Noise suppression method, device and equipment for MIMO power line communication channel

Abstract

The invention relates to a noise suppression method, device and equipment for an MIMO power line communication channel. The method comprises the following steps: superposing a preset noise signal on an output signal of an MIMO power line communication channel to obtain a main input signal; inputting the main input signal and the reference noise signal into an initial adaptive noise canceller for noise suppression processing to obtain an error signal; the reference noise signal comprises a Markov-Mildeton pulse noise signal and / or a Mildeton A-type pulse noise signal; performing parameter adjustment on an initial adaptive filter in the initial adaptive noise canceller based on the error signal to obtain a target adaptive noise canceller; the target adaptive noise canceller is used for noise suppression of an output signal of the MIMO power line communication channel. By adopting the method, the noise suppression effect for the MIMO PLC communication system can be comprehensively improved, and the communication quality of the MIMO PLC communication system is further improved.

Description

Technical Field

[0001] The present application relates to the field of power line communication technology, and in particular to a noise suppression method, apparatus, and device for a MIMO power line communication channel. Background Art

[0002] In the field of power line communication (PLC), noise severely restricts communication quality. Noise in power lines comes from a wide range of sources, primarily generated by electrical equipment and external noise, which can cause a large number of bit errors.

[0003] Existing noise suppression methods all have certain defects. For Multiple-Input Multiple-Output (MIMO) PLC communication systems, how to improve the noise suppression effect of MIMO PLC communication systems is a technical problem that needs to be solved urgently. Summary of the Invention

[0004] Based on this, it is necessary to provide a noise suppression method, device, computer equipment, computer-readable storage medium and computer program product for MIMO power line communication channels that can improve the noise suppression effect of MIMO PLC communication systems in response to the above technical problems.

[0005] In a first aspect, the present application provides a noise suppression method for a MIMO power line communication channel, comprising:

[0006] Superimposing a preset noise signal on the output signal of the MIMO power line communication channel to obtain a main input signal;

[0007] Inputting the main input signal and the reference noise signal into the initial adaptive noise canceller for noise suppression processing to obtain an error signal; the reference noise signal includes a Markov-Middleton impulse noise signal and / or a Middleton Class A impulse noise signal;

[0008] The parameters of the initial adaptive filter in the initial adaptive noise canceller are adjusted based on the error signal to obtain a target adaptive noise canceller; the target adaptive noise canceller is used to suppress noise on the output signal of the MIMO power line communication channel.

[0009] In one embodiment, the preset noise signal includes: an additive white Gaussian noise signal, and / or a reference noise signal.

[0010] In one embodiment, the main input signal and the reference noise signal are input into an initial adaptive noise canceller for noise suppression processing to obtain an error signal, including:

[0011] Inputting the main input signal and the reference noise signal into the initial adaptive filter for processing to obtain an estimated noise signal;

[0012] The main input signal and the estimated noise signal are input to the subtractor in the initial adaptive noise canceller to obtain an error signal.

[0013] In one embodiment, adjusting parameters of an initial adaptive filter in an initial adaptive noise canceller based on an error signal to obtain a target adaptive noise canceller includes:

[0014] Adjusting parameters of the initial adaptive filter based on the error signal to obtain an intermediate adaptive filter;

[0015] Constructing an intermediate adaptive noise canceller based on the intermediate adaptive filter;

[0016] Based on the intermediate adaptive noise canceller, the step of inputting the main input signal and the reference noise signal into the initial adaptive noise canceller for noise suppression processing to obtain an error signal is returned to be executed until a preset number of iterations is reached to obtain a target adaptive noise canceller.

[0017] In one embodiment, the method further comprises:

[0018] Inputting the input data into a binary phase shift keying modulator for modulation to obtain a first analog signal;

[0019] Inputting the first analog signal into an orthogonal frequency division multiplexing modulator for modulation to obtain a plurality of second analog signals; the plurality of second analog signals are matched with a MIMO power line communication channel;

[0020] The plurality of second analog signals are input into a space-time block code encoder for encoding, and then input into a MIMO power line communication channel.

[0021] In one embodiment, the method further comprises:

[0022] The denoised signal after noise suppression by the target adaptive noise canceller is input into the space-time block code combiner for combining, and then input into the orthogonal frequency division multiplexing demodulator to obtain a third analog signal;

[0023] inputting the third analog signal into a binary phase shift keying demodulator for demodulation to obtain output data;

[0024] Based on the input data and the output data, a bit error rate of the MIMO power line communication channel is determined; the bit error rate is used to characterize the noise suppression effect of the target adaptive noise canceller on the MIMO power line communication channel.

[0025] In a second aspect, the present application further provides a noise suppression device for a MIMO power line communication channel, comprising:

[0026] A signal processing module, configured to superimpose a preset noise signal on an output signal of the MIMO power line communication channel to obtain a main input signal;

[0027] a noise suppression module, configured to input a main input signal and a reference noise signal into an initial adaptive noise canceller for noise suppression processing to obtain an error signal; the reference noise signal comprises a Markov-Middleton impulse noise signal and / or a Middleton Class A impulse noise signal;

[0028] The adjustment module is used to adjust the parameters of the initial adaptive filter in the initial adaptive noise canceller based on the error signal to obtain a target adaptive noise canceller; the target adaptive noise canceller is used to suppress noise on the output signal of the MIMO power line communication channel.

[0029] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the noise suppression method for the MIMO power line communication channel in the first aspect are implemented.

[0030] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the noise suppression method for a MIMO power line communication channel in the first aspect.

[0031] In a fifth aspect, the present application further provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the noise suppression method for a MIMO power line communication channel in the first aspect.

[0032] The noise suppression method, apparatus, computer device, storage medium, and computer program product for a MIMO power line communication channel obtain a main input signal by superimposing a preset noise signal on the output signal of the MIMO power line communication channel; then, the main input signal and a reference noise signal are input into an initial adaptive noise canceller for noise suppression processing to obtain an error signal; wherein the reference noise signal includes a Markov-Middleton impulse noise signal and / or a Middleton Class A impulse noise signal; and further, based on the error signal, parameters of an initial adaptive filter in the initial adaptive noise canceller are adjusted to obtain a target adaptive noise canceller, which is used to perform noise suppression on the output signal of the MIMO power line communication channel. That is, in the noise suppression method proposed in the embodiment of the present application, on the one hand, the Markov-Middleton impulse noise model and / or the Middleton Class A impulse noise model are used as the reference impulse noise input of the adaptive noise eliminator, which can accurately simulate the main interference sources in the complex environment where the MIMO PLC communication system is located. On the other hand, in the process of adaptive filtering, by additionally superimposing a noise signal on the output signal of the MIMO power line communication channel, it is possible to further adapt to the internal and external complex, time-varying, and inter-channel noise interference suffered by the real channel. At the same time, with the help of the adaptive filtering principle to suppress noise, the noise suppression effect for the MIMO PLC communication system can be comprehensively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0034] Figure 1 FIG2 is a diagram illustrating an application environment of a noise suppression method for a MIMO power line communication channel according to an embodiment;

[0035] Figure 2 1 is a flow chart of a noise suppression method for a MIMO power line communication channel according to an embodiment;

[0036] Figure 3 1 is a flow chart of a noise suppression method for a MIMO power line communication channel according to another embodiment;

[0037] Figure 4 Schematic diagram of the structure of a MIMO PLC channel and an adaptive noise canceller in one embodiment;

[0038] Figure 5is an algorithm flow chart of an adaptive filter in one embodiment;

[0039] Figure 6 : is the bit error rate performance of two noise models in a MIMO PLC channel after LMS filtering in one embodiment;

[0040] Figure 7 : Bit error rate performance of two noise models in a MIMO PLC channel after RLS filtering in one embodiment;

[0041] Figure 8 is a structural block diagram of a noise suppression device for a MIMO power line communication channel in one embodiment;

[0042] Figure 9 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0044] In the field of power line communication (PLC), noise severely limits communication quality. Power line noise comes from a wide range of sources, primarily generated by electrical equipment and externally attached noise, and does not conform to the typical additive white Gaussian noise (AWGN) model. Impulse noise, despite its short duration, is a major source of interference in PLC systems, causing significant bit errors.

[0045] Among power line communication (PLC) technologies, impulse noise suppression suffers from numerous flaws and shortcomings. Regarding noise modeling, the s-α-s distribution is not commonly used for impulse noise modeling due to its lack of a closed-form expression and its non-Gaussian distribution, limiting its ability to accurately describe complex noise scenarios. While various noise reduction methods have been proposed, significant shortcomings remain. Techniques such as burst noise clipping and blanking strategies, convolutional coding, and interleaving can only partially improve communication performance but cannot effectively and fundamentally suppress impulse noise. While algorithms based on structured compressed sensing (SCS) and iterative thresholding have been used, their effectiveness remains to be improved. The former may have limitations in exploiting the spatial correlation of impulse noise, while the latter can be computationally inefficient in quantizing and eliminating impulse noise, and cannot guarantee stable and effective bit error rate reduction under varying interference intensities. While adaptive filtering technology is effective in single-input, single-output (SISO) PLC systems, it's underappreciated for pulse noise suppression in MIMO PLC systems. Multiple-input, multiple-output (MIMO) power line communication (PLC) systems require processing multiple signals, subject to coupling and interference between multiple channels. Simultaneously considering the transmission and noise suppression of multiple input and output signals presents a high level of complexity. In addition to pulse noise, MIMO PLC systems also contain additive white Gaussian noise (AWGN). The noise characteristics in multi-channel environments are even more complex and variable, requiring comprehensive suppression of multiple noises while also addressing the effects of noise propagation and coupling between different channels. Therefore, research on this technology is crucial for improving power line communication (PLC) performance, expanding PLC application scenarios, and promoting the development of communication technologies.

[0046] Based on this, an embodiment of the present application proposes an impulse noise adaptive filtering suppression technology for a MIMO (multiple-input, multiple-output) power line communication channel, which uses Markov-Middleton and / or Middleton Class A impulse noise as reference noise to approximately simulate the complex noise in the MIMO power line communication channel to improve the noise suppression effect of the MIMO power line communication channel.

[0047] The noise suppression method for MIMO power line communication channel provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the computer device 101 may include an adaptive noise suppression and cancellation device, such as an adaptive noise suppression canceller. The computer device 101 may perform adaptive noise suppression processing on the output signal of the MIMO power line communication channel, thereby eliminating the complex noise interference of the signal transmitted in the MIMO power line communication channel, thereby reducing the bit error rate of the MIMO power line communication channel and improving the communication quality of the MIMO power line communication channel.

[0048] In an exemplary embodiment, Figure 2 As shown, a noise suppression method for MIMO power line communication channel is provided, and the method is applied to Figure 1 The computer device in the embodiment is used as an example to illustrate the method, including the following steps 201 to 203.

[0049] Step 201 : superimposing a preset noise signal on an output signal of a MIMO power line communication channel to obtain a main input signal.

[0050] Among them, the output signal of the MIMO power line communication channel may include a multi-channel analog signal. For example, for the input data to be transmitted, it can be a binary digital signal, which is generated by coding modulation to generate a multi-channel analog signal, that is, a communication signal that is not interfered with by noise, and then transmitted through the MIMO power line communication channel. During the transmission process of the MIMO power line communication channel, the communication signal will be interfered with by different noises in the communication channel and different noises in the external environment of the communication channel. The output signal transmitted through the MIMO power line communication channel can be a communication signal after being interfered with by complex noise.

[0051] As an optional implementation method, binary phase shift keying modulation and orthogonal frequency division multiplexing modulation can be used to implement signal modulation of binary digital signals to convert digital signals into analog signals; illustratively, the input data can be first input into a binary phase shift keying modulator for modulation to obtain a first analog signal, and then the first analog signal can be input into an orthogonal frequency division multiplexing modulator for modulation to obtain multiple second analog signals, wherein the multiple second analog signals match the MIMO power line communication channel; and then, the multiple second analog signals are input into a space-time block code encoder for encoding and then input into the MIMO power line communication channel.

[0052] Among them, Binary Phase Shift Keying (BPSK) is a basic digital modulation technology that transmits information by changing the phase of the carrier. BPSK represents binary data by changing the phase of the carrier. When the transmitted bit is "1", the carrier phase changes 180 degrees, and when the transmitted bit is "0", the carrier phase remains unchanged. This phase change allows the BPSK signal to represent binary data 0 and 1 through different phases. During the BPSK modulation process, binary data (0 or 1) is converted into an analog signal, and the digital data is output by controlling the switching circuit to select carriers with different phases.

[0053] Orthogonal Frequency Division Multiplexing (OFDM) is a type of multicarrier modulation. By dividing a channel into several orthogonal subchannels, high-speed data signals are converted into parallel, lower-speed data streams, which are modulated and transmitted on each subchannel. This reduces interference between carriers. OFDM-modulated signals are better suited for MIMO power line communication channels.

[0054] For example, to better restore and adapt to the complex noise environment of the MIMO power line communication channel, a preset noise signal is superimposed on the output signal of the MIMO power line communication channel. The preset noise signal may include, but is not limited to, at least one of an additive white Gaussian noise signal and a reference noise signal. The reference noise signal may include one or more impulse noise signals of any type, such as a Markov-Middleton impulse noise signal, a Middleton Class A impulse noise signal, a Bernoulli-Gaussian impulse noise signal, etc. The output signal superimposed with the preset noise signal can serve as the main input signal, i.e., the primary input signal, of the adaptive noise canceller.

[0055] Step 202: Input the main input signal and the reference noise signal into an initial adaptive noise canceller for noise suppression processing to obtain an error signal.

[0056] Exemplarily, the reference noise signal may include a Markov-Middleton impulse noise signal and / or a Middleton Class A impulse noise signal, wherein the Markov-Middleton impulse noise signal is a noise signal based on the Markov-Middleton impulse noise model, which combines the characteristics of the Markov model and the Middleton Class A noise model and is a noise model with memory; the Middleton Class A impulse noise signal is a noise signal based on the Middleton Class A impulse noise model and is a noise model without memory; in this example, one or more types of reference noise signals can be input into the initial adaptive noise eliminator for noise suppression processing. In actual applications, other noise models besides the Markov-Middleton impulse noise model and the Middleton Class A impulse noise model can also be used to generate the reference noise signal.

[0057] Exemplarily, an adaptive noise canceller can suppress noise by adjusting the coefficients of an adaptive filter using the principle of adaptive filtering. The adaptive noise canceller can include an adaptive filter and a subtractor. As an optional implementation, a reference noise signal can be input into the adaptive filter for filtering to obtain an estimated noise signal that is approximately close to the true noise signal in the main input signal. Here, the reference noise signal, i.e., the impulse noise signal, is used as the reference input of the adaptive filter. On the one hand, it can assist in analyzing the characteristics of the impulse noise. On the other hand, it can enable the adaptive filter to adjust its parameters according to its characteristics, generate a reverse signal to offset the impulse noise, and improve signal quality.

[0058] Next, the main input signal and the estimated noise signal can be input into a subtractor to eliminate the estimated noise signal from the main input signal, thereby obtaining an error signal after noise elimination, which is approximately close to the effective communication signal in the main input signal without any noise interference.

[0059] In another implementation, both the main input signal and the reference noise signal may be input into an initial adaptive filter for processing to obtain an estimated noise signal. Subsequently, the main input signal and the estimated noise signal are input into a subtractor in the initial adaptive noise canceller to obtain an error signal. In this example, the adaptive filter may combine the main input signal and the reference noise signal, analyze the pulse signal characteristics of the reference noise signal, and learn an estimated noise signal that is closer to the actual noise signal in the main input signal. This allows the difference between the main input signal and the estimated noise signal to produce an error signal that is closer to the effective communication signal in the main input signal that is not interfered with by any noise, thereby further improving the noise suppression effect.

[0060] Step 203 , adjusting parameters of an initial adaptive filter in the initial adaptive noise canceller based on the error signal to obtain a target adaptive noise canceller; the target adaptive noise canceller is used to suppress noise on an output signal of the MIMO power line communication channel.

[0061] Exemplarily, after obtaining the error signal, the parameters of the initial adaptive filter in the initial adaptive noise eliminator can be adjusted based on the error signal to obtain a target adaptive filter after parameter adjustment, and then the target adaptive filter and the subtractor can be combined to form a target adaptive noise eliminator. The target adaptive noise eliminator is applied to the MIMO PLC communication system, which can effectively suppress the noise of the output signal of the power line communication channel, reduce the noise interference of the MIMO PLC communication system during the signal transmission process, and improve the communication quality of the MIMO PLC communication system.

[0062] Exemplarily, an adaptive filtering algorithm can be used to iteratively adjust the parameters of the initial adaptive filter based on the error signal until the error signal becomes closer and closer to the effective communication signal in the main input signal. After stopping the iteration, the target adaptive filter can be obtained. Optionally, the adaptive filtering algorithm can include but is not limited to the least mean square algorithm (LMS), the recursive least squares method (RLS), etc., wherein the LMS algorithm is an adaptive filtering algorithm based on gradient descent, which iteratively updates the filter coefficients to minimize the mean square value of the error between the filter output and the desired signal (i.e., the effective communication signal); the RLS algorithm is based on the basic principle of the least squares method, but continuously updates the filter parameters in a recursive manner to minimize the sum of the squares of the prediction error.

[0063] As an optional implementation, when performing adaptive parameter adjustment, the parameters of the initial adaptive filter can be first adjusted based on the error signal to obtain an intermediate adaptive filter, and an intermediate adaptive noise canceller can be constructed based on the intermediate adaptive filter. Then, based on the intermediate adaptive noise canceller, the step of inputting the main input signal and the reference noise signal into the initial adaptive noise canceller for noise suppression processing to obtain an error signal is returned to execution until a preset number of iterations is reached to obtain a target adaptive noise canceller; that is, the main input signal and the reference noise signal are re-inputted into the intermediate adaptive noise canceller for noise suppression processing to obtain a new error signal, and then the parameters of the intermediate adaptive filter are adjusted based on the new error signal, and this iteration is repeated multiple times until a preset iteration stop condition is met. Optionally, the preset iteration stop condition may include but is not limited to a preset number of iterations, a difference between the error signal and the valid communication signal being less than a preset difference, etc.

[0064] In the above-mentioned noise suppression method for a MIMO power line communication channel, a main input signal is obtained by superimposing a preset noise signal on the output signal of the MIMO power line communication channel; then, the main input signal and a reference noise signal are input into an initial adaptive noise eliminator for noise suppression processing to obtain an error signal; wherein the reference noise signal includes a Markov-Middleton impulse noise signal and / or a Middleton Class A impulse noise signal; further, based on the error signal, the parameters of the initial adaptive filter in the initial adaptive noise eliminator are adjusted to obtain a target adaptive noise eliminator, which is used to perform noise suppression on the output signal of the MIMO power line communication channel. That is, in the noise suppression method proposed in the embodiment of the present application, on the one hand, the Markov-Middleton impulse noise model and / or the Middleton Class A impulse noise model are used as the reference impulse noise input of the adaptive noise eliminator, which can accurately simulate the main interference sources in the complex environment where the MIMO PLC communication system is located. On the other hand, in the process of adaptive filtering, by additionally superimposing a noise signal on the output signal of the MIMO power line communication channel, it can further adapt to the complex internal and external, time-varying, and inter-channel noise interference suffered by the real channel. At the same time, with the help of the adaptive filtering principle to suppress noise, the noise suppression effect for the MIMO PLC communication system can be comprehensively improved, thereby improving the communication quality of the MIMO PLC communication system.

[0065] In an exemplary embodiment, for the communication signal after the target adaptive noise canceller performs noise suppression, the bit error rate of the communication signal can be further determined to judge the noise suppression effect of the target adaptive noise canceller. Figure 3 As shown, the above method may further include steps 301 to 303. In which:

[0066] Step 301: input the denoised signal after noise suppression by the target adaptive noise canceller into a space-time block code combiner for combining, and then input it into an orthogonal frequency division multiplexing demodulator to obtain a third analog signal.

[0067] Among them, the denoised signal output by the target adaptive noise eliminator is of the same type as the above-mentioned error signal, both of which are difference signals between the main input signal and the estimated noise signal, that is, the difference signal after the estimated noise signal is removed from the main input signal, and is also the denoised signal after noise suppression. The denoised signal is approximately close to the effective communication signal in the main input signal.

[0068] For example, the denoised signal can be first input into a space-time block code (STBC) combiner for multi-channel merging, and then the combined signal can be input into an orthogonal frequency division multiplexing (OFDM) demodulator for decoding to obtain a decoded third analog signal. The STBC combiner primarily combines multiple STBC-encoded analog signals to enhance the signal's resistance to fading and interference, thereby improving signal quality at the receiving end.

[0069] Step 302: Input the third analog signal into a binary phase shift keying demodulator for demodulation to obtain output data.

[0070] In this example, the modulation and coding process adapted to the signal is reversed during signal recovery. Initial demodulation is performed by an orthogonal frequency division multiplexing demodulator, followed by secondary demodulation by a binary phase shift keying demodulator to restore the original characteristics of the communication signal and determine its integrity and accuracy. Through demodulation, the inverse of modulation, the signal is converted into output bits according to the modulation rules, resulting in output data corresponding to the input data.

[0071] Step 303 : determining a bit error rate of the MIMO power line communication channel based on the input data and the output data; the bit error rate is used to characterize the noise suppression effect of the target adaptive noise canceller on the MIMO power line communication channel.

[0072] In this example, the performance of a MIMO PLC communication system with adaptive noise cancellation is evaluated by calculating the bit error rate (BER). By accurately comparing the original input data sent with the received output data, the BER is calculated. It directly reflects the accuracy of the communication system's transmission; a lower BER indicates better system performance. The BER is the ratio of the number of erroneous symbols transmitted to the total number of symbols transmitted. The total number of symbols transmitted can be determined by comparing the input data with the output data.

[0073] In this embodiment, the denoised signal after noise suppression is sequentially subjected to orthogonal frequency division multiplexing demodulation and binary phase shift keying demodulation to restore the original input data and obtain output data. Then, the bit error rate is determined by comparing the input data and the output data. The bit error rate can be used to evaluate the communication performance of the MIMO PLC communication system to ensure the noise suppression effect on the MIMO PLC communication system and improve the reliability of noise suppression.

[0074] The following combines system modeling and simulation experiments to verify the reliability of the noise suppression method proposed in the embodiment of the present application. The embodiment of the present application proposes a noise adaptive filtering suppression technology for MIMO power line communication channels. This technology suppresses noise by adjusting the filter coefficients with the help of the adaptive filtering principle. The adaptive filter is composed of an adaptive / digital filter and an adaptive algorithm. It updates the filter coefficients using an adaptive algorithm based on the error between the filter output and the expected signal. It accurately targets the key interference factor of Markov-Middleton and Middleton Class A impulse noise in power line communication, uses the adaptive filtering principle, and adjusts the filter coefficients with the help of LMS and / or RLS algorithms to effectively reduce the impact of noise. Experimental verification shows that the bit error rate performance can be significantly improved in both weakly interfered and severely interfered MIMO PLC channels, providing a solid guarantee for the accurate and stable transmission of power line communication data, expanding the application prospects of power line communication in smart homes, smart grids and other fields with strict communication quality requirements, and effectively promoting the development of power line communication technology. The specific methods and means are as follows:

[0075] Step 1: System modeling and data input

[0076] In the initial stage of technology implementation, it is necessary to build a suitable system model and input data. With the help of MATLAB R2020a software, a 6-way OSTBC-coded MIMO power line communication channel is carefully constructed. Figure 4 The model closely simulates a real-world power line communication environment, fully accounting for signal transmission between multiple transmitting and receiving antennas. Multicarrier modulation is employed for data input. Binary phase-shift keying (BPSK) modulation is first applied to precisely map the input bits to corresponding phase states and convert them into a complex sequence. Orthogonal frequency-division multiplexing (OFDM) modulation then further processes the signal, making it more adaptable to the power line communication channel. To simulate the complex noise interference encountered in actual communications, a noise-corrupted signal from a MIMO channel, superimposed with impulse noise and additive white Gaussian noise, is used as the primary input to the adaptive noise canceller (ANC), i.e., the main input signal x(n). The main input signal x(n) is the superposition of the desired signal and the noise signal. The desired signal is the valid communication signal uncorrupted by noise. The noise signal can include, but is not limited to, noise within the channel, noise from the external environment, additional impulse noise, and additive white Gaussian noise. Markov-Middleton and / or Middleton Class A impulse noise is also added as the reference input to the ANC, i.e., the reference noise signal.

[0077] Step 2: Adaptive filtering

[0078] refer to Figure 5As shown, it shows the algorithm flow of adaptive filtering. First, an adaptive filter composed of an adaptive / digital filter and an adaptive algorithm is constructed, and its coefficients are initialized, and the parameters of the adaptive filter are adjusted, such as setting some or all parameters of the adaptive filter to zero, or using random initialization, or setting initial values ​​based on prior knowledge; for example, if there is a certain understanding of the characteristics of the signal and noise, the initial coefficients can be set based on experience to speed up the convergence of the adaptive filter. Taking a linear adaptive filter as an example, its output is the weighted sum of the input signal and the adaptive filter coefficients. When the coefficients are all zero, the output of the adaptive filter is zero. When the coefficients are all 1 and there is no other operation to change the signal, the output signal of the adaptive filter is consistent with the input signal. When the coefficients are other values ​​except 0 and 1, the output signal of the adaptive filter is the weighted sum of the input signal and the adaptive filter coefficients.

[0079] During signal processing, the reference noise signal is filtered by an adaptive filter to obtain an estimated noise signal y(n) that is close to the actual noise signal in the main input signal. This means that processing the reference impulse noise signal helps the adaptive filter better suppress the noise in the main signal. For example, the input of the adaptive filter includes not only the reference noise signal but also the main input signal (not shown). An estimated noise signal is generated based on the main input signal and the reference noise signal. The estimated noise signal y(n) is then subtracted from the main input signal x(n) to obtain an error signal e(n). This error signal e(n) represents the difference between the output of the adaptive noise canceller and the desired signal. This error signal e(n) is the key basis for subsequent adjustments to the adaptive filter coefficients.

[0080] There are two algorithms to choose from for coefficient update. The LMS algorithm is based on the stochastic gradient descent method. The formula adjusts the weight vector, where the step size must be strictly controlled within In the range of , the coefficient vector that minimizes the cost function is found through continuous iteration; the RLS algorithm recursively finds the filter coefficient that minimizes the weighted linear least squares cost function, using the formula The gain vector is calculated and the filter coefficients are updated to better adapt to the dynamic changes of the signal.

[0081] Step 3: Signal processing and result output

[0082] After filtering is complete, the estimated noise signal output by the adaptive filter is precisely subtracted from the main input signal to obtain a preliminarily processed output signal, known as the error signal. This error signal may still exhibit some deviation. To optimize the filtering effect, the error signal is fed back to the adaptive filter. The error signal represents the difference between the current filtered output and the desired signal. The adaptive filter then adjusts the filter weights based on this error signal. By continuously fine-tuning the weights, the filter can better adapt to signal changes, bringing the output signal closer to the desired signal.

[0083] Then, continue to refer to Figure 4 , by performing a reshaping operation on the filtered and weighted denoised signal. During the modulation process, the input signal undergoes transformations such as BPSK and OFDM modulation. The reshaping operation aims to reverse these transformations, restoring the signal's original characteristics and ensuring signal integrity and accuracy. Finally, demodulation is performed. Demodulation is the inverse of modulation, converting the signal into output bits according to the modulation rules. For example, for a BPSK modulated signal, the received signal phase state is mapped back to the corresponding binary bit. This rigorous series of processing completes the entire communication process, outputting the final result.

[0084] It should be noted that during signal demodulation, channel estimation can also be performed on the output signals of a multi-input multi-output (MIMO) power line communication (PLC) channel to obtain information about its characteristics. PLC channels are subject to complexities such as noise, attenuation, and multipath effects, and their characteristics vary over time and the environment. Channel estimation can measure parameters such as channel gain and delay, providing a basis for subsequent signal modulation, coding, and demodulation. It helps the system adjust relevant parameters to compensate for channel fading and distortion, thereby improving the accuracy and reliability of signal transmission.

[0085] Step 4: Performance evaluation and comparison

[0086] The performance evaluation and comparison work at the receiving end is the key step in verifying the effectiveness of the technology. First, by accurately comparing the original input data sent and the received output data, the bit error rate is calculated. It directly reflects the accuracy of the communication system transmission. The lower the bit error rate, the better the system performance. In order to comprehensively measure the advantages of the technology, such as Figure 6 and Figure 7A Markov-Middleton noise reduction technique was compared with the Middleton Class A model. The effects of the LMS and RLS algorithms on bit error rates were thoroughly explored for varying noise intensities and signal-to-noise ratios. Bit error rate curves were plotted to visually demonstrate performance differences, comprehensively considering algorithm complexity and computational efficiency. The results demonstrated that the technique effectively suppressed noise and improved bit error rate performance in MIMO PLC channels with varying interference. Furthermore, comprehensive consideration of algorithm complexity and computational efficiency ensured that the technique achieved both efficient noise reduction and stable operation in practical applications, fully demonstrating its significant advantages in noise suppression and performance improvement.

[0087] In this embodiment, Markov-Middleton and Middleton Class A impulse noise are used to accurately describe complex noise scenarios. For OSTBC-coded MIMO power line communication channels, BPSK and OFDM modulation are used to improve the adaptability of MIMO signals. LMS and RLS algorithms are used to adjust filter coefficients to accurately suppress Markov-Middleton and Middleton Class A impulse noise. This significantly improves bit error rate performance in MIMO PLC channels with varying interference levels. Adaptive filtering technology based on LMS and RLS algorithms can improve impulse noise suppression performance overall. For example, by adaptively adjusting filter coefficients, not only can noise characteristics be more effectively utilized, avoiding the limitations of similar SCS algorithms in utilizing spatial correlation, but also, compared to iterative threshold algorithms, it can stably reduce bit error rates under varying interference intensities, improving noise suppression efficiency.

[0088] In addition, by combining a binary phase-shift keying modulator, an orthogonal frequency-division multiplexing modulator, a space-time block code encoder, etc. with a multi-input multi-output power line communication channel and an adaptive noise canceller, this specific architecture combination for the MIMO PLC system can effectively integrate signal modulation, coding and noise cancellation functions, thereby improving the reliability of the MIMO PLC system. In addition, this technology provides a guarantee for the stable and accurate transmission of power line communication data, expands its application in smart homes, smart grids and other fields, and strongly promotes the development of power line communication technology. Therefore, the noise suppression method proposed in the embodiment of the present application can produce a significant noise suppression effect in the MIMO power line communication channel.

[0089] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0090] Based on the same inventive concept, an embodiment of the present application further provides a noise suppression device for a MIMO power line communication channel for implementing the aforementioned noise suppression method for a MIMO power line communication channel. The implementation solution provided by the device is similar to the implementation solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the noise suppression device for a MIMO power line communication channel provided below can be found in the above-mentioned limitations of the noise suppression method for a MIMO power line communication channel, and will not be repeated here.

[0091] In an exemplary embodiment, Figure 8 As shown, a noise suppression device for a MIMO power line communication channel is provided, comprising: a signal processing module 801, a noise suppression module 802 and an adjustment module 803, wherein:

[0092] The signal processing module 801 is configured to superimpose a preset noise signal on the output signal of the MIMO power line communication channel to obtain a main input signal.

[0093] The noise suppression module is used to input the main input signal and the reference noise signal into the initial adaptive noise canceller for noise suppression processing to obtain an error signal; the reference noise signal includes a Markov-Middleton impulse noise signal and / or a Middleton Class A impulse noise signal.

[0094] The adjustment module 803 is used to adjust the parameters of the initial adaptive filter in the initial adaptive noise canceller based on the error signal to obtain a target adaptive noise canceller; the target adaptive noise canceller is used to suppress noise on the output signal of the MIMO power line communication channel.

[0095] In one embodiment, the preset noise signal includes: an additive white Gaussian noise signal, and / or a reference noise signal.

[0096] In one embodiment, the noise suppression module is specifically configured to input the main input signal and the reference noise signal into an initial adaptive filter for processing to obtain an estimated noise signal; and input the main input signal and the estimated noise signal into a subtractor in an initial adaptive noise canceller to obtain an error signal.

[0097] In one embodiment, the adjustment module 803 is specifically configured to adjust parameters of an initial adaptive filter based on an error signal to obtain an intermediate adaptive filter; construct an intermediate adaptive noise eliminator based on the intermediate adaptive filter; and, based on the intermediate adaptive noise eliminator, return to the step of inputting a main input signal and a reference noise signal into the initial adaptive noise eliminator for noise suppression processing to obtain an error signal, until a preset number of iterations is reached to obtain a target adaptive noise eliminator.

[0098] In one embodiment, the apparatus further comprises:

[0099] A first modulation module, configured to input input data into a binary phase shift keying modulator for modulation to obtain a first analog signal;

[0100] A second modulation module is used to input the first analog signal into an orthogonal frequency division multiplexing modulator for modulation to obtain a plurality of second analog signals; the plurality of second analog signals are matched with the MIMO power line communication channel;

[0101] The coding and transmission module is used to input the plurality of second analog signals into a space-time block code encoder for encoding, and then input the second analog signals into a MIMO power line communication channel.

[0102] In one embodiment, the apparatus further comprises:

[0103] A first demodulation module is configured to input the denoised signal after noise suppression by the target adaptive noise canceller into the space-time block code combiner for combining, and then input the denoised signal into the orthogonal frequency division multiplexing demodulator to obtain a third analog signal;

[0104] A second demodulation module is used to input the third analog signal into the binary phase shift keying demodulator for demodulation to obtain output data;

[0105] The determination module is used to determine the bit error rate of the MIMO power line communication channel based on the input data and the output data; the bit error rate is used to characterize the noise suppression effect of the target adaptive noise canceller on the MIMO power line communication channel.

[0106] Each module in the aforementioned noise suppression device for a MIMO power line communication channel can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or can be stored in a memory in the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0107] In an exemplary embodiment, a computer device is provided. The computer device may be an adaptive noise suppression and cancellation device, and its internal structure diagram may be as shown in FIG. Figure 9 As shown. The computer device includes a processor, a memory, an input / output interface, and a communication interface. The processor, the memory, and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC) or other technologies. When the computer program is executed by the processor, a noise suppression method for a MIMO power line communication channel is implemented.

[0108] Those skilled in the art will understand that Figure 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0109] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the noise suppression method for a MIMO power line communication channel in any of the above embodiments are implemented.

[0110] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the noise suppression method for a MIMO power line communication channel in any of the above embodiments are implemented.

[0111] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps of the noise suppression method for a MIMO power line communication channel in any of the above embodiments.

[0112] It should be noted that the data involved in this application (including but not limited to data used for analysis, storage, display, etc.) are all information and data fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0113] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), data processing logic devices based on quantum computing, and the like.

[0114] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0115] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A noise suppression method for a MIMO power line communication channel, characterized in that: The method comprises: Superimposing a preset noise signal on the output signal of the MIMO power line communication channel to obtain a main input signal; Inputting the main input signal and the reference noise signal into an initial adaptive noise canceller for noise suppression processing to obtain an error signal; the reference noise signal includes a Markov-Middleton impulse noise signal and / or a Middleton Class A impulse noise signal; The parameters of the initial adaptive filter in the initial adaptive noise canceller are adjusted based on the error signal to obtain a target adaptive noise canceller; the target adaptive noise canceller is used to suppress noise on the output signal of the MIMO power line communication channel.

2. The method according to claim 1, characterized in that The preset noise signal includes: an additive white Gaussian noise signal, and / or the reference noise signal.

3. The method according to claim 1, characterized in that The step of inputting the main input signal and the reference noise signal into an initial adaptive noise canceller for noise suppression processing to obtain an error signal comprises: Inputting the main input signal and the reference noise signal into the initial adaptive filter for processing to obtain an estimated noise signal; The main input signal and the estimated noise signal are input into a subtractor in the initial adaptive noise canceller to obtain the error signal.

4. The method according to claim 1, wherein The step of adjusting parameters of an initial adaptive filter in the initial adaptive noise canceller based on the error signal to obtain a target adaptive noise canceller includes: Adjusting parameters of the initial adaptive filter based on the error signal to obtain an intermediate adaptive filter; constructing an intermediate adaptive noise canceller based on the intermediate adaptive filter; Based on the intermediate adaptive noise canceller, the step of inputting the main input signal and the reference noise signal into the initial adaptive noise canceller for noise suppression processing to obtain an error signal is returned to be executed until a preset number of iterations is reached to obtain the target adaptive noise canceller.

5. The method according to any one of claims 1 to 4, characterized in that The method further comprises: Inputting the input data into a binary phase shift keying modulator for modulation to obtain a first analog signal; Inputting the first analog signal into an orthogonal frequency division multiplexing modulator for modulation to obtain a plurality of second analog signals; the plurality of second analog signals are matched with the MIMO power line communication channel; The plurality of second analog signals are input into a space-time block code encoder for encoding, and then input into the MIMO power line communication channel.

6. The method according to claim 5, characterized in that The method further comprises: Inputting the denoised signal after noise suppression by the target adaptive noise canceller into a space-time block code combiner for combining, and then inputting the denoised signal into an orthogonal frequency division multiplexing demodulator to obtain a third analog signal; Inputting the third analog signal into a binary phase shift keying demodulator for demodulation to obtain output data; Based on the input data and the output data, a bit error rate of the MIMO power line communication channel is determined; the bit error rate is used to characterize the noise suppression effect of the target adaptive noise canceller on the MIMO power line communication channel.

7. A noise suppression device for a MIMO power line communication channel, characterized in that: The device comprises: A signal processing module, configured to superimpose a preset noise signal on an output signal of the MIMO power line communication channel to obtain a main input signal; a noise suppression module, configured to input the main input signal and a reference noise signal into an initial adaptive noise canceller for noise suppression processing to obtain an error signal; the reference noise signal comprises a Markov-Middleton impulse noise signal and / or a Middleton Class A impulse noise signal; An adjustment module is used to adjust parameters of an initial adaptive filter in the initial adaptive noise canceller based on the error signal to obtain a target adaptive noise canceller; the target adaptive noise canceller is used to suppress noise on an output signal of the MIMO power line communication channel.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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