Power optimization method based on LED layout of visible light communication system
By applying the reverse learning strategy and the better set strategy in the visible light communication system, the LED layout is optimized, the problem of uneven optical power distribution is solved, the communication quality and fairness are improved, and the system cost is reduced.
Patent Information
- Application Number
- CN202411649433.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-19
- Publication Date
- 2025-03-28
AI Technical Summary
There is a problem of uneven optical power distribution in existing visible light communication systems, which affects communication quality and fairness.
An improved Harris Eagle optimization algorithm based on reverse learning strategy and good point set strategy is adopted to optimize the LED layout to improve the uniformity of the optical signal and the communication quality.
By optimizing the LED layout, the uniformity of received optical power is improved, signal transmission performance is enhanced, communication quality and fairness of visible light communication systems are improved, and system costs are reduced.
Smart Images
Figure CN119727909A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the field of visible light communication and relates to an optical signal coverage and power uniformity optimization method based on visible light communication (VLC) light emitting diode (LED) layout. Background Art
[0002] With the rapid development of information technology, traditional wireless communication technology faces problems such as tight spectrum resources and electromagnetic interference. In this context, visible light communication (VLC), as an emerging wireless communication technology, has become a hot topic of research due to its advantages such as rich spectrum resources, high security, energy saving and no electromagnetic interference. VLC technology uses light-emitting diodes (LEDs) as light sources and transmits information through high-speed light intensity modulation, thus realizing the dual functions of lighting and communication. As the light source of the VLC system, LED has the advantages of high efficiency, long life, green environmental protection, and high response rate. With the popularization of solid-state lighting technology, LED has gradually become the main lighting source. However, at present, there is often uneven distribution of optical power in indoor visible light communication systems. Optical power uniformity can provide a more stable communication environment and improve the communication quality and fairness of the VLC system. Therefore, in order to ensure the uniform distribution of optical signals, it is indispensable to optimize the uniformity of the system.
[0003] At present, the research on the uniformity of optical power distribution for visible light communication is mainly based on a variety of LED layout schemes, such as square, circular and mixed layouts. However, in practical applications, it is more necessary to optimize the existing random LED layout to achieve a balance between cost-effectiveness and system performance. In addition, the development of optimization algorithms has also shifted from classical optimization algorithms to heuristic algorithms. These algorithms have advantages in search range and accuracy, but they also have problems such as slow convergence and easy to fall into local optimality.
[0004] The present invention aims to propose a new optimization method, which synchronizes the LED layout and power uniformity of the VLC system by improving the layout strategy, so as to improve the optical signal uniformity and communication quality of the system and reduce the system cost. Summary of the invention
[0005] In view of this, the purpose of the present invention is to provide a power optimization method based on the LED layout of a visible light communication system, which uses a reverse learning strategy to fuse the best point set strategy to initialize the population, and combines the improved Harris Eagle optimization algorithm that introduces an adaptive energy factor to achieve the optimal parameter configuration of the LED layout, so as to improve the uniformity of the received optical power, enhance the signal transmission performance, and improve the communication quality and communication fairness of the visible light communication system.
[0006] In order to achieve the above object, the present invention provides the following technical solutions:
[0007] A power optimization method based on LED layout of a visible light communication system comprises the following steps:
[0008] S1: Use the good point set strategy to initialize the random distribution of LEDs in the simulated indoor visible light communication system;
[0009] S2: Integrate the reverse learning strategy, consider the reverse individuals of the population initialized in step S1, select individuals closer to the optimal particle and put them into the initial population to improve the quality of the initial population;
[0010] S3: input the intrinsic parameters of the visible light communication system and the relevant parameters of the improved adaptive Harris Hawk optimization algorithm;
[0011] S4: constructing a visible light communication system power model under the initial LED layout according to the inputs of step S2 and step S3;
[0012] S5: Based on the system model in step S4, parameters are adjusted by using the designed improved Harris Eagle optimization algorithm with the goal of optimizing optical coverage and received optical power;
[0013] S6: According to the optimization result of step S5, relevant parameters are updated to obtain the best LED layout mode and the optimized light coverage and received light power distribution.
[0014] Furthermore, in step S1, the number of LEDs at the transmitting end is input, and then the random distribution of LEDs in the simulated visible light communication system is initialized using the good point set strategy. The specific simulated distribution is as follows:
[0015] LED in communication space ceiling plane R n M LEDs are installed in the ceiling plane. n is an n-dimensional Euclidean space. n Point l n (x n ,y n )∈R n ,n=1,...,M, then the point set in the space can be expressed as follows:
[0016] P m (k)={(l1k),...,(l n k)}, k=1,2,...m
[0017] Take point l k =2cos(2πk / p), 1≤k≤n, where p is the smallest prime number satisfying (p-3) / 2≥n, then P m (k) is a set of good points, called lk is the best point. Deviation satisfy Where C(l,ε) is a constant that is only related to l and ε (ε>0).
[0018] The initial LED distribution positions simulated by the good point set strategy are represented by good points.
[0019] Further, in step S2, the reverse learning strategy is integrated, and the reverse individuals of the population in step S1 are considered. Step S2 specifically includes:
[0020] S21: The good point set P after the good point set method operation in step S1 m (k), consider its reverse individual P m '(k), its calculation formula is expressed as:
[0021] P m '(k)=lb+ub-P m (k)
[0022] Among them, lb and ub are the boundaries of the initial search range respectively.
[0023] S22: Consider the reverse individuals. The probability that they are closer to the optimal particle is 50%. Select the individuals closer to the optimal particle and put them into the initial population to obtain the reverse population P. m '(k), calculate the fitness values of all individuals, sort them from large to small, and select the top M individuals with the highest fitness values to form the initial population participating in the optimization.
[0024] S23: Select individuals that are closer to the optimal particle and put them into the initial population to improve the quality of the initial population.
[0025] Further, in step S3, the input parameters related to the visible light communication system are as follows:
[0026] S31: The inherent visible light communication system parameters include: communication space size, transmitting end LED device inherent parameters and their optimization ranges, and receiving end device inherent parameters;
[0027] S32: The relevant parameters of the improved adaptive Harris Hawk optimization algorithm include: the initialization population size, the algorithm search dimension, the maximum number of iterations and the random parameters.
[0028] Further, in step S4, the random distribution result of LEDs on the ceiling of the simulated visible light communication system is initialized by using the good point set method in step S2, and the model of the visible light communication system is built in combination with the inherent parameter input in step S3. The system model specifically includes:
[0029] S41: LED is regarded as Lambert radiation source, and the line-of-sight link and non-line-of-sight link (one reflection) channels in the communication space are considered at the same time. The light coverage and received optical power distribution in the communication space are calculated:
[0030] The light coverage ratio is R C To verify that the coverage area has improved:
[0031]
[0032] Among them, A FOV Indicates the communication space area directly covered by the LED field of view, A TOTAL The received optical power is calculated using the quality factor Q of the received power. SNR To evaluate:
[0033]
[0034] Where SNR = (R p P r ) 2 / σ 2 , represents the signal-to-noise ratio in the plane, R p and σ 2 They represent the photoelectric conversion efficiency and the received interference signal respectively.
[0035] S42:P r is the received optical power, which can be expressed as:
[0036] P r =P r,LOS +P r,NLOS =P t ·H LOS +∫ wall P t dH NLOS
[0037] Among them, P t Indicates the transmitted optical power, H LOS The line-of-sight link channel gain of the LED can be expressed as:
[0038]
[0039] Where A represents the effective receiving area of the receiver PD; d is the distance between the LED light source and the PD; φ is the irradiance angle of the LOS link; ψ is the incident angle of the LOS link; ψ c is the field of view of PD; T s (ψ) is the filter gain, g(ψ) is the concentrator gain, both of which are usually defined as 1; m is the Lambertian radiation order, which is defined as:
[0040] m=-log2 / log(cos(φ 1 / 2 ))
[0041] Among them, φ 1 / 2 It is the half-power half-angle of the LED, and for the cosine of the radiation angle of the LED and the cosine of the light incident angle of the PD, cos(φ)=cos(ψ)=h / d, where h represents the vertical height from the emitting end to the PD.
[0042] H NLOS represents the non-line-of-sight link channel gain of the LED. Consider dividing the wall into several areas of ldm 2 The center of each grid is a reflection point. The NLOS channel gain passing through the reflection point can be expressed as:
[0043]
[0044] Where d1 and d2 are the straight-line distances from the transmitting point to the reflection point and from the reflection point to the receiving point before and after reflection, respectively; φ N is the irradiance angle of the NLOS link; ψ N is the incident angle of the NLOS link; α and β are the incident angles of the reflection point and the receiving point respectively; ρ is the wall reflection coefficient in the communication space; dA wall is the reflection area.
[0045] Further, in step S5, based on the system model of step S4, the adaptive Harris Hawk optimization algorithm improved in step S5 is used to adjust parameters with the goal of optimizing light coverage and received light power to obtain the optimal LED layout mode. Step S5 specifically includes:
[0046] The adaptive escape energy factor ω(t) is introduced into the traditional Harris Hawk optimization algorithm, that is, based on the escape energy of each individual in the current population and the current number of iterations, the escape energy can be reduced at a faster rate in the early stage of the search and converge quickly in the later stage of the search after introducing an adaptive weight. The correct switching of the best behavior mode at different stages of the smoothing algorithm promotes the global optimization performance of the Harris Hawk optimization algorithm and quickly finds the optimal solution. The improved adaptive escape energy is expressed as:
[0047]
[0048] Where t represents the current iteration number, T represents the maximum iteration number, E0 represents the initial escape energy, and ω(t) represents the adaptive weight factor, which is calculated as follows:
[0049]
[0050] Further, in step S6, according to the optimization result of step S5, the quality of the iteration result is judged based on the light coverage and the uniformity of the received light power as evaluation indicators, and the optimal solution of the relevant parameters is polled and updated until the maximum number of iterations is reached, and the optimal solution is output to obtain the optimal LED layout mode and the optimized light coverage and received light power distribution.
[0051] The beneficial effect of the present invention is that the method of the present invention optimizes the configuration parameters of the LED layout by designing an adaptive Harris Hawk optimization algorithm based on the fusion of multiple strategies, so that the VLC technology in the communication space can maximize the communication quality while using a smaller number of LEDs, thereby improving the uniformity of the optical signal of the system and effectively reducing the system cost. The improved adaptive Harris Hawk optimization algorithm has the advantages of few parameters, easy implementation, smooth global search switching and local development strategy, and efficient convergence speed and accuracy. The optimization results show that this method can effectively reduce the fluctuation of the received optical power in the communication space and achieve good uniformity optimization effect. BRIEF DESCRIPTION OF THE DRAWINGS
[0052] In order to make the purpose, technical solution and beneficial effects of the present invention clearer, the present invention provides the following drawings for illustration:
[0053] Figure 1 The following is a summary of the present invention;
[0054] Figure 2 This is a model diagram of the visible light communication system described in the present invention;
[0055] Figure 3 This is a flow chart of solving the optimal related parameters by using the improved adaptive Harris Hawk optimization algorithm integrating the good point set and reverse learning strategy described in the present invention. DETAILED DESCRIPTION
[0056] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
[0057] The present invention configures LED layout parameters according to the size of the communication space, simulates the random distribution of LEDs based on the fusion of optimal point sets and reverse learning strategies; combines the improved adaptive Harris Eagle optimization algorithm to optimize the layout of LEDs in the communication space, thereby improving the uniformity of received optical power, eliminating communication blind spots and weak communication areas in the communication space, and has wide applicability.
[0058] like Figure 2 As shown, the power optimization method based on LED layout of visible light communication system described in the present invention specifically includes the following steps:
[0059] 1. Good point set strategy simulates random distribution
[0060] Considering a finite number of LEDs in practice, the number of transmitting LEDs M is set in the communication space of l×w×h, and then the random distribution of LEDs in the communication space is simulated using the good point set strategy.
[0061] LED in communication space ceiling plane R n M LEDs are installed in the ceiling plane. n is an n-dimensional Euclidean space. n Point l n (x n ,y n )∈R n ,n=1,...,M, then the point set in the space can be expressed as follows:
[0062] P m (k)={(l1k),...,(l n k)}, k=1,2,...m
[0063] Take point l k =2cos(2πk / p), 1≤k≤n, where p is the smallest prime number satisfying (p-3) / 2≥n, then P m (k) is a set of good points, called l k is the best point. Deviation satisfy Where C(l,ε) is a constant that is only related to l and ε (ε>0).
[0064] The initial LED distribution positions simulated by the good point set strategy are represented by good points.
[0065] 2. Integrate reverse learning strategy
[0066] 2.1. The good point set P after the good point set method operation in step S1 m (k), consider its reverse individual P m '(k), its calculation formula is expressed as:
[0067] P m '(k)=lb+ub-P m (k)
[0068] Among them, lb and ub are the boundaries of the initial search range respectively.
[0069] 2.2. Consider the reverse individuals. The probability that the two are closer to the optimal particle is 50%. Select the individuals closer to the optimal particle and put them into the initial population to obtain the reverse population P. m '(k), calculate the fitness values of all individuals, sort them from large to small, and select the top M individuals with the highest fitness values to form the initial population participating in the optimization.
[0070] 2.3. Select individuals that are closer to the optimal particle and put them into the initial population to improve the quality of the initial population.
[0071] 3. Input the intrinsic parameters of the visible light communication system and the related parameters of the improved adaptive Harris Hawk optimization algorithm. Table 1 gives the intrinsic parameters of the visible light communication system, and Table 2 gives the related parameters of the improved adaptive Harris Hawk optimization algorithm.
[0072] Table 1
[0073]
[0074] Table 2
[0075]
[0076] 4. According to the input system parameters, build a visible light communication system power model. The visible light communication system model is as follows: Figure 2 As shown. The size of the communication space is l×w×h. Consider a visible light communication system with M LEDs. The receiving end is equipped with a photodiode (PD) to receive the optical signal. The LED is regarded as a Lambert radiation source. Considering the line-of-sight link and non-line-of-sight link (one-reflection) channel in the communication space, the light coverage and received optical power distribution in the communication space are calculated:
[0077] The light coverage ratio is R C To verify that the coverage area has improved:
[0078]
[0079] Among them, A FOV Indicates the communication space area directly covered by the LED field of view, A TOTAL The received optical power is calculated using the quality factor Q of the received power. SNR To evaluate:
[0080]
[0081] Where SNR = (R p P r ) 2 / σ 2 , represents the signal-to-noise ratio in the plane, R p and σ 2 Respectively represent the photoelectric conversion efficiency and the received interference signal, P r is the received optical power, which can be expressed as:
[0082] P r =Pr,LOS +P r,NLOS =P t ·H LOS +∫ wall P t dH NLOS
[0083] Among them, P t Indicates the transmitted optical power, H LOS The line-of-sight link channel gain of the LED can be expressed as:
[0084]
[0085] Where A represents the effective receiving area of the receiver PD; d is the distance between the LED light source and the PD; φ is the irradiance angle of the LOS link; ψ is the incident angle of the LOS link; ψ c is the field of view of PD; T s (ψ) is the filter gain, g(ψ) is the concentrator gain, both of which are usually defined as 1; m is the Lambertian radiation order, which is defined as:
[0086] m=-log2 / log(cos(φ 1 / 2 ))
[0087] Among them, φ 1 / 2 It is the half-power half-angle of the LED, and for the cosine of the radiation angle of the LED and the cosine of the light incident angle of the PD, cos(φ)=cos(ψ)=h / d, where h represents the vertical height from the emitting end to the PD.
[0088] H NLOS represents the non-line-of-sight link channel gain of the LED. Consider dividing the wall into several areas of ldm 2 The center of each grid is a reflection point. The NLOS channel gain passing through the reflection point can be expressed as:
[0089]
[0090] Where d1 and d2 are the straight-line distances from the transmitting point to the reflection point and from the reflection point to the receiving point before and after reflection, respectively; φ N is the irradiance angle of the NLOS link; ψ N is the incident angle of the NLOS link; α and β are the incident angles of the reflection point and the receiving point respectively; ρ is the wall reflection coefficient in the communication space; dA wall is the reflection area.
[0091] 5. Based on the system model of step S4, in view of the uneven distribution of original optical power in the communication space, the parameters of the LED layout in the space are adjusted by using the improved adaptive Harris Hawk optimization algorithm with the goal of optimizing light coverage and received optical power to obtain the optimal LED layout mode. The improved adaptive Harris Hawk optimization algorithm can smoothly switch the optimal behavior mode of the algorithm at different stages, promote the global optimization performance of the Harris Hawk optimization algorithm, and quickly find the optimal solution.
[0092] In order to imitate the social behavior of Harris Hawk when hunting, the traditional Harris Hawk optimization algorithm is summarized into four strategies: soft encirclement, hard encirclement, soft siege (soft siege with progressive rapid dive), and hard siege (hard siege with progressive rapid dive). These four strategies simulate the different hunting strategies chosen by Harris Hawk when hunting prey with different escape energies E and escape probabilities ep, that is, four different strategies are selected under different escape energies E and escape probabilities ep to search for the optimal LED layout position within the search range. This requires the algorithm to have stronger global exploration capabilities in the early stages of the application and more sophisticated local development capabilities in the later stages. The linear energy factor in the traditional Harris Hawk algorithm is used as the basis for switching between global exploration and local development, but its linear characteristics lead to a linear decrease in the escape energy E, resulting in the consistency of the energy change during the iteration process, which will affect the correct selection of the optimal behavior model of the algorithm at different stages, and ultimately lead to the disadvantage of being easy to fall into the local optimum and difficult to escape. Therefore, in order to improve the search ability and convergence performance of the algorithm, an adaptive escape energy factor ω(t) is introduced, that is, based on the escape energy of each individual in the current population and the current number of iterations, the escape energy can be reduced at a faster rate in the early stage of the search and converge quickly in the later stage of the search after introducing an adaptive weight. The improved adaptive escape energy is expressed as:
[0093]
[0094] Where t represents the current iteration number, T represents the maximum iteration number, E0 represents the initial escape energy, and ω(t) represents the adaptive weight factor, which is calculated as follows:
[0095]
[0096] The process framework diagram of solving the optimal related parameters of the improved adaptive Harris Hawk optimization algorithm is as follows: Figure 2 Given.
[0097] The following will be combined with the attached Figure 3 The power optimization method based on the LED layout of the visible light communication system of the present invention is introduced in more detail. The specific process includes the following steps:
[0098] Input: Intrinsic parameters of the visible light communication system (including: the size of the communication space, the intrinsic parameters of the transmitter LED device, the intrinsic parameters of the receiver PD, etc.) and related parameters of the improved adaptive Harris Eagle optimization algorithm (including: search dimension, initialization population size, maximum number of iterations, search range, etc.);
[0099] Output: LED layout optimal parameter configuration;
[0100] Step 1: Build a visible light communication system power model including line-of-sight link and non-line-of-sight link channel gain based on the input;
[0101] Step 2: Determine the population size, maximum number of iterations, upper and lower limits of the search space, and initial energy of the prey. The local optimal value of each prey position is the current position, and the global optimal value is the optimal value of the current population;
[0102] Step 3: Initialize the initial distribution of LEDs in the simulated indoor visible light communication system by fusing the good point set and the reverse learning strategy, that is, the initial population of the improved adaptive Harris Hawk optimization algorithm;
[0103] Step 4: Take the individual position with the best fitness as the initial position of the current prey;
[0104] Step 5: The Harris Hawk selects a search strategy based on the escape energy E and the escape probability ep of the prey, and determines whether to conduct global exploration or detailed local development. It also selects the position update formulas of the four strategies of soft encirclement, hard encirclement, soft siege and hard siege to generate new solutions, i.e., the configuration parameters of the LED, according to different situations.
[0105] Step 6: Calculate the individual fitness value after the updated position, and use the individual position with a better fitness value as the new position of the prey;
[0106] Step 7: Update the optimal individual and group positions of Harris's hawks, which are the new solutions;
[0107] Step 8: According to the current number of iterations, calculate the current adaptive weight factor ω(t), as well as the corresponding escape energy E and escape probability ep of the next generation of iterative population;
[0108] Step 9: Return to step 5 and select a suitable strategy for position update according to the current escape energy E and escape probability ep;
[0109] Step 10: Determine whether the algorithm meets the termination condition. If so, stop the algorithm and execute step 11; if not, return to step 5;
[0110] Step 11: Output the optimal solution as the final LED layout pattern.
[0111] 6. Use light coverage and received light power uniformity as evaluation indicators to judge the quality of the iteration results, poll and update the optimal solution of the relevant parameters until the maximum number of iterations is reached, output the optimal solution, and obtain the best LED layout mode and the optimized light coverage and received light power distribution.
[0112] The light coverage ratio is R C To verify whether the coverage area has improved. C Indicates the coverage area ratio under LED layout, R C The larger the value, the better the light coverage in the communication space.
[0113]
[0114] Among them, A FOV Indicates the communication space area directly covered by the LED field of view, A TOTAL The received optical power is calculated using the quality factor Q of the received power. SNR To evaluate the fairness of the optical power received by users at different locations in the communication space.
[0115]
[0116] Where SNR = (R p P r ) 2 / σ 2 , represents the signal-to-noise ratio in the plane, R p and σ 2 They represent the photoelectric conversion efficiency and the received interference signal respectively.
[0117] 7. The final layout of LEDs optimized by combining the system model and the improved adaptive Harris Eagle optimization algorithm can obtain a higher light coverage rate and uniformly distributed received light power in the communication space.
[0118] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made in form and details without departing from the scope defined by the claims of the present invention.
Claims
1. A method for optimizing optical signal coverage and power uniformity based on visible light communication LED layout, characterized in that: The following steps are involved: S1: Use the good point set strategy to initialize the random distribution of LEDs in the simulated indoor visible light communication system; S2: Integrate the reverse learning strategy, consider the reverse individuals of the population in step S1, select individuals closer to the optimal particle and put them into the initial population to improve the quality of the initial population; S3: input the intrinsic parameters of the visible light communication system and the related parameters of the improved adaptive Harris Hawk optimization algorithm; S4: Building a visible light communication system power model including line-of-sight link and non-line-of-sight link channel gains according to the inputs of step S2 and step S3; S5: Based on the system model in step S4, parameters are adjusted by using an improved adaptive Harris Hawk optimization algorithm with the goal of optimizing optical coverage and received optical power; S6: According to the optimization result of step S5, relevant parameters are updated to obtain the best LED layout mode and the optimized light coverage and received light power distribution.
2. The optical signal coverage and power uniformity optimization method based on visible light communication LED layout according to claim 1 is characterized in that: In step S1, the number of LEDs at the transmitting end is input, and then the random distribution of LEDs in the simulated visible light communication system is initialized using the good point set strategy. The step S1 specifically includes: LED in communication space ceiling plane R n M LEDs are installed in the ceiling plane. n is an n-dimensional Euclidean space. n Point l n (x n ,y n )∈R n ,n=1,...,M, then the point set in the space can be expressed as follows: P m (k)={(l1k),...,(l n k)},k=1,2,...m Take point l k =2cos(2πk / p), 1≤k≤n, where p is the smallest prime number satisfying (p-3) / 2≥n, then P m (k) is a set of good points, called l k is the best point. Deviation satisfy Where C(l,ε) is a constant that is only related to l and ε (ε>0).
3. The optical signal coverage and power uniformity optimization method based on visible light communication LED layout according to claim 1 is characterized in that: In step S2, the reverse learning strategy is integrated, and the reverse individuals of the population in step S1 are considered. Step S2 specifically includes: S21: The good point set P after the good point set method operation in step S1 m (k), consider its reverse individual P m '(k), its calculation formula is expressed as: P m '(k)=lb+ub-P m (k) Among them, lb and ub are the boundaries of the initial search range respectively; S22: Consider the reverse individuals. The probability that they are closer to the optimal particle is 50%. Select the individuals closer to the optimal particle and put them into the initial population to obtain the reverse population P. m '(k), calculate the fitness values of all individuals, sort them from large to small, and select the M individuals with the highest fitness values to form the initial population participating in the optimization; S23: Select individuals that are closer to the optimal particle and put them into the initial population to improve the quality of the initial population.
4. The optical signal coverage and power uniformity optimization method based on visible light communication LED layout according to claim 1 is characterized in that: In step S3, the input intrinsic parameters of the visible light communication system include: the size of the communication space, the intrinsic parameters of the LED device at the transmitting end and its optimization range, and the intrinsic parameters of the device at the receiving end; The relevant parameters of the improved adaptive Harris Hawk optimization algorithm include: the number of initialized populations, the algorithm search dimension, the maximum number of iterations, and random parameters.
5. The optical signal coverage and power uniformity optimization method based on visible light communication LED layout according to claim 1 is characterized in that: In step S4, the random distribution result of LEDs on the ceiling in the simulated visible light communication system is initialized by the good point set method in step S2, and the model of the visible light communication system is built by combining the inherent parameter input in step S3. The LED is regarded as a Lambert radiation source, and the line-of-sight link and non-line-of-sight link (one reflection) channels in the communication space are considered at the same time, and the light coverage and received optical power distribution in the communication space are calculated: The light coverage ratio is R C To verify that the coverage area has improved: Among them, A FOV Indicates the communication space area directly covered by the LED field of view, A TOTAL Represents the plane area within the entire communication space; the received optical power uses the quality factor Q of the received power SNR To evaluate: Where SNR = (R p P r ) 2 / σ 2 , represents the signal-to-noise ratio in the plane, R p and σ 2 Respectively represent the photoelectric conversion efficiency and the received interference signal, P r is the received optical power, which can be expressed as: P r =P r,LOS +P r,NLOS =P t ·H LOS +∫ wall P t dH NLOS Among them, P t Indicates the transmitted optical power, H LOS The line-of-sight link channel gain of the LED can be expressed as: Where A represents the effective receiving area of the receiver PD; d is the distance between the LED light source and the PD; φ is the irradiance angle of the LOS link; ψ is the incident angle of the LOS link; ψ c is the field of view of PD; T s (ψ) is the filter gain, g(ψ) is the concentrator gain, both of which are usually defined as 1; m is the Lambertian radiation order, which is defined as: m=-log2 / log(cos(φ 1 / 2 )) Among them, φ 1 / 2 is the half-power half angle of the LED, and for the cosine of the radiation angle of the LED and the cosine of the light incident angle of the PD, cos(φ)=cos(ψ)=h / d, where h represents the vertical height from the emitter to the PD; H NLOS represents the non-line-of-sight link channel gain of the LED. Consider dividing the wall into several areas of ldm 2 The center of each grid is a reflection point. The NLOS channel gain passing through the reflection point can be expressed as: Where d1 and d2 are the straight-line distances from the transmitting point to the reflection point and from the reflection point to the receiving point before and after reflection, respectively; φ N is the irradiance angle of the NLOS link; ψ N is the incident angle of the NLOS link; α and β are the incident angles of the reflection point and the receiving point respectively; ρ is the wall reflection coefficient in the communication space; dA wall is the reflection area.
6. The optical signal coverage and power uniformity optimization method based on visible light communication LED layout according to claim 1 is characterized in that: In step S5, based on the system model of step S4, the adaptive Harris Hawk optimization algorithm improved in step S5 is used to adjust parameters with the goal of optimizing light coverage and received light power to obtain the optimal LED layout mode. Step S5 specifically includes: An adaptive weight factor ω(t) is introduced into the traditional Harris Hawk optimization algorithm, that is, based on the escape energy of each individual in the current population and the current number of iterations, the escape energy can be reduced at a faster speed in the early stage of the search and converge quickly in the later stage of the search after introducing an adaptive weight, so as to smoothly switch the optimal behavior mode of the algorithm at different stages, promote the global optimization performance of the Harris Hawk optimization algorithm, and quickly find the optimal solution. The improved adaptive escape energy factor is expressed as: Where t represents the current iteration number, T represents the maximum iteration number, E0 represents the initial escape energy, and ω(t) represents the adaptive weight factor, which is calculated as follows:
7. The optical signal coverage and power uniformity optimization method based on visible light communication LED layout according to claim 1 is characterized in that: In step S6, according to the optimization result of step S5, the quality of the iteration result is judged based on the light coverage and the uniformity of the received light power as evaluation indicators, and the optimal solution of the relevant parameters is updated in a polling manner until the maximum number of iterations is reached. The optimal solution is output and the optimal LED layout mode and the optimized light coverage and received light power distribution are obtained.