臺灣博碩士論文加值系統

可見光通訊(Visible-light communication system, VLC)，是近年來相當熱門的技術，它的優點在於能夠同時間實現照明與無線通訊，並且無電磁波干擾，節約能源等優點，雖然可見光通訊系統能夠提供多種好處，但是由於是多路徑傳輸通道的性質，因此會產生不均勻的照度及訊號雜訊比(signal to noise ratio ,SNR)波動變化大，如此一來容易造成不同位置所接收到的訊號品質不一，而導致訊號失真。
本論文主要研究為可見光通訊系統之降低訊雜比峰值偏差 (signal to noise ratio deviation from peak, SDP)及提升均勻照度比(Uniformity illuminance ratio, UIR)之技術，可應用在不同的LED陣列配置，以及提供可調控照度。
在本文中主要調控VLC系統中每盞燈具之功率透過連續型蟻群最佳化演算法(Ant colony optimization for continuous domains, ACO_R)找出一組最佳優化因子，來解決上述問題。由模擬結果顯示使用ACO演算法有效降低SDP從66.4%降低至37.1%及提升UIR從50.9%提升至80.6%在VLC系統中，其結果與基因演算法(Genetic Algorithm, GA)來相比，以同樣室內環境，燈具擺設為4×4 LED 燈具陣列下，由模擬結果顯示其ACO演算法約150 次疊代收斂，GA演算法約4500 次疊代收斂，故ACO演算法效能優於GA演算法，並且降低其計算複雜度，收斂速度提升30倍。

Visible light communication system has been the quite popular technology in recent years. The advantage is it can provide uniform wireless communications and illumination simultaneously, no electromagnetic waves and energy saving. Although VLC system can provide variety of benefits, it can result inhomogeneous illumination and signal to noise ratio change a lot because of multipath transmission channel characteristics. It is easy to receive uneven quality of signals with different locations and result in signal distortion.
In this thesis, the main research is signal to noise ratio deviation from peak and raise uniformity illuminance ratio of VLC. It can application in various LED array and provide regulation illumination.
In this thesis, we propose ant colony optimization for continuous domains to work in regulate lamp power in VLC. This thesis has presented the simulation results of using a ACO algorithm to reduce SDP to 37.1% from 66.4% and UIR of illuminance is improved 50.9% to 80.6% in VLC. The simulation results show ACO algorithm is better than Genetic algorithm, reduce computational complexity and improve 30 times on convergence.

目錄
摘要 i
Abstract ii
目錄 iii
表目錄 vi
圖目錄 vii
一、 緒論 1
1.1 前言 1
1.2 研究動機 1
1.3 論文架構 2
二、 可見光通訊系統基本介紹 3
2.1 白光LED介紹 3
2.2 可見光通訊發展歷史與簡介 4
2.3 可見光通訊的優點 5
三、 VLC系統模型與分析 7
3.1 水平接收光功率 7
3.2 接收端訊號雜訊比(SNR) 9
3.3 照度功能分析 11
四、 降低訊雜比峰值偏差方法 12
4.1 降低訊雜比峰值偏差定義 12
4.2 降低訊雜比峰值偏差之影響 12
4.3 降低訊雜比峰值偏差降低之方法 13
4.3.1 基因演算法（Genetic Algorithm ,GA）介紹 13
4.4 基因演算法複雜度問題 21
五、 同時降低訊雜比峰值偏差及系統複雜度方法 22
5.1 螞蟻演算法介紹 22
5.1.1 文獻回顧 22
5.1.2 離散型螞蟻演算法介紹 23
5.1.3 連續型螞蟻演算法介紹 26
5.2 ACOR演算法結合VLC系統降低SDP 29
5.3 ACOR演算法多目標優化VLC系統SDP及照度控制 33
六、 研究結果 35
6.1 可見光通訊室內環境模擬 35
6.2 ACO演算法與GA演算法模擬結果比較 37
6.3 ACO演算法在不同燈具陣列下優化結果 41
6.4 ACO演算法加上照度控制優化結果 42
七、 結論 44
參考文獻 45

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