臺灣博碩士論文加值系統

在這篇論文裡，我們提出一個全新的應用於多天線傳輸系統之奇異值分解偵測演算法。本演算法成功將傳統上限定於前置編碼系統的奇異質分解演算法，成功應用於一般性的非前置編碼多天線傳輸系統。
本演算法所提出的奇異值分解多天線偵測演算法(SVD-based MIMO Detection)，達到PER在0.08下與最大相似演算法誤差在0.5dB以內，並且與傳統之QR分解在同樣套用K-best架構下達到相同的誤差表現。
本演算法是在偵測前，將通道資訊陣列進行埃爾米特矩陣(Hermitain Matrix)轉換，並以此形式進行奇異值分解運算。最後配合以基礎等化器所估出之可能落點進行與星狀圖的偵測及調整，進而完成以寬度優先之搜尋分界的演算法。
實作於IEEE 802.11n的通訊平台上，提供4T4R、64QAM調變，在符合TGN-E所規範的通道模型中進行模擬。並與傳統之QR分解式K-best進行複雜度與擴充性比較，並且搭配矩陣相乘消除法 (Matrix Multiplication Reduction)進行各部分的複雜度降低與效能觀察。並在SVD架構下，能以規律計算之矩陣乘法換取降低矩陣分解之複雜度。

For enhancing spectral efficiency and reducing fading distortions, multiple-input multiple-output orthogonal frequency division multiplexing (MIMO OFDM) is widely adopted for recently wireless communication systems. In this work, a new SVD-based MIMO Detection using a clustering technique, namely SCMD, is proposed, which partitions the transmitted signals into clusters for deciding candidates according to minimum squared Euclidean distance metric. In additions, our SVD decomposition doesn’t require any pre-coding of transmitter to substitute for traditional QR decomposition.
Through simulations in an MIMO-OFDM system with frequency-selective fading (100-ns RMS delay spreading; 15 taps), the SNR loss of the proposed SCMD method is within 0.5dB, compared with maximum likelihood (ML) detector. It also indicates the less complexity than K-Best approach (K=12) to achieving the same performance.

摘要 I
Abstract II
誌謝 III
Table of Contents V
List of Figures Ⅵ
List of Tables Ⅷ
Chapter 1 Introduction 1
Chapter 2 System Assumptions 2
2.1 MIMO System Description 2
2.2 Motivation and Problem Statement 4
Chapter 3 SVD-based Clustering MIMO Detection in Non-precoding OFDM System 5
3.1 Introduction 5
3.2 SVD-based Clustering MIMO Detection 6
3.2.1 Steps of the SVD-based Clustering MIMO Detection 6
3.2.2 Pre-estimating 10
3.2.3 Hermitian Matrix Processing 11
3.2.4 SVD with Herimitian Matrix 11
3.2.5 Overlapped Clustering Algorithm 15
3.2.6 Dynamic Overlapped Clustering 17
3.2.7 Constellation Space Distortion 18
3.2.8 Detail Matching 19
3.2.9 Signal Readjustment 20
3.2.10 Matrix Multiplication Reduction 21
Chapter 4 Simulation Results 25
4.1 Performance Evaluation 26
4.2 Complexity Evaluation 37
Chapter 5 Future Works and Conclusion 40
Bibliography 41

[1] T. D. Chiueh and P. Y. Tsai, OFDM Baseband Receiver Design for Wireless Communications. Wiley, September 2007.
[2] P.W. Wolniansky, G.J. Foschini, G.D. Golden, and R.A. Valenzuela,“V-BLAST: An architecture for realizing very high data rates over the rich-scattering wireless channel,” Proc. IEEE ISSSE 1998, pp.295–300, Sept. 1998.
[3] X. Zhu and R. D. Murch, “Performance analysis of maximum likelihood detection in a MIMO antenna system,” IEEE Trans. Commun., vol. 50, pp. 187–191, Feb. 2002.
[4] Y. de Jong and T. Willink. "Iterative tree search detection for MIMO wireless systems," Communications, IEEE Transactions on, 53(6):930–935, June 2005.
[5] E. Viterbo and J. Boutros, “A universal lattice code decoder for fading channels,” IEEE Trans. Inf. Theory, vol.45, no.5, pp.1639–1642, July 1999.
[6] U. Fincle and M. Phost, "Improved methods for calculating vectors for short length in a lattice,includeing complexity analysis," Math. Comput., vol. 44, pp. 463-471, April. 1985.
[7] Rupp, M.; Gritsh, G.; Weinrichter, H., "Approximate ML detection for MIMO systems with very low complexity," ICASSP '04. IEEE International Conference on , vol.4, no., pp. iv-809-12 vol.4, 17-21 May 2004
[8] Y. de Jong and T. Willink. "Iterative tree search detection for MIMO wireless systems," Communications, IEEE Transactions on, 53(6):930–935, June 2005.
[9] G.A. Awater, A. van Zelst, and R. van Nee, “Reduced complexity space division multiplexing receivers,” Proc. IEEE VTC 2000, pp.11–15, May 2000.
[10] 802.11n standard, "TGn Sync Proposal Technical Specification", IEEE 802.11-04/0889r7, July 2005
[11] Takafumi FUJITA, Atsushi OHTA, TakeshiONIZAWA, and Takatoshi SUGIYAMA,“A Reduced-Complexity Signal Detection Scheme Employing ZF and K-Best Algorithms for OFDM_SDM”,IEICE TRANS. COMMUN., VOL.E88–B, NO.1 JANUARY 2005.
[12] Tao Cui and Chintha Tellambura,"Approximate ML Detection for MIMO Systems Using Multistage Sphere Decoding”, Signals, Systems and Computers, 2004. Conference Record of the Thirty-Eighth Asilomar Conference on Nov. 2004.
[13] H. C. Chang, Y. C. Liao, and H. C. Chang, “Low Complexity Prediction Techniques of K-best Sphere Decoding for MIMO Systems,” , IEEE Workshop on Signal Processing Systems, pp. 45 – 49, Oct. 2007.
[14] Hun Seok Kim , Weijun Zhu , Jatin Bhatia , Karim Mohammed , Anish Shah , Babak Daneshrad, A practical, hardware friendly MMSE detector for MIMO-OFDM-based systems, EURASIP Journal on Advances in Signal Processing, 2008, p.1-14, January 2008.
[15] Q. Li and Z. Wang, “An improved K-best sphere decoding architecture for MIMO systems,” Fortieth Asilomar Conference on Signals, Systems and Computers, pp.2190-2194, Oct.-Nov., 2006.
[16] L. Qingwei and W. Zhongfeng, “Improved K-Best sphere decoding algorithms for MIMO systems,” in Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS '06), pp. 1159–1162, May 2006.
[17] D. Wübben, R. Bohnke, V. Kuhn, and K.D. Kammeyer, “MMSE extension of V-BLAST based on sorted QR decomposition,” Proc. IEEE VTC Fall, 2003, vol.1, pp.508–512, 2003.
[18] D. Wübben, R. Bohnke, J. Rinas, V. Kuhn, and K.D. Kammeyer, “Efficient algorithm for decoding layered space-time codes,” Electron. Lett., vol.37, no.22, pp.1348–1350, Oct. 2001.
[19] Z. Guo and P. Nilsson, “Algorithm and implementation of the K-best sphere decoding for MIMO detection,” IEEE J. Sel. Areas Commun., vol. 24, pp. 491-503, Mar. 2006.
[20] A. Wiesel, X. Mestre, A. Pages, and J. R. Fonollosa, “Efficient implementation of sphere demodulation,” in Proceedings of the IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC '03), pp. 36–40, June 2003.
[21] M. Damen, H.El Gamal, and G. Caire, “On maximum-likelihood detection and the search for the closest lattice point,” IEEE Trans. Inf. Theory, vol. 49, no. 10, pp. 2389-2402, Oct. 2003.