臺灣博碩士論文加值系統

　　近年來,水下聲學通訊為提高頻寬效應,大多採用二相移鍵(binary phasse shift keying, BPSK)及四相移鍵(quadrature phase shift keying, QPSK)調變技術。
而後利用傳統訓練式決策迴授等化器克服多重路徑及都卜勒效應。
但傳統訓練式決策迴授等化器必須反覆訓練以適應通道,導致通訊的有效容量降低。
　　盲蔽可適性決策迴授等化器(blind adaptive decision feedback equalizer, BADFE)不需訓練過程，能達到較傳統訓練式決策迴授等化器優異的收斂速度及符元錯誤率，有效增加通訊容量。
為因應日益重要的水下通訊需求，本論文將BADFE起始模式中的遞迴濾波器改以遞迴最小平方(Recursive Least-Squares, RLS)演算法，
使等化器演算法有更快的收斂速度及更好的通道追縱能力，以提昇通訊的容量及品質。
　　考量系統的可靠性、可程式規劃及容易模組化的優點，選用TMS320C6711 DSK做為實現的平台，
撰寫盲蔽可適性決策迴授等化器的數位訊號處理器(Digital signal processor, DSP)程式，
所設定的系統參數為：中心頻率36 kHz、傳輸率12 kbps,期望日後能應用於水下即時通訊。

　In the field of underwater acoustic communications, digital communications systems generally use binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signaling. The multipath effects and Doppler frequency shift can be overcome by an adaptive trained decision feedback equalizer (DFE). Unfortunately, the trained DFE is inefficient, unless it is periodically retrained, for digital communications through a water channel. But it will reduce effective capacity.
　The blind adaptive decision feedback equalization (BADFE) exhibits good convergence rate and symbol-error rate (SER) as the conventional trained decision feedback equalizer (DFE) 　　does, but it requires no training. In this thesis, a new method for BADFE is discussed. An recursive-least-square (RLS) algorithm is employed for updating the parameters of the recursive whitening filter in BADFE during the starting period, and the revised structure is called BADFE(RLS). BADFE(RLS) can speed up the convergence rate and get better tracking capabilities. This will improve the quality of communication and increase the capacity.
　Because of the advantages of the reliable, programmable and model of the DSP system, we complier the BADFE program based on TMS320C6711 DSK. The testing system uses a QPSK modulation with 12 kbps data rate and the carrier frequency is 36 kHz.

1 序論
1.1 研究動機和目的 . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 各章節內容概述 . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
2 水下聲波衰減
2.1 多重路徑 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 都卜勒頻率位移 . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3 水下聲波傳輸環境 . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.4 水下通道數學模型 . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.5 全數位接收機 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3 適應性等化器
3.1 適應性等化器簡介 . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.2 等化器架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.2.1 線性等化器 (LE) . . . . . . . . . . . . . . . . . . . . . . . 15
3.2.2 決策迴授等化器 (DFE) . . . . . . . . . . . . . . . . . . . 16
3.3 傳統的適應性演算法 . . . . . . . . . . . . . . . . . . . . . . . . 18
3.3.1 逼零演算法 (ZF) . . . . . . . . . . . . . . . . . . . . . . . 18
3.3.2 最小均方演算法 (LMS) . . . . . . . . . . . . . . . . . . . 19
3.3.3 遞迴最小平方演算法 (RLS) . . . . . . . . . . . . . . . . 21
3.4 盲蔽式演算法 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.4.1 Sato類型的盲蔽等化器 . . . . . . . . . . . . . . . . . . . 24
3.4.2 Godar類型的盲蔽等化器 . . . . . . . . . . . . . . . . . 25
4 盲蔽可適性決策迴授等化器收斂速度之改良
4.1 簡介 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.2 盲蔽可適性決策迴授等化器運作流程 . . . . . . . . . . . . . . . 28
4.2.1 起始模式 (R 在 T 之前) . . . . . . . . . . . . . . . . . . . 28
4.2.2 追縱模式 (T 在 R 之前) . . . . . . . . . . . . . . . . . . . . 31
4.2.3 追縱模式的適應性演算法 . . . . . . . . . . . . . . . . . . 32
4.3 模擬結果與分析 . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.3.1 系統結果與通道描述 . . . . . . . . . . . . . . . . . . . . 34
4.3.2 模擬結果與分析. . . . . . . . . . . . . . . . . . . . . . . 38
4.3.3 水槽離線分析. . . . . . . . . . . . . . . . . . . . . . . . 44
4.4 盲蔽可適性決策迴授化器 DSP 程式設計 . . . . . . . . . . . 47
4.4.1 TMS320C6711模組 . . . . . . . . . . . . . . . . . . . . . . 47
4.4.2 程式設計流程 . . . . . . . . . . . . . . . . . . . . . . . . 48
4.4.3 程式撰寫 . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
4.4.4 程式最佳化 . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.4.5 程式測試與效能分析 . . . . . . . . . . . . . . . . . . . . 51
5 結論與未來發展

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