A Visible Light Communication Method Based on Entropy Loading

Abstract

This invention discloses a visible light communication method based on entropy loading, comprising the following steps: S1, constructing a non-line-of-sight channel model for an indoor visible light communication system; S2, based on the impact of DC bias setting on system capacity, constructing an algorithm to obtain the optimal DC bias constant using a simple lookup table; S3, based on the non-line-of-sight channel model, proposing an adaptive DC bias entropy loading scheme, and allocating a DC bias constant that maximizes the generalized mutual information of subcarriers to each subcarrier according to the estimated signal-to-noise ratio.

Description

Technical Field

[0001] This invention relates to the field of communication technology, particularly visible light communication (VLC), and specifically to a visible light communication method based on entropy loading. Background Technology

[0002] VLC based on light-emitting diodes (LEDs) has become a highly anticipated solution for indoor wireless communication due to its advantages such as low cost and high data rate, and is considered a key candidate technology for 6G. However, the limited bandwidth of LED devices and channel dispersion result in significant frequency response fading and fluctuations in the optical channel, limiting the achievable information rate (AIR) of VLC systems. Since improvements to the equipment are extremely costly, research has focused on optimizing the input signal at the transmitter by preprocessing the signal before transmission, thereby further increasing system capacity without increasing transmission power.

[0003] Orthogonal Frequency Division Multiplexing (OFDM), as an efficient spectrum modulation scheme, has been widely used in VLC systems without increasing equipment costs. Bit-loaded OFDM based on a multi-carrier water-filling strategy has been proposed to approximate bandwidth-limited channel capacity, but its use of discrete bit levels still results in a capacity gap compared to the continuously fluctuating VLC channel. With probabilistic constellation shaping gaining popularity in optical communications due to its excellent capacity approximation performance, entropy-loading (EL) OFDM, combining probabilistic shaping (PS), loads continuous entropy onto subcarriers to achieve optimal performance. This has recently been used for VLC channel approximation, and its advantages have been experimentally verified.

[0004] However, VLC utilizes intensity modulation and direct detection (IM / DD) for data transmission, requiring a positive DC bias to be added to the OFDM signal to meet the non-negative constraint. However, a low DC bias can negatively impact channel capacity due to residual negative signal, while a high DC bias ensures a positive signal but may cause the signal to exceed the dynamic range limit or result in lower power efficiency. Existing VLC capacity approximation schemes often employ a fixed-size or fixed-proportion DC bias. Furthermore, experimental verification in these schemes is primarily conducted on line-of-sight (LOS) paths. Summary of the Invention

[0005] This invention addresses the problem of limited system transmission efficiency caused by the limited bandwidth of LED devices and the fading and large fluctuations in the frequency response of indoor visible light multipath channels. It proposes a novel Adaptive Bias Entropy-Loading (ABEL) scheme, specifically an entropy-loaded visible light communication method. This method sets an optimal DC bias constant for each subcarrier based on the estimated signal-to-noise ratio (SNR) of the channel, maximizing the generalized mutual information (GMI) on each subcarrier, thereby further approaching the capacity limit of indoor multipath VLC systems.

[0006] According to one aspect of the present invention, the following technical solution is proposed:

[0007] A visible light communication method based on entropy loading includes the following steps: S1, constructing a non-line-of-sight channel model for an indoor visible light communication system; S2, based on the impact of DC bias setting on system capacity, constructing an algorithm to obtain the optimal DC bias constant using a simple lookup table; S3, based on the non-line-of-sight channel model, proposing an adaptive DC bias entropy loading scheme, and allocating a DC bias constant that maximizes the generalized mutual information of subcarriers to each subcarrier according to the estimated signal-to-noise ratio.

[0008] Furthermore, the channel characteristics of the non-line-of-sight channel model are expressed as follows:

[0009]

[0010] Where Y(t) represents the current signal received by the receiver; r represents the photoelectric conversion efficiency of the photodetector, in amperes per watt; X(t) represents the light intensity signal transmitted by the transmitter; h(t) represents the impulse response of the channel; and N(t) represents the additive noise of the system.

[0011] Furthermore, the unit impulse response of the non-line-of-sight channel model is:

[0012]

[0013] Among them, h i (0) (t;dA i R) represents a photon being emitted by a group of areas A R The unit impulse response produced when a reflective surface composed of infinitesimal elements with reflectivity ρ reflects light, where t represents the photon arrival time, and dA is the reflectance. i This represents a point light source derived from reflection, where R represents the receiver; ρ iThe reflection coefficient represents the reflection of this event; N represents the number of photons emitted; θ represents the angle between the incident ray and the unit normal vector of the receiver; d represents dA. i The distance to R; φ represents the angle between the emitted ray and the unit normal vector of the light source; θ FOV Indicates the receiving field of view; T PI Let represent the time it takes for a photon to travel from emission to this reflection; c represents the photon propagation speed; δ(·) is the impulse function; and r(·) is the receiving rectangle function, defined as follows:

[0014]

[0015] The impulse response h of k reflections (k) (t;S,R) is:

[0016]

[0017] Where S represents the transmitter, P (k) It is the set of photons that have not been absorbed after k reflections;

[0018] Set time slot T ts Contribution power of photons in each time slot The calculation formula is as follows:

[0019]

[0020] in, T represents the power contribution of the i-th photon, which undergoes k reflections, to the unit impulse response. i k This indicates the time when the i-th photon, after undergoing k reflections, arrives at the receiver, and j represents the time slot number.

[0021] Further, step S2 specifically includes: S21, the transmitting end first performs serial-to-parallel conversion to divide the bit sequence into parallel data streams, and each data stream passes through a constant component distribution matcher to generate probabilistic shaped signals with the same probabilistic shaped modulation format; S22, Hermitian symmetry is applied to the IFFT signal to obtain a real-valued probabilistic shaped OFDM signal; a positive constant DC bias is added to remove the remaining negative signal and obtain a single-level signal that conforms to LED modulation; S23, a 256th-order probabilistic shaped QAM signal is simulated through an additive white Gaussian noise channel, different DC bias and probabilistic shaped format parameters are set, and the generalized mutual information, normalized generalized mutual information and signal-to-noise ratio relationship of all probabilistic shaped modulation formats with different DC bias constants are stored in the first table arrG and the second table arrN. The normalized generalized mutual information is used as a threshold, and the optimal DC bias constant under the set signal-to-noise ratio is obtained through a simple lookup table.

[0022] Furthermore, the unipolar signal x(t) obtained in step S22 is:

[0023]

[0024] Where Re represents taking a real number, M is the number of IFFT points, and X... n It is a signal component, w n ω is the angular frequency, j is the imaginary unit, d0 is the DC bias constant, and T is the signal period.

[0025] Furthermore, in step S3, the adaptive DC bias entropy loading scheme first adds a cyclic prefix and parallel-to-serial conversion to each subcarrier at the transmitting end, and then uses a lookup table to superimpose different DC bias constants that maximize the generalized mutual information of the subcarriers based on the estimated channel signal-to-noise ratio. Then, the remaining negative signals are removed to obtain the optimized unipolar signal.

[0026] Furthermore, the adaptive DC bias entropy loading scheme described in step S3, after sequentially performing serial-to-parallel conversion, removing the cyclic prefix, and performing FFT at the receiving end, estimates the subcarrier response and performs frequency domain equalization using the pilot sequence. After removing the pilot, the generalized mutual information value of each subcarrier is calculated based on the received and transmitted symbol sequences. After inverse QAM mapping, inverse constant component distribution matching mapping, and parallel-to-serial conversion, the original bits are restored.

[0027] According to another aspect of the present invention, a computer-readable storage medium is provided on which a computer program is stored, which, when executed by a processor, can implement the steps of the aforementioned visible light communication method.

[0028] The beneficial effects of this invention are as follows: Based on traditional entropy loading, this invention sets an optimal DC bias constant for each subcarrier according to the estimated signal-to-noise ratio of the channel, thereby maximizing the generalized mutual information on each subcarrier and further approximating the channel capacity. This invention considers an indoor single-source non-line-of-sight (NLOS) model and verifies it through simulation in a constructed multipath VLC channel. The proposed scheme exhibits performance closer to the channel capacity under both forward error correction (FEC) overhead (OH) conditions, and also has a significant advantage in forward error correction overhead while maintaining a high information rate. Attached Figure Description

[0029] Figure 1 This is a basic channel model diagram of indoor wireless optical communication according to an embodiment of the present invention.

[0030] Figure 2 This is a flowchart illustrating the visible light communication method based on entropy loading according to an embodiment of the present invention.

[0031] Figure 3 This is a graph showing the variation of the generalized mutual information (GMI) of the PS-256QAM with the signal-to-noise ratio (SNR) under different DC biases in embodiments of the present invention.

[0032] Figure 4-1 This is the time-domain response of the indoor NLoS VLC channel in an embodiment of the present invention.

[0033] Figure 4-2 This is the frequency domain response of the indoor NLoS VLC channel in an embodiment of the present invention.

[0034] Figure 5 This is the estimated signal-to-noise ratio (SNR) of the multipath channel in this embodiment of the invention.

[0035] Figure 6-1 This is the load entropy of each subcarrier when the NGMI threshold is 0.9 in this embodiment of the invention.

[0036] Figure 6-2 This is the load entropy of each subcarrier when the NGMI threshold is 0.75 in this embodiment of the invention.

[0037] Figure 7 This is the constellation probability distribution at three different observation points in an embodiment of the present invention.

[0038] Figure 8-1 This refers to the DC bias of each subcarrier when the NGMI threshold is 0.9 in this embodiment of the invention.

[0039] Figure 8-2 This refers to the DC bias of each subcarrier when the NGMI threshold is 0.9 in this embodiment of the invention.

[0040] Figure 9-1 This refers to the GMI of each subcarrier under 11.1% FEC-OH conditions (NGMI threshold is 0.9) in Embodiment 1 of the present invention.

[0041] Figure 9-2 This refers to the GMI of each subcarrier under 33.3% FEC-OH (NGMI threshold is 0.75) in Embodiment 3 of the present invention. Detailed Implementation

[0042] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0043] Traditional entropy loading schemes combine probabilistic shaping (PS) and orthogonal frequency division multiplexing (OFDM), exhibiting excellent capacity approximation advantages in VLC channels, but still showing a gap with channel capacity. This invention proposes a novel adaptive bias entropy-loading (ABEL) scheme. This scheme sets an optimal DC bias constant for each subcarrier based on the estimated signal-to-noise ratio (SNR) of the channel, maximizing the generalized mutual information (GMI) on each subcarrier, thereby further approaching the capacity limit of indoor multipath VLC systems. Based on this, this invention proposes a visible light communication method based on entropy loading. This method includes: constructing a non-line-of-sight channel model of an indoor visible light communication system; constructing an algorithm to obtain the optimal DC bias constant using a simple lookup table based on the impact of DC bias settings on system capacity; and proposing an adaptive DC bias entropy loading scheme based on the non-line-of-sight channel model, allocating a DC bias constant that maximizes the GMI of each subcarrier according to the estimated SNR. Furthermore, the effectiveness of the ABEL scheme in this embodiment of the invention was verified through simulation comparison.

[0044] like Figure 1 The diagram shown is a channel model of an indoor VLC system. The channel characteristics of the system can be represented by the impulse response.

[0045]

[0046] Where Y(t) represents the current signal received by the receiver; r represents the photoelectric conversion efficiency of the photodetector, in amperes per watt; X(t) represents the light intensity signal transmitted by the transmitter; h(t) represents the impulse response of the channel; and N(t) represents the additive noise of the system.

[0047] To simplify the model, specular reflection and transmission of visible light are ignored, and all wall surfaces are considered rough surfaces. It is assumed that all reflective surfaces obey ideal Lambertian reflection and that the radiation pattern is independent of the angle of incidence. Both walls and floors are considered to be composed of a certain number of surfaces with area A. R A reflective surface is composed of infinitesimal elements with reflectivity ρ. When reflection occurs, the infinitesimal elements are regarded as a new emission source with emission power of ρdP, where dP represents the receiver power of the emission source.

[0048] When a photon is reflected, it is equivalent to a point light source with power 1 / N sending out new light rays, and the resulting unit impulse response is:

[0049]

[0050] in, This indicates that photons are distributed across several areas A. R The unit impulse response produced when a reflective surface composed of infinitesimal elements with reflectivity ρ reflects light, where t represents the photon arrival time, and dA is the reflectance. i This represents a point light source derived from reflection, where R represents the receiver; ρ i The reflection coefficient represents the reflection of this event; N represents the number of photons emitted; θ represents the angle between the incident ray and the unit normal vector of the receiver; d represents dA. i The distance to R; φ represents the angle between the emitted ray and the unit normal vector of the light source; θ FOV Indicates the receiving field of view; T PI Let represent the time it takes for a photon to travel from emission to this reflection; c represents the photon propagation speed; δ(·) is the impulse function; and r(·) is the receiving rectangle function, defined as follows:

[0051]

[0052] Therefore, the impulse response h of the kth reflection (k) (t;S,R) is:

[0053]

[0054] Where S represents the transmitter, P (k) Let be the set of photons that are not absorbed after k reflections.

[0055] N photons emitted by an LED are tracked, and their arrival time at the receiver and their power contribution to the receiver are calculated. After tracking of all photons is complete, a small time slot T is set. ts For example, in one embodiment of the present invention, a T is provided. ts For a time slot of 1E-9s, summing the power received by the receiver within that time slot and then dividing by N yields the value of the unit impulse response for that time slot. In the set time slot T... ts Below, the contribution power of photons in each time slot The calculation formula is as follows:

[0056]

[0057] in, T represents the power contribution of the i-th photon, which undergoes k reflections, to the unit impulse response. i k This indicates the time when the i-th photon, after undergoing k reflections, arrives at the receiver, and j represents the time slot number.

[0058] This invention improves upon traditional entropy loading schemes and proposes the ABEL scheme for NLOS channels in indoor VLC systems, such as... Figure 2 The diagram illustrates the flow chart of the visible light communication method based on entropy loading proposed in this embodiment of the invention. The transmitting end first converts the bit sequence from serial to parallel, dividing it into parallel data streams. Then, each data stream undergoes probabilistic shaping encoding via a Constant Composition Distribution Matcher (CCDM) to generate probabilistically shaped signals with the same probabilistic shaping modulation format. Based on a pre-estimated channel SNR, different symbol distributions conforming to the Maxwell-Boltzmann (MB) distribution are determined, followed by quadrature amplitude modulation (QAM). After the modulation (QAM) mapping, pilot sequences for multipath channel estimation and equalization are inserted and loaded into the corresponding subcarriers; OFDM itself is a bipolar signal, but for visible light communication systems using intensity modulation-direct detection, the input signal needs to be a positive real value. Therefore, Hermitian symmetry is applied to the input IFFT signal to obtain a real-valued probability-shaped OFDM signal. In order to obtain a single-stage signal that conforms to LED modulation, a positive constant DC bias voltage should be added to remove the remaining negative signal, thereby obtaining a single-stage signal x(t) that conforms to LED modulation, as shown in equation (6):

[0059]

[0060] Where Re{·} represents taking a real number, M is the number of IFFT points, and X n It is a signal component, w n ω is the angular frequency, j is the imaginary unit, d0 is the DC bias constant, and T is the signal period.

[0061] Figure 3 The variation of 256th-order probabilistic shaped QAM (PS-QAM) with signal-to-noise ratio (SNR) under different DC biases is shown, where λ represents the degree of PS shaping, with values ​​ranging from [0,1]. A larger value indicates a higher degree of shaping. It can be seen that there exists a DC bias setting that maximizes the generalized mutual information (GMI) at a specific SNR. Therefore, through AWGN channel simulation, the relationships between GMI, NGMI, and SNR for all PS modulation formats with different DC bias constants are stored in tables arrG and arrN. Using normalized generalized mutual information (NGMI) as a threshold, the optimal DC bias constant under the current channel SNR is obtained through a simple lookup table.

[0062] Therefore, unlike traditional entropy loading schemes, the adaptive DC bias entropy loading scheme proposed in this embodiment of the invention, at the transmitting end, before adding cyclic prefixes (CP) and parallel and serial (P / S) conversion to each subcarrier, uses a lookup table to superimpose different DC bias constants that maximize the GMI of each subcarrier based on the estimated signal-to-noise ratio. The algorithm for determining the optimal DC bias constant is shown in Table 1. Then, the remaining negative signal is removed to obtain the optimized single-pole signal. At the receiving end, after serial-to-parallel conversion, cyclic prefix removal, and FFT, the subcarrier response is estimated using the pilot sequence and frequency domain equalization is performed. After removing the pilot, the generalized mutual information value of each subcarrier is calculated based on the received and transmitted symbol sequences. After inverse QAM mapping, inverse constant component distribution matching converter mapping, and parallel-to-serial conversion, the original bits are recovered.

[0063] The indoor multipath channels that are traversed can be constructed using the aforementioned non-line-of-sight channel model and the parameters summarized in Table 2.

[0064] The indoor scene space is set to 5m×5m×3m. The transmitter LED is located in the center of the ceiling wall, and the receiver is located at a height of 0.8m above the ground. To ensure accuracy, the micro-element area is set to 1cm². 2 The maximum number of reflections considered is 3. The obtained time-domain and frequency-domain impulse responses of the indoor VLC channel are as follows: Figure 4-1 and Figure 4-2 As shown, the time domain response of the NLOS link exhibits an exponential decay after the first maximum peak, while the frequency domain response shows a deep fading at 3.5MHz.

[0065] Table 1. Pseudocode of the algorithm for determining the optimal DC bias constant.

[0066]

[0067] The algorithm for determining the optimal DC bias constant includes the following steps:

[0068] 1) Initialization parameters: A lookup table for the relationship between GMI and SNR for different PS modulation formats under different DC bias constants is denoted as arrG; a lookup table for the relationship between NGMI and SNR is denoted as arrN; and the NGMI threshold is denoted as NGMI. T The signal-to-noise ratio of each subcarrier is denoted as SNR. k ;

[0069] 2) Subcarrier signal-to-noise ratio (SNR) k Rounding down gives the integer snr k According to SNR k Find its corresponding column j in lookup tables arrG and arrN;

[0070] 3) First, iterate through the j-th column of the NGMI and SNR relationship lookup table arrN to find NGMI values ​​less than NGMI. T If the value of the row corresponding to the row does not meet the NGMI threshold, set the value of the row in the j-th column of the GMI and SNR lookup table arrG to 0.

[0071] 4) Then iterate through the j-th column of the GMI and SNR relationship lookup table arrG, find the maximum GMI value in that column, corresponding to row r;

[0072] 5) Based on r, obtain the DC bias constant b and probability shaping factor λ that maximize GMI.

[0073] This invention aims to study and verify the effectiveness of the proposed adaptive DC bias entropy loading scheme for visible light communication based on entropy loading. A traditional entropy loading scheme is compared in the same indoor multipath VLC system. The same simulation parameters are set, including a 512 FFT length, 200 effective subcarriers, and pilot and CP overhead of 21.3%. First, the SNR of the constructed multipath channel is estimated by transmitting a uniform 4QAM-OFDM signal, such as... Figure 5 As shown, to ensure fairness in comparing the adaptive DC bias entropy loading scheme and the traditional entropy loading scheme in this embodiment of the invention, the same NGMI threshold is set, and both are probabilistically shaped using 256-QAM. Under the premise of satisfying the NGMI threshold, for the traditional entropy loading scheme, 256-QAMs with different probability constellation distributions are allocated to different subcarriers based on the estimated SNR. For the adaptive DC bias entropy loading scheme, the probability constellation distribution that maximizes the generalized mutual information (GMI) under the optimal DC bias is selected based on the estimated SNR.

[0074] Table 2 Indoor VLC Channel Simulation Parameter Settings

[0075]

[0076] Since normalized generalized mutual information (NGMI) affects the achievable information rate (AIR) of the probabilistic shaping scheme, in order to better reflect the performance of the adaptive DC bias entropy loading scheme proposed in this embodiment, this embodiment considers two NGMI threshold cases, namely 0.9 and 0.75, corresponding to 11.1% and 33.3% forward error correction overhead (FEC-OH). Figure 6-1 and Figure 6-2 The relationship between source entropy and subcarrier index is shown for two schemes (traditional entropy loading and adaptive DC bias entropy loading). In both schemes, the source entropy decreases as the subcarrier channel SNR deteriorates, indicating that the probabilistically shaped QAM signal becomes more formed. Figure 7 As shown. Figure 8-1 and Figure 8-2The diagram shows the DC bias settings for each subcarrier under two different NGMI thresholds for two different schemes. Compared with the fixed DC bias applied by the traditional entropy loading scheme, the adaptive DC bias entropy loading scheme of this invention sets a smaller DC bias for subcarriers with a higher degree of shaping, and a larger DC bias for subcarriers with a lower degree of shaping. Figure 9-1 and Figure 9-2 The GMI calculated by two loading schemes under two different thresholds after passing through the same multipath VLC channel is shown. The adaptive DC bias entropy loading scheme of this embodiment exhibits performance closer to the channel capacity. When FEC-OH is 11.1%, the average generalized mutual information (GMI) of the adaptive DC bias entropy loading scheme and the conventional entropy loading scheme are 5.90 bits / symbol and 5.75 bits / symbol, respectively, with a GMI increment of 0.15 bits / symbol, corresponding to a 7.5 Mb / s improvement in the achievable information rate (AIR) for a 50 MHz bandwidth. Notably, when FEC-OH is 33.3%, the average GMI of the adaptive DC bias entropy loading scheme is 5.93 bits / symbol, while the average GMI of the conventional entropy loading scheme is 5.90 bits / symbol, showing little difference from the GMI of the adaptive DC bias entropy loading scheme when FEC-OH is 11.1%.

[0077] As can be seen, the visible light communication method based on entropy loading proposed in this invention can maintain a high system information rate with low forward error correction overhead, solving the problems of frequency response fading, large fluctuations, and high forward error correction overhead caused by capacity approximation in visible light multipath channels. Furthermore, it can set an optimal DC bias constant for each subcarrier based on the estimated signal-to-noise ratio of the channel, maximizing the generalized mutual information on each subcarrier, thereby further approaching the capacity limit of indoor multipath VLC systems. This solves the problem that the capacity of visible light communication systems is limited by the bandwidth of LED devices and channel dispersion, while traditional entropy loading schemes ignore the important influence of DC bias setting on system capacity.

[0078] According to another embodiment of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, can implement the steps of the visible light communication method described in the foregoing embodiments. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (e.g., a CD-ROM, USB flash drive, portable hard drive, etc.), including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the method of the present invention.

[0079] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, several equivalent substitutions or obvious modifications can be made without departing from the concept of the present invention, and all such modifications, achieving the same performance or purpose, should be considered within the scope of protection of the present invention.

Claims

1. A visible light communication method based on entropy loading, characterized in that, Includes the following steps: S1. Construct a non-line-of-sight channel model for an indoor visible light communication system; S2. Based on the impact of DC bias setting on system capacity, construct an algorithm to obtain the optimal DC bias constant using a simple lookup table; S3. Based on the non-line-of-sight channel model, an adaptive DC bias entropy loading scheme is proposed, which allocates a DC bias constant that maximizes the generalized mutual information of each subcarrier according to the estimated signal-to-noise ratio. S2 specifically includes: S21. The transmitting end first performs serial-to-parallel conversion to divide the bit sequence into parallel data streams. Each data stream passes through a constant component distribution matcher to generate a probability-shaped signal with the same probability-shaped modulation format. S22. Perform Hermitian symmetry on the IFFT signal to obtain a real-valued probability-shaped OFDM signal; add a positive constant DC bias voltage to remove the remaining negative signal and obtain a unipolar signal that conforms to LED modulation. S23. Using a 256th-order probabilistic shaped QAM signal, simulate through an additive white Gaussian noise channel. Set different DC bias and probabilistic shaped format parameters. Store the generalized mutual information, normalized generalized mutual information and signal-to-noise ratio relationship of all probabilistic shaped modulation formats with different DC bias constants in the first table arrG and the second table arrN. Use the normalized generalized mutual information as a threshold and obtain the optimal DC bias constant under the set signal-to-noise ratio through a simple lookup table. The adaptive DC bias entropy loading scheme described in step S3 involves adding a cyclic prefix and parallel-to-serial conversion to each subcarrier at the transmitting end before adding the cyclic prefix and parallel-to-serial conversion. Based on the estimated channel signal-to-noise ratio, a lookup table is used to superimpose different DC bias constants that maximize the generalized mutual information of the subcarriers to each subcarrier. Then, the remaining negative signals are removed to obtain the optimized unipolar signal. The adaptive DC bias entropy loading scheme described in step S3 involves sequentially performing serial-to-parallel conversion, removing the cyclic prefix, and performing FFT at the receiving end. Then, the subcarrier response is estimated using the pilot sequence, and frequency domain equalization is performed. After removing the pilot, the generalized mutual information value of each subcarrier is calculated based on the received and transmitted symbol sequences. The original bits are then recovered after inverse QAM mapping, inverse constant component distribution matching mapping, and parallel-to-serial conversion.

2. The visible light communication method based on entropy loading as described in claim 1, characterized in that, The channel characteristics of the non-line-of-sight channel model are expressed as follows: Where Y(t) represents the current signal received by the receiver; r represents the photoelectric conversion efficiency of the photodetector, in amperes per watt; X(t) represents the light intensity signal transmitted by the transmitter; h(t) represents the impulse response of the channel; and N(t) represents the additive noise of the system.

3. The visible light communication method based on entropy loading as described in claim 1, characterized in that, The unit impulse response of the non-line-of-sight channel model is: in, This indicates that photons are distributed across several areas A. R The unit impulse response produced when a reflective surface composed of infinitesimal elements with reflectivity ρ reflects light, where t represents the photon arrival time, and dA is the reflectance. i This represents a point light source derived from reflection, where R represents the receiver; ρ i The reflection coefficient represents the reflection of this event; N represents the number of photons emitted; θ represents the angle between the incident ray and the unit normal vector of the receiver; d represents dA. i The distance to R; φ represents the angle between the emitted ray and the unit normal vector of the light source; θ FOV Indicates the receiving field of view; T PI Let represent the time it takes for a photon to travel from emission to this reflection; c represents the photon propagation speed; δ(·) is the impulse function; and r(·) is the receiving rectangle function, defined as follows: The impulse response h of k reflections (k) (t;S,R) is: Where S represents the transmitter, P (k) It is the set of photons that have not been absorbed after k reflections; Set time slot T ts Contribution power of photons in each time slot The calculation formula is as follows: in, T represents the power contribution of the i-th photon, which undergoes k reflections, to the unit impulse response. i k This indicates the time when the i-th photon, after undergoing k reflections, arrives at the receiver, and j represents the time slot number.

4. The visible light communication method based on entropy loading as described in claim 1, characterized in that, The unipolar signal x(t) obtained in step S22 is: Where Re represents taking a real number, M is the number of IFFT points, and X... n It is a signal component, w n ω is the angular frequency, j is the imaginary unit, d0 is the DC bias constant, and T is the signal period.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it can implement the steps of the visible light communication method according to any one of claims 1-4.

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