Visible light communication data transmission method based on dynamic modulation

Abstract

The invention discloses a visible light communication data transmission method based on dynamic modulation, and particularly 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 spectral response feature set; performing segmented analysis on the frequency domain distortion evolution trend based on the frequency spectrum response feature set to obtain a plurality of frequency 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 driving 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; and the target signal sequence is demodulated, complete data is recovered, visible light communication data transmission is completed, and the optical communication signal quality and the data recovery capability in a complex modulation environment are effectively improved.

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 communication widely uses fixed modulation parameters for signal transmission, making it difficult to adapt to the spectral form imbalance and signal distortion caused by the change of the response characteristics of light-emitting diodes under different working conditions and the dynamic disturbances of the external environment (such as light source fluctuations, channel instability, etc.). At the same time, existing methods generally rely on static modulation structures, unable to perceive the process of frequency-domain distortion change in real time, and lacking a dynamic matching mechanism for modulation parameters, which restricts the data transmission accuracy and stability of visible light communication in complex scenarios.

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

[0004] In order to overcome the above 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 background art.

[0005] To achieve the above object, the present invention provides the following technical solutions: A visible light communication data transmission method based on dynamic modulation, comprising the following steps: S1: Collect the actual light intensity signals emitted by the light-emitting diode within a preset dynamic modulation period, and perform non-linear dynamic feature extraction to generate a spectral response feature set corresponding to different modulation parameter intervals; S2: Based on the spectral response feature set, perform segmented analysis on the evolution trend of frequency-domain distortion in the dynamic modulation state to obtain multiple spectral distortion sub-intervals; S3: Optimize the local modulation parameters for each spectral distortion sub-interval respectively to obtain a dynamic local modulation parameter set; S4: Map the dynamic local modulation parameter set to a modulation drive strategy sequence, and use the orthogonal frequency division multiplexing modulation method to generate a dynamic modulation waveform signal sequence; S5: Perform optoelectronic detection on the dynamic modulation waveform signal sequence, extract the corresponding time-frequency feature matrix, and based on the noise suppression algorithm, obtain the target signal sequence after noise suppression; 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.

[0006] In a preferred embodiment, S1 is specifically: During the dynamic modulation period, continuously collect the real-time light intensity of the light-emitting diode within different modulation parameter intervals through a photodetector; Classify the real-time light intensity according to different modulation parameter intervals to obtain multiple real-time light intensity data sets corresponding to different modulation parameter intervals; Process each real-time light intensity data set respectively using a non-linear dynamic feature extraction method to extract dynamic feature data from each real-time light intensity data set; Perform Fourier transform on each dynamic feature data to generate a set of spectral response features corresponding to different modulation parameter intervals respectively.

[0007] In a preferred embodiment, S2 is specifically: Based on the set of spectral response features corresponding to different modulation parameter intervals, perform frequency-domain distortion degree calculation on each set of spectral response features respectively to obtain the frequency-domain distortion degree values corresponding to each modulation parameter interval; Divide the frequency-domain distortion degree values of each modulation parameter interval into multiple different frequency-domain distortion degree intervals according to a preset frequency-domain distortion threshold; Perform statistical analysis on the multiple different frequency-domain distortion degree intervals obtained by division to obtain the distribution range of the set of spectral response features in each frequency-domain distortion degree interval; According to the distribution range of the set of spectral response features corresponding to each frequency-domain distortion degree interval, divide the frequency-domain distortion evolution trend into multiple spectral distortion sub-intervals.

[0008] In a preferred embodiment, S3 is specifically: Perform frequency-domain response curve fitting analysis on the set of spectral response features corresponding to each spectral distortion sub-interval respectively to obtain the frequency-domain response fitting curve of each spectral distortion sub-interval; According to each frequency-domain response fitting curve, calculate the difference value between the frequency-domain response curve and the ideal frequency-domain response curve within each spectral distortion sub-interval; Based on the difference value between the frequency-domain response curve and the ideal frequency-domain response curve within each spectral distortion sub-interval, perform local adjustment and optimization processing on the modulation frequency, modulation depth, and modulation amplitude parameters respectively, and integrate them to form a set of dynamic local modulation parameters.

[0009] In a preferred embodiment, S4 is specifically: Sort the modulation frequency parameter, modulation depth parameter, and modulation amplitude parameter in the set of dynamic local modulation parameters in time sequence to form a modulation driving strategy sequence for driving the light-emitting diode; According to the time sequence sorting order of the modulation driving strategy sequence, set the carrier frequency, sub-carrier amplitude, and sub-carrier interval of the orthogonal frequency division multiplexing modulation method one by one; Generate a dynamic modulation waveform signal corresponding to each set of dynamic local modulation parameters based on the set orthogonal frequency division multiplexing modulation method, and obtain a sequence of dynamic modulation waveform signals emitted by the light-emitting diode within the dynamic modulation period.

[0010] In a preferred embodiment, S5 is specifically as follows: Use a photodetector to perform real-time photoelectric conversion detection on the sequence of dynamic modulation waveform signals to obtain a sequence of electrical signal waveforms corresponding to each dynamic modulation waveform signal; Extract the time-domain signal characteristics of the sequence of electrical signal waveforms respectively; Extract the frequency-domain signal characteristics of the sequence of electrical signal waveforms respectively; Based on the extracted time-domain signal characteristics and frequency-domain signal characteristics, construct a time-frequency feature matrix; Input the time-frequency feature matrix into a noise suppression algorithm to perform the identification and elimination of structured noise; Perform inverse transformation processing on the time-frequency feature matrix after structured noise elimination processing respectively to obtain a target signal sequence after noise suppression.

[0011] In a preferred embodiment, S6 is specifically as follows: Perform spectrum transformation on each target signal respectively to obtain a spectrum signal corresponding to each target signal; Based on each spectrum signal, identify the subcarrier number and subcarrier frequency position of the orthogonal frequency division multiplexing modulation method corresponding to the target signal; Perform frequency compensation and phase correction processing on each subcarrier frequency position in each target signal respectively 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 respectively to complete the demodulation process of each subcarrier spectrum; Integrate the demodulation results of each subcarrier spectrum and perform data reconstruction to obtain a data recovery result corresponding to each target signal, and complete the visible light communication data transmission based on dynamic modulation.

[0012] The technical effects and advantages of the visible light communication data transmission method based on dynamic modulation of the present invention: By collecting the actual light intensity signals of light-emitting diodes within a dynamic modulation period and extracting non-linear dynamic features, the time-varying characteristics of optical signals under modulation conditions can be reflected, providing a basis for spectral distortion analysis. By constructing a spectral response feature set and analyzing the evolution trend of frequency-domain distortion, the abnormal change intervals of the spectrum can be accurately divided, improving the recognition ability of non-ideal transmission behaviors. By separately performing local optimization of modulation frequency, modulation depth, and modulation amplitude parameters for different spectral distortion sub-intervals, the modulation strategy is adaptively adjusted to enhance the robustness against complex channel environments. Mapping the optimized modulation parameters to a modulation drive strategy sequence and combining it with the orthogonal frequency division multiplexing modulation method can improve the modulation efficiency and spectral utilization rate. By photoelectric detection, time-frequency feature extraction, and combining with a noise suppression algorithm, structural interference can be effectively suppressed while retaining the main components of modulation information, ensuring the clarity of received signals. Finally, through the processes of spectrum recognition, compensation, correction, and demapping, complete data recovery is achieved, significantly improving the communication accuracy and reliability under dynamic modulation conditions. Brief Description of the Drawings

[0013] Figure 1 Schematic diagram of a visible light communication data transmission method based on dynamic modulation according to the present invention. Detailed Embodiments

[0014] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0015] Embodiment 1:

[0016] Figure 1 A visible light communication data transmission method based on dynamic modulation according to the present invention is provided, which includes the following steps: S1: Collect the actual light intensity signals emitted by light-emitting diodes within a preset dynamic modulation period, and perform non-linear dynamic feature extraction to generate a spectral response feature set corresponding to different modulation parameter intervals; S2: Based on the spectral response feature set, perform segmented analysis on the evolution trend of frequency-domain distortion under dynamic modulation conditions to obtain multiple spectral distortion sub-intervals; S3: Perform local modulation parameter optimization on each spectral distortion sub-interval respectively to obtain a dynamic local modulation parameter set; S4: Map the dynamic local modulation parameter set to a modulation drive strategy sequence, and use the orthogonal frequency division multiplexing modulation method to generate a dynamic modulation waveform signal sequence; S5: Perform optoelectronic 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.

[0017] S1: Collect the actual light intensity signals emitted by the light-emitting diode within a preset dynamic modulation period, perform non-linear dynamic feature extraction, and generate a spectral response feature set corresponding to different modulation parameter intervals, including: Within the dynamic modulation period, continuously collect the real-time light intensity of the light-emitting diode within different modulation parameter intervals through a photodetector; Within the dynamic modulation period, use a high-sensitivity photodetector to continuously collect the real-time light intensity generated by the light-emitting diode under different dynamic modulation parameters. The collected real-time light intensity changes in real time with the dynamic modulation parameters. A silicon-based photodetector can be selected as the high-sensitivity photodetector to ensure the response consistency and sensitivity to different-intensity light signals within the dynamic modulation period. For example, the dynamic modulation period can be divided into several consecutive small time periods, each small time period corresponding to a set of specific dynamic modulation parameters. All real-time light intensity values generated by the light-emitting diode within each small time period are continuously collected through the silicon-based photodetector, ensuring the integrity of the real-time light intensity values.

[0018] Classify the real-time light intensity according to different modulation parameter intervals to obtain multiple real-time light intensity data groups corresponding to different modulation parameter intervals; Classify all the collected real-time light intensity values according to the intervals to which their corresponding dynamic modulation parameters belong, that is, group all the real-time light intensity values corresponding to each set of dynamic modulation parameters to form multiple real-time light intensity data groups corresponding to different dynamic modulation parameter intervals. The dynamic modulation parameters include modulation frequency, modulation depth, and modulation amplitude. Define the dynamic modulation parameter intervals with specific modulation frequency ranges, modulation depth ranges, and modulation amplitude ranges, and classify the real-time light intensity values based on the dynamic modulation parameter intervals. For example, when the modulation frequency is within a certain specific frequency range, the modulation depth is within a certain specific depth range, and the modulation amplitude is within a certain specific amplitude range, all the continuously collected real-time light intensity values during this period are attributed to the real-time light intensity data group corresponding to this dynamic modulation parameter interval.

[0019] Process each real-time light intensity data group separately using a non-linear dynamic feature extraction method to extract dynamic feature data from each real-time light intensity data group; Use a non - linear fitting algorithm to perform characteristic fitting on the amplitude change trend of each real - time light intensity data group, obtaining an amplitude change trend characteristic curve corresponding to each real - time light intensity data group; perform spectral analysis on each group of real - time light intensity data using the frequency - domain periodic analysis method, extract the main frequency components of the modulation frequency change, and determine the modulation frequency change characteristics of the real - time light intensity data group; use a non - linear time - series analysis algorithm to analyze the non - linear change pattern of the light intensity, and determine the specific pattern characteristics of the non - linear change, such as a periodic fluctuation pattern or an irregular fluctuation pattern. Integrate the above amplitude change trend, modulation frequency change characteristics, and non - linear change pattern of the light intensity to form dynamic characteristic data corresponding to each real - time light intensity data group.

[0020] Perform Fourier transform on each dynamic characteristic data, respectively generating a set of spectral response characteristics corresponding to different modulation parameter intervals; Use the discrete Fourier transform method for spectral transformation: sample the dynamic characteristic data at equal intervals in the time domain to obtain a data sequence of equal - interval time sampling; then multiply each sampling value of each data sequence by the sine function and cosine function of the Fourier transform respectively, and perform cumulative calculation on the product results within the sampling range of the entire data sequence to obtain the frequency - domain characteristic components corresponding to each dynamic characteristic data, which is the set of spectral response characteristics.

[0021] S2: Based on the set of spectral response characteristics, perform segmented analysis on the evolution trend of frequency - domain distortion in the dynamic modulation state, obtaining multiple frequency - domain distortion sub - intervals, including: Based on the set of spectral response characteristics corresponding to different modulation parameter intervals, perform frequency - domain distortion degree calculation on each set of spectral response characteristics respectively, obtaining the frequency - domain distortion degree values corresponding to each modulation parameter interval; Each set of spectral response characteristics consists of multiple frequency components and their corresponding amplitudes. Using the spectral fitting error calculation method, fit the set of spectral response characteristics into an ideal reference curve in the frequency domain, sum and take the absolute value of the difference between the actual amplitude of each frequency point in the set of spectral response characteristics and the amplitude of the corresponding point on the ideal reference curve, and finally obtain the frequency - domain distortion degree value. The frequency - domain distortion degree value reflects the deviation degree of the optical signal spectral response in the current modulation parameter interval from the ideal spectral form.

[0022] Divide the frequency - domain distortion degree values of each modulation parameter interval into multiple different frequency - domain distortion degree intervals according to a pre - set frequency - domain distortion threshold; The value range of the frequency-domain distortion threshold is divided into several continuous and non-overlapping frequency-domain distortion degree intervals. The boundary of each interval is set manually or determined by training data to ensure that all frequency-domain distortion degree values can be uniquely attributed to a certain frequency-domain distortion degree interval. For example, three level intervals of low distortion, medium distortion, and high distortion are set, and their corresponding numerical ranges are defined respectively. The distortion degree values of each modulation parameter interval are classified into the corresponding levels.

[0023] Statistical analysis is performed on the multiple different frequency-domain distortion degree intervals obtained by division to obtain the distribution range of the spectral response feature set for each frequency-domain distortion degree interval; Count the number of spectral response feature sets included in each frequency-domain distortion degree interval, and analyze indicators such as the main energy distribution frequency, the number of spectral main peaks, the bandwidth range, and the spectral center offset trend of the spectral response within the frequency-domain distortion degree interval. Taking the medium distortion degree interval as an example, the main energy frequency of the spectral response feature set in the medium distortion degree interval is mainly concentrated in the medium frequency band, the bandwidth is of medium width, and the spectral shape shows an edge lift compared with the low distortion interval. By statistically summarizing the spectral characteristics of each frequency-domain distortion degree interval, an index set describing the distribution law of the spectral response set of the frequency-domain distortion degree interval is formed.

[0024] According to the distribution range of the spectral response feature set corresponding to each frequency-domain distortion degree interval, the frequency-domain distortion evolution trend is divided into multiple spectral distortion sub-intervals.

[0025] According to the difference degree of the frequency-domain distribution characteristics of the spectral response sets within each frequency-domain distortion degree interval, determine the boundary positions between different spectral shapes. Taking the frequency-domain main peak transfer frequency as the division basis, divide the change process of the spectral response from concentrated distribution in the low frequency to multi-peak distribution in the medium frequency into different sub-intervals. The spectral response feature sets within each sub-interval show a relatively consistent evolution pattern. Thus, multiple spectral distortion sub-intervals are formed for local modulation parameter optimization.

[0026] S3: Perform local modulation parameter optimization on each spectral distortion sub-interval to obtain a dynamic local modulation parameter set, including: Perform frequency-domain response curve fitting analysis on the spectral response feature sets corresponding to each spectral distortion sub-interval to obtain the frequency-domain response fitting curve of each spectral distortion sub-interval; The operations of frequency-domain response curve fitting include: using frequency as the abscissa and amplitude as the ordinate, and adopting a point-by-point polynomial fitting method to construct a continuous fitting curve. The fitting curve is used to describe the overall change form of the spectral response set. The fitting process adopts a smooth weighted fitting strategy to avoid the influence of edge jumps or local outliers on the curve form. The fitting accuracy of the fitting curve is controlled by calculating the mean square error.

[0027] According to each frequency-domain response fitting curve, calculate the difference value between the frequency-domain response curve and the ideal frequency-domain response curve within each spectral distortion sub-interval; For the frequency-domain response fitting curve of each spectral distortion sub-interval, calculate the difference value between it and the ideal frequency-domain response curve. The ideal frequency-domain response curve can be preset based on the design objective, for example, presenting a symmetric single-peak band-pass characteristic or having a linear spectral distribution characteristic. The calculation method of the difference value is as follows: calculate the amplitude difference between the current frequency-domain response fitting curve and the ideal frequency-domain response curve at the same frequency points, calculate the amplitude differences of the two curves at each frequency point respectively, and perform an integration or weighted average operation after taking the absolute value to obtain a scalar value for quantifying the spectral response difference degree, which is called the spectral response difference degree value. The larger the difference degree value, the greater the deviation of the current spectral response curve from the ideal target.

[0028] Based on the difference values between the frequency-domain response curves and the ideal frequency-domain response curves within each spectral distortion sub-interval, perform local adjustment and optimization processing on the modulation frequency, modulation depth, and modulation amplitude parameters respectively, and integrate them to form a dynamic local modulation parameter set; According to the spectral response difference degree of each spectral distortion sub-interval, perform local optimization adjustment processing of the modulation parameters in sequence. The modulation parameters to be optimized include three aspects: modulation frequency, modulation depth, and modulation amplitude. The optimization uses the spectral response difference degree as a feedback index, performs parameter perturbation tests on each modulation parameter respectively, gradually adjusts the parameter values and synchronously calculates the change of the spectral response difference degree. For each sub-interval, adopt a local search strategy to find the modulation frequency parameter, modulation depth parameter, and modulation amplitude parameter that minimize the spectral response difference degree. 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 result, the search step size, search range, and convergence condition of each parameter need to be set according to the system stability requirements.

[0029] Integrate the optimized modulation frequency parameters, optimized modulation depth parameters, and optimized modulation amplitude parameters obtained for each spectral distortion sub-interval to form a dynamic local modulation parameter set. The dynamic local modulation parameter set is stored in units of sub-intervals, recording the optimal combination of modulation parameters corresponding to each spectral distortion sub-interval, and is used for the construction of the modulation drive strategy sequence and the generation of dynamic waveform signals.

[0030] S4: Map the dynamic local modulation parameter set to a modulation drive strategy sequence, and use the orthogonal frequency division multiplexing modulation method to generate a dynamic modulation waveform signal sequence, including: Perform time-series sorting on the modulation frequency parameters, modulation depth parameters, and modulation amplitude parameters in the dynamic local modulation parameter set respectively to form a modulation drive strategy sequence for driving the light-emitting diode; Extract the modulation frequency parameter, modulation depth parameter, and modulation amplitude parameter 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 (OFDM) modulation method. The modulation depth parameter is used to define the dynamic range of the amplitude variation in the modulation signal. The modulation amplitude parameter is used to set the initial emission intensity of each frequency component in the modulation signal. Sort the modulation frequency parameter, modulation depth parameter, and modulation amplitude parameter in chronological order. The sorting principle is: arrange them according to the time sequence of their appearance in the entire dynamic modulation period. After sorting, combine the above three parameters 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 set of modulation parameters, and each parameter set includes modulation frequency, modulation depth, and modulation amplitude.

[0031] According to the timing sorting order of the modulation drive strategy sequence, set the carrier frequency, sub-carrier amplitude, and sub-carrier interval of the orthogonal frequency division multiplexing modulation method one by one; Based on the timing sorting order of the modulation drive strategy sequence, use each set of modulation parameters as input parameters respectively to perform setting operations on the corresponding parameters in the orthogonal frequency division multiplexing modulation method. The setting process is as follows: Set the modulation frequency parameter as the carrier frequency in the orthogonal frequency division multiplexing modulation method. The modulation frequency parameter determines the center frequency position of the modulation signal. Set the modulation depth parameter as the sub-carrier interval in the orthogonal frequency division multiplexing modulation method. The modulation depth parameter affects the bandwidth and orthogonality of the signal sub-channels. Set the modulation amplitude parameter as the sub-carrier amplitude control value in the orthogonal frequency division multiplexing modulation method. The modulation amplitude parameter directly affects the luminous intensity ratio of each sub-carrier.

[0032] To avoid inter-carrier interference, the setting of the above three parameters needs to meet the continuity constraint conditions of non-overlapping frequency intervals, stable amplitude ratios, and no jumps between time segments.

[0033] 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 sequence of dynamic modulation waveform signals emitted by the light-emitting diode during the dynamic modulation period; After completing the parameter setting, call the orthogonal frequency division multiplexing modulation method. According to the modulation frequency parameter, modulation depth parameter, and modulation amplitude parameter corresponding to each time segment in the modulation drive strategy sequence, generate dynamic modulation waveform signals in sequence: For each set of modulation parameters, construct a set of sub-carrier signals. The frequency of each sub-carrier signal is jointly determined by the set carrier frequency and sub-carrier interval, and the amplitude is controlled by the modulation amplitude parameter. Perform parallel orthogonal superposition on all sub-carriers to form a set of modulation waveform signals.

[0034] Arrange and splice multiple sequentially generated dynamic modulation waveform signals in 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 diode within the entire dynamic modulation period according to the set modulation frequency, modulation depth, and modulation amplitude. The dynamic modulation waveform signal sequence is used to drive the optical signal output and provide a physical carrier for photoelectric detection and data demodulation.

[0035] 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, including: Use a photoelectric detector 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; Perform real-time photoelectric conversion detection on the dynamic modulation waveform signal sequence. The light-emitting diode outputs a continuously changing optical signal within the dynamic modulation period according to the modulation drive strategy sequence. After the optical signal is transmitted through space to the receiving end, it is subjected to real-time photoelectric conversion by a high-sensitivity photoelectric detector. The photoelectric detector used should meet the requirement that the response speed is higher than the upper limit of the dynamic modulation frequency and have stable linear response performance. Each group of dynamic modulation waveform signals output by the light-emitting diode is respectively irradiated onto the photosensitive surface of the photoelectric detector and converted into a corresponding voltage signal sequence through the photoelectric effect. Each voltage signal sequence is the electrical signal waveform sequence, which records the amplitude fluctuation and frequency change of the optical signal in the time dimension.

[0036] Extract the time-domain signal features from the electrical signal waveform sequences respectively; Extract the time-domain signal features from each electrical signal waveform sequence respectively. The extraction contents include: extracting the time-domain amplitude, calculating the difference between the maximum value and the minimum value at each time sampling point; extracting the time-domain phase, using the Hilbert transform method to convert the real-valued electrical signal into an analytic signal, and obtaining the instantaneous phase feature by calculating the phase component; extracting the time-domain period, using the autocorrelation function method to judge the main period of the electrical signal.

[0037] Extract the frequency-domain signal features from the electrical signal waveform sequences respectively; Extract the frequency-domain signal features from each electrical signal waveform sequence respectively. Convert the electrical signal waveform sequence into a frequency-domain signal sequence through fast Fourier transform, and extract the following frequency-domain features: the frequency-domain main frequency component, select the frequency component with the largest amplitude as the main frequency value; the spectral bandwidth, calculate the frequency range corresponding to the amplitude dropping to half on both sides of the main frequency within the energy concentration region; the spectral amplitude, record the amplitude value corresponding to the main frequency component as the main energy index.

[0038] Construct a time-frequency feature matrix based on the extracted time-domain signal features and frequency-domain signal features; Combine the time-domain features (including time-domain amplitude, time-domain phase, and time-domain period) and frequency-domain features (including frequency-domain main frequency component, spectrum bandwidth, and spectrum amplitude) corresponding to each dynamic modulation waveform signal in a row vector structure to obtain a set of time-frequency feature vectors for each dynamic modulation waveform signal. Arrange all the time-frequency feature vectors in chronological order to form a time-frequency feature matrix, which represents the signal change characteristics during the entire dynamic modulation period.

[0039] Input the time-frequency feature matrix into the noise suppression algorithm to perform the identification and elimination of structured noise; Structured noise refers to repetitive interference signals introduced by modulation mechanisms, environmental reflections, or system nonlinearities, which are different from traditional white noise or thermal noise. Identify interference patterns with obvious periodicity or specific frequency patterns through statistical analysis, and adopt a selective filtering algorithm based on frequency masking to reduce the characteristic dimensions occupied by structured noise. The noise determination rule is set according to the modulation range and bandwidth characteristics of the target system.

[0040] Perform inverse transformation processing on the time-frequency feature matrix after structured noise elimination to obtain the target signal sequence after noise suppression; Perform inverse transformation processing on the time-frequency feature matrix after structured noise elimination to recover the target signal. Resynthesize the main frequency component and the sub-main frequency component into a spectrum according to the frequency-domain features, combine the corresponding time-domain period and phase information, and generate a time-domain signal sequence through inverse Fourier transform. The obtained signal is the target signal sequence after noise suppression, which retains the main components of the modulation information, removes the periodic structured noise, and can be used for the demodulation and data signal recovery processes.

[0041] 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: Perform spectrum transformation on each target signal to obtain the spectrum signal corresponding to each target signal; Use the fast Fourier transform algorithm to convert the target signal from the time domain to the frequency domain to obtain the spectrum signal corresponding to the target signal. The spectrum signal uses frequency as the abscissa and complex amplitude as the ordinate, and can reflect the energy distribution of the modulation signal on different frequency components. Spectrum transformation is used to reveal the frequency-domain characteristics of the subcarrier signals during the dynamic modulation process.

[0042] Based on each spectrum signal, identify the subcarrier number and subcarrier frequency position of the orthogonal frequency division multiplexing modulation method corresponding to the target signal; The recognition process is completed by the frequency-domain energy detection method, specifically as follows: perform a local peak scanning operation on the spectrum signal within the set frequency distribution range, screen out all frequency points whose amplitudes exceed the set energy threshold, and sequentially number them according to the frequency value to obtain the subcarrier sequence number; use the center frequency of each subcarrier as the subcarrier frequency position. For each target signal, a complete subcarrier recognition table is established.

[0043] Perform frequency compensation and phase correction processing on each subcarrier frequency position in each target signal respectively to obtain the subcarrier spectrum after frequency compensation and phase correction; Perform frequency compensation and phase correction processing on each subcarrier frequency position in each target signal respectively. Due to the slight offset of the frequency response of the light-emitting diode under different modulation driving states and the phase change in the optical propagation path, there is a deviation in the subcarrier frequency position and phase state observed at the receiving end. To correct the deviation, a frequency offset estimation algorithm is used for frequency compensation processing, specifically as follows: set a frequency search window near each subcarrier frequency, measure the peak position of the signal amplitude through a matched filter, and compare the peak position of the signal amplitude with the expected center frequency to obtain the compensation value; for phase correction, a phase rotation algorithm is used, multiply each subcarrier spectrum by a complex exponential function with a unit amplitude, and its phase angle is opposite to the estimated phase error value to achieve the correction operation.

[0044] Perform amplitude demapping and phase demapping processing on each subcarrier spectrum after frequency compensation and phase correction respectively to complete the demodulation process of each subcarrier spectrum; Amplitude demapping restores the amplitude information of each subcarrier to the original modulation amplitude symbol; phase demapping matches the phase value represented by the corrected subcarrier complex number with the known modulation depth range in the modulation parameter set to extract the corresponding phase symbol. To ensure the demapping accuracy, the mapping table of amplitude and phase needs to be established in advance through the calibration experiment of the light-emitting diode under different driving states and corresponds one-to-one with the modulation parameter space. After demapping processing, each subcarrier spectrum outputs two symbol quantities, which respectively represent the data units carried by the subcarrier within the current time segment; Integrate and reconstruct the data of the demodulation results of each subcarrier spectrum to obtain the data recovery result corresponding to each target signal, and complete the visible light communication data transmission based on dynamic modulation; Integrate and reconstruct the data of the demodulation results of all subcarrier spectra in each target signal: sort all subcarrier data within the same target signal according to the subcarrier sequence number, and splice the data segments of multiple target signals in sequence according to the time order of the dynamic modulation period to finally form a bit data stream or symbol sequence (i.e., the data recovery result). The data recovery result will be used for error checking, decoding, and application layer processing to complete the last functional link of the visible light communication data transmission process based on dynamic modulation.

[0045] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain a formula that is closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0046] The above embodiments can be implemented in whole or in part by 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 includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. 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 by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that includes one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0047] Those of ordinary skill in the art can realize that the modules and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0048] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0049] In several embodiments provided by the present 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 illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0050] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0051] In addition, in each embodiment of the present application, the functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0052] 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 essence of the technical solution of the present application, or the part that contributes to the prior art, or this part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0053] As described above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0054] Finally, the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A visible light communication data transmission method based on dynamic modulation, characterized in that, It includes the following steps: S1: Collect the actual light intensity signals emitted by the light-emitting diodes within a preset dynamic modulation period, and perform non-linear dynamic feature extraction to generate a spectral response feature set corresponding to different modulation parameter intervals; S2: Based on the spectral response feature set, perform a segmented analysis on the evolution trend of frequency-domain distortion under dynamic modulation to obtain multiple spectral distortion sub-intervals; S3: Optimize the local modulation parameters for each spectral distortion sub-interval respectively to obtain a dynamic local modulation parameter set; S4: Map the dynamic local modulation parameter set to a modulation driving strategy sequence, and use the orthogonal frequency division multiplexing modulation method to generate a dynamic modulation waveform signal sequence; S5: Perform optoelectronic 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, output the corresponding data recovery result, and complete the visible light communication data transmission based on dynamic modulation.

2. The method for transmitting visible light communication data based on dynamic modulation according to claim 1, wherein, S1 is specifically as follows: Within the dynamic modulation period, continuously collect the real-time light intensity of the light-emitting diodes within different modulation parameter intervals through a photodetector; Classify and process the real-time light intensity according to different modulation parameter intervals to obtain multiple real-time light intensity data groups corresponding to different modulation parameter intervals; Process each real-time light intensity data group respectively using a non-linear dynamic feature extraction method to extract dynamic feature data from each real-time light intensity data group; Perform Fourier transform on each dynamic feature data to generate a spectral response feature set corresponding to different modulation parameter intervals respectively.

3. The method for transmitting visible light communication data based on dynamic modulation according to claim 2, characterized in that, S2 is specifically as follows: Based on the spectral response feature set corresponding to different modulation parameter intervals, calculate the frequency-domain distortion degree for each spectral response feature set respectively to obtain the frequency-domain distortion degree values corresponding to each modulation parameter interval; Divide the frequency-domain distortion degree values of each modulation parameter interval into multiple different frequency-domain distortion degree intervals according to a preset frequency-domain distortion threshold; Perform statistical analysis on the multiple different frequency-domain distortion degree intervals obtained by division to obtain the distribution range of the spectral response feature set for each frequency-domain distortion degree interval; According to the distribution range of the spectral response feature set corresponding to each frequency-domain distortion degree interval, divide the evolution trend of frequency-domain distortion into multiple spectral distortion sub-intervals.

4. A visible light communication data transmission method based on dynamic modulation according to claim 3, characterized in that, S3 is specifically as follows: Perform frequency-domain response curve fitting analysis on the spectral response feature set corresponding to each spectral distortion sub-interval respectively to obtain the frequency-domain response fitting curve for each spectral distortion sub-interval; According to each frequency-domain response fitting curve, calculate the difference value between the frequency-domain response curve and the ideal frequency-domain response curve within each spectral distortion sub-interval; Based on the difference value between the frequency-domain response curve and the ideal frequency-domain response curve within each spectral distortion sub-interval, perform local adjustment and optimization processing on the modulation frequency, modulation depth, and modulation amplitude parameters respectively, and integrate them to form a dynamic local modulation parameter set.

5. A visible light communication data transmission method based on dynamic modulation according to claim 4, characterized in that S4 is specifically as follows: Sort the modulation frequency parameters, modulation depth parameters, and modulation amplitude parameters in the dynamic local modulation parameter set respectively in time sequence to form a modulation driving strategy sequence for driving the light-emitting diodes; Set the carrier frequency, sub - carrier amplitude, and sub - carrier interval of the orthogonal frequency - division multiplexing (OFDM) modulation method one by one according to the timing sorting order of the modulation drive strategy sequence; Generate a dynamic modulation waveform signal corresponding to each set of dynamic local modulation parameters based on the set OFDM modulation method, and obtain a sequence of dynamic modulation waveform signals emitted by the light - emitting diode within the dynamic modulation period.

6. The method for transmitting visible light communication data based on dynamic modulation according to claim 5, wherein, S5 is specifically as follows: Use a photodetector to perform real - time optoelectronic conversion detection on the sequence of dynamic modulation waveform signals to obtain a sequence of electrical signal waveforms corresponding to each dynamic modulation waveform signal; Extract the time - domain signal features from the sequence of electrical signal waveforms respectively; Extract the frequency - domain signal features from the sequence of electrical signal waveforms respectively; Construct a time - frequency feature matrix based on the extracted time - domain and frequency - domain signal features; Input the time - frequency feature matrix into a noise suppression algorithm to perform the identification and elimination of structured noise; Perform inverse transformation processing on the time - frequency feature matrix after structured noise elimination respectively to obtain a target signal sequence after noise suppression.

7. A visible light communication data transmission method based on dynamic modulation according to claim 6, characterized in that S6 is specifically as follows: Perform spectral transformation on each target signal respectively to obtain a spectral signal corresponding to each target signal; Based on each spectral signal, identify the sub - carrier number and sub - carrier frequency position of the OFDM modulation method corresponding to the target signal; Perform frequency compensation and phase correction processing on each sub - carrier frequency position in each target signal respectively to obtain a sub - carrier spectrum after frequency compensation and phase correction; Perform amplitude demapping and phase demapping processing on each sub - carrier spectrum after frequency compensation and phase correction respectively to complete the demodulation process of each sub - carrier spectrum; Integrate the demodulation results of each sub - carrier spectrum and perform data reconstruction to obtain a data recovery result corresponding to each target signal, and complete the visible - light communication data transmission based on dynamic modulation.

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