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研究生:曾益龍
研究生(外文):Yi-Lung Tseng
論文名稱:適應性向量量化振幅偵測於無線通訊之應用
論文名稱(外文):Application of Adaptive Vector Quantization Magnitude Detection for Wireless Communications
指導教授:翁萬德
指導教授(外文):Wan-De Weng
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:電機工程系碩士班
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:2005
畢業學年度:93
語文別:中文
論文頁數:66
中文關鍵詞:單權值式向量演算法向量量化相位振幅式量化簡易振幅和差量化適應性量化器
外文關鍵詞:Phase and Simple Amplitude SummaryVector QuantizationSimple Amplitude Summary and DifferentSingle Weight Vector Quantization AlgorithmAdaptive Quantizer
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本篇論文主要針對運用最佳量化結構來解決無線通訊系統振幅偵測之平均量化誤差問題,提出一個新的相位及簡易振幅和量化法 (phase and simple amplitude summary, PSAS)。靠著運用單權值向量量化演算於振幅偵測,來達到同時估測外加白色高斯雜訊 (additive white Gaussian noise, AWGN) 以及通道衰退 (fading),其表現優於傳統的均勻量化器約 10~24.8%。模擬結果顯示 PSAS 演算之收斂速度比單一衰退適應系統來得快,此外我們所提之演算更達成比單一 AWGN 適應系統更低的平均誤差。最後,為了改良 PSAS 演算法,我們提出另一種簡易振幅和差量化法 (simple amplitude summary and different, SASD),它不但繼承了 PSAS 演算之優點,而且運算上可以更加有效率的來達成。
In this thesis, an optimal quantization structure is proposed to solve the mean quantization error problem in amplitude detection for wireless communication systems. It is found that, when exploiting single weight vector quantization algorithm for amplitude detection to simultaneously estimate additive white Gaussian noise (AWGN) and channel fading, the performance of the proposed phase and simple amplitude summary (PSAS) quantization method is better than that of a traditional uniform quantizer by 10 to 24.8%. Simulation results show that the convergence speed of PSAS algorithm is faster than a single fading adaptive system. And moreover, the proposed algorithm achieves lower mean quantization error than a single AWGN adaptive system. Finally, to improve the PSAS algorithm, we further present another quantization method which is named simple amplitude summary and different (SASD). The SASD not only inherits the advantages of the PSAS, but also exhibits much more efficient computations.
中文摘要 III
英文摘要 IV
致謝 V
目錄 VI
圖目錄 VIII
表目錄 IX
第一章 緒論 1
1.1研究動機 1
1.2研究目的 1
1.3研究方法與內容 3
1.4各章提要 4
第二章 無線電波量化誤差之基本概念 5
2.1 通道訊號量化誤差量的估測 5
2.2 通道模型的二維高斯分佈 6
第三章 量化架構及最佳量化模型 7
3.1 二維均勻量化之最佳化 7
3.2 新式相位振幅量化法 10
第四章 特徵向量相關的處理概念 15
4.1 遮罩處理 15
4.2 如何判斷高斯訊號特徵大小及位移 18
第五章 量化系統的實現 23
5.1 最佳化振幅碼書應用在通道適應性偵測 23
5.2 單權值向量量化振幅偵測演算法 26
5.3 系統估測限制 31
5.4 運用新式相位振幅量化法的適應性演算 32
5.5 運用簡易振幅和差量化法的適應性演算 34
第六章 模擬結果分析與討論 38
6.1 適應性量化實際範例與應用 38
6.2 運用新式相位振幅量化法的實際範例與應用 44
6.3 運用簡易振幅和差量化法的實際範例與應用 47
6.4 穩定收斂的定義及訓練時間的考量 51
第七章 結論與展望 55
參 考 文 獻 56
簡 歷 57
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[9] S. A. Rizvi and N. M. Narabadi, “An Efficient Euclidean Distance Computation for Vector Quantization Using a Truncated Look-up table,” IEEE Trans. on Circuits and System for Video Technology , Vol.5, NO.4, August 1995.
[10] C. C. Chang, J. S. Chou and T. S. Chen, “ An Efficiet Computation of Eulidean Distances Using Approximated Look-up table,’’ Trans. on Circuits and System for Video Technology, Vol.5, NO.4, June 2000.
[11] H. Hashemi, “The Indoor Radio Propagation Channel,” Proc. IEEE, vol.81, no.7, pp.943-968, July 1993.
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[13] L. C. Lin, “The Implementation of the 3G Mobile Communication FDD Fading Channel Simulator,” Master dissertation, Department of Electrical Engineering, National Yunlin University of Science and Technology, June 2004.
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