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研究生:馮秉森
研究生(外文):Feng, Bin-sen
論文名稱:應用於估測無線網路參數之階層式支持向量機平行演算法
論文名稱(外文):Determination of Wireless Networks Parameters Through Parallel Hierarchical Support Vector Machines
指導教授:張適宇張適宇引用關係
指導教授(外文):Chang, Shih-yu
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:54
中文關鍵詞:無線網路頻道估測位置量測支持向量機平行演算法
外文關鍵詞:wireless networkschannel noise estimationnode localizationsupport vector machineparallel learning
相關次數:
  • 被引用被引用:0
  • 點閱點閱:173
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
此篇論文考慮兩個問題:(1)在室內無線網路環境中估測節點的實體位置,(2)估測無線通訊網路的雜訊強度。在無線網路環境中,正確評估這些參數對許多工作而言是件重要的事,例如:網路管理、事件偵測、位置相關服務以及路由選擇。在此篇論文中,我們設計了 H-SVM (Hierarchical Support Vector Machine) 架構來解決以上問題。H-SVM具有以下優點:第一、因為 H-SVM 的階層式架構,它可以提供有效率的分散式計算程序。第二、 H-SVM 只需要簡單的網路資訊即可用來判斷這些參數,而不需要額外的硬體支援。第三、在 H-SVM 的架構下,估測誤差的平均值及標準差可保證在一定範圍之內。並且,我們設計了平行式學習演算法來降低計算複雜度,使在SVM的訓練資料為 n 時,計算複雜度由原本的 O(n^3) 降低為 O(n^2)。最後,模擬結果將驗證 H-SVM 的有效性。
Abstract—We consider the problems of (1)estimating the physical locations of nodes in an indoor wireless network, (2)estimating the channel noise in a MIMO wireless network, since knowing these parameters are important to many tasks of a wireless network such as network management, event detection, location-based service, and routing. A hierarchical support vector machines (H-SVM) scheme is proposed with the following advantages. First, H-SVM offers an efficient evaluation procedure in a distributed manner due to hierarchical structure. Second, H-SVM could determine these parameters based only on simpler network information, e.g., the hop counts, without require particular ranging hardware. Third, the exact mean and the variance of the estimation error introduced by HSVM
are derived which are seldom addressed in previous works. Furthermore, we present a parallel learning algorithm to reduce the computation time required for the proposed H-SVM. Thanks for the quicker matrix diagonization technique, our algorithm can reduce the traditional SVM learning complexity from O(n3) to O(n2) where n is the training sample size. Finally, the simulation results verify
the validity and effectiveness for the proposed H-SVM with parallel learning algorithm.
1 Introduction
2 Related Works
3 Preliminary of Support Vector Machine
4 Hierarchical Support Vector Machine and Estimation Procedure
5 Estimation Error Analysis
6 Parallel Learning Algorithm for H-SVM
7 Numerical Results
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