Joint Illumination-Communication Optimization in Visible Light Communication
Zhongqiang Yao, Hui Tian and Bo FanState Key Laboratory of Networking and Switching Technology
Beijing University of Posts and Telecommunications (BUPT), ChinaEmail: [email protected]
Presenter: Zhongqiang Yao
Outlines
2
4 Proposed Algorithm
1 Introduction
2 System Model
5 Simulation
3 Problem Formulation
Outlines
3
1 Introduction
Introduction
4
Introduction
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Illuminance
VLC
VLC : Visible Light Communication
Communication
Introduction
Motivation
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Jointly improving the illumination uniformity and communication signal quality is a challenging problem in VLC systems.
Existing works target at achieving uniform SNR or maximizing average SNR.
Conventional evolutionary algorithm (EA) is generally adopted as the optimization tool in references.
The goal is rendering uniform illumination and guaranteeing the minimum SNR threshold.
Outlines
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1 Introduction
2 System Model
System Model
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Indoor VLC system
๐ท (๐น ๐ )=โ๐=๐
๐ต
{๐๐๐ท ๐๐ฏ๐ (๐ ;๐บ๐ ,๐น ๐ )+โซ๐๐๐ท ๐๐ ๐ฏ ๐๐๐ (๐ ;๐บ๐ ,๐น ๐)}
System Model
Received Signal Power
Receiver SNR
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: the receiver: the LED lighting soruce: the number of LED lighting source: the line-of-sight(LOS) channel gain: the channel LOS gain on the first reflection
๐บ๐ต๐น (๐น ๐ )=ยฟยฟ: the detector responsivity: the shot noise variance: the thermal noise variance
(1)
(2)
System Model
Illuminance Feature
where is the horizontal illuminance of the light undergoing exactly order reflections.
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๐ฌ=โ๐=๐
โ
๐ฌ๐
๐ฌ๐
๐
(3)
Outlines
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1 Introduction
2 System Model
3 Problem Formulation
Problem Formulation
Optimization Factors The LED lighting source can be adjusted by a corresponding optimization factor
Received Signal Power The Eq.(1) can be rewritten as:
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๐ท (๐น ๐ )=โ๐=๐
๐ต
{๐๐๐ท ๐๐ฏ๐ (๐ ;๐บ๐ ,๐น ๐ )+โซ๐๐๐ท ๐๐ ๐ฏ ๐๐๐ (๐ ;๐บ๐ ,๐น ๐)} (4)
Problem Formulation
Evenness Measure Function
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๐ฟ (๐พ )=100โ100(max
๐๐ (๐ ๐)โmin
๐๐ (๐ ๐)
max๐๐ (๐ ๐)
)
To facilitate the description, the vectordenotes the optimization factor of the N LED lighting sources.
(5)
Problem Formulation
Mathematical formulation
UIR: The uniformity illuminance ratio (UIR) is defined as the ratio of the minimum to the average illuminance.
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Subject to:
(6)
Outlines
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4 Proposed Algorithm
1 Introduction
2 System Model
3 Problem Formulation
Proposed Algorithm
Harmony Annealing algorithm (HA) HA is constructed on the benchmark of improved harmony search (IHS) algorithm and simulated annealing (SA) algorithm.
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โข Weak Stability is poorโข Advantage Searching
efficiency is highIHS
โข Weak Searching efficiency is poor
โข Advantage A powerful global optimization
SA
Proposed Algorithm
Algorithm Flowchart
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HM: harmony memory,the library of solution vectors
NI: the stopping criterion
Proposed Algorithm
IHS
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๐๐
The procedure of generating a new solution vector.
๐ฒ ๐๐๐
HMCR: help find global solution vectorsPAR: the fine tuning probability
Proposed Algorithm
Algorithm Flowchart
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is multiply sampled using Metropolis Criterion.
Outlines
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4 Proposed Algorithm
1 Introduction
2 System Model
5 Simulation
3 Problem Formulation
Simulation
Simulation Parameter
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Simulation
Convergence curves of the HA, EA, IHS and SA
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โข SA doesnโt arrive in a stable state
โข IHS converges after about 3800 iterations
โข EAโs convergence speed is 5.3% slower than IHS.
โข HA is 50% faster than EA.
Simulation
Optimization Result
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Initial illuminance distribution and power distribution
Simulation Optimized using HA
Optimized using EA
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โข 58.53% from the peak valueโข UIR is 0.82
โข 56.53% from the peak valueโข UIR is 0.77
Conclusion The optimization scheme works well.
The proposed HA algorithm outperforms EA algorithm.
The universality of HA algorithm is need further verification.
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