Signal noise reduction method, receiving device and medium

Abstract

The present application relates to a noise cancellation method, a receiving device, and a medium for a power line communication system. The noise cancellation method is used for a power line system including a transmitting device, a receiving device, and a power line, and the transmitting device communicates with the receiving device through the power line. The method includes: the receiving device receives a first signal sent by the transmitting device through the power line, and divides the first signal into a plurality of first signal segments; the receiving device determines the noise interval to which the signal energy value of each first signal segment belongs, and performs noise cancellation on the signal using a noise cancellation method corresponding to the noise interval. Through the method of the present application, the receiving device can calculate and determine the noise interval parameter and the noise interval of the noise interval parameter through the signal energy value of the noise segment in the actual signal sent by the transmitting device, and can perform noise cancellation more accurately for different signals sent by the transmitting device.

Description

Technical Field

[0001] The present application relates to communication technology, and more particularly to a signal noise reduction method, a receiving device, and a medium. Background Art

[0002] Power networks are widely present in large, medium and small cities. Multiple communication devices can communicate through the power lines of the power network, which is called Power Line Communication (PLC). In current power line communication technology, Multiple-Input Multiple-Output (MIMO) technology is widely used. Multiple-Input Multiple-Output refers to the communication between multiple communication devices through multiple transmitting antennas and multiple receiving antennas. Figure 1 As shown, routers 100 and 200 supporting power line communication are connected to power sources 300 and 400, respectively. Routers 100 and 200 communicate via the power line, live wire, neutral wire, and ground wire between power sources 300 and 400. In other words, routers 100 and 200 use the live wire, neutral wire, and ground wire as transmitting and receiving antennas, respectively, to form a MIMO-PLC channel for communication.

[0003] exist Figure 1 During power line communication between routers 100 and 200, the signals transmitted between routers 100 and 200 may be interfered with by various noises. For example, the use of high-power electrical appliances may generate pulse noise in the power line communication. The pulse noise may increase interference with the power line communication signal, significantly affecting the communication quality. An existing method for denoising pulse noise in power line communication signals is to compare the energy of the signal with a preset noise energy threshold, and denoise the signal when the energy of the signal exceeds the preset noise energy threshold. However, the noise energy threshold here is often determined based on simulation data or empirical judgment. Using this noise energy threshold for actual power line communication signals may result in inaccuracies. Summary of the Invention

[0004] The purpose of the present application is to provide a signal denoising method, a receiving device and a medium. Through the method of the present application, the receiving device can calculate and determine the noise interval parameter and the noise interval of the noise interval parameter based on the signal energy value of the noise segment in the actual signal sent by the sending device, so that different signals sent by the sending device can be denoised more accurately.

[0005] A first aspect of the present application provides a signal noise reduction method for a power line communication system, characterized in that the power line system includes a transmitting device, a receiving device, and a power line, wherein the transmitting device communicates with the receiving device via the power line;

[0006] Methods include:

[0007] The receiving device receives the first signal sent by the sending device through the power line and divides the first signal into a plurality of first signal segments;

[0008] The receiving device determines the noise interval to which the signal energy value of each first signal segment belongs, and performs noise reduction processing on the signal using a noise reduction method corresponding to the noise interval, wherein

[0009] There are multiple noise intervals, and the division of the multiple noise intervals is determined based on the signal energy value of at least one noise segment in the second signal received by the receiving device from the sending device before the receiving device receives the first signal.

[0010] In a possible implementation of the first aspect, the receiving device divides the noise interval in the following manner:

[0011] The receiving device divides the second signal into a plurality of second signal segments, and selects at least one noise segment from the plurality of second signal segments;

[0012] The receiving device determines a plurality of noise thresholds based on the signal energy value of at least one noise segment, and divides the noise intervals according to the plurality of noise thresholds.

[0013] In a possible implementation of the first aspect, the receiving apparatus selects at least one noise segment from the plurality of second signal segments in the following manner:

[0014] A second signal segment whose signal energy value is greater than a preset energy threshold is selected as a noise segment.

[0015] That is, in an embodiment of the present application, the sending device and the receiving device may be a router or a home gateway, and may also be referred to as a sending end and a receiving end. The sending device sends a first signal and a second signal to the receiving device through the power network, where the first signal and the second signal may be optical fiber signals or cable signals connected to the sending device. The receiving device may divide the first signal and the second signal into N first signal segments and M second signal segments, respectively, according to a preset time slot. The signal energy value may be the average value of the energy within the time slot of the first signal segment or the second signal segment. The receiving device filters out the second signal segment whose signal energy value is greater than a preset energy threshold from the second signal segment as a noise segment.

[0016] In a possible implementation of the first aspect, the receiving apparatus determines multiple noise thresholds based on a signal energy value of at least one noise segment, including:

[0017] The signal energy value of the noise segment is calculated, and a plurality of noise thresholds are determined according to a linear relationship between the signal energy value and the plurality of noise thresholds.

[0018] That is, in the embodiments of the present application, the noise threshold can be referred to as a noise interval parameter, or a noise reduction parameter. The linear relationship can be a linear regression equation between the signal energy value and the noise threshold. The linear regression equation reflects the law that the noise interval parameter changes accordingly as the signal energy value corresponding to different noise segments changes.

[0019] In a possible implementation of the first aspect above, the signal energy value of the noise segment includes an average energy value of the noise segment, a variance value of the energy value, or an unbiased estimate of the energy value.

[0020] In a possible implementation of the first aspect, the multiple noise thresholds include a first noise threshold and a second noise threshold, and the first noise threshold a and the second noise threshold T are determined by the following formula:

[0021] a=AB*x1+C*x2+D*x3

[0022] T=E+F*x1-G*x2+H*x3

[0023] Among them, A, B, C, D, E, F, G, and H are constants, and x1, x2, and x3 are respectively one of the average energy value, the variance value of the energy value, or the unbiased estimate of the energy value of the noise segment.

[0024] That is, in the embodiment of the present application, the first noise threshold a and the second noise threshold T may be noise cancellation parameters a and T, and one form of the formula corresponding to the first noise threshold a and the second noise threshold T may be:

[0025] a=f1(x1,x2,…)=1.4160-4.6880*x1+6.8142*x2+0.0554*x3

[0026] T=f1(x1,x2,…)=0.1771+1.9721*x1-1.0565*x2+0.0171*x3

[0027] In a possible implementation of the first aspect, the noise interval includes a first noise interval [T, a*T] and a second noise interval (a*T, +∞).

[0028] That is, in the embodiment of the present application, [T, a*T] represents an interval greater than or equal to T and less than or equal to a*T, and (a*T, +∞) represents an interval greater than a*T.

[0029] In a possible implementation of the first aspect, performing noise reduction processing on the signal using a noise reduction method corresponding to the noise interval includes:

[0030] When the signal energy value of the noise segment belongs to the first noise interval, performing a sign operation on the signal energy value of the noise segment to obtain a signal after noise elimination processing;

[0031] When the signal energy value of the noise segment belongs to the second noise interval, the signal energy value of the noise segment is reset to zero to obtain a signal after noise removal processing.

[0032] That is, in an embodiment of the present application, when the signal energy value of the noise segment belongs to the noise interval [T, a*T], noise removal is performed by taking the product of the sign function and T of the signal energy value of the noise segment; when the signal energy value of the noise segment belongs to the noise interval (a*T, +∞), it means that the signal energy value of the noise segment is too large, so the signal energy value of the noise segment is set to zero.

[0033] In a possible implementation of the first aspect, the receiving apparatus divides the first signal into multiple first signal segments in the following manner:

[0034] The duration of the first signal segment and the noise segment is the same as the preset time interval.

[0035] That is, in the embodiment of the present application, the time slots of the first signal segment and the noise segment are the same.

[0036] A second aspect of the present application provides a receiving device, characterized in that it includes: a processor, when the processor calls a computer program or instruction in the memory, executes the signal noise reduction method provided in the first aspect.

[0037] The third aspect of the present application provides a computer-readable storage medium, characterized in that the computer-readable storage medium includes a computer program or instructions, which, when the computer program or instructions are run on a computer, enables the computer to execute the signal denoising method provided in the first aspect above. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 According to an embodiment of the present application, a process of transmitting a signal via a power line between a transmitting device and a receiving device is shown;

[0039] FIG2( a ) and FIG2( b ) are schematic structural diagrams showing a sending device and a receiving device according to an embodiment of the present application;

[0040] Figure 3 A flow chart of a signal denoising method according to an embodiment of the present application is shown;

[0041] Figure 4 According to an embodiment of the present application, a flow chart of a sending device processing an input signal is shown;

[0042] Figure 5 According to an embodiment of the present application, an energy distribution diagram of a signal is shown;

[0043] Figure 6(a) and 6(b) According to an embodiment of the present application, a curve showing the energy variation of a signal over time is shown;

[0044] Figure 7 According to an embodiment of the present application, a schematic diagram of denoising a noise segment is shown;

[0045] Figure 8 According to an embodiment of the present application, a schematic diagram of denoising a noise segment using a noise recognition model is shown;

[0046] Figure 9 According to an embodiment of the present application, another schematic diagram of denoising a noise segment using a noise recognition model is shown. DETAILED DESCRIPTION

[0047] The embodiments of the present application include but are not limited to a signal noise reduction method and a receiving device. To make the purpose, technical solution and advantages of the present application clearer, the embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

[0048] The following will Figure 1 The router 100 is described as a signal sending end 100, and the router 200 is described as a signal receiving end 200 for explanation.

[0049] In an embodiment of the present application, in order to solve the problem in the background technology, the above-mentioned noise capability threshold is determined based on the actual signal sent by the transmitter 100. Specifically, after the receiving end 200 receives the signal sent by the transmitter 100, the receiving end 200 divides the received signal into multiple signal segments according to a preset time length, and selects a noise segment from the multiple signal segments. Then, a plurality of energy parameters for representing the energy size of the noise signal in the noise segment are obtained, and the noise capability threshold is determined according to different types of energy parameters, and the energy of the signal is divided into multiple noise intervals. Different noise cancellation methods are used to cancel the noise of the signal according to the different noise intervals in which the energy of the signal is located. The noise interval parameter and the noise interval of the noise interval parameter in the embodiment of the present application are calculated and determined by the energy parameter of the noise segment in the actual signal sent by the transmitter 100, and noise cancellation can be performed more accurately for signals sent by different transmitters 100. In the following, the noise interval parameter can be represented by the noise cancellation parameter.

[0050] For example, the transmitter 100 sends a signal to the receiver 200. If a pulse arrives in the power grid, the signal will be interfered with for a period of time when the pulse arrives. Therefore, the signal will be added with pulse noise after passing through the power grid. The receiver 200 receives the signal and divides the signal into N signal segments according to a preset time slot (i.e., duration). For example, the preset time slot here can be the duration of the pulse noise. Since the duration of the pulse noise is relatively fixed, assuming it is 0.4ms, the receiver 200 can divide the signal into N signal segments with a time slot of 0.4ms. After obtaining the N signal segments, the receiver 200 can detect the energy corresponding to the N signal segments using a sliding window with the same length as the preset time slot. If a signal segment exceeds a preset energy threshold, the receiver 200 determines that the signal segment is a noise segment. After the receiver 200 filters out M noise segments from the N signal segments, it calculates the eigenvalues ​​corresponding to each of the M noise segments and establishes a linear regression equation between the eigenvalues ​​and the noise cancellation parameters a and T. After receiving end 200 determines the noise cancellation parameters a and T, it then determines at least three noise intervals, such as (,T), [T,a*T], and (a*T,) based on the values ​​of T and a*T. It then performs noise cancellation on the received signal based on the correspondence between the received signal and these three intervals. The specific calculation process is described in detail below.

[0051] It can be understood that the linear regression equation for calculating the denoising parameters a and T based on the eigenvalues ​​corresponding to the noise segments is a regression of the dependent variable and the independent variable, which reflects the law that the denoising parameters a and T change accordingly with the changes in the eigenvalues ​​corresponding to different noise segments.

[0052] In the embodiments of the present application, the transmitting end 100 and the receiving end 200 include, but are not limited to, routers, home gateways, and the like. Figures 2(a) and 2(b) illustrate schematic diagrams of the structures of the transmitting end 100 and the receiving end 200 according to the embodiments of the present application. As shown in Figure 2(a), the transmitting end 100 includes: a processor 110, a signal input module 120, an encoding module 130, a power line communication module 140, a storage module 150, and a power supply module 160.

[0053] The processor 110 may include one or more processing units. For example, the processor 110 may include an application processor (AP), a modem processor, a controller, a baseband processor, and / or a neural-network processing unit (NPU). The different processing units may be independent devices or integrated into one or more processors.

[0054] The signal input module 120 may include at least one signal input interface, for example, a physical interface for connecting a signal input, such as an optical fiber signal interface and a cable signal interface, for acquiring a signal.

[0055] The encoding module 130 can encode the signal, for example, by first modulating the signal using 256-bit Quadrature Amplitude Modulation (QAM) and Orthogonal Frequency Division Multiplexing (OFDM), then performing space-time block coding on the modulated signal to obtain the encoded signal. Finally, an Inverse Fast Fourier Transform (IFFT) is used to transform the signal into a time-domain signal for transmission.

[0056] The power line communication module 140 may send a signal to the receiving end 200 via a power line of the power network.

[0057] The storage module 150 can be used to store executable program code, which includes instructions. The storage module 150 may include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function. The data storage area may store data generated during base station use. In embodiments of the present application, the storage module 150 may store a program for converting signals into signal segments.

[0058] The power supply module 160 is used to connect the transmitting end 100 to the power network to supply power to the transmitting end 100 .

[0059] As shown in FIG2( b ), relative to the transmitting end 100 , the receiving end 200 includes: a processor 210 , a signal output module 220 , a noise cancellation module 230 , an encoding module 240 , a power line communication module 250 , a storage module 260 and a power supply module 270 .

[0060] The processor 210 may include one or more processing units. For example, the processor 110 may include an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU). The different processing units may be independent devices or integrated into one or more processors.

[0061] The signal output module 220 may include at least one signal output interface, for example, an optical fiber signal interface, a cable signal interface, a wireless interface, etc., a wired / wireless interface for outputting signals.

[0062] After receiving the signal at the receiving end 200, the noise cancellation module 230 performs noise cancellation on the signal. In the embodiment of the present application, the noise cancellation module 230 may store noise cancellation parameters for performing noise cancellation on the signal.

[0063] The decoding module 240 may decode the signal, for example, by performing Fast Fourier Transform (FFT) on the signal and then performing space-time block coding decoding on the signal to obtain a decoded signal.

[0064] The power line communication module 250 may receive a signal transmitted from the transmitting end 100 through a power line of the power network.

[0065] The storage module 260 can be used to store executable program code, which includes instructions. The storage module 260 can include a program storage area and a data storage area. The program storage area can store an operating system and at least one application required for a function.

[0066] The power supply module 270 is used to connect the transmitting end 100 to the power network to supply power to the transmitting end 100 .

[0067] In an embodiment of the present application, the process of implementing noise reduction on the transmitted signal between the transmitter 100 and the receiver 200 may include: the transmitter 100 maps the bit stream of the signal to be transmitted through 256QAM, modulates it into a continuous OFDM symbol stream using orthogonal frequency division multiplexing, and then obtains a transmission symbol stream through a space-time block code encoding scheme, and finally transforms it into a time domain signal through an inverse fast Fourier transform for transmission. When the communication device 200 is the receiving end, the signal is received by the signal transmission module 203, and the processor 201 first performs pulse noise reduction on the signal, and then transforms it into the frequency domain through a fast Fourier transform to obtain the signal symbol, and then obtains the signal symbol stream according to the space-time block code decoding scheme.

[0068] The following describes the functions implemented by the transmitting end 100 and the receiving end 200 to illustrate the signal denoising method of the present application. The signal denoising method of the present application can be implemented by executing relevant programs by the processors of the transmitting end 100 and the receiving end 200. The method may include: S301 to S303, wherein the embodiment of the present application does not limit the execution order of S301 to S303 by the transmitting end 100. Figure 3 As shown, the signal noise reduction method of the embodiment of the present application includes but is not limited to the following steps:

[0069] S301: Acquire a signal containing impulse noise and filter out noise segments.

[0070] Here, the signal obtained by the receiving end 200 may be sent by the transmitting end 100. It can be understood that before the transmitting end 100 sends a signal to the receiving end 200, the transmitting end 100 can obtain the input signal, such as an optical fiber signal, through its own signal input module 120. Then, the transmitting end 100 performs 256 orthogonal amplitude modulation and orthogonal frequency division multiplexing modulation on the input signal, thereby enhancing the signal and reducing signal distortion; performs space-time block coding on the modulated signal, and sends the encoded signal to the receiving end 200 through the MIMO-PLC channel. The process of processing the input signal performed by the transmitting end 100 will be described later, such as Figure 4 S301a to S301e shown are described in detail.

[0071] Specifically, after receiving the signal, the receiving end 200 may divide the signal into n signal segments, each of which may be denoted as x(n), where n may be an integer greater than or equal to 1.

[0072] For example, for a signal with a duration of 4 seconds, the time slot of each signal segment is 0.4 ms, where 0.4 ms can be the duration of each signal segment, and the 4-second signal can be divided into 10,000 signal segments. Figure 5is the energy distribution diagram corresponding to the signal including n signal segments. Figure 5 As shown, when the signal contains impulse noise, the signal will be disturbed during the duration of the impulse noise, and the signal fluctuation is relatively large. When there is no additional impulse noise, the signal is relatively stable, that is, no noise removal is required when there is no impulse noise.

[0073] In the embodiment of the present application, when n signal segments are directly converted into Figure 5 After the energy distribution diagram is shown, the curve in the energy distribution diagram is not smooth, which will make the calculation of the energy of the signal segment inaccurate. Figure 5 The energy distribution diagram shown is then filtered through a low-pass filter to obtain a filtered energy-over-time curve, which makes the curve corresponding to the energy of the signal segment smoother, which is conducive to accurately calculating the energy of the signal segment. Figure 6(a) shows a schematic diagram of the result after low-pass filtering.

[0074] Then, in the embodiment of the present application, the receiving end 200 can calculate Figure 5 and the average power of each of the n signal segments in Figure 6(a), and using the average power as the energy corresponding to the n signal segments. The method for calculating the energy corresponding to the signal segments may include: the receiving end 200 may set a sliding window, the window length of the sliding window being the same as the time slot of the signal segment, for example, the window length of the sliding window being 0.4 ms. The receiving end 200 uses the sliding window to slide along the energy versus time curve in Figure 6(b). After each sliding window slide, the sliding window moves to a signal segment, and the receiving end 200 calculates the average power of the signal segments within the sliding window as the energy of the signal segment. As shown in Figure 6(b), the receiving end 200 takes the arithmetic average of the values ​​on the energy versus time curve of the signal segments within sliding window 1 and uses the result as the energy of the signal segment. For example, if the values ​​on the energy versus time curve of the signal segments within sliding window 1 obtained by the receiving end 200 are (0.4, 0.7, 0.9, 0.7, 0.5), the receiving end 200 determines that the energy of the signal segments within sliding window 1 is 0.64.

[0075] Finally, the receiving end 200 compares the energy corresponding to each of the n signal segments obtained with a preset energy threshold, and takes the signal segments whose energy is greater than the energy threshold as noise segments, thereby obtaining at least m noise segments x′1(m) from the n signal segments, where m is less than or equal to n. For example, the energy threshold can be set to 0.6, such as Figure 7 As shown, by comparing with the energy threshold, it can be determined Figure 7 There are 3 noise segments in , namely, noise segment 1 to noise segment 3.

[0076] S302: Calculate the eigenvalue corresponding to the noise segment, and determine the denoising parameter corresponding to the noise segment based on the eigenvalue;

[0077] In an embodiment of the present application, the receiving end 200 can obtain a characteristic value corresponding to a noise segment, and the characteristic value is used to represent the energy characteristics of the noise segment. For example, the characteristic value may include, but is not limited to, one or more of the following information: the average value x1 of the absolute value of the energy of the noise segment, the unbiased estimate x2 of the energy of the noise segment, and the maximum absolute value x3 of the energy of the noise segment; x1, x2, and x3 are expressed as follows, where x′1(m) represents the noise segment, mean represents the calculated average value, var represents the calculated unbiased estimate, and max represents the calculated maximum value: The unbiased estimate here refers to a statistical estimator whose mathematical expectation of the energy variance is equal to the estimated quantity.

[0078] x1=mean(|x′1(m)|); x2=var(x′1(m)); x3=max(|x′1(m)|);

[0079] The receiving end 200 further calculates the noise removal parameters corresponding to the noise segment based on the eigenvalues ​​corresponding to the noise segment. The eigenvalues ​​corresponding to the noise segment and the noise removal parameters (a, T) corresponding to the noise segment satisfy the linear relationship of multiple regression.

[0080] a=f1(x1, x2, ...), T=f2(x1, x2, ...), the specific implementation of the linear relationship f1 and f2 can be shown in the following formula (1). It can be understood that the parameters in the linear relationship shown in formula (1), for example, 1.4160 is only an example, which is a correlation coefficient used to reflect the linear correlation between the denoising parameter and the eigenvalue corresponding to the noise segment. It is an adjustable coefficient, such as the Pearson correlation coefficient. In other embodiments of the present application, other linear relationships can also be applied between the eigenvalue corresponding to the noise segment and the denoising parameter corresponding to the noise segment. Wherein, a and T respectively represent two denoising parameters obtained by the eigenvalue corresponding to the noise segment and the linear relationship. The numerical units of the denoising parameters a and T can be the same as the numerical units of the energy corresponding to the signal segment.

[0081] a=f1(x1,x2,…)=1.4160-4.6880*x1+6.8142*x2+0.0554*x3

[0082] T=f1(x1,x2,…)=0.1771+1.9721*x1-1.0565*x2+0.0171*x3

[0083] Formula (1)

[0084] S303: Determine a noise interval according to the noise removal parameter, and perform noise removal on at least one noise segment that meets the noise interval.

[0085] Here, the receiving end 200 can determine three intervals (0, T), [T, a*T] and (a*T, +∞) based on the noise cancellation parameters a and T, where (0, T) represents an interval less than T, [T, a*T] represents an interval greater than or equal to T and less than or equal to a*T, and (a*T, +∞) represents an interval greater than a*T, where [T, a*T] and (a*T, +∞) are noise intervals.

[0086] In an embodiment of the present application, the receiving end 200 determines the correspondence between the value of each signal segment and the three noise intervals from n signal segments, and applies the following noise reduction method to perform noise reduction processing on the signal segments.

[0087]

[0088] Wherein, x1(n) is a normal signal segment received by the receiving end 200, x′1(n) is a signal segment containing impulse noise received by the receiving end 200, x2(n) is a signal segment after noise removal, and the sign() function is a sign extraction function.

[0089] When the signal segment x1(n) belongs to the noise interval (0,T), it means that the energy of the signal segment x1(n) does not reach the energy of the noise, and the signal segment x1(n) is a normal signal, so the signal segment x1(n) is not subjected to denoising. When the signal segment x′1(n) belongs to the noise interval [T,a*T], it means that the signal segment x′1(n) contains noise, such as impulse noise or Gaussian white noise, so denoising is performed by taking the product of the sign function and T of the signal segment x′1(n). When the signal segment x′1(n) belongs to the noise interval (a*T,+∞), it means that the energy of the signal segment x′1(n) is too large, so the signal segment x′1(n) is set to zero.

[0090] It can be understood that the linear relationship (a, T) = f(x1, x2, ...) in the above step S302 is exemplary. When the type of signal input to the signal input module 120 of the transmitting end 100 is different, the linear relationship will also be different. For example, for an optical fiber signal, the corresponding linear relationship can be the linear relationship a = f1(x1, x2, ...), T = f2(x1, x2, ...) in step S302; for a cable signal, the corresponding linear relationship can be a = g1(x1, x2, ...), T = g2(x1, x2, ...), that is, the linear relationship between the cable signal and the noise cancellation parameters corresponding to the cable signal. Here, the form of g1 and g2 can be the same as f1 and f2 in formula (1), but the related parameters of the two can be different. Different linear relationships obtained based on different signals can be obtained by setting the update equations h1 = (1-μ)f1 + μg1 and h2 = (1-μ)f2 + μg2 to obtain new linear relationships in an adaptive manner, where μ is the update factor. Through the above-mentioned adaptive method, the linear relationship used to obtain the noise reduction parameters can be automatically adjusted according to the characteristic values ​​of different signals to obtain more accurate results.

[0091] In an embodiment of the present application, before step S301, the process of processing the input signal performed by the transmitting end 100 includes:

[0092] S301a: Obtain input signal.

[0093] The input signal here may be a signal obtained by the transmitting end 100 through its own signal input module 120, for example, an optical fiber signal interface.

[0094] Next, the transmitting end 100 performs S301b: modulating the signal using 256-bit quadrature amplitude modulation. After the signal undergoes 256-bit quadrature amplitude modulation, both the amplitude and phase of the signal can be enhanced simultaneously.

[0095] Then, the transmitting end 100 performs S301c: modulating the signal using orthogonal frequency division multiplexing. After the signal is modulated by orthogonal frequency division multiplexing, the distortion of the signal can be reduced.

[0096] Afterwards, the transmitting end 100 executes S301d: performing space-time block coding on the modulated signal. Here, the signal is encoded in two dimensions, space and time, so that the signal can be transmitted through multiple antennas.

[0097] Finally, the transmitting end 100 executes S301e: after performing inverse fast Fourier transform on the encoded signal, it sends it to the receiving end 200. Here, inverse fast Fourier transform can convert the time domain signal into a frequency domain signal, making the signal transmission efficiency higher. The transmitting end 100 converts the converted frequency domain signal into a frequency domain signal through the following method: Figure 4 The MIMO-PLC channel composed of transmitting antenna 1, transmitting antenna 2, receiving antenna 1 and receiving antenna 2 is sent to the receiving end 200. After the signal passes through the MIMO-PLC channel, impulse noise will be added.

[0098] In the signal denoising method from S301 to S303 above, step S302 uses the linear relationship of multiple regression to obtain the denoising parameters (a, T) according to the characteristic values ​​corresponding to the noise segments. In another embodiment of the present application, the receiving end 200 can also input the signal segments into the noise recognition model in the server 300. The noise recognition model can extract and train the features of the signal segments to obtain the denoising parameters (a, T) output by the model. The denoising parameters (a, T) are then updated to the denoising module 230 of the receiving end 200. The denoising module 230 can use the updated denoising parameters (a, T) to denoise the signal using the method of step S303. The method for obtaining the denoising parameters (a, T) through the noise recognition model in the server 300 can be as follows: Figure 8 Shown, including:

[0099] S401: The receiving end 200 obtains a signal containing impulse noise and sends it to the server 200.

[0100] S401 here is similar to S301, except that after receiving the signal, the receiving end 200 can directly send the signal to the server 200. The process of dividing the signal into multiple signal segments and extracting features from the signal segments will be performed by the server 200.

[0101] S402: The server 200 filters out noise segments from the received signal and calculates feature values ​​corresponding to the noise segments.

[0102] S402 here is similar to S302, the server 200 inputs the received signal into the noise recognition model, such as Figure 9 As shown, the noise recognition model can use the sliding window method described in S302 to filter out noise segments from the signal, for example, noise segments 1 to noise segments 3, and calculate the characteristic values ​​of the noise segments. The characteristic values ​​here may also include but are not limited to one or more of the following information: the average value x1 of the absolute value of the energy of the noise segment, the unbiased estimate x2 of the energy variance of the noise segment, and the maximum value x3 of the absolute value of the energy of the noise segment.

[0103] S403: The server 200 trains noise reduction parameters according to the characteristics of the noise segment.

[0104] Here, the noise recognition model of the server 200 can train noise cancellation parameters according to the characteristic values ​​of the noise segments.

[0105] Specifically, the server 200 can input the characteristic value of each noise segment into the noise recognition model for training, and then compare the output of the model (i.e., the noise reduction parameter corresponding to the noise segment) with the data representing the noise, calculate the error (i.e., the difference between the two), calculate the partial derivative of the aforementioned error, and update the weight according to the partial derivative. Until the model finally outputs the noise reduction parameter corresponding to the noise segment. It can be understood that the noise recognition model can continuously adjust the weight by training with a large amount of noise segment data. When the output error reaches a very small value (for example, meeting a predetermined error threshold), it is considered that the model has converged and the noise recognition model has been trained.

[0106] It is understood that the noise recognition model herein can be applied to various neural network models, such as convolutional neural networks (CNN), deep neural networks (DNN), recurrent neural networks (RNN), binary neural networks (BNN), etc. In a specific implementation, the number of layers of the neural network model, the number of nodes in each layer, and the connection parameters of the two connected nodes (i.e., the weights on the line connecting the two nodes) can all be pre-set according to actual needs.

[0107] S404: The server 200 updates the receiving end 200 using the trained noise cancellation parameters.

[0108] Here, after the server 200 sends the trained noise cancellation parameters to the receiving end 200 , the noise cancellation module 230 of the receiving end 200 may update the stored noise cancellation parameters.

[0109] S405: The receiving end 200 performs noise reduction on the signal.

[0110] S405 here is similar to S303. The receiving end 200 can determine a noise interval based on the noise cancellation parameters and perform noise cancellation on at least one noise segment that meets the noise interval. For example, the receiving end 200 can determine three intervals (0, T), [T, a*T], and (a*T, +∞) based on the noise cancellation parameters a and T, where (0, T) represents an interval less than T, [T, a*T] represents an interval greater than or equal to T and less than or equal to a*T, and (a*T, +∞) represents an interval greater than a*T. [T, a*T] and (a*T, +∞) are noise intervals.

[0111] The signal denoising method of the embodiment of the present application uses a linear regression algorithm to calculate the denoising parameters from the eigenvalues ​​of the noise segments. This method has low computational complexity and can achieve good denoising results even in terminal devices with limited computing power. Furthermore, the embodiment of the application also provides a method for obtaining denoising parameters through a server-based neural network model. This method utilizes the capabilities of a remote cloud server to train the collected data. Once the denoising parameters are obtained, the denoising parameters of the terminal device are updated in real time, thereby improving the efficiency of the denoising process.

[0112] It should be understood that although the terms "first," "second," and the like may be used herein to describe various features, these features should not be limited by these terms. These terms are used merely to distinguish and should not be understood as indicating or implying relative importance. For example, a first feature may be referred to as a second feature, and similarly, a second feature may be referred to as a first feature, without departing from the scope of the exemplary embodiments.

[0113] Furthermore, various operations will be described as multiple, separate operations in a manner that is most helpful for understanding the illustrative embodiments; however, the order of description should not be construed to imply that the operations must be performed in order of description, and many of the operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can terminate when the described operations are completed, but can also have additional operations not included in the figures. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0114] References in the specification to "one embodiment," "an embodiment," "an illustrative embodiment," etc., indicate that the described embodiment may include a particular feature, structure, or property, but that every embodiment may or may not necessarily include the particular feature, structure, or property. Furthermore, these phrases are not necessarily referring to the same embodiment. Furthermore, while particular features may be described in conjunction with a specific embodiment, the knowledge of those skilled in the art may affect how those features may be combined with other embodiments, whether or not those embodiments are explicitly described.

[0115] Unless the context dictates otherwise, the terms "comprising," "having," and "including" are synonymous. The phrase "A / B" means "A or B." The phrase "A and / or B" means "(A), (B), or (A and B)."

[0116] As used herein, the term "module" may refer to, be part of, or include: memory (shared, dedicated, or group) for running one or more software or firmware programs, application-specific integrated circuits (ASICs), electronic circuits and / or processors (shared, dedicated, or group), combinational logic circuits, and / or other suitable components that provide the functionality.

[0117] In the accompanying drawings, some structural or method features may be shown in a particular arrangement and / or order. However, it should be understood that such a particular arrangement and / or order is not required. Rather, in some embodiments, these features may be illustrated in a manner and / or order different from that shown in the illustrative drawings. In addition, the inclusion of structural or method features in a particular drawing does not mean that all embodiments need to include such features. In some embodiments, these features may not be included, or they may be combined with other features.

[0118] The embodiments of the present application are described in detail above with reference to the accompanying drawings. However, the application of the technical solution of the present application is not limited to the various applications mentioned in the embodiments of the present patent. Various structures and variations can be easily implemented with reference to the technical solution of the present application to achieve the various beneficial effects mentioned herein. Various changes made within the knowledge of ordinary technicians in this field without departing from the purpose of the present application should fall within the scope of coverage of the patent application.

Claims

1. A signal noise reduction method for a power line communication system, characterized in that: The power line system includes a transmitting device, a receiving device, and a power line, wherein the transmitting device communicates with the receiving device via the power line; The method comprises: The receiving device receives the first signal sent by the sending device through the power line and divides the first signal into a plurality of first signal segments; The receiving device determines the noise interval to which the signal energy value of each first signal segment belongs, and performs noise reduction processing on the signal using a noise reduction method corresponding to the noise interval, wherein the signal energy value of the first signal segment is the average value of the energy within the time slot of the first signal segment, There are multiple noise intervals, and the division of the multiple noise intervals is determined based on the signal energy value of at least one noise segment in the second signal received by the receiving device from the sending device before the receiving device receives the first signal.

2. The method according to claim 1, characterized in that The receiving device divides the noise interval in the following manner: The receiving device divides the second signal into a plurality of second signal segments, and selects at least one noise segment from the plurality of second signal segments; The receiving device determines a plurality of noise thresholds based on the signal energy value of the at least one noise segment, and divides the noise interval according to the plurality of noise thresholds.

3. The method according to claim 2, characterized in that The receiving device selects at least one noise segment from the plurality of second signal segments in the following manner: The second signal segment whose signal energy value is greater than a preset energy threshold is selected as the noise segment.

4. The method according to claim 2, characterized in that The receiving device determines a plurality of noise thresholds based on the signal energy value of the at least one noise segment, including: The signal energy value of the noise segment is calculated, and the multiple noise thresholds are determined according to a linear relationship between the signal energy value and the multiple noise thresholds.

5. The method according to claim 4, characterized in that The signal energy value of the noise segment includes an average energy value of the noise segment, a variance value of the energy value, or an unbiased estimated value of the energy value.

6. The method according to claim 4, characterized in that The multiple noise thresholds include a first noise threshold and a second noise threshold, and the first noise threshold a and the second noise threshold T are determined by the following formula: a=AB*x1+C*x2+D*x3 T=E+F*x1-G*x2+H*x3 Among them, A, B, C, D, E, F, G, and H are constants, and x1, x2, and x3 are respectively one of the average energy value, the variance value of the energy value, or the unbiased estimate of the energy value of the noise segment.

7. The method according to claim 6, wherein The noise interval includes a first noise interval [T, a*T] and a second noise interval (a*T, +∞).

8. The method according to claim 1, wherein The signal is subjected to noise reduction processing using a noise reduction method corresponding to the noise interval, including: When the signal energy value of the noise segment belongs to the first noise interval, performing a sign operation on the signal energy value of the noise segment to obtain a signal after noise removal processing; In a case where the signal energy value of the noise segment belongs to the second noise interval, the signal energy value of the noise segment is set to zero to obtain a signal after noise removal processing.

9. The method according to claim 1, characterized in that The receiving device divides the first signal into a plurality of first signal segments in the following manner: The duration of the first signal segment and the noise segment is the same as a preset time interval.

10. A receiving device, characterized in that: include: A processor, when the processor calls a computer program or instruction in a memory, executes the method according to any one of claims 1 to 9.

11. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a computer program or instructions. When the computer program or instructions are executed on a computer, the computer is caused to perform the method according to any one of claims 1 to 9.

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