Research on Modeling and Application of Downhole Visible Light Channel

Fig. 5. Original stone walls and reflection surface element division diagrams. (a)(b) Original stone walls; (c)(d) reflection surface element division diagrams

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Fig. 6. Normal vectors of stone wall surfaces. (a) L wall; (b) R wall

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Fig. 7. Top view of receiver

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Fig. 8. First reflection powers and total received powers under different conditions

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Fig. 9. Positioning flow chart

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Fig. 10. BP neural network topology

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Fig. 11. Flow chart of GA-BP algorithm

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Fig. 12. GA-BP positioning effect under different conditions

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Table 1. Simulation parameters

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Table 1. Simulation parameters

Symbol	Parameter	Value
$A$	Physical area of photo-detector / $c m^{2}$	1
$ρ$	Reflection coefficient	0.5
$T^{*}$	Gain of optical filter	1
$n$	Refractive index of lens at PD	1.5
$Ω$	Field of view /（°）	70
$Φ_{1 / 2}$	Semi-angle at half power /（°）	60

Table 2. Average received power and average contribution ratio of first reflection

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Table 2. Average received power and average contribution ratio of first reflection

Simulation situation	Average total power /W	Average power of first reflection /W	Average contribution ratio of first reflection /%
Ideal condition	0.1487	0.0226	15.22
Uneven wall	0.1811	0.0550	30.37
Uneven wall and inclined receiver	0.1674	0.0493	29.48

Table 3. Training parameters of neural network
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Table 3. Training parameters of neural network
Parameter Value
Number of hidden layer nodes 20
Evolutionary algebra 50
Population size 10
Crossover probability 0.3
Mutation probability 0.1

Table 4. Root mean square positioning error
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Table 4. Root mean square positioning error
Channel model Only considering direct light Considering first reflection
Traditional channel model 2.49 2.51
Channel model built in this study 9.83 13.4

Table 5. Root mean square positioning errors and elapsed time of BP and GA-BP neural networks
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Table 5. Root mean square positioning errors and elapsed time of BP and GA-BP neural networks
Positioning algorithm Root mean square positioning error /cm Elapsed time /s
BP neural network 84.7 0.0156
GA-BP neural network 13.4 0.0137

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Sijie Lei, Xiaoli Hu, Ling Qin, Fengying Wang, Qian Wang. Research on Modeling and Application of Downhole Visible Light Channel[J]. Chinese Journal of Lasers, 2023, 50(11): 1106005

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Paper Information

Category: Fiber optics and optical communication

Received: Oct. 17, 2022

Accepted: Nov. 25, 2022

Published Online: May. 29, 2023

The Author Email: Xiaoli Hu (hxl7756@163.com), Ling Qin (qinling1979@imust.edu.cn)

DOI:10.3788/CJL221335

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Simulation parameters

Table 1. Simulation parameters

Table 2. Average received power and average contribution ratio of first reflection

Table 2. Average received power and average contribution ratio of first reflection

Table 3. Training parameters of neural network

Table 3. Training parameters of neural network

Table 4. Root mean square positioning error

Table 4. Root mean square positioning error

Table 5. Root mean square positioning errors and elapsed time of BP and GA-BP neural networks

Table 5. Root mean square positioning errors and elapsed time of BP and GA-BP neural networks