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研究生:潘維宏
研究生(外文):Wei-Hung Pan
論文名稱:盲蔽式適應性多用戶偵測使用滑動視窗遞回最小平方演算法於DS-CDMA系統中
論文名稱(外文):Blind Adaptive Multiuser Detection for DS-CDMA System Based on Sliding Window RLS Algorithm
指導教授:陳巽璋
指導教授(外文):Shiunn-Jang Chern
學位類別:碩士
校院名稱:國立中山大學
系所名稱:電機工程學系研究所
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:2004
畢業學年度:92
語文別:英文
論文頁數:52
中文關鍵詞:直序式分碼多工系統滑動視窗遞回最小平方演算法強窄頻干擾盲蔽式多用戶偵測限制最佳化適應性濾波器
外文關鍵詞:Blind Multiuser DetectionNarrow Band InterferenceAdaptive FilterDS-CDMA SystemSliding Window Recursive Least Squares AlgorithmConstrained optimization
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直序式分碼多工技術被視為是一重要多工技術在無線通訊領域中。在直序式分碼多工系統架構中,每位用戶自身都有一組獨特的展頻碼使其可與其他用戶在相同頻帶上做資訊傳送且在接收端被解出。現今的直序式分碼多工系統在受到多重路徑衰減且有遠近效應的環境下大都使用耙式接收機以強化系統效能。倘若使用訓練資訊(training sequence),則可藉由求解限制性Wiener估測解以求得最小均方誤差多用戶接收機。然而,若沒有使用訓練資訊,則盲蔽式多用戶接收機是另一種可以讓系統效能接近於最小均方誤差架構的方法。
本論文提出使用最大/最小方法的盲蔽式多用戶接收器。在本篇論文中,限制條件是以參數的形式表示且亦參與求取接收機參數最佳解的動作。限制條件參數與所要訊號的多重路徑資訊有關。在劇烈變動的環境中,由於通道特性隨多重接取干擾、遠近效應以及強窄頻干擾訊號的加入等影響所呈現的劇烈時變,致使傳統限制性最小均方值演算法的與限制性遞回最小平方演算法無法獲得好的效能。因此,推衍滑動視窗限制性遞回最小平方演算法基於最大/最小法則以求得最佳盲蔽式接收機。從我們的電腦模擬結果中證明我們所提出的新技術對於在直序式分碼多工系統受到多重路徑衰減的環境中抑制之多重接取干擾相較於傳統限制性遞回最小平方演算法以及限制性最小均方值演算法有更佳的效能;且對於強窄頻干擾及遠近效應的問題更具有強健性。
Direct sequence code division multiple access (DS-CDMA) technique is one of the significant multiplexing technologies used in wireless communication services. In the DS-CDMA framework, all users have been assigned distinct signature code sequence to achieve multiple accesses within the same frequency band, and allow signal separating at the receiver. Under multipath fading environment with near-far effect, the current CDMA systems employed the RAKE receiver, to enhance the system performance. It is known that if training data is available the minimum mean squares error (MMSE) multiuser receiver, in which the average power of the receiver output is minimized subject to appropriate constraints, could be obtained by solving directly by the constrained Wiener estimation solution. However, if this is not the case, the blind multiuser receiver is an alternative approach to achieve desired performance closed to the one with the MMSE approach.
In this thesis, based on the max/min criterion, the blind multiuser receiver, with linear constraints, is devised. Here constraint equations are written in parametric forms, which depend on the multipath structure of the signal of interest. Constraint parameters are jointly optimized with the parameters of the linear receiver to obtain the optimal parameters. In consequence, the sliding window linearly constrained RLS (SW-LC-RLS) algorithm is employed to implement the optimal blind receiver, with max/min approach. This new proposed scheme can be used to deal with multiple access interference (MAI) suppression for the environments, in which the narrow band interference (NBI) due to other systems is joined suddenly to the DS-CDMA systems, and having serious near-far effect. Under such circumstance, the channel character due to the NBI and near-far effect will become violent time varying, such that the conventional LC-RLS algorithm as well as LC-LMS algorithms could not perform well. Via computer simulation it confirms that our proposed scheme has better capability for MAI suppression in DS-CDMA systems than other existing schemes, and is more robust against the NBI and near-far problems.
Acknowledgement(i)
Abstract
Contents
List of Figures
List of Tables
Chapter 1 Introduction
Chapter 2 DS-CDMA System with Linearly Constrained Minimum (LCMV) Variance Receiver
2.1 Overview of the DS-CDMA System
2.2 System Model Description
2.3 Linearly Constrained Minimum Variance Receiver
Chapter 3 Adaptive Filtering Algorithms with Linear Constraints
3.1 Introduction
3.2 Linearly Constrained Least Mean Square (LC-LMS) Algorithm
3.3 Linearly Constrained Recursive Least Square (LC-RLS) Algorithm
Chapter 4 Blind Adaptive Multiuser Detection for DS-CDMA System Based On Sliding Window RLS Algorithm
4.1 Blind Adaptive Multiuser Detection
4.2 Blind Multiuser Receiver with Modified Sliding Window RLS Algorithm
4.3 Computer Simulation Results
Chapter 5 Conclusions and Future Study
Appendix A
Appendix B
Appendix C
Appendix D
References
[1] J. G. Proakis, “Digital Communications”, 4th, New York: McGraw-Hill, 2001
[2] Roger L. Peterson, Rodger E. Ziemer and David E. Borth, Introduction To Spread Spectrum Communication, Prentice Hall International Inc.
[3] R. L. Pickholtz, D. L. Schilling, and L. B. Milstein, “Theory of spread spectrum communications: A tutorial,” IEEE Trans. Commun., vol. 30, pp. 855–884, May 1982.
[4] L. B. Milstein, “Interference rejection techniques in spread-spectrum communications,” Proc. IEEE, vol. 76, pp. 657–671, June 1988.
[5] T. S. Rappaport, “Wireless Communications”. Englewood Cliffs, NJ: Prentice-Hall, 2002.
D. H. Johnson and D. E. Dudgeon,” Array Signal Processing: Concepts and Techniques.” Englewood Cliffs, NJ: Prentice-Hall, 1993.
[7] Haykin, “Adaptive Filter Theory”, 4th, Englewood Cliffs, NJ: Prentice Hall, 2002
[8] B. D. Van Veen, “Adaptive radar detection and estimation,” in Minimum Variance Beamforming, S. Haykin and A. Steinhardt, Eds. New York: Wiley, 1992.
[9] U.Madhow, and M. L. Honig, ”MMSE interference suppression for direct-sequence , spread spectrum CDMA,” IEEE Trans. Commun., vol. 42, pp. 3178-3188, Dec. 1994
[10] M. Honig, U. Madhow, and S. Verdu “Blind Adaptive Interference Suppression for Direct-Sequence CDMA”, IEEE Trans. on Information Theory, vol. 86, pp. 2049-2069,Oct. 1998
[11] E. Moulines, P. Duhamel, J. –F. Cardoso, and S. Mayrargue, “Subspace methods for the blind identification of multichannel FIR filters,” IEEE Trans. on Signal Processing, vol. 43, pp. 516-525,Feb. 1995
[12] Bensley, S. E.; Aazhang, B.;” Subspace-based channel estimation for code division multiple access communication systems,” IEEE Trans. Commun., vol. 44, pp.1009-1020, Aug. 1996.
[13] M. L. Honig, U. Madhow, and S. Verd´ u, “Blind Adaptive Multiuser Detection,” IEEE Trans. on Information Theory, pp. 944–960, July 1995
[14] M. K. Tsatsanis, ”Blind Estimation of Direct Sequence spread Spectrum signals in Multipath, ” IEEE Trans. on Signal Processing, vol. 45, No. 5, May 1997
[15] O. L. Frost III, “An Algorithm for Linearly-Constrained Adaptive Array Processing, ” IEEE Proc., Vol. 60, pp. 926-935, Aug. 1972.
[16] J. B. Schodorf and D. B. Williams, “Array processing techniques for multiuser detection,” IEEE Trans. Commun., vol. 45, pp. 1375-1378, Nov. 1997.
[17] Jeffrey B. Schodorf and Douglas B. Williams, “A Constraint Optimization Approach to Multiuser Detection,” IEEE Trans. on Signal Processing, vol. 45, No.1, January 1997
[18] M. K. Tsatsanis and G. B. Giannakis, “Optimal decorrelating receivers for DS-CDMA systems: A signal processing framework,” IEEE Trans. Signal Processing, vol. 44, pp. 3044-3055, Dec. 1996.
[19] D. R. Brown, D. L. Anair, C. R. Johnson Jr., ”Fractionally Sampled Linear Detection for DS-CDMA”, Proc. Asilomar Conf. on Signals, Systems, and Computer ,Nov. 1998
[20] M. K. Tsatsanis, “Inverse filtering criteria for CDMA systems,” IEEE Trans. Signal Processing, vol. 45, pp. 102-112, Jan. 1997
[21] M. K. Tsatsanis, “Performance analysis of minimum variance CDMA receivers,” IEEE Trans. Signal Processing, vol. 46, pp. 3014-3022, Nov. 1998.
[22] Z. Xu and M. K. Tsatsanis,” Blind Adaptive Algorithms for Minimum Variance CDMA Receivers”, IEEE Trans. Signal Processing, vol. 41, no1 , pp. 180-194, Jan. 2001.
[23] C. H. Sun and S. J. Chern and H. P. Lee, “ Adaptive Rake Receiver for Multiuser DS-CDMA System over Multipath Fading Channel with Sliding Window Linearly Constrained RLS Algorithm,” IEICE Trans. on Commun., Vol. E87-B, no.7, July 2004 (to appear).
[24] X. Wang and H. V. Poor, “Blind multiuser detection: A subspace approach,” IEEE Trans. Inform. Theory, vol. 44, pp. 677-690, Mar. 1998.
[25] S. Verdu, “Minimum probability of error for asynchronous Gaussian multiple-
access channels,” IEEE Trans. Inform. Theory, vol. 32 , pp:85 - 96 , Jan 1986.
[26] S. Verdu, ”Multiuser detection,” in Advances in Statistical signal Processing, V. Poor, Ed. New York, JAI,1993,vol. 2, pp. 369-409
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