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: yaozhongqiang2019@163.com

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

5

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

𝑷 (𝑹 𝒋 )=∑𝒊=𝟏

𝑵

{𝒌𝒊𝑷 𝒕𝑯𝒅 (𝟎 ;𝑺𝒊 ,𝑹 𝒋 )+∫𝒌𝒊𝑷 𝒕𝒅𝑯 𝒓𝒆𝒇 (𝟎 ;𝑺𝒊 ,𝑹 𝒋)}

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

𝑺𝑵𝑹 (𝑹 𝒋 )=¿¿: 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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𝑬=∑𝒏=𝟎

∞

𝑬𝒏

𝑬𝒏

𝒏

(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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𝑷 (𝑹 𝒋 )=∑𝒊=𝟏

𝑵

{𝒌𝒊𝑷 𝒕𝑯𝒅 (𝟎 ;𝑺𝒊 ,𝑹 𝒋 )+∫𝒌𝒊𝑷 𝒕𝒅𝑯 𝒓𝒆𝒇 (𝟎 ;𝑺𝒊 ,𝑹 𝒋)} (4)

Problem Formulation

Evenness Measure Function

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𝛿 (𝐾 )=100−100(max

𝑗𝑃 (𝑅 𝑗)−min

𝑗𝑃 (𝑅 𝑗)

max𝑗𝑃 (𝑅 𝑗)

)

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

15

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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• Weak Stability is poor• Advantage Searching

efficiency is highIHS

• Weak Searching efficiency is poor

• 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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𝒌𝒊

The procedure of generating a new solution vector.

𝑲 𝒏𝒆𝒘

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

20

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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• SA doesn’t arrive in a stable state

• IHS converges after about 3800 iterations

• EA’s convergence speed is 5.3% slower than IHS.

• 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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• 58.53% from the peak value• UIR is 0.82

• 56.53% from the peak value• 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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