應用最佳化法則降低都會區無線通訊傳輸信號之信號衰減

機構典藏 > College of Engineering > Graduate Institute & Department of Electrical Engineering > Thesis > Item 987654321/52553

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Title:	應用最佳化法則降低都會區無線通訊傳輸信號之信號衰減
Other Titles:	Path loss reduction in an urban area by applying optimization method
Authors:	張家偉;Chang, Chai–wei
Contributors:	淡江大學電機工程學系碩士班丘建青;Chiu, Chieh-ching
Keywords:	戶外無線通訊;訊號衰減;基因演算法;群聚粒子演算法;動態差異演算法;SBR/Image-method;outdoor environments;antenna patterns;Path loss;Genetic Algorithm;particle swarm optimization algorithm;dynamic differential evolution algorithm.
Date:	2010
Issue Date:	2010-09-23 17:54:51 (UTC+8)
Abstract:	在本論文中所呈現的是戶外無線通訊系統的通道特性分析。然而在戶外的環境中，有許多阻擋物會干擾和減弱接收訊號的功率，例如車輛樹木和建築物。所以我們的目的是要分析與了解戶外無線通訊系統的通道特性。我們考慮了三種不同的天線形式來評估戶外環境中的路徑損失。而且藉由使用遺傳基因演算法，群聚粒子演算法，動態差異演算法三種演算法，來改善陣列天線的天線場型使得無線通訊訊號傳輸具有良好的指向性並減少環境的多路徑干擾產生的路徑損失。在本文中，吾人利用遺傳基因演算法，群聚粒子演算法與動態差異演算法來把具有指向性的天線陣列加入戶外環境裡，用射線彈跳-影像法（SRB-image method）來模擬傳播通道,進而分別分析三種演算法如何改良天線場型，使得所需區域的路徑損失(Path Loss)下降更多，並減少基地台上的功率浪費，並比較三種演算法在不同形狀的天線陣列中的各項優缺點。第二章是模擬戶外環境的描述及建立，如射線彈跳-影像法技術(SBR-image method )、路徑損失(Path Loss)的觀念等等。第三章描述天線陣列的觀念，如分集技術、天線陣列技術、天線陣列模擬操作等等。第四章中，介紹遺傳基因演算法則、群聚粒子演算法則以及動態差異演算法則三種不同演算法的設定與特性，第五章則是描述以上三種演算法則來產生天線陣列場型所減少環境中多路徑干擾造成的接收點收到訊號之路徑損失，最後第六章則比較上述三種演算法產生天線場之結論。 In this paper, we use the SBR/Image method to compute the path loss for different outdoor environments in the commercial area of Taipei. Three types of antenna arrays such as L shape, Y shape, and circular shape arrays are used in the base station and their corresponding path loss on several routes in the outdoor environment are calculated. Moreover, three algorithms are employed to optimize the excitation voltages and phases for antenna arrays to form proper antenna patterns. The performance in reduction of path loss by the optimization algorithm is investigated for these antenna arrays. The particle swarm optimization algorithm and dynamic differential evolution algorithm have more advantages than genetic algorithm. By the obtained antenna patterns, we can know the route with the lowest path loss; meanwhile, transmission power using this route in the base station can be reduced. The dynamic differential evolution algorithm has better optimization result than genetic algorithm in LOS case. But the particle swarm optimization has better optimization result in NLOS case. The investigated results can help communication engineers improve their planning and design of outdoor communication system.
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