論文使用權限 Thesis access permission:校內校外完全公開 unrestricted
開放時間 Available:
校內 Campus: 已公開 available
校外 Off-campus: 已公開 available
論文名稱 Title |
時變衰減通道下直序分碼多重接取通訊系統之低複雜度適應性H∞等化器 Low-Complexity Adaptive H∞ Equalizer for DS-CDMA Communication System in Time-Varying Dispersive Fading Channels |
||
系所名稱 Department |
|||
畢業學年期 Year, semester |
語文別 Language |
||
學位類別 Degree |
頁數 Number of pages |
82 |
|
研究生 Author |
|||
指導教授 Advisor |
|||
召集委員 Convenor |
|||
口試委員 Advisory Committee |
|||
口試日期 Date of Exam |
2000-06-09 |
繳交日期 Date of Submission |
2000-06-21 |
關鍵字 Keywords |
等化器、H∞ 演算法、分碼多重接取 H∞ algorithm, CDMA, Equalizer |
||
統計 Statistics |
本論文已被瀏覽 5688 次,被下載 2917 次 The thesis/dissertation has been browsed 5688 times, has been downloaded 2917 times. |
中文摘要 |
碼際干擾(Intersymbol Interference, ISI)是影響通訊系統效能的一個重要因素,為了達到高速率數位傳輸與可靠的通訊,使用等化器可以有效地消除由通道頻寬或多重路徑所造成的碼際干擾。 在本論文中,我們介紹一適應性H∞等化演算法,和傳統的遞迴最小平方(Recursive Least Square, RLS)濾波演算法不同的是,適應性H∞濾波演算法是最差情況下的最佳化,它可以最小化最差擾動(包括輸入雜訊和模態誤差(Modeling Error))對等化誤差的影響。適應性H∞等化演算法已經被證實較RLS演算法對於模態誤差和任意雜訊環境具有較不敏感的優點,然而,H∞演算法的缺點是有非常大的計算量。因此我們利用子權重分割技術去降低H∞演算法的計算量,由電腦模擬中可發現H∞演算法也可以減少因子權重分割所產生的動態估測誤差(Dynamic Estimation Error)。 最後,為了解決多重接取干擾(Multiple Access Interference, MAI)、切片間的干擾(Interchip Interference, ICI)、通道時變效應所造成的影響及計算複雜度等問題,我們將經子權重分割的基於H∞演算法(H∞-based)應用於直序分碼多重接取(Direct Sequence-Code Division Multiple Access, DS-CDMA)的多使用者偵測系統中,電腦模擬將驗證基於H∞等化演算法的優點。 |
Abstract |
Intersymbol interference(ISI) is an important factor which affects the performance of communication systems. To achieve highspeed digital transmission and reliable communication, an equalizer can effectively eliminate ISI caused by band-limited channel or multipath. In this paper, we introduce an adaptive H∞ equalizing algorithm. Different form conventional recursive least square filtering algorithm, the adaptive H∞ filtering algorithm is a worst case optimization. It can minimize the effect of the worst disturbances (including input noise and modeling error) on the equalization error. It has been proven that the adaptive H∞ filtering algorithm has the advantage of reduction of sensitivity to modeling error and suitability for arbitrary ambient noise over RLS algorithm. However, the computational burden of the H∞ algorithm is enormous. To reduce the computational complexity, the subweight partition technique is employed to the H∞ algorithm. Computer simulation also shows that the H∞ algorithm can reduce the dynamic estimation error resulting from subweight partition. Finally, in order to overcome multiple access interference, interchip interference, time-varying effects from the channel and computational complexity, the H∞ algorithm with subweight partition (termed H∞-based) is then further extended to the multiuser detection in code division multiple access (CDMA) system. Simulation results are presented to demonstrate the advantage of the H∞-based equalizing algorithm. |
目次 Table of Contents |
感謝詞.................................I 中文摘要...............................II 英文摘要...............................III 目錄...................................IV 圖表目錄...............................VI 第一章 緒言............................1 1.1 文獻探討...........................1 1.2 研究動機...........................3 第二章 適應性等化器演算法..............5 2.1 適應性等化器.......................5 2.1.1 數位調變訊號、通道與碼際干擾.....6 2.1.2 線性等化器(LE)...................8 2.1.3 判斷回授等化器(DFE)..............9 2.2 等化演算法.........................10 2.2.1 逼零演算法(ZF)...................10 2.2.2 最小均方演算法(LMS)..............11 2.2.3 遞迴最小平方演算法(RLS)..........13 2.3 電腦模擬及分析...................14 第三章 適應性H∞等化演算法...........25 3.1 系統介紹...........................26 3.2 判斷回授等化器的適應性濾波演算法...26 3.2.1 濾波(RLS)演算法..................27 3.2.2 濾波演算法.......................28 3.2.3 適應性H∞濾波演算法..............30 3.3 電腦模擬及分析...................32 第四章 基於H∞適應性等化演算法及其應用.41 4.1 子權重分割技術 .....................41 4.2 應用子權重分割技術的適應性H∞等化演算法.43 4.2.1 基於RLS濾波演算法................44 4.2.2 基於H∞濾波演算法................46 4.2.3 計算複雜度分析...................48 4.3 應用基於H∞適應性等化演算法於多使用者偵測系統.....................................49 4.3.1 信號模型介紹.....................49 4.3.2 接收器結構.......................50 4.4 電腦模擬及分析...................54 第五章 討論與建議....................63 參考文獻...............................65 附錄 A.................................i 附錄 B.................................iv 縮寫...................................v |
參考文獻 References |
[1] Theodore S. Rappaport, “Wireless communications: principles and practice”, Prentice-Hall, NJ, 1996. [2] Y. Sato, “A method of self-recovering equalization for multilevel amplitude-modulation systems,” IEEE Trans. Communication, vol. 23, pp. 679-682, June. 1975. [3] L. Tong, G. Xu, and T. Kailath, “Blind identification and equalization based on second-order statistics: A time domain approach,” IEEE Trans. Information Theory, vol. 40, pp. 340-349, Mar. 1994. [4] E. Moulines, P. Duhamel, J. F. Cardoso, and S. Mayrargue, “Subspace methods for the blind identification of multichannel FIR filters,” IEEE Trans. Signal Processing, vol. 43, pp. 516-525, Feb. 1995. [5] G. Xu, H. Liu, L. Tong, and T. Kailath, “A least-squares approach to blind equalization,” IEEE Trans. Signal Processing, vol. 43, pp. 2982-2993, Dec. 1995. [6] D. T. M. Sock, “Blind fractionally-spaced equalization, perfect-reconstruction filter banks and multichannel linear prediction,” in Proc. ICASSP Conference, Adelaide, Australia, Apr. 1994, pp. IV.585-IV.588. [7] D. Gesbert, P. Duhamel, and S. Mayrargue, “On-line blind multichannel eualization based on mutually-referenced filters,” IEEE Trans. Signal Processing, vol. 45, pp. 2307-2317, Sept. 1997. [8] G. Giannakis and S. Halford, “Blind fractionally spaced equalization of noisy FIR channels: Direct and adaptive solutions,” IEEE Trans. Signal Processing, vol. 45, pp. 2277-2292, Sept. 1997. [9] H. Liu and G. Xu, “Closed-form blind symbol estimation in digital communications,” IEEE Trans. Signal Processing, vol. 43, pp. 2714-2723, Nov. 1995. [10] A. J. van der Veen, S. Talwar, and A. Paulraj, “A subspace approach to blind space-time signal processing for wireless communication systems,” IEEE Trans. Signal Processing, vol. 45, pp. 173-190, Jan. 1997. [11] W. A. Gardner, “A new method of identification,” IEEE Trans. Communication, vol. 39, pp. 813-817, June. 1991. [12] L. Tong, G. Xu, and T. Kailath, “Fast blind equalization via antenna arrays,” in Proc. ICASSP, vol. 4, pp. 272-275, 1993. [13] A. Salmasi and K. S. Gilhousen, “On the system design aspects of code division multiple access(CDMA) applied to digital cellular and personal communications networks,” in IEEE 41st Vehicular Technology Conference, pp. 57-62, 1991. [14] R. Lupas and S. Verdu, “Linear multiuser detectors for synchronous code-division multiple-access channels,” IEEE Trans. Communication, vol. 35, pp. 123-136, Jan. 1989. [15] R. Lupas and S. Verdu, “Near-far resistance of multiuser detectors in asynchronous channels,” IEEE Trans. Communication, vol. 38, no. 4, pp. 496-508, Apr. 1990. [16] Majeed Abdulrahman, etc., “Decision feedback equalization for CDMA in indoor wireless communications,” IEEE JSAC vol. 12, no. 4, pp. 698-706, May. 1994. [17] X. Wang, and H. V. Poor, “Blind equalization and multiuser detection in dispersive CDMA channels,” IEEE Trans. Communication, vol. 46, no. 1, pp. 91-103, Jan. 1998. [18] Po-Wei Fu and Kwang Cheng Chen, “Linear-complexity equalized multiuser receivers for wideband CDMA in time varying channels,” Vehicular Technology Conference, vol. 3, pp. 2338-2342, 1999. [19] Anja Klein, Ghassan Kawas Kaleh, and Paul Walter Baier, “Equalizers for multi-user detection in code division multiple access mobile radio systems,” in IEEE 44th Vehicular Technology Conference, pp. 762-766, vol. 2, 1994. [20] Brian Hart and Rittwik Jana, “Optimal multiuser detection of bandlimited DS-CDMA signals, distorted by time-varying, frequency-selective multipath channels,” in IEEE 48th Vehicular Technology Conference, pp. 2292-2296, vol. 3, 1998. [21] Peter Hoeher, “A statistical discrete-time model for the WSSUS multipath channel,” IEEE Trans. Vehicular Technology, vol. 41, no. 4, pp. 461-468, Nov. 1992. [22] D. N. Kalofonos, M. Stojanovic, and J. G. Proakis, “On the performance of adaptive MMSE detectors for a MC-CDMA systems in fast fading Rayleigh channels,” The Ninth IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, vol. 3, pp. 1309-1313, 1998. [23] Weihua Zhuang, “Adaptive channel equalization for wireless personal communications,” IEEE Trans. Vehicular Technology, vol. 48, no. 1, pp. 126-136, January 1999. [24] 楊憲東、葉芳柏, “線性與非線性 控制理論,” 全華圖書公司, 1997. [25] Shiann-Jeng Yu and Ju-Hong Lee, “Adaptive array beamforming based on an efficient technique,” IEEE Trans. Antennas and Propagation, vol. 44, no. 8, pp.1094-1101, August 1996. [26] S. Haykin, “Adaptive filter theory,” Prentice-Hall, Englewood Cliffs, NJ, 1996. [27] U.shaked ang Y. Theodor, “ -optimal estimation: A tutorial,” in Proc. 31st IEEE CDC, Tucson, AZ, pp. 2278-2286, Dec. 1992. [28] X. Shen and L. Deng, “Discrete filter design with application to speech enhancement,” in Proc. ICASSP’95, Detroit, MI, pp. 1504-1507, May 1995. [29] I. Yaesh and U. Shaked, “Game theoty approach to state estimation of linear discrete-time processes and its relation to -Optimal estimation,” Int. J. Control, vol. 55, pp. 1443-1452, 1992. [30] J. M. Cioffi and T. Kailath, “Fast, recursive-least-squares transversal filters for adaptive filtering,” IEEE Trans. Acoustic, Speech, Signal Processing, vol. 32, pp. 304-337, Apr. 1984. [31] G. H. Golub and C. F. Van Loan, Matrix computations, Baltimore, MD: Johns Hopkins Univ. Press. 1983 |
電子全文 Fulltext |
本電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。 論文使用權限 Thesis access permission:校內校外完全公開 unrestricted 開放時間 Available: 校內 Campus: 已公開 available 校外 Off-campus: 已公開 available |
紙本論文 Printed copies |
紙本論文的公開資訊在102學年度以後相對較為完整。如果需要查詢101學年度以前的紙本論文公開資訊,請聯繫圖資處紙本論文服務櫃台。如有不便之處敬請見諒。 開放時間 available 已公開 available |
QR Code |