A visible light communication data transmission method based on dynamic modulation

Abstract

The present invention discloses a visible light communication data transmission method based on dynamic modulation, which specifically relates to the field of visible light technology communication. The method comprises the following steps: performing nonlinear dynamic feature extraction on an actual light intensity signal emitted by a light-emitting diode to generate a spectrum response feature set; performing segmented analysis on the frequency domain distortion evolution trend based on the spectrum response feature set to obtain multiple spectrum distortion subintervals; performing local modulation parameter optimization on each spectrum distortion subinterval to obtain a dynamic local modulation parameter set; mapping the dynamic local modulation parameter set into a modulation drive strategy sequence, and generating a dynamic modulation waveform signal sequence; performing detection and time-frequency feature extraction on the dynamic modulation waveform signal sequence to obtain a target signal sequence after noise suppression; demodulating the target signal sequence to recover complete data, completing visible light communication data transmission, and effectively improving the optical communication signal quality and data recovery capability under complex modulation environments.

Description

Technical Field

[0001] The present invention relates to the technical field of visible light communication, and more specifically, to a visible light communication data transmission method based on dynamic modulation. Background Art

[0002] Existing visible light communications (VLC) widely use fixed modulation parameters for signal transmission, making them difficult to adapt to the spectral morphology imbalance and signal distortion caused by variations in the response characteristics of light-emitting diodes under different operating conditions and dynamic external environmental disturbances (such as light source fluctuations and channel instability). Furthermore, existing methods generally rely on static modulation structures, are unable to perceive the changing frequency domain distortion in real time, and lack a dynamic matching mechanism for modulation parameters, limiting the accuracy and stability of VLC data transmission in complex scenarios.

[0003] In order to solve the above problems, a technical solution is now provided. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a visible light communication data transmission method based on dynamic modulation to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A visible light communication data transmission method based on dynamic modulation includes the following steps:

[0007] S1: Collect the actual light intensity signal emitted by the light-emitting diode within a preset dynamic modulation period, perform nonlinear dynamic feature extraction, and generate a set of spectrum response features corresponding to different modulation parameter intervals;

[0008] S2: Based on the spectrum response feature set, the frequency domain distortion evolution trend under dynamic modulation state is segmented and analyzed to obtain multiple spectrum distortion sub-intervals;

[0009] S3: Optimize the local modulation parameters of each spectral distortion sub-interval to obtain a dynamic local modulation parameter set;

[0010] S4: mapping the dynamic local modulation parameter set into a modulation driving strategy sequence, and using an orthogonal frequency division multiplexing modulation method to generate a dynamic modulation waveform signal sequence;

[0011] S5: Perform photoelectric detection on the dynamic modulation waveform signal sequence, extract the corresponding time-frequency feature matrix, and obtain the target signal sequence after noise suppression based on the noise suppression algorithm;

[0012] S6: Demodulate the target signal sequence and output the corresponding data recovery result to complete the visible light communication data transmission based on dynamic modulation.

[0013] In a preferred embodiment, S1 is specifically:

[0014] During the dynamic modulation period, the real-time light intensity of the light emitting diode in different modulation parameter ranges is continuously collected by the photodetector;

[0015] Classifying the real-time light intensity according to the different modulation parameter intervals to which it belongs, and obtaining a plurality of real-time light intensity data groups corresponding to different modulation parameter intervals;

[0016] Each real-time light intensity data group is processed using a nonlinear dynamic feature extraction method to extract dynamic feature data from each real-time light intensity data group;

[0017] Fourier transform is performed on each dynamic feature data to generate spectrum response feature sets corresponding to different modulation parameter intervals.

[0018] In a preferred embodiment, S2 is specifically:

[0019] Based on the spectrum response feature sets corresponding to different modulation parameter intervals, frequency domain distortion degree calculation is performed on each spectrum response feature set to obtain a frequency domain distortion degree value corresponding to each modulation parameter interval;

[0020] The frequency domain distortion degree values of each modulation parameter interval are divided into a plurality of different frequency domain distortion degree intervals according to a preset frequency domain distortion threshold;

[0021] Statistical analysis is performed on the multiple frequency domain distortion degree intervals obtained by division to obtain the distribution range of the spectrum response feature set of each frequency domain distortion degree interval;

[0022] According to the distribution range of the spectrum response feature set corresponding to each frequency domain distortion degree interval, the frequency domain distortion evolution trend is divided into multiple spectrum distortion sub-intervals.

[0023] In a preferred embodiment, S3 is specifically:

[0024] Performing frequency domain response curve fitting analysis on the spectrum response feature set corresponding to each spectrum distortion sub-interval, respectively, to obtain a frequency domain response fitting curve for each spectrum distortion sub-interval;

[0025] According to each frequency domain response fitting curve, the difference value between the frequency domain response curve in each spectrum distortion subinterval and the ideal frequency domain response curve is calculated;

[0026] Based on the difference between the frequency domain response curve and the ideal frequency domain response curve in each spectral distortion sub-interval, local adjustment optimization processing is performed on the modulation frequency, modulation depth and modulation amplitude parameters respectively, and then integrated to form a dynamic local modulation parameter set.

[0027] In a preferred embodiment, S4 is specifically:

[0028] Sequencing the modulation frequency parameter, the modulation depth parameter, and the modulation amplitude parameter in the dynamic local modulation parameter set respectively to form a modulation driving strategy sequence for driving the light emitting diode;

[0029] According to the timing order of the modulation driving strategy sequence, the carrier frequency, subcarrier amplitude and subcarrier spacing of the orthogonal frequency division multiplexing modulation method are set one by one;

[0030] A dynamic modulation waveform signal corresponding to each dynamic local modulation parameter set is generated based on the set orthogonal frequency division multiplexing modulation method, and a dynamic modulation waveform signal sequence emitted by the light emitting diode within the dynamic modulation period is obtained.

[0031] In a preferred embodiment, S5 is specifically:

[0032] A photoelectric detector is used to perform real-time photoelectric conversion detection on the dynamic modulation waveform signal sequence to obtain an electrical signal waveform sequence corresponding to each dynamic modulation waveform signal;

[0033] Extract time domain signal features from the electrical signal waveform sequence respectively;

[0034] Extract frequency domain signal features from the electrical signal waveform sequence;

[0035] Based on the extracted time domain signal features and frequency domain signal features, a time-frequency feature matrix is constructed;

[0036] The time-frequency feature matrix is input into the noise suppression algorithm to perform identification and elimination of structured noise;

[0037] The time-frequency feature matrix after structured noise elimination is subjected to inverse transformation to obtain the target signal sequence after noise suppression.

[0038] In a preferred embodiment, S6 is specifically:

[0039] Perform spectrum transformation on each target signal to obtain the spectrum signal corresponding to each target signal;

[0040] Based on each spectrum signal, identifying the subcarrier sequence number and subcarrier frequency position of the orthogonal frequency division multiplexing modulation method corresponding to the target signal;

[0041] Performing frequency compensation and phase correction processing on each subcarrier frequency position in each target signal to obtain a subcarrier spectrum after frequency compensation and phase correction;

[0042] Perform amplitude demapping and phase demapping processing on each subcarrier spectrum after frequency compensation and phase correction to complete the demodulation process of each subcarrier spectrum;

[0043] The demodulation results of each subcarrier spectrum are integrated and data is reconstructed to obtain the data recovery results corresponding to each target signal, completing the visible light communication data transmission based on dynamic modulation.

[0044] The technical effects and advantages of the visible light communication data transmission method based on dynamic modulation of the present invention are as follows:

[0045] By collecting the actual light intensity signal of the light-emitting diode during the dynamic modulation period and extracting nonlinear dynamic characteristics, the time-varying characteristics of the optical signal under the modulation state can be reflected, providing a basis for spectrum distortion analysis; by constructing a spectrum response feature set and performing frequency domain distortion evolution trend analysis, the spectrum abnormal change interval can be accurately divided, and the ability to identify non-ideal transmission behavior can be improved; by performing local optimization of the modulation frequency, modulation depth and modulation amplitude parameters for different spectrum distortion sub-intervals, adaptive adjustment of the modulation strategy is achieved, and the robustness to complex channel environments is enhanced; the optimized modulation parameters are mapped into a modulation drive strategy sequence and combined with the orthogonal frequency division multiplexing modulation method to improve modulation efficiency and spectrum utilization; through photoelectric detection and time-frequency feature extraction combined with noise suppression algorithm, structural interference is effectively suppressed and the main components of the modulation information are retained to ensure the clarity of the received signal; finally, through the spectrum identification, compensation, correction and demapping process, complete data recovery is achieved, significantly improving the communication accuracy and reliability under dynamic modulation conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a schematic diagram of a visible light communication data transmission method based on dynamic modulation according to the present invention. DETAILED DESCRIPTION

[0047] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0048] Example 1:

[0049] Figure 1 The present invention provides a visible light communication data transmission method based on dynamic modulation, which includes the following steps:

[0050] S1: Collect the actual light intensity signal emitted by the light-emitting diode within a preset dynamic modulation period, perform nonlinear dynamic feature extraction, and generate a set of spectrum response features corresponding to different modulation parameter intervals;

[0051] S2: Based on the spectrum response feature set, the frequency domain distortion evolution trend under dynamic modulation state is segmented and analyzed to obtain multiple spectrum distortion sub-intervals;

[0052] S3: Optimize the local modulation parameters of each spectral distortion sub-interval to obtain a dynamic local modulation parameter set;

[0053] S4: mapping the dynamic local modulation parameter set into a modulation driving strategy sequence, and using an orthogonal frequency division multiplexing modulation method to generate a dynamic modulation waveform signal sequence;

[0054] S5: Perform photoelectric detection on the dynamic modulation waveform signal sequence, extract the corresponding time-frequency feature matrix, and obtain the target signal sequence after noise suppression based on the noise suppression algorithm;

[0055] S6: Demodulate the target signal sequence and output the corresponding data recovery result to complete the visible light communication data transmission based on dynamic modulation.

[0056] S1: Collect the actual light intensity signal emitted by the light-emitting diode within the preset dynamic modulation period, and perform nonlinear dynamic feature extraction to generate a set of spectrum response features corresponding to different modulation parameter intervals, including:

[0057] During the dynamic modulation period, the real-time light intensity of the light emitting diode in different modulation parameter ranges is continuously collected by the photodetector;

[0058] During the dynamic modulation cycle, a highly sensitive photodetector is used to continuously collect the real-time light intensity generated by the LED under different dynamic modulation parameters. The collected real-time light intensity changes in real time with the dynamic modulation parameters. Silicon-based photodetectors can be used as high-sensitivity photodetectors to ensure consistent and sensitive response to light signals of varying intensities during the dynamic modulation cycle. For example, the dynamic modulation cycle can be divided into several consecutive small time periods, each corresponding to a specific set of dynamic modulation parameters. The silicon-based photodetector continuously collects all real-time light intensity values generated by the LED within each small time period, ensuring the integrity of the real-time light intensity values.

[0059] Classifying the real-time light intensity according to the different modulation parameter intervals to which it belongs, and obtaining a plurality of real-time light intensity data groups corresponding to different modulation parameter intervals;

[0060] All collected real-time light intensity values are classified according to the intervals to which their corresponding dynamic modulation parameters belong. That is, all real-time light intensity values corresponding to each set of dynamic modulation parameters are grouped to form multiple groups of real-time light intensity data corresponding to different dynamic modulation parameter intervals. Dynamic modulation parameters include modulation frequency, modulation depth, and modulation amplitude. The dynamic modulation parameter interval is defined by a specific modulation frequency range, modulation depth range, and modulation amplitude range. The real-time light intensity values are classified based on the dynamic modulation parameter interval. For example, when the modulation frequency is within a specific frequency range, the modulation depth is within a specific depth range, and the modulation amplitude is within a specific amplitude range, all real-time light intensity values continuously collected during this period are classified into the real-time light intensity data group corresponding to the dynamic modulation parameter interval.

[0061] Each real-time light intensity data group is processed using a nonlinear dynamic feature extraction method to extract dynamic feature data from each real-time light intensity data group;

[0062] A nonlinear fitting algorithm is used to fit the amplitude variation trend characteristics of each real-time light intensity data set, resulting in an amplitude variation trend characteristic curve corresponding to each real-time light intensity data set. Frequency domain periodic analysis is then used to perform spectrum analysis on each set of real-time light intensity data, extracting the primary frequency components of its modulation frequency variation and determining the modulation frequency variation characteristics of the real-time light intensity data set. A nonlinear time series analysis algorithm is then used to analyze the nonlinear variation pattern of light intensity, determining the specific pattern characteristics of the nonlinear variation, such as periodic or irregular fluctuation patterns. The above amplitude variation trends, modulation frequency variation characteristics, and light intensity nonlinear variation patterns are then integrated to form dynamic characteristic data corresponding to each real-time light intensity data set.

[0063] Performing Fourier transform on each dynamic feature data to generate spectrum response feature sets corresponding to different modulation parameter intervals;

[0064] The discrete Fourier transform method is used for spectrum transformation: the dynamic feature data is sampled at equal intervals in the time domain to obtain a data sequence sampled at equal intervals; then each sampling value of each data sequence is multiplied by the sine function and cosine function of the Fourier transform, and the product results are accumulated within the sampling range of the entire data sequence to obtain the frequency domain feature components corresponding to each dynamic feature data, which is the spectrum response feature set.

[0065] S2: Based on the spectrum response feature set, the frequency domain distortion evolution trend under dynamic modulation state is segmented and analyzed to obtain multiple spectrum distortion sub-intervals, including:

[0066] Based on the spectrum response feature sets corresponding to different modulation parameter intervals, frequency domain distortion degree calculation is performed on each spectrum response feature set to obtain a frequency domain distortion degree value corresponding to each modulation parameter interval;

[0067] Each spectral response feature set consists of multiple frequency components and their corresponding amplitudes. Using a spectrum fitting error calculation method, the spectral response feature set is fitted to an ideal reference curve in the frequency domain. The difference between the actual amplitude of each frequency point in the spectral response feature set and the amplitude of the corresponding point on the ideal reference curve is summed and taken its absolute value to obtain a frequency domain distortion value. The frequency domain distortion value reflects the degree of deviation of the optical signal spectral response from the ideal spectral shape within the current modulation parameter range.

[0068] The frequency domain distortion degree values of each modulation parameter interval are divided into a plurality of different frequency domain distortion degree intervals according to a preset frequency domain distortion threshold;

[0069] The frequency domain distortion threshold value range is divided into several continuous and non-overlapping frequency domain distortion level intervals. The boundaries of each interval are manually set or determined through training data to ensure that all frequency domain distortion level values can be uniquely attributed to a frequency domain distortion level interval. For example, three level intervals of low distortion, medium distortion, and high distortion are set, and the corresponding value ranges are defined for each level. The distortion level values in each modulation parameter interval are classified into the corresponding level.

[0070] Statistical analysis is performed on the multiple frequency domain distortion degree intervals obtained by division to obtain the distribution range of the spectrum response feature set of each frequency domain distortion degree interval;

[0071] The number of spectral response feature sets contained in each frequency domain distortion interval is counted, and indicators such as the main energy distribution frequency, the number of main spectral peaks, the bandwidth range, and the spectral center offset trend of the spectral response within the frequency domain distortion interval are analyzed. Taking the medium distortion interval as an example, the main energy frequency of the spectral response feature set in the medium distortion interval is mainly concentrated in the mid-frequency band, the bandwidth is medium-wide, and the spectral morphology shows an edge lift compared to the low distortion interval. By statistically summarizing the spectral characteristics of each frequency domain distortion interval, a set of indicators is formed to describe the distribution pattern of the spectral response sets in the frequency domain distortion interval.

[0072] According to the distribution range of the spectrum response feature set corresponding to each frequency domain distortion degree interval, the frequency domain distortion evolution trend is divided into multiple spectrum distortion sub-intervals.

[0073] The boundaries between different spectral forms are determined based on the degree of difference in the frequency domain distribution characteristics of the spectral response set within each frequency domain distortion interval. Based on the frequency domain main peak transition frequency, the transition process from a concentrated low-frequency distribution to a multi-peak distribution at mid-frequency is divided into different sub-intervals. The spectral response characteristics within each sub-interval exhibit a relatively consistent evolutionary pattern. This results in multiple spectral distortion sub-intervals for local modulation parameter optimization.

[0074] S3: Optimize the local modulation parameters for each spectral distortion sub-interval to obtain a dynamic local modulation parameter set, including:

[0075] Performing frequency domain response curve fitting analysis on the spectrum response feature set corresponding to each spectrum distortion sub-interval, respectively, to obtain a frequency domain response fitting curve for each spectrum distortion sub-interval;

[0076] Frequency domain response curve fitting involves constructing a continuous fitting curve using a point-by-point polynomial fitting method, with frequency as the horizontal axis and amplitude as the vertical axis. The fitting curve describes the overall variation of the spectral response set. A smooth weighted fitting strategy is used to avoid edge jumps or local outliers that affect the curve shape. The fitting accuracy of the fitting curve is controlled by calculating the mean square error.

[0077] According to each frequency domain response fitting curve, the difference value between the frequency domain response curve in each spectrum distortion subinterval and the ideal frequency domain response curve is calculated;

[0078] For the frequency domain response fitting curve of each spectral distortion subinterval, calculate the difference between it and the ideal frequency domain response curve. The ideal frequency domain response curve can be preset based on the design goal, for example, it can present a symmetrical single-peak bandpass characteristic or have a linear spectral distribution characteristic. The difference value is calculated as follows: the amplitude difference between the current frequency domain response fitting curve and the ideal frequency domain response curve is calculated at the same frequency point, and the amplitude difference of the two curves at each frequency point is calculated respectively. After taking the absolute value, an integral or weighted average operation is performed to obtain a scalar value used to quantify the degree of spectral response difference, which is called the spectral response difference degree value. The larger the difference degree value, the greater the degree to which the current spectral response curve deviates from the ideal target.

[0079] Based on the difference between the frequency domain response curve and the ideal frequency domain response curve in each spectral distortion sub-interval, local adjustment optimization processing is performed on the modulation frequency, modulation depth and modulation amplitude parameters respectively, and the results are integrated to form a dynamic local modulation parameter set;

[0080] According to the degree of spectral response difference in each spectral distortion sub-interval, local optimization and adjustment of the modulation parameters are performed in sequence. The modulation parameters that need to be optimized include modulation frequency, modulation depth and modulation amplitude. The optimization uses the degree of spectral response difference as a feedback indicator, performs parameter perturbation tests on each modulation parameter, gradually adjusts the parameter value and simultaneously calculates the change in the degree of spectral response difference. For each sub-interval, a local search strategy is used to find the modulation frequency parameter, modulation depth parameter and modulation amplitude parameter that minimize the degree of spectral response difference. The optimization algorithm can adopt a gradient-based numerical update method or an enumeration search strategy based on a regular grid. To ensure the stability of the optimization results, the search step size, search range and convergence condition of each parameter need to be set according to the system stability requirements.

[0081] The optimized modulation frequency, depth, and amplitude parameters obtained for each spectral distortion subinterval are integrated to form a dynamic local modulation parameter set. This dynamic local modulation parameter set is stored per subinterval and records the optimal combination of modulation parameters for each spectral distortion subinterval. This is used to construct a modulation drive strategy sequence and generate dynamic waveform signals.

[0082] S4: Mapping the dynamic local modulation parameter set to a modulation drive strategy sequence, and using an orthogonal frequency division multiplexing modulation method to generate a dynamic modulation waveform signal sequence, including:

[0083] Sequencing the modulation frequency parameter, the modulation depth parameter, and the modulation amplitude parameter in the dynamic local modulation parameter set respectively to form a modulation driving strategy sequence for driving the light emitting diode;

[0084] The modulation frequency parameter, modulation depth parameter, and modulation amplitude parameter are extracted from the dynamic local modulation parameter set respectively. The modulation frequency parameter is used to control the center frequency of the carrier in the orthogonal frequency division multiplexing modulation method, the modulation depth parameter is used to define the dynamic range of amplitude variation in the modulation signal, and the modulation amplitude parameter is used to set the initial emission intensity of each frequency component in the modulation signal. The modulation frequency parameter, modulation depth parameter, and modulation amplitude parameter are sorted in chronological order. The sorting principle is: arrange them according to the time sequence in which the modulation parameters appear in the entire dynamic modulation cycle. After the sorting is completed, the above three parameters are combined in sequence to construct a modulation drive strategy sequence for driving the light-emitting diode. The structure of the modulation drive strategy sequence is: each time segment corresponds to a complete modulation parameter group, and each parameter group contains modulation frequency, modulation depth, and modulation amplitude.

[0085] According to the timing order of the modulation driving strategy sequence, the carrier frequency, subcarrier amplitude and subcarrier spacing of the orthogonal frequency division multiplexing modulation method are set one by one;

[0086] Based on the timing order of the modulation drive strategy sequence, each set of modulation parameters is used as input parameters to set the corresponding parameters in the OFDM modulation method. The setting process is as follows: the modulation frequency parameter is set to the carrier frequency in the OFDM modulation method. The modulation frequency parameter determines the center frequency position of the modulated signal; the modulation depth parameter is set to the subcarrier spacing in the OFDM modulation method. The modulation depth parameter affects the bandwidth and orthogonality of the signal subchannel; the modulation amplitude parameter is set to the subcarrier amplitude control value in the OFDM modulation method. The modulation amplitude parameter directly affects the luminous intensity ratio of each subcarrier.

[0087] In order to avoid inter-subcarrier interference, the settings of the above three parameters must meet the continuity constraints of non-overlapping frequency intervals, stable amplitude ratios, and no jumps between time segments.

[0088] Generate a dynamic modulation waveform signal corresponding to each dynamic local modulation parameter set based on the set orthogonal frequency division multiplexing modulation method, and obtain a dynamic modulation waveform signal sequence emitted by the light-emitting diode within the dynamic modulation period;

[0089] After completing the parameter setting, the orthogonal frequency division multiplexing modulation method is called to generate dynamic modulation waveform signals in sequence according to the modulation frequency parameters, modulation depth parameters and modulation amplitude parameters corresponding to each time segment in the modulation drive strategy sequence: for each modulation parameter group, a group of subcarrier signals is constructed. The frequency of each subcarrier signal is determined by the set carrier frequency and subcarrier interval, and the amplitude is controlled by the modulation amplitude parameter. Parallel orthogonal superposition is performed on all subcarriers to form a group of modulated waveform signals.

[0090] Multiple dynamically modulated waveform signals are sequentially arranged and spliced according to the chronological order of the modulation drive strategy sequence to construct a dynamic modulation waveform signal sequence. The dynamic modulation waveform signal sequence is emitted by the light-emitting diodes at the set modulation frequency, modulation depth, and modulation amplitude throughout the dynamic modulation cycle. This dynamic modulation waveform signal sequence is used to drive the optical signal output, providing the physical carrier for photoelectric detection and data demodulation.

[0091] S5: Perform photoelectric detection on the dynamic modulation waveform signal sequence, extract the corresponding time-frequency feature matrix, and obtain the noise-suppressed target signal sequence based on the noise suppression algorithm, including:

[0092] A photoelectric detector is used to perform real-time photoelectric conversion detection on the dynamic modulation waveform signal sequence to obtain an electrical signal waveform sequence corresponding to each dynamic modulation waveform signal;

[0093] Real-time photoelectric conversion detection is performed on the dynamic modulation waveform signal sequence. The light-emitting diode outputs a continuously changing light signal according to the modulation drive strategy sequence within the dynamic modulation period. After the light signal is transmitted through space to the receiving end, it is converted into photoelectricity in real time by a high-sensitivity photodetector. The photodetector used should meet the requirement of a response speed higher than the upper limit of the dynamic modulation frequency and have stable linear response performance. Each set of dynamic modulation waveform signals output by the light-emitting diode is irradiated onto the photosensitive surface of the photodetector and converted into a corresponding voltage signal sequence through the photoelectric effect. Each voltage signal sequence is an electrical signal waveform sequence, which records the amplitude fluctuation and frequency change of the light signal in the time dimension.

[0094] Extract time domain signal features from the electrical signal waveform sequence respectively;

[0095] Time-domain signal feature extraction is performed on each electrical signal waveform sequence. This includes: extracting the time-domain amplitude by calculating the difference between the maximum and minimum values at each time sampling point; extracting the time-domain phase by converting the real-valued electrical signal into an analytical signal using the Hilbert transform method and calculating the instantaneous phase characteristics by calculating the phase component; and extracting the time-domain period by determining the dominant period of the electrical signal using the autocorrelation function method.

[0096] Extract frequency domain signal features from the electrical signal waveform sequence;

[0097] Frequency domain signal feature extraction is performed on each electrical signal waveform sequence. The electrical signal waveform sequence is converted into a frequency domain signal sequence through fast Fourier transform, and the following frequency domain features are extracted: the frequency domain dominant frequency component, selecting the frequency component with the largest amplitude as the dominant frequency value; the spectrum bandwidth, calculating the frequency range corresponding to the point where the amplitude on both sides of the dominant frequency drops to half within the energy concentration area; and the spectrum amplitude, recording the amplitude value corresponding to the dominant frequency component as the main energy indicator.

[0098] Based on the extracted time domain signal features and frequency domain signal features, a time-frequency feature matrix is constructed;

[0099] The time-domain features (including amplitude, phase, and period) and frequency-domain features (including the dominant frequency component, spectral bandwidth, and spectral amplitude) corresponding to each dynamically modulated waveform signal are combined in a row vector structure to obtain a set of time-frequency feature vectors for each dynamically modulated waveform signal. All time-frequency feature vectors are arranged in chronological order to form a time-frequency feature matrix, which represents the signal variation characteristics throughout the entire dynamic modulation cycle.

[0100] The time-frequency feature matrix is input into the noise suppression algorithm to perform identification and elimination of structured noise;

[0101] Structured noise refers to repetitive interference signals introduced by modulation mechanisms, environmental reflections, or system nonlinearities, distinguishing it from traditional white noise or thermal noise. Statistical analysis identifies interference patterns with distinct periodicity or specific frequency patterns. A selective filtering algorithm based on frequency masking is then employed to reduce the characteristic dimensions occupied by structured noise. Noise determination rules are set based on the modulation range and bandwidth characteristics of the target system.

[0102] Perform inverse transform processing on the time-frequency feature matrix after structured noise elimination processing to obtain the target signal sequence after noise suppression;

[0103] After structured noise removal, the time-frequency feature matrix is inversely transformed to recover the target signal. The primary and secondary frequency components are resynthesized into a spectrum based on the frequency domain characteristics. Combined with the corresponding time domain period and phase information, a time domain signal sequence is generated through an inverse Fourier transform. The resulting signal is the target signal sequence after noise suppression, retaining the main components of the modulation information while removing periodic structured noise. This signal can be used for demodulation and data signal recovery.

[0104] S6: Demodulate the target signal sequence and output the corresponding data recovery result to complete the visible light communication data transmission based on dynamic modulation, including:

[0105] Perform spectrum transformation on each target signal to obtain the spectrum signal corresponding to each target signal;

[0106] Using the Fast Fourier Transform (FFT) algorithm, the target signal is converted from the time domain to the frequency domain, generating the corresponding spectrum. The spectrum, with frequency as the horizontal axis and complex amplitude as the vertical axis, reflects the energy distribution of the modulated signal across different frequency components. Spectral transformations are used to reveal the frequency-domain characteristics of subcarrier signals during dynamic modulation.

[0107] Based on each spectrum signal, identifying the subcarrier sequence number and subcarrier frequency position of the orthogonal frequency division multiplexing modulation method corresponding to the target signal;

[0108] The identification process is accomplished through frequency-domain energy detection. Specifically, a local peak scan is performed on the spectrum signal within a set frequency distribution range, screening out all frequency points whose amplitudes exceed the set energy threshold. These points are then numbered sequentially according to their frequency values to obtain subcarrier numbers. Each subcarrier's center frequency is used as the subcarrier's frequency position. A complete subcarrier identification table is created for each target signal.

[0109] Performing frequency compensation and phase correction processing on each subcarrier frequency position in each target signal to obtain a subcarrier spectrum after frequency compensation and phase correction;

[0110] Frequency compensation and phase correction are performed on each subcarrier frequency position in each target signal. Due to slight offsets in the frequency response of the light-emitting diode under different modulation drive states and phase changes in the light propagation path, the subcarrier frequency position and phase state observed by the receiving end deviate. In order to correct the deviation, a frequency offset estimation algorithm is used for frequency compensation. Specifically, a frequency search window is set near each subcarrier frequency, the signal amplitude peak position is measured through a matched filter, and the signal amplitude peak position is compared with the expected center frequency to obtain a compensation value; the phase correction uses a phase rotation algorithm to multiply each subcarrier spectrum by a complex exponential function of unit amplitude, whose phase angle is opposite to the estimated phase error value, to achieve the correction operation.

[0111] Perform amplitude demapping and phase demapping processing on each subcarrier spectrum after frequency compensation and phase correction to complete the demodulation process of each subcarrier spectrum;

[0112] Amplitude demapping restores the amplitude information of each subcarrier to the original modulation amplitude symbol; phase demapping matches the phase value of the corrected subcarrier complex representation with the known modulation depth range in the modulation parameter set to extract the corresponding phase symbol. In order to ensure the accuracy of demapping, the mapping table of amplitude and phase needs to be established in advance through calibration experiments of light-emitting diodes under different driving states, and correspond one-to-one with the modulation parameter space. After demapping, each subcarrier spectrum outputs two symbol quantities, which represent the data units carried by the subcarrier in the current time segment;

[0113] The demodulation results of each subcarrier spectrum are integrated and data is reconstructed to obtain the data recovery results corresponding to each target signal, completing the visible light communication data transmission based on dynamic modulation;

[0114] The demodulation results of all subcarrier spectra within each target signal are integrated and reconstructed: all subcarrier data within the same target signal are sorted by subcarrier sequence number, and the data segments of multiple target signals are sequentially spliced according to the time sequence of the dynamic modulation cycle, ultimately forming a bit data stream or symbol sequence (i.e., the data recovery result). The data recovery result is used for error checking, decoding, and application layer processing, completing the functional link of the dynamic modulation-based visible light communication data transmission process.

[0115] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0116] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0117] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0118] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0119] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0120] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0121] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0122] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0123] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0124] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A visible light communication data transmission method based on dynamic modulation, characterized in that: The steps include: S1: Collect the actual light intensity signal emitted by the light-emitting diode within a preset dynamic modulation period, perform nonlinear dynamic feature extraction, and generate a set of spectrum response features corresponding to different modulation parameter intervals; S2: Based on the spectrum response feature set, the frequency domain distortion evolution trend under dynamic modulation state is segmented and analyzed to obtain multiple spectrum distortion sub-intervals; S3: Optimize the local modulation parameters of each spectral distortion sub-interval to obtain a dynamic local modulation parameter set; S4: mapping the dynamic local modulation parameter set into a modulation driving strategy sequence, and using an orthogonal frequency division multiplexing modulation method to generate a dynamic modulation waveform signal sequence; S5: Perform photoelectric detection on the dynamic modulation waveform signal sequence, extract the corresponding time-frequency feature matrix, and obtain the target signal sequence after noise suppression based on the noise suppression algorithm; S6: Demodulate the target signal sequence and output the corresponding data recovery result to complete the visible light communication data transmission based on dynamic modulation.

2. The method for transmitting data via visible light communication based on dynamic modulation according to claim 1, wherein: S1, specifically: During the dynamic modulation period, the real-time light intensity of the light emitting diode in different modulation parameter ranges is continuously collected by the photodetector; Classifying the real-time light intensity according to the different modulation parameter intervals to which it belongs, and obtaining a plurality of real-time light intensity data groups corresponding to different modulation parameter intervals; Each real-time light intensity data group is processed using a nonlinear dynamic feature extraction method to extract dynamic feature data from each real-time light intensity data group; Fourier transform is performed on each dynamic feature data to generate spectrum response feature sets corresponding to different modulation parameter intervals.

3. The method for transmitting data via visible light communication based on dynamic modulation according to claim 2, wherein: S2, specifically: Based on the spectrum response feature sets corresponding to different modulation parameter intervals, frequency domain distortion degree calculation is performed on each spectrum response feature set to obtain a frequency domain distortion degree value corresponding to each modulation parameter interval; The frequency domain distortion degree values of each modulation parameter interval are divided into a plurality of different frequency domain distortion degree intervals according to a preset frequency domain distortion threshold; Statistical analysis is performed on the multiple frequency domain distortion degree intervals obtained by division to obtain the distribution range of the spectrum response feature set of each frequency domain distortion degree interval; According to the distribution range of the spectrum response feature set corresponding to each frequency domain distortion degree interval, the frequency domain distortion evolution trend is divided into multiple spectrum distortion sub-intervals.

4. The method for transmitting data via visible light communication based on dynamic modulation according to claim 3, wherein: S3, specifically: Performing frequency domain response curve fitting analysis on the spectrum response feature set corresponding to each spectrum distortion sub-interval, respectively, to obtain a frequency domain response fitting curve for each spectrum distortion sub-interval; According to each frequency domain response fitting curve, the difference value between the frequency domain response curve in each spectrum distortion subinterval and the ideal frequency domain response curve is calculated; Based on the difference between the frequency domain response curve and the ideal frequency domain response curve in each spectral distortion sub-interval, local adjustment optimization processing is performed on the modulation frequency, modulation depth and modulation amplitude parameters respectively, and then integrated to form a dynamic local modulation parameter set.

5. The method for transmitting data via visible light communication based on dynamic modulation according to claim 4, wherein: S4, specifically: Sequencing the modulation frequency parameter, the modulation depth parameter, and the modulation amplitude parameter in the dynamic local modulation parameter set respectively to form a modulation driving strategy sequence for driving the light emitting diode; According to the timing order of the modulation driving strategy sequence, the carrier frequency, subcarrier amplitude and subcarrier spacing of the orthogonal frequency division multiplexing modulation method are set one by one; A dynamic modulation waveform signal corresponding to each dynamic local modulation parameter set is generated based on the set orthogonal frequency division multiplexing modulation method, and a dynamic modulation waveform signal sequence emitted by the light emitting diode within the dynamic modulation period is obtained.

6. The method for transmitting data via visible light communication based on dynamic modulation according to claim 5, wherein: S5, specifically: A photoelectric detector is used to perform real-time photoelectric conversion detection on the dynamic modulation waveform signal sequence to obtain an electrical signal waveform sequence corresponding to each dynamic modulation waveform signal; Extract time domain signal features from the electrical signal waveform sequence respectively; Extract frequency domain signal features from the electrical signal waveform sequence; Based on the extracted time domain signal features and frequency domain signal features, a time-frequency feature matrix is constructed; The time-frequency feature matrix is input into the noise suppression algorithm to perform identification and elimination of structured noise; The time-frequency feature matrix after structured noise elimination is subjected to inverse transformation to obtain the target signal sequence after noise suppression.

7. The method for transmitting data via visible light communication based on dynamic modulation according to claim 6, wherein: S6, specifically: Perform spectrum transformation on each target signal to obtain the spectrum signal corresponding to each target signal; Based on each spectrum signal, identifying the subcarrier sequence number and subcarrier frequency position of the orthogonal frequency division multiplexing modulation method corresponding to the target signal; Performing frequency compensation and phase correction processing on each subcarrier frequency position in each target signal to obtain a subcarrier spectrum after frequency compensation and phase correction; Perform amplitude demapping and phase demapping processing on each subcarrier spectrum after frequency compensation and phase correction to complete the demodulation process of each subcarrier spectrum; The demodulation results of each subcarrier spectrum are integrated and data is reconstructed to obtain the data recovery results corresponding to each target signal, completing the visible light communication data transmission based on dynamic modulation.

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