A Survey of Channel Models
for Underwater Optical Wireless
Communication
IWOW 2013
Laura Johnson, Roger Green, Mark Leeson
Overview
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Motivation
Underwater channel fundamentals
Natural waters
Criteria for models
Assessment of models
Summary of findings
Conclusion
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Motivation
• Lowered attenuation for visible wavelengths
• Short- to mid-ranged market
visible band
106
Attenuation
coefficient
(m-1)
10-2 -14
10
10-10
10-6
10-2
• Defining real world scenarios
102
106
3
Underwater Channel Fundamentals
Compared to free-space:
• Generally higher attenuation
• Only 1 transmission window
• Greater variability
4
Natural Waters: Composition Variability
1
2
3
Image: Google Earth (accessed 25/09/13)
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Natural Waters: Composition Variability
• Organic composition
linked to chlorophyll
concentration.
• Chlorophyll varies with:
– time,
– laterally,
– with depth.
attenuation coefficient (m-1)
0
0.1
0.2
0
-50
ocean
-100
depth
(m)
-150
-200
-250
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Natural Waters: Refractive Variability
refractive index
1.341
1.344
1.347
0
200
ocean
400
depth
(m)
600
• Fluctuations:
– Small-medium,
(scattering)
– Large (turbulence).
• Gradient with depth
800
1000
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Criteria for Models
A. Ability to model:
– varying composition and refractive index with
depth,
– localised turbulence,
– sunlight at the receiver.
B. Gives information about: power reduction, spatial
light distribution, temporal distribution.
C: Simplicity, ease of use
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Assessment of Modelling Schemes
• Qualitative study due to nature of schemes
• The modelling schemes of interest:
- Beer’s Law
- Radiative transfer equation
- Approximate analytical solution of the RTE
- Numerical solutions (Monte Carlo, Discrete
ordinates, imbedding invariants)
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Summary of Findings
A. Ability to model:
– Varying composition and refractive index with
depth.
All capable (no orientation issues for Monte
Carlo and RTE)
– Localised turbulence
RTE and solutions
– Sunlight at the receiver
RTE and solutions (orientation issues)
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Summary of Findings
B. Gives information about:
– power reduction, all show
– Spatial, RTE and solutions
– Temporal RTE and numerical solutions
C. Simplicity, ease of use
– Beer’s law is simplest
– RTE most complex, cannot be solved
– Numerical solutions are a compromise
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Conclusion
• Monte Carlo most fit for purpose
– Relatively simple scheme to describe complex
situations
– Works at any orientation
• Resulting work
– Modelling in mid-ranged ocean links
– “Monte Carlo Simulation for Underwater Optical
Wireless Communications” (Poster session)
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Any Questions?
E-mail: laura.j.johnson@warwick.ac.uk
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