noise whitening filter are updated based on the gradient information to obtain the target tap coefficients; The colored noise is whitened based on the target tap coefficient; The construction of the error function based on the equalization signal and training sequence includes: Construct a noise whitening filter; The equalization signal and the training sequence are input into the noise whitening filter for noise filtering to obtain a first filtered signal and a second filtered signal. The error function is obtained by calculating based on the first and second filtered signals. The step of updating the tap coefficients of the noise whitening filter based on the gradient information to obtain the target tap coefficients includes: The gradient direction and gradient step size are determined based on the gradient information. The tap coefficients of the noise whitening filter are adjusted along the gradient direction according to the gradient step size until convergence is achieved, thus obtaining the target tap coefficients.

2. The method as described in claim 1, characterized in that, Before constructing the error function based on the equalization signal and training sequence, the following steps are also included: Obtain an initial symbol sequence, wherein the initial symbol sequence is a symbol sequence with inter-symbol interference received by the communication receiver; The initial symbol sequence is input into a linear equalizer for linear equalization to obtain an equalized signal.

3. The method as described in claim 2, characterized in that, The step of inputting the initial symbol sequence into a linear equalizer for linear equalization to obtain an equalized signal includes: The initial symbol sequence is linearly equalized by the linear equalizer to obtain a symbol sequence with inter-symbol interference removed and equalized and enhanced colored noise. Based on the symbol sequence with inter-symbol interference removed and the colored noise with equalization enhancement, an equalized signal is obtained.

4. The method as described in claim 3, characterized in that, After obtaining the initial symbol sequence, the process further includes: The initial symbol sequence is input into a nonlinear equalizer for nonlinear equalization to obtain an equalized signal.

5. The method as described in claim 1, characterized in that, After whitening the colored noise based on the target tap coefficient, the process further includes: The maximum likelihood sequence estimate is determined based on the target tap coefficient; The target symbol sequence is obtained by decoding the controllable inter-symbol interference caused by the noise whitening filter using the maximum likelihood sequence estimation.

6. A noise whitening device, characterized in that, The noise whitening device includes: The building block is used to construct an error function based on the equalization signal and the training sequence; The calculation module is used to perform calculations based on the error function to obtain gradient information; The update module is used to update the tap coefficients of the noise whitening filter based on the gradient information to obtain the target tap coefficients; The whitening module is used to whiten colored noise based on the target tap coefficient; The construction module is further configured to construct a noise whitening filter; input the equalization signal and the training sequence into the noise whitening filter for noise filtering to obtain a first filtered signal and a second filtered signal; and calculate an error function based on the first filtered signal and the second filtered signal. The update module is further configured to determine the gradient direction and gradient step size based on the gradient information; adjust the tap coefficients of the noise whitening filter along the gradient direction according to the gradient step size until convergence is achieved, thereby obtaining the target tap coefficients.

7. A noise whitening device, characterized in that, The noise whitening device includes: a memory, a processor, and a noise whitening program stored in the memory and executable on the processor, the noise whitening program being configured to implement the noise whitening method as described in any one of claims 1 to 5.

8. A storage medium, characterized in that, The storage medium stores a noise whitening program, which, when executed by a processor, implements the noise whitening method as described in any one of claims 1 to 5.

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