在無線通訊系統中，直接序列分碼多工(direct sequence code division multiple access; DS-CDMA)是其中一種最有發展性之技術。同時直接序列分碼多工也是第三代無線通訊系統中之寬頻分碼多工(wideband CDMA; WCDMA)系統的核心技術。在DS-CDMA系統中，由於展頻碼之非完全正交(incomplete orthogonal)特性，使得多重擷取干擾(multiple access interference; MAI)成為最嚴重也是不可避免的問題。並且，當干擾用戶的傳輸功率甚大於主要用戶(desired user)的功率時，遠近效應便產生。此情況會造成系統效能下降，因而使得系統的通道容量(capacity)受到限制。為了防止上述問題發生，許多利用限制性條件(constraint)設計之最小平方誤差(minimum mean square error; MMSE)和最小輸出能量(minimum output energy; MOE)多用戶檢測器紛紛被提出。此外，為了減少多路徑衰減(multipath fading)的影響，耙式接收機(RAKE receiver)是公認可有效用來消除因多路徑所引起之符碼干擾(inter-symbol interference; ISI)以提昇系統效能。
為了執行盲蔽式(blind)多用戶檢測器，最小輸出能量逼近加上限制性條件，或稱線性限制性最小變異數(linearly constrained minimum variance; LCMV)逼近常被使用。然而，以LCMV所設計的接收機對於不精確的通道估測所造成之不匹配(mismatch)是相當敏感的，而常模(constant modulus; CM)法則便可用來解決這個問題。在本論文中，在考慮多路徑通道環境之下，我們首先提出結合線性限制性常模(LCCM)逼近和Min/Max法則之盲蔽式適應性多用戶檢測器之設計。此技術可同時抑制MAI和ISI，且對於因主要用戶之同步時間的不精確所造成之不匹配問題有更強健性(robust)的效果。為了減少計算複雜度，我們將前述以LCCM-RLS演算法設計之適應性接收機，以廣義旁波帶消除器(generalized sidelobe canceller; GSC)之架構執行，稱為CM-GSC-RLS演算法。
傳統上通常考慮使用指數型視窗(exponential window; EW)來執行LC-RLS或LCCM-RLS演算法。然而，為了更進一步改善在時變通道下之系統效能，我們發展出滑動視窗(sliding window; SW) LCCM-RLS 或SW CM-GSC-RLS 演算法。此演算法可在DS-CDMA多路徑通道環境中遭受突然加入之強大窄頻干擾時，使系統效能獲得改善。同時在論文中，我們也對EW LC-RLS和SW LC-RLS演算法提出理論分析，證明輸入信號的自相關矩陣之初始值的選擇所造成的影響。然而，很不幸地，主要用戶之振幅卻對LCCM法則的效能有極大的影響，但實際上在接收機端卻無法得知主要用戶的振幅。故而在本論文的最後，提出線性限制性條件之適應型常模遞迴式最小平方(linearly constrained adaptive constant modulus recursive least squares; LC-ACM-RLS)之改良型演算法來改善此一問題。當使用LC-ACM-RLS演算法設計盲蔽式DS-CDMA接收機，對於通道環境改變而造成主要用戶的振幅改變，可做適應性之追蹤。

The direct sequence (DS) code division multiple access (CDMA) is one of the most promising multiplexing technologies for wireless communications. It is also a core technology used in the wideband CDMA (WCDMA) system for the third generation (3G) wireless communication systems. In practice, in the CDMA systems the incomplete orthogonal of the spreading codes between users may introduce the so-called multiple access interference (MAI). Usually, the near-far problem exists when the interfering users are assigned powers much higher than the desired user. Such that the system performance might degrade, dramatically, and thus limits the system capacity. To circumvent the above-mentioned problems many effective adaptive multiuser detectors, based on the minimum mean square error (MMSE) and the minimum output energy (MOE) criteria subject to certain constraints have been proposed. In addition, to mitigate multipath fading effect, RAKE receiver was adopted due to the advantages of path diversity, thus, enhances the system performance. To implement the blind adaptive multiuser detector the linearly constrained minimum variance (LCMV), which is the constrained version of MOE, has been suggested. Further, the LCMV-based receivers exhibit high sensitivity to the channel mismatch caused by the unreliable estimation. To deal with this problem the constant modulus (CM) criterion was considered. In this dissertation, to deal with diverse phenomena encountered in practical channels, we first propose new blind adaptive multi-user detectors, based on the Min/Max criterion associated with the LCCM approach. For implementation the LC exponential window (EW) recursive least-square (RLS) algorithm is derived, and is referred to as the EW LCCM-RLS receiver. It can be used to effectively suppress the MAI and ISI, simultaneously, over multipath fading channels and are robust to mismatch problem caused by inaccuracies in the acquisition of timing and spreading code of the desired user. To reduce the complexity of the above-mentioned blind adaptive multi-user receiver with the LCCM-RLS algorithm, the so-called generalized sidelobe-canceller (GSC) structure is adopted, results in obtaining new CM-GSC-RLS algorithm. Moreover, to further improve the system performance for multipath fading and time-varying channel, the sliding window (SW) LCCM-RLS and SW CM-GSC-RLS algorithms are developed. It can be employed for multipath fading channel with the rapidly changing strong narrowband interference (NBI), which is joined suddenly to the CDMA systems. To look more inside the effect of selecting the initial value of the input signals autocorrelation matrix, some theoretical analyses for the SW LC-RLS as well as EW LC-RLS are provided. Since, unfortunately, the LCCM criterion is known to highly depend on the exact knowledge of the desired user amplitude that is not known exactly at receiver. In the final of this dissertation, a novel linearly constrained adaptive constant modulus RLS (LC-ACM-RLS) algorithm for blind DS-CDMA receiver is proposed. With this new proposed LC-ACM-RLS algorithm, the amplitude variation of the desired user, due to changing characteristics of the channel, can be tracked adaptively. Thus, better performance achievement, in terms of output signal-to-interference-plus-noise ratio (SINR) and bit error rate (BER), over the conventional LCCM-LMS and LCCM-RLS algorithms can be expected.

Chapter 1 Introduction...1
1.1 Problem Statement and Literature Survey...2
1.2 Objective of this Dissertation...4

Chapter 2 Description of DS-CDMA Systems...7
2.1 Introduction...7
2.2 Overview of DS-CDMA Systems...8
2.2.1 Problem Description of DS-CDMA Systems...9
2.2.2 System Model Description of DS-CDMA Systems under Multipath Channel Environments...11
2.2.3 Rake Receiver for DS-CDMA Systems...14
2.3 Conventional Adaptive Multiuser Detectors for DS-CDMA systems...18
2.3.1 Multiuser Detectors with the Minimum Mean Square Error (MMSE) Approach...18
2.3.2 Multiuser Detectors with Linearly Constrained Minimum Output Energy (MOE) Approach...20
2.3.3 Multiuser Detectors with Linearly Constrained Constant Modulus (CM) Criterion...21

Chapter 3 Pilot Symbol-Aided DS-CDMA RAKE Receiver with Sliding Window Linearly Constrained RLS Algorithm...23
3.1 Introduction...23
3.2 Conventional Adaptive RAKE Receivers of DS-CDMA System for Multipath Fading Channels...25
3.3 Adaptive RAKE Receiver with Sliding Window Linearly Constrained RLS Algorithms for Multipath Fading DS-CDMA System...29
3.3.1 LC-RLS Algorithm based on LSE approach...30
3.3.2 SW-LC-RLS Algorithm based on LSE approach...31
3.4 Steady State Performance Analysis for Windowed Linearly Constrained RLS Algorithms...35
3.4.1 Steady-State Performance of the Exponential Window LC-RLS Algorithm...36
3.4.2 Steady-State Performance of the Sliding Window LC-RLS Algorithm...41
3.5 Simplified Sliding Window Linearly Constrained RLS Algorithm...47
3.6 Computer Simulation Results...49
3.7 Summary...55

Chapter 4 Blind DS-CDMA Multiuser Detector Based on Min/Max Criterion with Linearly Constrained RLS Algorithms for Multipath Channels...69
4.1 Introduction...69
4.2 Signal Model Description...70
4.3 Blind DS-CDMA Multiuser Detector Using MOE Approach Based on Min/Max Criterion...72
4.3.1 LC-LS Algorithm Based on Min/Max Criterion...72
4.3.2 GSC-RLS Algorithm Based on Min/Max Criterion...73
4.4 Constant Modulus GSC-RLS Algorithm Based on Min/Max Criterion for Mismatch Problem...76
4.4.1 CM-GSC-RLS Algorithm Based on Min/Max Criterion...76
4.4.2 SW CM-GSC-RLS Algorithm Based on Min/Max Criterion...79
4.5 Computer Simulation Results...82
4.6 Summary...84

Chapter 5 Linearly Constrained Adaptive Constant Modulus RLS Algorithm for Blind DS-CDMA Multiuser Receiver in Time-Varying Channels...89
5.1 Introduction...89
5.2 Signal Model Description...90
5.3 Linearly Constrained Adaptive Constant Modulus RLS Algorithm...91
5.3.1 Derivation of the LC-ACM-RLS Algorithm...92
5.3.2 Optimal MMSE solution of the LCCM Algorithm...95
5.4 LC-ACM-RLS Receiver for Multipath Fading Channels...98
5.4.1 System Model in Multipath Channel...99
5.4.2 The Optimal Weight Solution and Its Adaptive Implementation...99
5.5 Computer Simulation Results...101
5.6 Summary...104

Chapter 6 Conclusions...109

[1] J. G. Proakis, Digital Communications, 4th Eds. McGraw-Hall, Inc., New York, 2001.
[2] Roger L. Peterson, Rodger E. Ziemer and David E. Borth, Introduction To Spread Spectrum Communication, Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 1991.
[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] S. Verdú, Multiuser Detection, in Advances in Detection and Estimation. JAI Press, 1993.
[6] M. Honig, U. Madhow and S. Verdú, “Blind Adaptive Multiuser Detection,” IEEE Trans. on Information Theory, vol. 41, no. 4, pp. 944 –960, July 1995.
[7] U. Madbow and M. L. Honig, “MMSE interference suppression for direct-sequence spread spectrum CDMA,” IEEE Trans. Commun., vol. 42, no. 12, pp. 3178-3188, Dec. 1994.
[8] J. F. Weng and T. Le-Ngoc, “RAKE Receiver Using Blind Adaptive Minimum Output Energy Detection for DS/CDMA Over Multipath Fading Channels,” IEE Journal on Proc-Communication, vol. 14, no. 6, pp. 385-392, Dec. 2001.
[9] S. R. Kim, Y. G. Jeong and I. K. Choi, “A Constrained MMSE Receiver for DS/CDMA Systems in fading channels,” IEEE Trans. on Communication, vol. 48, no. 11, pp. 1793-1796, Nov. 2000.
[10] S. R. Kim, Y. G. Jeong, I.-K. Choi and S. Lee, “An Adaptive Pilot Symbol-Aided MMSE Receiver in fading channels,” IEEE International Conference on Communications, vol. 1, pp. 555-558, 1999.
[11] M. Latva-aho and M. Juntti, “LMMSE Detection for DS-CDMA Systems in Fading Channels,” IEEE Trans. Commun., vol. 48, pp. 194-199, Feb. 2000.
[12] M. Latva-aho and M. Juntti, “Modified Adaptive LMMSE Receiver for DS-CDMA Systems in Fading Channels,” The 8th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, vol. 2, pp. 554-558, Sep. 1997.
[13] H. F. Hsiao, M. H. Hsieh and C. H. Wei, “Narrow-band Interference Rejection in OFDM-CDMA Transmission System,” Proceedings of the 1998 IEEE International Symposium on Circuits and Systems, vol. 4, pp. 437-440, 31 May-3 Jun 1998.
[14] W. S. Hou, L. M. Chen and B. S. Chen, “Adaptive Narrowband Interference Rejection in DS-CDMA Systems: A Scheme of Parallel Interference Cancellers,” IEEE Journal on Selected Areas in Communication, vol. 19, no. 6, pp.1103-1114, June 2001.
[15] J. Luo, C. Davis and D. Ho, “Performance study on adaptive LMS Filter to NBI Rejection in CDMA Signals,” Proceedings of IEEE ICSP2000, vol. 1, pp. 193 –196, 2000.
[16] B.-Y. CHOI and Z. BIEN, “Sliding-Windowed Weighted Recursive Least-Squares Method for Parameter Estimation,” Electronics Letters, vol. 25, no. 20, pp. 1381-1382, Sep. 1989.
[17] L. S. Resende, J. Marcos, T. Romano and Maurice G., “A FAST Least-Squares Algorithm for Linearly Constrained Adaptive Filtering,” IEEE Trans. on Signal Processing, vol. 44, no.5, pp. 1168-1174, May 1996.
[18] V. Nagesha and S. Kay, “On frequency estimation with IQML algorithm,” IEEE Trans. Signal Processing, vol. 42, no. 9, pp. 2509-2513, Sep. 1994.
[19] J. S. Thompson, P. M. Grant, and B. Mulgrew, “Smart antenna arrays for CDMA systems,” IEEE Personal Commun., vol. 3, no. 5, pp. 16-25, Oct. 1996.
[20] M. K. Tsatsanis and Z. Xu, “Performance analysis of minimum variance CDMA receivers,” IEEE Trans. Signal Processing, vol. 46, no. 11, pp. 3014-3022, Nov. 1998.
[21] Z. Xu and M. K. Tsatsanis, “Blind adaptive algorithms for minimum variance CDMA receivers,” IEEE Trans. Commun., vol. 49, no. 1, pp. 180-194, Jan. 2001.
[22] S. Haykin, Adaptive Filter Theory, 3rd Eds. Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 1991.
[23] S. J. Chern, C. H. Sun and H. P. Lee, “Adaptive RAKE receiver with sliding window linearly constrained RLS algorithm for multipath fading DS-SS CDMA system,” IEICE Trans. on Commun., vol. E87-B, no. 7, pp. 1970-1976, July 2004.
[24] H. V. Poor and X. Wang, “Code-aided interference suppression for DS/CDMA communications-Part II: Parallel blind adaptive implementations,” IEEE Trans. Commun. vol. 45, no. 9, pp. 1112-1122, Sep. 1997.
[25] A. Giorgetti, M. Chiani and M. Z. Win, “ The effect of narrowband interference on wideband wireless communication systems,” IEEE Transactions on Communications, vol. 53, no. 12, Dec.2005.
[26] 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.
[27] B. Sklar, Digital Communications, Fundamentals and Applications. Englewood Cliffs, NJ: Prentice-Hill, 1988.
[28] P. P. Vaidyanathan, Multirate Systems and Filter Banks. Englewood Cliffs, NJ: Prentice-Hall, 1993.
[29] A. F. Naguib, A. Paulraj, and T. Kailath, “Capacity improvement with base-station antenna arrays in cellular CDMA,”IEEE Trans. Vehicular Technology, vol. 43, no. 3, pp. 691-698, Aug. 1994.
[30] S. J. Chern and C. Y. Chang, “Adaptive MC-CDMA receiver with constrained constant modulus IQRD-RLS algorithm for MAI suppression,” Signal Process., vol. 83, no. 10, pp. 2209-2226, Oct. 2003.
[31] S. J. Chern and C. Y. Chang, “Direct implementation of space-time MC-CDMA receiver with adaptive linearly constrained constant modulus algorithm,” IEICE Trans. on Commun., vol. E88-B, no. 2, pp. 698-705, Feb. 2005.
[32] J. Miquez and L. Castedo, “ A linearly constrained constant modulus approach to blind adaptive multiuser interference suppression,” IEEE commun. Lett., vol.2, no.8, pp.217-219, Aug. 1998.
[33] C. Xu and G.Feng, “Comments on “A linearly constrained constant modulus approach to blind adaptive multiuser interference suppression”,” IEEE commun. Lett., vol.4, no.9, pp.280-282, Sept. 2000.
[34] Z. T. Zhixing. and Y. Yao, “Closed-form analysis of linearly constrained CMA-based blind multiuser detector,” IEEE Commun. Lett., vol. 4, no.9, pp. 273-276, Set. 2000.
[35] C. Xu and K. S. Kwak, “ Comments on: Closed-form analysis of linearly constrained CMA-based blind multiuser detector,” IEEE Commun. Lett., vol. 5, no.7, pp. 290-291, July 2001.
[36] J. P. de Villiers and L. P. Linde, ”On the convexity of the LCCM cost function for DS-CDMA blind multiuser detection”, IEEE Commun. Lett., vol. 8, no.6, pp. 351-353, June 2004.
[37] D.N. Godard, “Self-recovering equalization and carrier tracking in two-dimension data communication systems,” IEEE Trans. Commun., vol. COM-28, no. 11, pp. 1867-1875, Nov. 1980.
[38] J. Míguez and L. Castedo, “A constant modulus blind adaptive receiver for multiuser interference suppression,” Signal Process., vol. 71, no. 1, pp. 15-27, Nov. 1998.
[39] T. Miyajima, "Blind adaptive detection using differential CMA for CDMA systems," Electronics and Communications in Japan: Part 3, vol. 85, no. 1, pp. 1-13, Jan. 2002.
[40] N. E. Hubing and S. T. Alexander, “Statistical analysis of the soft constrained initialization of recursive least squares algorithms,” IEEE ICASS,. pp. 1277-1280, April 1990.
[41] Theodore S. Rappaport, Wireless Communications: Principles and Practice, 2nd Eds. Prentice-Hall, Inc., Englewood Cliffs, New Jersey, 2001.
[42] A. Chockalingasm and L. B. Milstein, “Open-loop power control performance in DS-CDMA networks with frequency selective fading and non-stationary base stations,” Wireless Networks, vol. 4, no. 3, pp. 249-261, March 1998.
[43] R. C. de Lamare and R. Sampaio-Neto, “Blind Adaptive Code-Constrained Constant Modulus Algorithms for CDMA Interference Suppression in Multipath Channels,” IEEE Commun. Lett., vol. 9, no.4, pp. 334-336, April 2005.
[44] B. Yang “Projection approximation subspace tracking,” IEEE Trans. Signal Processing, vol. 43, no. 1, pp. 95-107, Jan. 1995
[45] Special Issue, “IMT-2000: Standards efforts of the ITU,” IEEE Personal Commun., vol. 4, Aug. 1997.
[46] T. Ojanperä and R. Prasad, “An overview of air interference multiple access for IMT-2000/UMTS,” IEEE Commun. Mag., vol. 36, no. 9, pp 82-95, Sept. 1998.
[47] E. Dahlman, B. Gudmundson, M. Nilsson, and J. Sköld, “UMTS/IMT-2000 based on wideband CDMA,” IEEE Commun. Mag., vol. 36, no. 9, pp. 70-80, Sept. 1998.
[48] F. Adachi, M. Sawahashi, and H. Suda, “Wideband DS-CDMA for next-generation mobile communications systems,” IEEE Commun. Mag., vol. 36, no. 9, pp. 56-69, Sept. 1988.
[49] J. C. Liberti and T. S. Rappaport, “Analytical results for capacity improvements in CDMA,” IEEE Trans. Veh. Techol., vol. 43, no. 3, pp. 680-690, Aug. 1994.
[50] A. F. Naguib and A. Paulraj, “Performance of wireless CDMA with M-ary orthogonal modulation and cell site antenna arrays,” IEEE J. Select. Areas Commun., vol. 14, no. 9, pp. 1770-1783, Dec. 1996.
[51] X. Wang and H. V. Poor, “Space-time multiuser detection in multipath CDMA channels,” IEEE Trans. on Signal Processing, vol. 47, no.9, pp.2356-2374, Sept. 1999.
[52] C. B. Papadias and H. Huang, “Linear space-time multiuser detection for multipath CDMA channels,” IEEE J. Select. Area Commun. Vol. 19, no. 2, pp. 254-265, Feb. 2001.
[53] Y. Huang, and H. Leib, “SINR maximizing space-time filtering for asynchronous DS-CDMA,” IEEE J. Select. Areas Commun. Vol. 18, no. 7, pp. 1191-1202, July 2000.
[54] H. K. Wu, W. H. Fang and M. L. Wu, “A cascaded constrained beamforming and multiuser detection using cyclostationarity,” IEEE Proc. VTC’02, pp. 1921-1925, 2002.
[55] S. Haykin, A. H. Sayed, J. R. Zeidler, P. Yee, and P. C. Wei, “Adaptive tracking of linear time-variant systems by extended RLS algorithms,” IEEE Trans. on Signal Processing, vol. 45, no.5, pp.1118-1128, May 1997.
[56] E. Eleftheriou and D. D. Falconer, “Tracking properties and steady-state performance of RLS adaptive filter algorithms,” IEEE Trans. Signal Processing, vol. 34, no. 5, pp. 1079-1110, Oct. 1986.
[57] S. Verdú, Mutiuser Detection, Cambridge Univ. Press, New York, 1998.
[58] O. L. Frost III, “An Algorithm for Linearly Constrained Adaptive Array Processing,” IEEE Proc., vol. 60, pp. 926-935, Aug. 1972.
[59] H. Liu and K. Li, “A Decorrelating RAKE Receiver for CDMA Communications over Frequency-Selective Fading Channels,” IEEE Trans. Commun., vol. 47, no. 7, July 1999.
[60] J. B. Schodorf and D. B. Williams, “A Constrained Optimization Approach to Multiuser Detection,” IEEE Trans. Signal Processing, vol. 45, no. 1, pp. 258-262, Jan. 1997.
[61] R. S. C. Wong, A. A. Jr, and V. K. Bhargava, “Evaluation of Predetection Diversity Techniques for RAKE Receivers,” IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, vol. 1, pp. 227-230, Aug. 1997.
[62] C. H. Sun, S. J. Chern, and J. N. Li, “Adaptive Multiuser RAKE Detector for DS-CDMA System with Constant Modulus Linear Constrained IQRD-RLS Algorithm,” IEEE ISPACS’ 02, Ambassador Hotel, Kaohsiung Taiwan, R.O.C. pp. 17-21, Nov. 21-24, 2002.
[63] P. Yuvapoositanon and J. A. Chambers, “A CMOE-CMA RAKE Receiver Structure for Near-Far Frequency-Selective Fading CDMA Channels,” IEEE Sixth International Symposium on Signal Processing and its Applications, vol. 2, pp. 418-421, Aug. 2001.
[64] R. V. Nee and R. Prasad, OFDM Wireless Multimedia Communications. Boston: Artech House, 2000.
[65] A. Al-Bayati and S. Prasad, “Modified Constant Modulus Algorithm for Blind DS/CDMA Detection,” Electron. Lett., pp. 2005-2006, vol. 35, no.23, Nov. 1999.
[66] A. N. Barbosa and S. L. Miller, “Adaptive detection for DS/CDMA signals in fading channels,” IEEE Trans. Commun., vol. 46, pp. 115-124, Jan. 1998.
[67] S. Verdú, “Minimum Probability of Error for Asynchronous Gaussian Muliple-access Channels,” IEEE Trans. Inform. Theory, vol. IT-32, pp. 85-96, Jan. 1986.
[68] R.Lupas and S. Verd, ”Linear multiuser detectors for synchronous code-division multiple-access channel, ”IEEE Trans. Inform., Theory , vol. 35 pp. 123-136, Jan.1989.
[69] ―,”Near-far resistance of multiuser detectors in asynchronous channels,” IEEE Trans. Commu., vol.38, Mar.1990.
[70] H. Chen and S. C. Chan, “Blind linear MMSE receivers for MC-CDMA systems,” IEEE Transactions on Circuits and Systems: Accepted for future publication.
[71] U. Meahow and M. Honing, “MMSE Interference Suppression for Direct-Sequence Spread Spectrum CDMA,” IEEE Trans. Commun., vol. 42,no.12,pp. 3178-3188, Dec. 1994.
[72] Shiunn-Jang Chern, Chun-Hung Sun, ”Constrained Adaptive Constant Modulus RLS Algorithm for Blind DS-CDMA Multiuser Receiver Under Time-Varying Channels,” The IEICE Trans. on Fundamentals,(Accepted).