Layout Optimization for Pointtopoint Wireless Optical Networks via Simulated Annealing

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Layout Optimization for Point-to-point Wireless
Optical Networks via Simulated Annealing
Genetic Algorithm

• Present by Min Fang

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Guideline

• Problem Specification System Modeling
• Simulated Annealing
• Genetic Algorithm
• Results Conclusion

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Problem Specification System Modeling

• Introduction to wireless optical networks
• Problem specification
• System modeling

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Wireless Optical Networks
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Wireless Optical Networks

• Advantages
• Speed
• Cost
• Convenience
• Limitation
• Weather
• Moving buildings
• Flying Objects

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Problem Specification

• Configuration
• How to optimize the layout for a given area with
  given topology for possible laser locations and
  potential user locations?

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System Modeling

• Combinational Optimization Problem
• Configuration
• Two dimensional
• Potential subscriber
• Possible locations for base station(laser)
• System topology
• System constrains

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Simulated Annealing
Imperfect order, Perfect
order, has has higher energy
minimum energy
How to reach the low energy state anneal
the material. get it very hot gives atoms energy
to move around. cool it very slowly gently
restricts range of motion till everything freezes
into a low energy configuration.
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Simulated Annealing
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Simulated Annealing
Start with the system in a known configuration,
at known energy E T temperature hot frozen
false while ( ! frozen ) repeat Perturb
system slightly (e.g., move a particle) Compute
.E ,change in energy due to perturbation if (?E
lt 0 ) then accept this perturbation, this is
the new system config else accept maybe, with
probability exp(-?E/KT) until (the system is
in thermal equilibrium at this T) If (?E still
decreasing over the last few temperatures) then
T 0.9 T // cool the temperature do more
perturbations else frozen true return (final
configuration as low-energy solution)
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Simulated Annealing Implementation

• Configuration
• Cost function
• Move set
• Cooling schedule
• Thermal equilibrium
• When to freeze
• Melt

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Simulated Annealing Implementation

• Ray Tracing
• Data Structure
• Testing Annealer

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Simulated Annealing Result
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Simulated Annealing Result
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Simulated Annealing Result
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Simulated Annealing Result
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Genetic Algorithm

• Selection
• Crossover
• Mutation
• Elitism
• Scaling

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Genetic Algorithm
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Genetic Algorithm Result
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Genetic Algorithm Result
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Genetic Algorithm Result
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Results Conclusion

• Scalability
• Accuracy
• Performance
• Applicability
• Future Work