Signal uniformity optimization method for random LED distribution in indoor visible light communication system

Abstract

The invention relates to a signal uniformity optimization method for random LED distribution of an indoor visible light communication system, belonging to the technical field of optical communication, and comprising the following steps: S1: using a binomial point process BPP to simulate the random distribution of LEDs in the indoor visible light communication system; S2: inputting inherent parameters of the indoor visible light communication system and initialization parameters of an adaptive bat algorithm; S3: building a system model of indoor visible light communication according to the simulation result of step S1 and the input of step S2, and obtaining the distribution of the original indoor received illumination and received optical power; S4: based on the system model of step S3, optimizing the half angle, central luminous intensity and emission power of LEDs at half power by using a designed adaptive bat algorithm, and obtaining the optimal parameter configuration of LED distribution; S5: obtaining the distribution of the indoor optimized received illumination and received optical power according to the optimization result of step S4.

Description

Technical Field

[0001] The invention belongs to the technical field of optical communication and relates to a method for optimizing signal uniformity of random LED distribution in an indoor visible light communication system. Background Art

[0002] In recent years, light-emitting diodes (LEDs) have been widely used due to their advantages of energy saving, environmental protection and high response rate. LEDs have replaced traditional light sources as a new generation of green light sources. LED-based visible light communication (VLC) is an attractive short-range wireless communication technology. LEDs are the most suitable light source for VLC due to their unique high switching rate characteristics, modulating information bits through light intensity. Generally speaking, there is often an uneven distribution of optical signals in indoor visible light communication systems. However, uniform received illumination can provide a more comfortable lighting environment, and uniform received optical power can improve the communication quality and fairness of the VLC system. Therefore, in order to ensure the uniform distribution of optical signals, it is essential to optimize the uniformity of the system.

[0003] At present, the research on the uniformity of optical signal distribution for visible light communication mainly includes indoor square geometric layout, circular geometric layout and square-circle mixed layout. In recent years, random distribution has been proposed. However, in the practical application of indoor VLC technology, we need to optimize the existing random LED layout so that indoor VLC technology can be widely used while maximizing cost savings. The optimization algorithm studied has also progressed from the classic optimization algorithm to the heuristic algorithm. The heuristic algorithm has the characteristics of wide search range and high search accuracy, but it also has the disadvantages of slow convergence speed and easy to fall into local optimum. Therefore, the current problem of this type of solution is how to select or improve the appropriate algorithm to search for the best solution. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a signal uniformity optimization method for random LED distribution in an indoor visible light communication system, and utilize the designed adaptive bat algorithm based on adaptive speed strategy to optimize the half-angle, central luminous intensity and transmission power of the LED at half power, so as to achieve the optimal parameter configuration of LED distribution, so as to optimize the uniformity of visible light signals and improve the lighting quality and communication fairness of the system.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A method for optimizing signal uniformity of random LED distribution in an indoor visible light communication system comprises the following steps:

[0007] S1: Using Binomial Point Process (BPP) to simulate the random distribution of LEDs in indoor visible light communication systems;

[0008] S2: Input the inherent parameters of the indoor visible light communication system and the initialization parameters of the adaptive bat algorithm;

[0009] S3: Building a system model of indoor visible light communication according to the simulation result of step S1 and the input of step S2, and obtaining the distribution of the original received illumination and received optical power indoors;

[0010] S4: Based on the system model in step S3, the half angle, center luminous intensity and emission power of the LED at half power are optimized by the designed adaptive bat algorithm to obtain the optimal parameter configuration of the LED distribution;

[0011] S5: According to the optimization result of step S4, the distribution of the indoor optimized received illumination and received optical power is obtained.

[0012] Furthermore, in step S1, the number of LEDs at the transmitting end is input, and then the binomial point process BPP is used to simulate the random distribution of the LEDs. The specific simulation distribution method is as follows:

[0013] The LEDs are binomially distributed in the plane R at the top of the room. M LEDs are randomly placed in the square plane R with a length of L. The points (x u ,y u ):x u ,y u ∈R(0,L), u=1,…,M, the points of the BPP random distribution are located according to the random variable X, and the probability density function of the binomial distribution is as follows:

[0014]

[0015] Therefore, there is a point (x u ,y u ) probability is:

[0016]

[0017] The probability p R Given by:

[0018] p R =∫ R p X (x)dx (15)

[0019] The probability density function of the number and position of randomly distributed points is given by p R Closely linked together.

[0020] Further, the system inherent parameters in step S2 include: the size of the input indoor room, the inherent parameters of the LED device and its optimization range, the number of receivers, and the inherent parameters of the receiver device;

[0021] The initialization parameters of the adaptive bat algorithm include the initialization population size, search dimension, maximum number of iterations and random parameters.

[0022] Furthermore, the random distribution of indoor LEDs obtained in step S1 is combined with the inherent parameters of step S2 to build a system model for indoor visible light communication. Only the indoor line-of-sight link is considered, and the radiation intensity model of the LED is set as the Lambertian model. The distribution of the original received illumination and received optical power in the room is calculated.

[0023] The original indoor received illumination is:

[0024]

[0025] Where K represents the number of photoelectric receivers PD, M represents the number of transmitting LEDs, I(0) is the central luminous intensity of the LED, θ is the exit angle, ψ is the incident angle, and d ij Indicator LED i With photoelectric receiver PD j The Euclidean distance between them is expressed as:

[0026]

[0027] Where (X i ,Y i ,Z i ) indicates LED i The spatial coordinates of j ,y j ,z j ) indicates PD j The spatial coordinates of

[0028] m is the order of Lambertian emission, given by the half-power angle θ 1 / 2 express:

[0029]

[0030] The original received optical power indoors is:

[0031]

[0032] Where P ti For LED i The transmission power, H ij is the channel gain, expressed as:

[0033]

[0034] Among them A PD is the effective receiving area of ​​the receiver, h is the height of the receiving plane from the ground, T s (ψ) represents the optical filter gain, g(ψ) represents the optical concentrator gain, and FOV represents the receiver field of view; the model of the rectangular function rect(x) is expressed as:

[0035]

[0036] Where |x| represents the absolute value of the ratio of the restricted interval independent variable incident angle to the field of view angle.

[0037] Further, in step S4, the designed adaptive bat algorithm is: introducing an adaptive speed strategy into the standard bat algorithm, that is, based on the fitness value of each individual and the current number of iterations, multiplying its speed by an adaptive weight w when updating, to construct an adaptive bat algorithm;

[0038] The adaptive bat algorithm is used to optimize the half angle, center luminous intensity and emission power of indoor LEDs at half power; the improved speed update rule is given by the following formula:

[0039]

[0040] Where w is the velocity weight, v is the flight speed, x is the displacement, and f is the sound wave frequency. The calculation rule of w is:

[0041]

[0042] Where κ is a constant, factor i Given by:

[0043]

[0044] Among them fitness i is the fitness value of individual bats in the same generation, fitness max is the maximum fitness value in the same generation, min is the minimum fitness value in the same generation.

[0045] Furthermore, in step S4, the quality of the algorithm solution is judged according to the evaluation function, and the best solution of the algorithm is polled and saved until the algorithm is terminated and the optimal solution is output as the final LED parameter configuration solution.

[0046] Furthermore, in step S5, the optimal parameter configuration obtained by optimization in step S4 is brought into the system model of step S3 to obtain the distribution of the indoor optimized received illumination and received optical power, thereby achieving uniform distribution of optical signals of random LEDs in the indoor visible light communication system.

[0047] The beneficial effects of the present invention are as follows: the present invention optimizes the random LED layout in practical applications, uses the adaptive bat algorithm to optimize the configuration parameters of the LED, and enables indoor VLC technology to be widely used while maximizing cost savings. The adaptive bat algorithm has the advantages of fast convergence speed, strong global optimization ability and few parameters. The optimization results show that the proposed scheme can simultaneously meet the indoor lighting requirements and communication fairness, and obtain a higher quality coefficient. The method can achieve stable and rapid reduction of the fluctuations of the received illumination and received optical power on the receiving plane.

[0048] Other advantages, objectives and features of the present invention will be described in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the following examination and study, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below in conjunction with the accompanying drawings, wherein:

[0050] Figure 1 Model diagram of indoor visible light system;

[0051] Figure 2 Optimization flow chart of the adaptive bat algorithm. DETAILED DESCRIPTION

[0052] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0053] Among them, the drawings are only used for illustrative explanations, and they only represent schematic diagrams rather than actual pictures, and should not be understood as limitations on the present invention. In order to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0054] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if the terms "upper", "lower", "left", "right", "front", "rear", etc. indicate the orientation or position relationship, they are based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation. Therefore, the terms describing the position relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0055] See also Figure 1-2 , a signal uniformity optimization method for random LED distribution in an indoor visible light communication system, comprising the following steps:

[0056] (1) Considering the random distribution problem of LEDs in practice, a binomial point process is used to simulate the random distribution of LEDs.

[0057] The input of this method is: the number of LEDs at the transmitting end, and then the binomial point process is used to simulate the random distribution of LEDs.

[0058] In the BPP random distribution of indoor LEDs, the LEDs are binomially distributed in the plane R at the top of the room. M LEDs are randomly placed in a square plane R with a length of L. The points (x m ,y m ):x m ,y m ∈R(0,L), m=1,…,M, the points of the BPP random distribution are located according to the random variable X, and the probability density function of the binomial distribution is as follows:

[0059]

[0060] Therefore, there is a point (x u ,y u ) probability is:

[0061]

[0062] The probability p R Given by:

[0063] p R =∫ R p X (x)dx (27)

[0064] The probability density function of the number and position of randomly distributed points is given by p R Closely linked together.

[0065] (2) Input the inherent parameters of the indoor visible light communication system and the initialization parameters of the adaptive bat algorithm. Table 1 gives the inherent parameters of the indoor VLC system, and Table 2 gives the experimental parameters of the adaptive bat algorithm.

[0066] Table 1

[0067] parameter symbol Numeric <![CDATA[Room size (m 3 )]]> L×W×H 5×5×3 Receiving plane height (m) <![CDATA[H R ]]> 0.85 LEDs per lamp LED_nxn 7×7 Half angle of LED at half power (degree) <![CDATA[θ 1 / 2 ]]> 20～80 LED center luminous intensity (cd) I(0) 5～20 LED emission light power (W) Pt 0.1～1 Array LED light spacing (m) S 0.01m <![CDATA[The area of PD (cm 2 )]]> <![CDATA[A PD ]]> 1 Optical filter gain <![CDATA[T s (ψ)]]> 1 Optical concentrator gain G(ψ) 1 Photoelectric conversion efficiency (A / W) r 1 Photodetector field of view (degree) FOV 90

[0068] Table 2

[0069] parameter symbol Numeric Search Dimensions D 16 Population size <![CDATA[N pop ]]> 200 Maximum number of iterations T 50 Scaling parameters δ 0.008 Sound wave loudness attenuation coefficient λ 0.9 Pulse frequency enhancement factor γ 0.9

[0070] (3) According to the input system parameters, a system model of indoor visible light communication is built. The indoor visible light system model is as follows: Figure 1 As shown. The size of the indoor room model is L×W×H, and the receiving plane is 0.85m from the ground. Consider an indoor VLC system with M LEDs, and consider K photo-diodes (PDs) on the receiving plane. At the same time, we only consider the line-of-sight (LOS) link of the indoor VLC system. Assuming that the radiation intensity model of the LED is a Lambertian model, calculate the distribution of the original received illuminance and received optical power indoors. The original received illuminance indoors is:

[0071]

[0072] Where I(0) is the central luminous intensity of the LED, θ is the exit angle, ψ is the incident angle, and d ij Indicator LED i With photoelectric receiver PD j The Euclidean distance between them is expressed as:

[0073]

[0074] m is the order of Lambertian emission, given by the half-power angle θ 1 / 2 express:

[0075]

[0076] The original received optical power indoors is:

[0077]

[0078] Where P ti For LED i The transmission power, H ij is the channel gain, expressed as:

[0079]

[0080] Among them APD is the effective receiving area of ​​the receiver, T s (ψ) represents the optical filter gain, g(ψ) represents the optical concentrator gain, and FOV represents the receiver field of view. The model of the rectangular function rect(x) is expressed as:

[0081]

[0082] (4) Aiming at the situation of uneven distribution of original light signals, a bat algorithm with an adaptive speed strategy is proposed to optimize the half-power angle, central luminous intensity and emission power of LEDs in indoor VLC systems. The adaptive speed strategy can effectively prevent the algorithm from falling into local optimality and improve the search capability of the algorithm.

[0083] In order to utilize the bionic principle of bat echolocation during the random search process of bats, three idealized rules need to be assumed in the basic bat algorithm. In this algorithm, it is assumed that the bat population is N, which represents the possible solutions of N LED states, and the dimension of the search space is represented by D. The initial bat population consists of {x1,x2,x3,…,x N} indicates that its parameters are randomly initialized and the position of each bat in each generation and speed The update rule is given by:

[0084] f i =f min +(f max -f min )β (10)

[0085]

[0086]

[0087] where β∈[0,1] is a random vector, x best is the current local optimal solution in the group, f i is the frequency of the sound waves emitted by bats, and the adjustment range is [f min ,f max ]. New local solutions are generated using a random walk:

[0088] x new =x old +δεA t (13)

[0089] Where ε∈[-1,1] is a random number, A t is the average loudness of the entire population in the same generation. In particular, from an implementation perspective, we choose to provide a scaling parameter δ to control the step size.

[0090] When bats are searching for prey, they will constantly adjust the loudness and frequency of the sound waves they emit according to the distance from the target prey to improve their hunting efficiency. As they gradually approach the prey, the spatial range in which the bat searches for prey is gradually decreasing, so they will gradually reduce the loudness to a fixed value while continuously increasing the frequency in order to quickly and dynamically grasp the location of the target prey. and frequency The update rule is given by:

[0091]

[0092]

[0093] Where λ∈[0,1] is the sound wave loudness attenuation coefficient; γ>0 is the pulse frequency enhancement coefficient; Represents the initial pulse frequency of bat i.

[0094] As a key part of the algorithm, the fitness function considers the flatness and LED utilization in the uniformity optimization of the indoor VLC system. Different from the variance used in the past, we consider making the average value of the optimization result as high as possible and select the following fitness function:

[0095]

[0096]

[0097] Bat individuals inherit the speed and the best individual position of the previous generation and fly off. When the algorithm reaches the later stage, most bat individuals fall into local optimization, and the speed of the bat cannot make it out of the local optimization. Based on this, we propose an adaptive speed strategy. The improvement is based on the fitness of each individual and the current number of iterations, so that its speed is multiplied by an adaptive weight w when updating. Obviously, if an individual in the population is closer to the optimal solution than other individuals, its fitness value is lower. We should quickly slow down the speed of the individual so that the individual converges to the optimal solution. In this case, we give a smaller weight value. On the contrary, if the fitness value of the individual is very high, it means that the individual is far from the optimal solution. We need to give it a larger weight value. At the same time, we also consider the impact of the number of iterations on the optimization. The strategy of gradually reducing the speed weight as the number of iterations increases can not only ensure the global and local search capabilities of the algorithm, but also speed up the overall convergence speed of the algorithm. Finally, the bat individual with the lowest fitness value in all generations is the best solution.

[0098] Therefore, the improved velocity update rule is given by:

[0099]

[0100] Where w is the speed weight, and the calculation rule of w is:

[0101]

[0102] Where κ is 2.5, factor i Given by:

[0103]

[0104] Among them fitness i is the fitness value of individual bats in the same generation, fitness max is the maximum fitness value in the same generation, fitness min is the minimum fitness value in the same generation. The optimization process of the adaptive bat algorithm is as follows: Figure 2 Given.

[0105] The following will be combined with the attached Figure 2 The signal uniformity optimization method of random LED distribution in the indoor visible light communication system of the present invention is introduced in more detail. The specific process can be divided into the following steps:

[0106] Input: Input the inherent parameters of the indoor visible light communication system model (including: the size of the indoor room, the inherent parameters of the LED device and its optimization range, the number of receivers, the inherent parameters of the receiver device, etc.) and the parameters required for the algorithm execution (including: the number of initialized populations, the search dimension, the maximum number of iterations and random parameters);

[0107] Output: optimal parameter configuration for each LED;

[0108] Step 1: Establish an indoor visible light communication system model and initialize the population of the adaptive bat algorithm based on the input;

[0109] Step 2: According to step 1, a new solution is generated by adjusting the frequency and loudness of the bat's search, i.e., the configuration parameters of each LED;

[0110] Step 3: Calculate the weight w of the current adaptive speed according to the current number of iterations;

[0111] Step 4: Update the bat’s speed and position, where the position is the new solution;

[0112] Step 5: According to the parameter adjustment range of the LED, determine whether the new solution is out of range. If so, jump to step 2; otherwise, continue to step 6;

[0113] Step 6: Determine whether the random number is greater than the pulse frequency r. If so, proceed to step 7; otherwise, jump to step 10;

[0114] Step 7: Based on the new solution updated in step 4, perform a local search around the currently selected best solution;

[0115] Step 8: Update the bat's speed and position;

[0116] Step 9: Determine whether the new solution is out of bounds. If so, jump to step 7; otherwise, continue to step 10;

[0117] Step 10: Calculate the fitness value of the current solution according to the fitness function;

[0118] Step 11: According to the input maximum number of iterations, determine whether the current number of iterations t is less than the maximum number of iterations T. If so, jump to step 2; otherwise, continue to step 12;

[0119] Step 12: Output the optimal solution as the final LED parameter configuration solution.

[0120] (5) The quality of the algorithm solution is judged according to the evaluation function, and the best solution of the algorithm is polled and saved until the algorithm terminates and the optimal solution is output as the final LED parameter configuration plan.

[0121] The root mean square error coefficient of variation (CV) (RMSE) is used as the quality coefficient to test the illumination uniformity of the system. The smaller the CV (RMSE), the better the illumination uniformity.

[0122]

[0123] Where E av is the mean illuminance and RMSE is the standard deviation of illuminance.

[0124] The quality factor is considered to evaluate the fairness of the indoor VLC system to the user's received power. The quality factor is given by the following formula.

[0125]

[0126] Where Λ=(rP r ) 2 / σ 2 , r is the photoelectric conversion efficiency, σ 2 is the noise variance, mean(Λ) is the mean value of Λ, and var(Λ) is the variance of Λ.

[0127] (6) Combining the initial system model and the algorithm-optimized LED parameter configuration scheme, the uniformly distributed received illumination and received optical power in the room are obtained.

[0128] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solution, which should be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing signal uniformity of random LED distribution in an indoor visible light communication system, characterized in that: The following steps are involved: S1: Using binomial point process BPP to simulate the random distribution of LEDs in indoor visible light communication system; S2: Input the inherent parameters of the indoor visible light communication system and the initialization parameters of the adaptive bat algorithm; S3: Building a system model of indoor visible light communication according to the simulation result of step S1 and the input of step S2, and obtaining the distribution of the original received illumination and received optical power indoors; S4: Based on the system model in step S3, the half angle, center luminous intensity and emission power of the LED at half power are optimized by the designed adaptive bat algorithm to obtain the optimal parameter configuration of the LED distribution; The designed adaptive bat algorithm is as follows: an adaptive speed strategy is introduced into the standard bat algorithm, that is, based on the fitness value of each individual and the current number of iterations, its speed is multiplied by an adaptive weight w when updating, so as to construct an adaptive bat algorithm; The adaptive bat algorithm is used to optimize the half angle, center luminous intensity and emission power of indoor LEDs at half power; the improved speed update rule is given by the following formula: Where w is the velocity weight, v is the flight speed, x is the displacement, x best is the current local optimal solution in the group, f is the sound wave frequency, and the calculation rule of w is: Where κ is a constant, T is the maximum number of iterations, factor i Given by: Among them fitness i is the fitness value of individual bats in the same generation, fitness max is the maximum fitness value in the same generation, fitness min is the minimum fitness value in the same generation; S5: According to the optimization result of step S4, the distribution of the indoor optimized received illumination and received optical power is obtained.

2. The signal uniformity optimization method for random LED distribution in an indoor visible light communication system according to claim 1, characterized in that: In step S1, the number of LEDs at the transmitting end is input, and then the binomial point process BPP is used to simulate the random distribution of LEDs. The specific simulation distribution method is as follows: The LEDs are binomially distributed in the plane R at the top of the room. M LEDs are randomly placed in the square plane R with a length of L. The points (x u ,y u ):x u ,y u ∈R(0,L),u=1,…,M,the points of BPP random distribution are located according to the random variable X, and the probability density function of binomial distribution is as follows: Therefore, there is a point (x u ,y u ) probability is: The probability p R Given by: p R =∫ R p X (x)dx (3) The probability density function of the number and position of randomly distributed points is given by p R Closely linked together.

3. The signal uniformity optimization method for random LED distribution in an indoor visible light communication system according to claim 1, characterized in that: The system inherent parameters in step S2 include: the size of the input indoor room, the inherent parameters of the LED device and its optimization range, the number of receivers, and the inherent parameters of the receiver device; The initialization parameters of the adaptive bat algorithm include the initialization population size, search dimension, maximum number of iterations and random parameters.

4. The signal uniformity optimization method for random LED distribution in an indoor visible light communication system according to claim 1, characterized in that: The random distribution of indoor LEDs obtained in step S1 is combined with the inherent parameters of step S2 to build a system model for indoor visible light communication. Only the indoor line-of-sight link is considered. The radiation intensity model of the LED is set as the Lambertian model, and the distribution of the indoor original received illumination and received optical power is calculated. The original indoor received illumination is: Where K represents the number of photoelectric receivers PD, M represents the number of transmitting LEDs, I(0) is the central luminous intensity of the LED, θ is the exit angle, ψ is the incident angle, and d ij Indicator LED i With photoelectric receiver PD j The Euclidean distance between them is expressed as: Where (X i ,Y i ,Z i ) represents the spatial coordinates of LEDi, where (x j ,y j ,z j ) indicates PD j The spatial coordinates of m is the order of Lambertian emission, given by the half-power angle θ 1 / 2 express: The original received optical power indoors is: Where P ti For LED i The transmission power, H ij is the channel gain, expressed as: Among them A PD is the effective receiving area of ​​the receiver, h is the height of the receiving plane from the ground, T s (ψ) represents the optical filter gain, g(ψ) represents the optical concentrator gain, and FOV represents the receiver field of view; the model of the rectangular function rect(x) is expressed as: Where |x| represents the absolute value of the ratio of the restricted interval independent variable incident angle to the field of view angle.

5. The signal uniformity optimization method for random LED distribution in an indoor visible light communication system according to claim 4, characterized in that: In step S4, the quality of the algorithm solution is judged according to the evaluation function, and the best solution of the algorithm is polled and saved until the algorithm is terminated and the optimal solution is output as the final LED parameter configuration solution.

6. The signal uniformity optimization method for random LED distribution in an indoor visible light communication system according to claim 1, characterized in that: In step S5, the optimal parameter configuration obtained by optimization in step S4 is brought into the system model of step S3 to obtain the distribution of indoor optimized received illumination and received optical power, thereby achieving uniform distribution of optical signals of random LEDs in the indoor visible light communication system.

Images