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摘要:
针对可见光通信系统存在的光照度和接收平面功率分配不均匀的问题，提出了基于多种群遗传算法的光源布局模型。以15个LED灯为例，构造和接收功率方差有关的适应度函数，采用多个种群协同进化的方式，对LED灯的位置坐标信息进行寻优。经Matlab R2016a仿真结果表明，优化后的功率分布直观上更均匀，功率方差达到1.5744 dBm，照度范围为889 lx ~1009 lx，照度均匀度亦达到91.73%，均优于传统遗传算法优化的布局和多种群遗传算法优化的矩形布局，从而为系统优化LED灯布局使得用户获得更好的通信体验提供了一种借鉴方案。
Abstract:To solve the unevenness of distributions of optical illuminance and power in visible light communication system, a light source layout based on multi-population genetic algorithm is proposed. Taking 15 LED lamps as an example, the position coordinates were optimized under the fitness function related to variance of received power through the co-evolution of multi-populations. The simulation results on Matlab R2016a showed that, after being optimized, the distribution of power was evener intuitively, the variance of power reached 1.5744 dBm, the illuminance fell in a range between 889 lx and 1009 lx and the uniformity ratio of illuminance was 91.73%, all of which were better than those of the layout optimized by traditional genetic algorithm and the rectangular layout optimized by multi-population genetic algorithm. This experiment provides a feasible solution for optimizing the visible light communication system so that users can have a more comfortable communication trip in this system.
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Overview: With the rapid development of technology, traditional wireless communication can't quite meet the needs of fast-growing data service gradually. Researchers are seeking new ways to overcome this conundrum. Since the light communication has the advantages of high SNR, high modulation rate and high security, it is promising to achieve a new height in data communication system. Visible light communication also becomes a hot field for scientists to explore. However, there are many problems to solve in order to make a perfect visible light communication system. Due to the LED lamps discretely mounted on the ceiling, distributions of illuminance and power are incredibly uneven on the receiving plane, so that user experiences can't be exhilarating. To create a better atmosphere for communication, a layout optimized by multi-population genetic algorithm is proposed. Traditional genetic algorithm may get involved in premature convergence or running into a local optimization solution. The strategy of multi-population co-evolution is introduced into multi-population genetic algorithm to get rid of these problems. The immigration operation strengthens the bond of multi-populations, and the elitism strategy makes sure that the result is found out under our request. A room with dimensions 5 m×3 m×3 m plays the role of simulation model. Particularly, the base of the model is rectangular, which is different from most of the previous studies. 15 specific LED lamps are mounted on the ceiling and serve as sources of optical illuminance and power. The position coordinates of lamps make up chromosome individuals. A function related to the variance of the receiving power is constructed as the fitness function. After being optimized by the algorithm, parameters are plugged into the model simulated on Matlab R2016a. Furthermore, to illustrate the effectiveness of the proposed method, layout optimized by traditional genetic algorithm and rectangular layout optimized by multi-population genetic algorithm are taken as comparisons. The diagrams show that parameters of the proposed method are the evenest intuitively. Through the numerical analysis, the variance of power reaches 1.5744 dBm, the illuminance falls in a range between 889 lx and 1009 lx and the uniformity ratio of illuminance is 91.73%, all of these parameters in multi-population genetic algorithm (MPGA) are the best among the three methods mentioned above. Therefore, the feasibility of this optimization method is evidently proved by this experiment. It can provide references when people tend to find a way to properly design the LED layout, thus finally contributes to building the visible light communication system.
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图 4 MPGA优化参数分布图。(a)照度；(b)功率
Figure 4. Distributions of parameters under the proposed layout optimized by MPGA. (a) Illuminance; (b) Power
图 5 GA优化参数分布图。(a)照度；(b)功率
Figure 5. Distributions of parameters under the proposed layout optimized by GA. (a) Illuminance; (b) Power
图 6 MPGA优化矩形布局参数分布图。(a)照度；(b)功率
Figure 6. Distributions of parameters under rectangular layout optimized by MPGA. (a) Illuminance; (b) Power
图 7 LED灯布局图。(a) MPGA优化；(b) GA优化；(c)矩形布局
Figure 7. Layouts of LED lamps. (a) Optimized by MPGA; (b) Opimized by GA; (c) The rectangular layout
表 1 系统仿真参数
Table 1. Parameters of the simulated system
Parameter Value Room size 5 m×3 m×3 m Distance between the ceiling and the receiving plane h/m 2.15 LED lamp number 15 Central luminous intensity of LED I(0)/cd 23.81 Single LED bulb power Pt/mW 452 Semi-angle at half power Φ1/2/(°) 60 Number of LED in each lamp 7×7 Physical area A/cm2 1 Field of view at receiver Ψc/(°) 85 Gain of optical filter Ts(Ψ) 1 Reflective index of concentrator n 1.5 Photodiode responsivity 0.54 
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表 2 算法参数表
Table 2. Parameters of the algorithm
Parameter Value The number of populations (PMP) 10 Population size (CNIND) 40 Dimension of variables (NVAR) 7 Precision of variables (BPRECI) 20 Generation gap (GGAP) 0.9 Maximum generation (MAXGEN) 15 Probability of crossover (pc) 0.7+(0.9-0.7)×rand(PMP, 1) Probability of mutation (pm) 0.001+(0.05-0.001)×rand(PMP, 1)
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表 3 布局参数表
Table 3. Parameters of layouts
Layout Minimum receiving power/dBm Maximum receiving power/dBm Average receiving power/dBm Received power variance/dBm Illumination range/lx Uniformity ratio of illuminance/% The proposed layout optimized by MPGA -1.6189 -1.0705 -1.2458 1.5744 889~1009 91.73 The proposed layout optimized by GA -1.5932 -0.9193 -1.1183 2.0802 895~1045 89.60 The rectangular layout optimized by MPGA -1.7892 -0.4947 -0.9395 6.81 855~1152 82.06
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