Visible light communication method based on precoding and adaptive dynamic compression

Abstract

The invention discloses a visible light communication method based on precoding and adaptive dynamic compression. The method comprises the following steps: data layering and precoding processing; signal conversion and clipping; regulating and controlling a dynamic peak value and generating a signal; receiving end signal recovery and processing; decoding and recovering the signal; and separating and demodulating the signal. The method comprises the following steps of: layering original data by adopting a modulation mode of mixing ACO-OFDM and PAM-DMT at a transmitting end; layered signals are preprocessed through a precoder, the peak-to-average power ratio of the signals is reduced, converted time domain signals are input into a generative adversarial network-based framework, and the framework adaptively performs amplitude compression and dynamic clipping on the signals through a feedback mechanism of a convolution feature extraction module and a discriminator, so that the peak-to-average power ratio is effectively reduced; a convolutional feature extraction module at a receiving end generates a recovered signal, and nonlinear distortion is reduced; the signal recovery precision and the spectrum efficiency of a communication system are remarkably improved, and meanwhile the reliability and stability of communication are guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a visible light communication method based on precoding and adaptive dynamic compression. Background Art

[0002] Visible light communication (VLC) transmits data through LEDs, which not only avoids the occupation of radio frequency spectrum resources, but also provides high-speed, large-capacity wireless communication services indoors. Orthogonal frequency division multiplexing (OFDM) technology has been widely used in VLC systems due to its high spectrum utilization and ability to effectively resist multipath fading and signal interference.

[0003] Optical wireless communication systems are usually based on intensity modulation / direct detection (IM / DD), which requires the transmitted signal to be real and positive. Although many O-OFDM schemes have been proposed, these schemes still have certain performance defects. Among the many schemes, HACO-OFDM provides a good trade-off between spectral efficiency, computational complexity and transmission performance. However, the peak-to-average power ratio (PAPR) of the HACO-OFDM system is high, which leads to large fluctuations in the signal amplitude, making it easy for the LED to enter the nonlinear working area, thereby causing power loss and increased bit error rate problems. These problems limit the performance of HACO-OFDM in practical applications, especially in scenarios with high data rates and low power consumption requirements, and it is difficult to effectively improve system performance.

[0004] The Chinese patent publication number is "CN202210773144.5", and its name is "A method for improving the performance of hybrid HACO-OFDM using predistortion technology". This method uses predistortion technology for the first time to eliminate the interference between ACO-OFDM and PAM-DMT at the transmitting end. After Fourier transforming the ACO-OFDM signal, the interference noise is extracted and compensated on the frequency domain data of the PAM-DMT signal. This technology effectively eliminates the interference of ACO-OFDM to PAM-DMT, avoids the complex serial interference processing at the receiving end in the traditional method, and simplifies the processing at the receiving end. Although it reduces the system complexity and improves the bit error rate performance to a certain extent, it does not optimize the peak-to-average power ratio (PAPR), which may cause power amplifier distortion caused by high PAPR and affect the system transmission quality. Therefore, designing a modulation technology that can effectively suppress the peak-to-average power ratio (PAPR) and improve the stability of the system while reducing the bit error rate is an important problem that the present invention needs to solve. Summary of the invention

[0005] The technical solution of the present invention to solve the above technical problem is to provide a visible light communication method based on precoding and adaptive dynamic compression, comprising the following steps:

[0006] Step 1: Data stratification and precoding processing:

[0007] The signal source is layered, the first layer of data is allocated to the odd subcarrier position for ACO-OFDM modulation, and the second layer of data is allocated to the even subcarrier position for PAM-DMT modulation;

[0008] A Vandermonde-like matrix is ​​created, and the ACO-OFDM frequency domain signal and the PAM-DMT frequency domain signal are multiplied by the Vandermonde-like matrix respectively to obtain a precoded ACO-OFDM signal and a precoded PAM-DMT signal;

[0009] Step 2: Signal transformation and trimming:

[0010] Perform Hermitian symmetric transform and inverse Fourier transform (IFFT) on the two-layer precoded signals to generate ACO-OFDM and PAM-DMT bipolar time domain signals;

[0011] Perform negative signal clipping on the bipolar time domain signal, and superimpose the clipped time domain positive signal to obtain a superimposed signal;

[0012] Step 3: Dynamic peak control and signal generation:

[0013] The superimposed signal is subjected to μ-law mapping to obtain a mapping signal, and the mapping signal is input into a dynamic peak control module to perform dynamic peak reduction to obtain a reduced and clipped low peak-to-average power ratio signal;

[0014] The processed signal is input to the discriminator 1 in the DPC-GAN framework. The discriminator 1 analyzes the amplitude distribution and characteristics of the signal, optimizes the signal processing process, and the convolution feature extraction module generates an output signal with amplitude compression and dynamic peak reduction.

[0015] Step 4: Signal recovery and processing at the receiving end:

[0016] The received time domain signal is input into the dynamic signal recovery module in the DPC-GAN framework to recover the signal cut and adjusted by the dynamic threshold;

[0017] Perform μ-law inverse mapping on the restored signal to obtain an inverse mapping signal;

[0018] The inverse mapped signal is input to the discriminator 2 for comparative analysis to evaluate the quality and accuracy of signal recovery. The convolution feature extraction module generates the signal that has been restored and inverse mapped;

[0019] Step 5: Signal decoding and recovery:

[0020] Perform fast Fourier transform and precoding decoding on the received signal, and use the reversibility of the Vandermonde-like precoding matrix to obtain a precoding decoding signal, which contains mixed data of ACO-OFDM and PAM-DMT;

[0021] Step 6: Signal separation and demodulation:

[0022] Directly detect and extract the ACO-OFDM signal from the received signal and restore its original data information;

[0023] The clipped noise of the ACO-OFDM signal is reconstructed and removed from the received mixed signal to obtain the complete PAM-DMT signal, achieving accurate separation and demodulation of the two signals.

[0024] Furthermore, the method of creating a Vandermonde-like matrix in step 1 includes: generating a Vandermonde-like matrix using a monomial, and replacing the monomial with a polynomial at a node to create a Vandermonde-like matrix.

[0025] Furthermore, the dynamic peak control module in step 3 includes:

[0026] Determine a dynamic threshold value, and calculate the dynamic threshold value according to the maximum amplitude value of the mapped signal;

[0027] The peak characteristics of the signal are optimized. When the signal amplitude exceeds the threshold, it is cut to within the threshold range, and the cut part is redistributed to the zero value position of the signal to obtain the target signal.

[0028] Furthermore, the DPC-GAN framework in step 3 includes:

[0029] Convolution feature extraction module, used to extract the peak features of the signal, output amplitude compression and dynamically reduce the peak of the generated signal;

[0030] The discriminator 1 is used to analyze the amplitude distribution and characteristics of the generated signal and the target signal to improve the quality of the generated signal.

[0031] Furthermore, the convolutional feature extraction module includes a fully connected layer module, a convolutional block module and an output layer module, and the discriminator consists of a convolutional module, a flattening module and a discrimination module.

[0032] Furthermore, the dynamic signal recovery module in step 4 includes:

[0033] Dynamic threshold estimation, recovering the corresponding threshold according to the maximum amplitude value of the received signal;

[0034] Signal classification mechanism, which determines the signal status based on the relationship between the received signal and the dynamic threshold;

[0035] The original signal is restored and reconstructed, and the signal is linearly superimposed using a dynamic threshold according to the signal classification result to restore the original signal.

[0036] Furthermore, the discriminator 2 in step 4 is used to evaluate the quality and accuracy of signal recovery and optimize the feature extraction capability of the convolutional feature extraction module through feedback.

[0037] Furthermore, the precoding and decoding process in step 5 utilizes the reversibility of the Vandermonde-like precoding matrix to perform an inverse operation on the signal to obtain a precoding and decoding signal.

[0038] Furthermore, the signal separation and demodulation process in step 6 includes directly detecting and extracting the ACO-OFDM signal, and reconstructing and removing the clipping noise of the ACO-OFDM signal to obtain a complete PAM-DMT signal.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] 1. The present invention provides a precoding method based on a Vandermonde matrix and Chebyshev node selection, which effectively reduces inter-symbol interference by reducing the autocorrelation of matrix elements, suppresses the increase of peak-to-average power ratio (PARA), solves the problem that high peak-to-average power ratio and inter-symbol interference in optical communication systems affect transmission performance, and improves system stability.

[0041] 2. The present invention provides a dynamic compression framework based on DPC-GAN adaptation. First, the signal is compressed by the μ-law mapping module, and the part exceeding the threshold is cut in the dynamic peak control module; then, according to the feedback result of the discriminator 1, the convolution feature extraction module extracts the signal features and generates adaptive compression and clipping signals, which effectively solves the problems of signal distortion and excessive peak in traditional visible light communication.

[0042] 3. The present invention provides a signal recovery framework based on DPC-GAN. First, the received signal is recovered by a dynamic recovery module; then, the inverse mapping signal is generated by a μ-law inverse mapping module; then, the convolution feature extraction module is optimized according to the feedback result of the discriminator 2, thereby generating a high-precision recovery signal. This method can significantly improve the accuracy and quality of signal recovery, while enhancing the stability of signal transmission and ensuring the integrity and reliability of the signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0044] Figure 1 A flowchart of the steps of the visible light communication method based on precoding and adaptive dynamic compression according to the present invention;

[0045] Figure 2 A schematic diagram of the structure of a visible light communication method based on precoding and adaptive dynamic compression according to the present invention;

[0046] Figure 3 Flow chart of the convolution feature extraction module of the DPC-GAN framework of the present invention;

[0047] Figure 4 Flowchart of the discriminator of the DPC-GAN framework of the present invention. DETAILED DESCRIPTION

[0048] The present invention proposes a visible light communication method based on precoding and adaptive dynamic compression, aiming to improve the signal recovery accuracy and the spectrum efficiency of the communication system while ensuring the reliability and stability of communication.

[0049] The visible light communication method based on precoding and adaptive dynamic compression proposed by the present invention will be described below in a specific embodiment:

[0050] Embodiment 1:

[0051] A visible light communication method based on precoding and adaptive dynamic compression, such as Figure 1 As shown, the following steps are included:

[0052] Step 1: Data stratification and precoding processing:

[0053] The signal source is layered, the first layer of data is allocated to the odd subcarrier position for ACO-OFDM modulation, and the second layer of data is allocated to the even subcarrier position for PAM-DMT modulation;

[0054] A Vandermonde-like matrix is ​​created, and the ACO-OFDM frequency domain signal and the PAM-DMT frequency domain signal are multiplied by the Vandermonde-like matrix respectively to obtain a precoded ACO-OFDM signal and a precoded PAM-DMT signal;

[0055] Step 2: Signal transformation and trimming:

[0056] Perform Hermitian symmetric transform and inverse Fourier transform (IFFT) on the two-layer precoded signals to generate ACO-OFDM and PAM-DMT bipolar time domain signals;

[0057] Perform negative signal clipping on the bipolar time domain signal, and superimpose the clipped time domain positive signal to obtain a superimposed signal;

[0058] Step 3: Dynamic peak control and signal generation:

[0059] The superimposed signal is subjected to μ-law mapping to obtain a mapping signal, and the mapping signal is input into a dynamic peak control module to perform dynamic peak reduction to obtain a reduced and clipped low peak-to-average power ratio signal;

[0060] The processed signal is input to the discriminator 1 in the DPC-GAN framework. The discriminator 1 analyzes the amplitude distribution and characteristics of the signal, optimizes the signal processing process, and the convolution feature extraction module generates an output signal with amplitude compression and dynamic peak reduction.

[0061] Step 4: Signal recovery and processing at the receiving end:

[0062] The received time domain signal is input into the dynamic signal recovery module in the DPC-GAN framework to recover the signal cut and adjusted by the dynamic threshold;

[0063] Perform μ-law inverse mapping on the restored signal to obtain an inverse mapping signal;

[0064] The inverse mapped signal is input to the discriminator 2 for comparative analysis to evaluate the quality and accuracy of signal recovery. The convolution feature extraction module generates the signal that has been restored and inverse mapped;

[0065] Step 5: Signal decoding and recovery:

[0066] Perform fast Fourier transform and precoding decoding on the received signal, and use the reversibility of the Vandermonde-like precoding matrix to obtain a precoding decoding signal, which contains mixed data of ACO-OFDM and PAM-DMT;

[0067] Step 6: Signal separation and demodulation:

[0068] Directly detect and extract the ACO-OFDM signal from the received signal and restore its original data information;

[0069] The clipped noise of the ACO-OFDM signal is reconstructed and removed from the received mixed signal to obtain the complete PAM-DMT signal, achieving accurate separation and demodulation of the two signals.

[0070] Furthermore, the method of creating a Vandermonde-like matrix in step 1 includes: generating a Vandermonde-like matrix using a monomial, and replacing the monomial with a polynomial at a node to create a Vandermonde-like matrix.

[0071] Furthermore, the dynamic peak control module in step 3 includes:

[0072] Determine a dynamic threshold value, and calculate the dynamic threshold value according to the maximum amplitude value of the mapped signal;

[0073] The peak characteristics of the signal are optimized. When the signal amplitude exceeds the threshold, it is cut to within the threshold range, and the cut part is redistributed to the zero value position of the signal to obtain the target signal.

[0074] Furthermore, the DPC-GAN framework in step 3 includes:

[0075] Convolution feature extraction module, used to extract the peak features of the signal, output amplitude compression and dynamically reduce the peak of the generated signal;

[0076] The discriminator 1 is used to analyze the amplitude distribution and characteristics of the generated signal and the target signal to improve the quality of the generated signal.

[0077] Furthermore, the convolutional feature extraction module includes a fully connected layer module, a convolutional block module and an output layer module, and the discriminator consists of a convolutional module, a flattening module and a discrimination module.

[0078] Furthermore, the dynamic signal recovery module in step 4 includes:

[0079] Dynamic threshold estimation, recovering the corresponding threshold according to the maximum amplitude value of the received signal;

[0080] Signal classification mechanism, which determines the signal status based on the relationship between the received signal and the dynamic threshold;

[0081] The original signal is restored and reconstructed, and the signal is linearly superimposed using a dynamic threshold according to the signal classification result to restore the original signal.

[0082] Furthermore, the discriminator 2 in step 4 is used to evaluate the quality and accuracy of signal recovery and optimize the feature extraction capability of the convolutional feature extraction module through feedback.

[0083] Furthermore, the precoding and decoding process in step 5 utilizes the reversibility of the Vandermonde-like precoding matrix to perform an inverse operation on the signal to obtain a precoding and decoding signal.

[0084] Furthermore, the signal separation and demodulation process in step 6 includes directly detecting and extracting the ACO-OFDM signal, and reconstructing and removing the clipping noise of the ACO-OFDM signal to obtain a complete PAM-DMT signal.

[0085] Embodiment 2:

[0086] A visible light communication method based on precoding and adaptive dynamic compression, such as Figure 2 The method comprises the following steps:

[0087] Step 1: Data stratification and precoding processing:

[0088] First, the signal source is layered. Then, the first layer of data is allocated to odd subcarrier positions and ACO-OFDM modulation is performed. The second layer of data is allocated to even subcarrier positions and PAM-DMT modulation is performed on the signal. Finally, the two layers of signals are input into the precoding module respectively to obtain two layers of precoded signals.

[0089] The specific steps include:

[0090] First, create a Vandermonde-like matrix using the monomial (1, x, x 2 ,…,x n ) generates the Vandermonde matrix, at the node Replace the monomial with a polynomial (p0(x), p1(x), …p n (x)) to create a Vandermonde-like matrix, and the expression of the Vandermonde-like matrix with m rows and n columns is as follows:

[0091]

[0092] Finally, the ACO-OFDM frequency domain signal and the PAM-DMT frequency domain signal are multiplied by the Vandermonde matrix X pre =X VLM =P(m,n)X, the precoded ACO-OFDM signal is represented by X pre , precoded PAM-DMT is represented by Y pre .

[0093] Step 2: Signal transformation and trimming:

[0094] The two-layer precoded signals are subjected to Hermitian symmetric transformation and inverse Fourier transform (IFFT) respectively to generate ACO-OFDM and PAM-DMT bipolar time domain signals; then, the bipolar time domain signals are subjected to negative signal clipping, and the clipped time domain positive signals are superimposed. Finally, the superimposed signals are subjected to μ-law mapping to obtain the mapped signals;

[0095] The specific steps include:

[0096] Firstly, the precoded ACO-OFDM frequency domain signal and the precoded PAM-DMT frequency domain signal are Hermitian symmetric to obtain the ACO-OFDM frequency domain signal and the PAM-DMT frequency domain signal respectively.

[0097] Secondly, the two-layer frequency domain signals are subjected to inverse Fourier transform (IFFT) to obtain the ACO-OFDM time domain bipolar signal x pre,n and PAM-DMT time domain bipolar signal pre,n , the time domain symmetry of ACO-OFDM and PAM-DMT signals is as follows:

[0098] x pre,n =-x pre,n+N / 2 ,n=0,1,…,N / 2-1;

[0099] y pre,n =-y pre,N-n ,n=0,1,…,N / 2-1;

[0100] In order to facilitate the subsequent design of the present invention, combined with the symmetry of the signal, the index variable k is used to represent the signal side. Assuming that it is the mth symbol, the ACO-OFDM signal is represented by x m,k , using the side information as the first side of the signal in the range of n, denoted as x m,1 ; The second side using the side information as the signal in the range n+N / 2 is represented as x m,2 ; PAM-DMT signal is represented by y m,k , using the side information for the first side of the signal in the range n, denoted as y m,1 ; Use side information in the range Nn for the second side of the signal, denoted as y m,2 .

[0101] Finally, the time domain positive signal is obtained by trimming the negative signal, and the trimmed time domain positive signal is superimposed to obtain the superimposed time domain signal x haco .

[0102] Step 3: Dynamic peak control and signal generation:

[0103] The time domain signal is input into the DPC-GAN framework designed by the present invention, and after processing, an output signal with amplitude compression and dynamic peak reduction is generated. The time domain signal is input into the μ-law mapping module to obtain a mapping signal; then, the mapping signal is input into the dynamic peak control module to obtain a reduced and clipped low peak-to-average power ratio signal, and then the signal is input into the discriminator 1. Specifically, it includes the following steps:

[0104] (1) Nonlinear compression and dynamic peak reduction process:

[0105] The input time domain signal first passes through the μ-law mapping module to perform nonlinear compression on the signal amplitude, reduce the dynamic range, and thus reduce the peak-to-average power ratio (PARA) of the signal to obtain the signal x μhaco As shown below:

[0106]

[0107] The mapped signal x μhaco The signal is input to the dynamic peak control module of the present invention, and the peak characteristics of the signal are optimized by the module. The specific steps include:

[0108] Determine the dynamic threshold γ m According to the maximum amplitude value x of the mapped signal max,m Calculate a dynamic threshold, dynamic threshold γ m The expression is as follows:

[0109] γ m =(1-α)·x max,m ;

[0110] Among them, α is the weight parameter, which controls the influence weight of the maximum amplitude value, and the value range is α∈[0,1], x amax,m is the maximum amplitude value of the mth signal.

[0111] By dynamically adjusting the amplitude of the input signal, it ensures that its amplitude does not exceed the preset threshold. When the amplitude of the signal is less than or equal to the threshold, the signal is directly output; when the signal amplitude exceeds the threshold, it is cut to within the threshold range, and the cut part is redistributed to the zero value position of the signal, so as to maintain the overall balance of the signal energy while limiting the signal amplitude.

[0112] (2) Signal feature extraction process:

[0113] The original time domain signal is input into the convolution feature extraction module to extract the peak features of the signal and generate a signal that has been dynamically compressed and reduced to ensure that it meets the quality requirements; then, the generated signal and the target signal that has been processed by nonlinear compression and dynamic peak reduction are input into the discriminator 1 for comparative analysis. The discriminator 1 analyzes the amplitude distribution and characteristics of the two, and evaluates the amplitude and characteristics of the generated signal, thereby optimizing the signal processing process and improving the generation effect.

[0114] like Figure 3As shown in the figure, all convolution feature extraction modules are composed of a fully connected layer module, a convolution block module and an output layer module. The fully connected layer module contains a fully connected layer, which is used to convert the features of the input signal into a high-dimensional representation. The convolution block module is composed of five convolution blocks, each of which contains upsampling, 1D convolution layer, batch normalization and ReLU activation function. The local features of the signal are extracted through upsampling and downsampling operations. Batch normalization improves training stability. The ReLU activation function increases the nonlinear ability of the network and helps extract complex features. The output layer module consists of a 1D convolution layer and a ReLU activation function, which is used to further process the extracted features to ensure efficient and accurate representation of the signal.

[0115] like Figure 4 As shown in the figure, all discriminators are composed of a convolution module, a flattening module and a discrimination module. The convolution module contains six convolution blocks, each of which contains a 1D convolution layer, a batch normalization layer and a LeakyReLU activation function; this design can effectively extract the time domain features of the signal and enhance the nonlinear expression ability of the model through nonlinear activation functions. After the output of the convolution module is converted into a one-dimensional vector through the flattening module, it is passed to the discrimination module; the discrimination module consists of two fully connected layers, the first layer uses the LeakyReLU activation function for feature processing, and the second layer uses the Sigmoid activation function to output the discrimination result of the signal, indicating the similarity between the generated signal and the target signal. This structure not only improves the ability to extract signal features, but also enhances the discriminant accuracy of the signal.

[0116] (3) Feedback optimization process:

[0117] The feedback results of the discriminator are used to optimize the convolutional feature extraction function, thereby improving the quality of signal generation; first, the discriminator 1 calculates its discrimination loss based on the input generated signal and the real signal, and evaluates the difference between the generated signal and the real signal. The discriminator continuously improves its discrimination ability by maximizing the discrimination probability of the real signal and minimizing the discrimination probability of the generated signal. The feedback results of the discriminator are passed to the generator through the back propagation mechanism. The generator updates the parameters of its convolutional feature extraction module based on these feedbacks. The goal of the convolutional feature extraction module is to maximize the probability that the generated signal is judged as the target signal by the discriminator, and to promote the convolutional feature extraction module to produce outputs that are closer to the real signal.

[0118] The loss function of the convolutional feature extraction module is composed of the mean square error loss function between the generated signal and the true signal, which is defined as follows:

[0119]

[0120] in, To generate a signal, D is the real signal, n is the number of samples of the signal, for each sample is the signal generated by the convolutional feature extraction module, and d i is the target signal.

[0121] The loss function of the discriminator is composed of a weighted combination of the class loss and the similarity loss of the generated signal. The class loss is directly calculated by cross entropy, while the similarity loss is calculated by the similarity between the generated signal and the original signal. The loss function is defined as follows:

[0122]

[0123] Among them, λ1,λ2 are weight factors, L ce (p ic ) is the cross entropy loss, calculated as follows:

[0124]

[0125] Among them, p ic The predicted probability that observation sample i belongs to category c, The structural similarity loss is calculated as follows:

[0126]

[0127] Step 4: Signal recovery and processing at the receiving end:

[0128] At the receiving end, the received signal is input into the DPC-GAN framework designed by the present invention, and the output restores the signal after dynamic threshold reduction and adjustment at the sending end.

[0129] The specific steps include:

[0130] (1) Dynamic recovery and inverse mapping process:

[0131] The received time domain signal is input to the dynamic signal recovery module, which performs reverse recovery for the dynamic threshold reduction and zero-value signal relocation operations performed by the transmitter, reconstructs the peak characteristics of the signal, and obtains a reconstructed signal. Among them, the specific steps of a dynamic signal recovery module of the present invention are as follows:

[0132] Dynamic threshold estimation: Since the transmitter sets different dynamic thresholds γ for the signal m The receiving end needs to restore the corresponding threshold value based on the signal received by the photoelectric intensity modulator By detecting the maximum amplitude value in the received signal, the receiver can infer the threshold used by the sender. and the threshold γ at the sender m near.

[0133] Signal classification mechanism: based on the received signal and dynamic threshold The relationship between the two signals is used to determine the state of the signal and classify the received signal. The steps of the signal classification mechanism are as follows: Judgment of uncut signals: If the amplitude of the signal is less than or equal to the threshold, it means that the symbol has not been cut, and the receiving end directly uses the received signal.

[0134] Judgment of the signal being cut: If the signal amplitude is greater than the threshold, it means that the signal has been cut and adjusted, and the receiving end needs to restore the signal to the original signal. By comparing the received signal with the threshold, it is determined which side is cut.

[0135] Restore the original signal and reconstruct it: The receiver determines the type of signal through the signal classification mechanism, and uses a dynamic threshold to linearly superimpose the signal based on the classification result to restore the original signal. Then, the reconstructed signal z is obtained by reconstructing the restored signal with the information in the zero value area. n .

[0136] For the reconstructed signal z n Perform μ-law inverse mapping to restore the compressed signal to the approximate value of the original signal and obtain the inverse mapping signal. The inverse mapping signal expression is z μn As shown below:

[0137]

[0138] (2) Signal feature extraction process:

[0139] The time domain signal at the receiving end is first input into the convolution feature extraction module, which is responsible for extracting the time domain amplitude characteristics of the signal and generating a generated signal after dynamic recovery and inverse mapping processing; then, the generated signal is input into the discriminator 2 for comparison and analysis with the inverse mapping signal obtained through dynamic recovery and inverse mapping processing; the discriminator 2 analyzes the characteristics and amplitude distribution of the two to evaluate the quality and accuracy of signal recovery.

[0140] (3) Discriminator feedback optimization process:

[0141] The feedback result of discriminator 2 is used to optimize the feature extraction capability of the convolution feature extraction module, thereby improving the quality of signal generation; first, discriminator 2 receives the generated signal from the convolution feature extraction module, and compares and analyzes it with the inverse mapping signal after dynamic recovery and inverse mapping, and evaluates the differences in amplitude distribution and characteristics between the two; according to the results of the comparative analysis, discriminator 2 inputs the feedback signal into the convolution feature extraction module through the back propagation mechanism, prompting it to adjust the feature extraction process to generate an output that is closer to the inverse mapping signal; with the iterative update of the feedback, the convolution feature extraction module gradually improves its generation capability, and finally outputs the restored signal after dynamic recovery and inverse mapping.

[0142] Step 5: Signal decoding and recovery:

[0143] The signal is subjected to fast Fourier transform and precoding decoding, wherein the Vandermonde precoding matrix (VLM) is reversible, and the precoding decoding signal can be obtained by inverse operation of the signal, which contains the mixed data of ACO-OFDM and PAM-DMT. n .

[0144] Step 6, signal separation and demodulation:

[0145] When receiving the signal r n In the process, the ACO-OFDM signal is first directly detected and extracted to restore its original data information x aco-ofdm Next, the clipping noise of the ACO-OFDM signal is reconstructed and removed from the received mixed signal to eliminate the interference to the signal, and finally the complete PAM-DMT signal is obtained. pam-dmt , to achieve accurate separation and demodulation of the two signals.

[0146] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A visible light communication method based on precoding and adaptive dynamic compression, characterized in that: The following steps are involved: Step 1: Data stratification and precoding processing: The signal source is layered, the first layer of data is allocated to the odd subcarrier position for ACO-OFDM modulation, and the second layer of data is allocated to the even subcarrier position for PAM-DMT modulation; A Vandermonde-like matrix is ​​created, and the ACO-OFDM frequency domain signal and the PAM-DMT frequency domain signal are multiplied by the Vandermonde-like matrix respectively to obtain a precoded ACO-OFDM signal and a precoded PAM-DMT signal; Step 2: Signal transformation and trimming: Perform Hermitian symmetric transform and inverse Fourier transform (IFFT) on the two-layer precoded signals to generate ACO-OFDM and PAM-DMT bipolar time domain signals; Perform negative signal clipping on the bipolar time domain signal, and superimpose the clipped time domain positive signal to obtain a superimposed signal; Step 3: Dynamic peak control and signal generation: The superimposed signal is subjected to μ-law mapping to obtain a mapping signal, and the mapping signal is input into a dynamic peak control module to perform dynamic peak reduction to obtain a reduced and clipped low peak-to-average power ratio signal; The processed signal is input to the discriminator 1 in the DPC-GAN framework. The discriminator 1 analyzes the amplitude distribution and characteristics of the signal, optimizes the signal processing process, and the convolution feature extraction module generates an output signal with amplitude compression and dynamic peak reduction. Step 4: Signal recovery and processing at the receiving end: The received time domain signal is input into the dynamic signal recovery module in the DPC-GAN framework to recover the signal cut and adjusted by the dynamic threshold; Perform μ-law inverse mapping on the restored signal to obtain an inverse mapping signal; The inverse mapped signal is input to the discriminator 2 for comparative analysis to evaluate the quality and accuracy of signal recovery. The convolution feature extraction module generates the signal that has been restored and inverse mapped; Step 5: Signal decoding and recovery: Perform fast Fourier transform and precoding decoding on the received signal, and use the reversibility of the Vandermonde-like precoding matrix to obtain a precoding decoding signal, which contains mixed data of ACO-OFDM and PAM-DMT; Step 6: Signal separation and demodulation: Directly detect and extract the ACO-OFDM signal from the received signal and restore its original data information; The clipped noise of the ACO-OFDM signal is reconstructed and removed from the received mixed signal to obtain the complete PAM-DMT signal, achieving accurate separation and demodulation of the two signals.

2. The visible light communication method based on precoding and adaptive dynamic compression according to claim 1, characterized in that: The method of creating a Vandermonde-like matrix in step 1 includes: generating a Vandermonde-like matrix using a monomial, and replacing the monomial with a polynomial at a node to create the Vandermonde-like matrix.

3. The visible light communication method based on precoding and adaptive dynamic compression according to claim 1, characterized in that: The dynamic peak control module in step 3 includes: Determine a dynamic threshold value, and calculate the dynamic threshold value according to the maximum amplitude value of the mapping signal; The peak characteristics of the signal are optimized. When the signal amplitude exceeds the threshold, it is cut to within the threshold range, and the cut part is redistributed to the zero value position of the signal to obtain the target signal.

4. The visible light communication method based on precoding and adaptive dynamic compression according to claim 1, characterized in that: The DPC-GAN framework in step 3 includes: Convolution feature extraction module, used to extract the peak features of the signal, output amplitude compression and dynamically reduce the peak of the generated signal; The discriminator 1 is used to analyze the amplitude distribution and characteristics of the generated signal and the target signal to improve the quality of the generated signal.

5. The visible light communication method based on precoding and adaptive dynamic compression according to claim 4, characterized in that: The convolution feature extraction module includes a fully connected layer module, a convolution block module and an output layer module, and the discriminator consists of a convolution module, a flattening module and a discrimination module.

6. The visible light communication method based on precoding and adaptive dynamic compression according to claim 1, characterized in that: The dynamic signal recovery module in step 4 includes: Dynamic threshold estimation, recovering the corresponding threshold according to the maximum amplitude value of the received signal; Signal classification mechanism, which determines the signal status based on the relationship between the received signal and the dynamic threshold; The original signal is restored and reconstructed, and the signal is linearly superimposed using a dynamic threshold according to the signal classification result to restore the original signal.

7. The visible light communication method based on precoding and adaptive dynamic compression according to claim 1, characterized in that: The discriminator 2 in step 4 is used to evaluate the quality and accuracy of signal recovery and optimize the feature extraction ability of the convolutional feature extraction module through feedback.

8. The visible light communication method based on precoding and adaptive dynamic compression according to claim 1, characterized in that: The precoding and decoding process in step 5 utilizes the reversibility of the Vandermonde-like precoding matrix to perform an inverse operation on the signal to obtain a precoding and decoding signal.

9. The visible light communication method based on precoding and adaptive dynamic compression according to claim 1, characterized in that: The signal separation and demodulation process in step 6 includes directly detecting and extracting the ACO-OFDM signal, and reconstructing and removing the clipping noise of the ACO-OFDM signal to obtain a complete PAM-DMT signal.

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