應用於多使用者波束成型器系統的盲目並行濾波器之低複雜度演算法

、線上人數：81

、訪客IP：3.15.34.244

姓名	余奇紘(YU,CI-HONG) 查詢紙本館藏	畢業系所	通訊工程學系
論文名稱	應用於多使用者波束成型器系統的盲目並行濾波器之低複雜度演算法 (Reduce Antenna Algorithm for blind-CAF in multi-user beamforming system)
檔案	[Endnote RIS 格式] [Bibtex 格式] [相關文章] [文章引用] [完整記錄] [館藏目錄] 至系統瀏覽論文 ( 永不開放)
摘要(中)	在本篇論文中，我們先介紹CMA+DD﹐CMA+MAP﹐CMA+SC-MAP﹐CMA+RSC-MAP等並行濾波器的演算法﹐並在不同的情境下比較以上四個演算法可以得知，在BER系統效能的觀點 CMA+RSC-MAP演算法和CMA+SC-MAP演算法相較於其他的演算法是最佳的，以系統複雜度的觀點可以得知CMA+DD為最佳CMA+RSC-MAP次之，CMA+SC-MAP則最差。由此可知CMA+RSC-MAP具有最佳的BER效能還擁有與CMA+DD相近的系統複雜度，而本篇論文所提出的降低天線演算法(Reduce Antenna Algorithm)加入各個演算法中不但不會影響BER系統效能且有效的降低系統複雜度，也解決了並濾波器高複雜度的問題。
摘要(英)	In this paper﹐we introduce four concurrent adaptive filter (CAF) as CMA+DD algorithm﹐CMA+MAP algorithm﹐CMA+RSC-MAP algorithm﹐and compare their performance of bit error rate (BER) in different scenario﹐we found that CMA+RSC-MAP algorithm and CMA+SC-MAP algorithm has the best performance of bit error rate﹐but in system complexity CMA+DD algorithm is the best ﹐CMA+RSC-MAP algorithm is second ﹐CMA+SC-MAP algorithm is the worst。 So we can know CMA+RSC-MAP has the best performance of bit error rate and it system complexity is very close with CMA+DD。 The reduce antenna algorithm ﹐we propose in this paper﹐it can reduce each concurrent adaptive system complexity effectively﹐ and does not affect their performance of bit error rate﹐therefore reduce antenna algorithm resolve the problem of high complexity for concurrent adaptive filter。
關鍵字(中)	★ 多使用者波束成型器 ★ 盲目並行濾波器	關鍵字(英)	★ beamforming system ★ blind-CAF
論文目次	目錄摘要 v Abstract vi 目錄 viii 圖目錄 x 表目錄 xii 第一章緒論 (Introduction) 1 第二章智慧型天線 (Smart Antenna) 4 2-1等距線性天線陣列通道模型(ULA) 6 2-2 盲目演算法與並行調適性濾波器用於波束形成器(Blind Concurrent Adaptive Filter for Beamformer) 8 2-2.1 星座點演算法 (Reduced Constellation Algorithm) 11 2-2.3 恆模數演算法 (Constant Modulus Algorithm) 13 2-2.2 多模數演算法 (Multi-Modulus Algorithm) 14 2-2.4 恆模數與硬式決策演算法 (Constant Modulus Algorithm and Decision Directed Algorithm) 17 2-2.5 恆模數與最大事後機率演算法 (Constant Modulus Algorithm and Maximum-A-Posterior Probability Algorithm) 19 2-2.6 對稱限制最大事後機率演算法(Symmetric-Constraint MAP Algorithm) 22 2-2.7 實數對稱限制最大事後機率演算法(Real-Valued Symmetric-Constraint MAP Algorithm) 25 第三章 31 3-1 動機 31 3-2 Introduce Reduce Antenna Algorithm 34 第四章模擬結果 (Simulation results) 37 4-1 系統模擬架構(Simulation System) 37 情境A(Scenario A) 38 情境B(Scenario B) 48 星座圖(Constellation) 53 波束圖樣(Beam pattern) 55 複雜度(Complexity) 59 結論(Conclusion) 62 參考文獻 (Reference) 63 圖目錄圖一 MIMO模型架構 4 圖二波束形成器，加強目標使用者信號，抑制干擾使用者信號 5 圖三天線陣列的種類 5 圖四等距線性天線陣列模型 7 圖五傳統調適性線性波束形成器 9 圖六波束圖樣(beam pattern)圖示例子 10 圖七 RCA的決策原則圖示 12 圖八 CMA的決策原則圖示 14 圖九 MMA的決策原則圖示 16 圖十低複雜度最大事後機率演算法示意圖 21 圖十一並行調適性濾波器輸出訊號星座圖 30 圖十二各演算法複雜度分析圖 31 圖十三天線訓練過程中每根天線的 33 圖十四 Reduce Antenna Algorithm 35 圖十五 Reduce Antenna Algorithm 流程圖 36 圖十六各演算法之BER效能 39 圖十七 CMA+DD演算法在天線訓練過程中每根天線的 40 圖十八 CMA+DD&RA之BER效能 41 圖十九 CMA+RSC-MAP演算法在天線訓練過程中每根天線的 42 圖二十 CMA+RSC-MAP&RA之BER效能 43 圖二十一 CMA+SC-MAP演算法在天線訓練過程中每根天線的 44 圖二十二 CMA+SC-MAP&RA之BER效能 45 圖二十三 CMA+MAP演算法在天線訓練過程中每根天線的 46 圖二十四 CMA+MAP&RA之BER效能 47 圖二十五各演算法在SNR變動下BER效能比較圖 48 圖二十六 CMA+DD和CMA+DD &RA在不同SNR下BER效能比較圖 49 圖二十七 CMA+RSC-MAP和CMA+RSC-MAP &RA在不同SNR下BER效能比較圖 50 圖二十八 CMA+SC-MAP和CMA+SC-MAP&RA在不同SNR下BER效能比較圖 51 圖二十九 CMA+MAP和CMA+MAP &RA在不同SNR下BER效能比較圖 52 圖三十 CMA+DD、CMA+DD&RA、CMA+MAP、CMA+MAP&RA星座圖 53 圖三十一 CMA+SC-MAP、CMA+SC-MAP&RA、CMA+RSC-MAP、CMA+RSC-MAP&RA 54 圖三十二 CMA+DD與CMA+DD&RA(T=0.2)之波束圖樣比較圖 55 圖三十三 CMA+RSC-MAP與CMA+RSC-MAP&RA(T=0.2)之波束圖樣比較圖 56 圖三十四 CMA+SC-MAP與CMA+SC-MAP&RA(T=0.2)之波束圖樣比較圖 57 圖三十五 CMA+MAP與CMA+MAP&RA(T=0.2)之波束圖樣比較圖 58 圖三十六各演算法與加入RA之天線平均個數圖 60 圖三十七各演算法與加入RA之複雜度比較圖 61 表目錄表一各演算法之參數設定 38 表二各演算法與加入降低天線演算法之運算複雜度 59
參考文獻	[1] S. Haykin, Adaptive filter theory, 4th edition. Englewood Cliffs, NJ: Prentice-Hall, 2002. [2] L. C. Godara, “Application of antenna arrays to mobile communications, part II: beam-forming and direction-of-arrival estimation,” Proc. IEEE, vol. 85, pp. 1195-1245, 1997. [3] A. I. Sulyman, and M. Hefnawi, “Adaptive MIMO beamforming algorithm based on gradient search of the channel capacity in OFDM-SDMA systems,” IEEE Commun. Lett., vol. 12, pp. 642-644, 2008. [4] S. Chen, A. Livingstone, H. Q. Du, and L. Hanzo, “Adaptive minimum symbol error rate beamforming assisted detection for quadrature amplitude modulation,” IEEE Trans. Wirel. Commun., vol. 7, pp. 1140-1145, 2008. [5] Y. J. Chang and C. L. Ho, “Reduced Symmetric Self-Constructing Fuzzy Neural Network Beamforming Detectors,” IET Microw. Antennas Propag., vol. 5, pp. 676-684, 2011. [6] L. Zhang, W. Liu, and R. J. Langley, “A class of constrained adaptive beamforming algorithms based on uniform linear arrays,” IEEE Trans. Signal Process., vol. 58, pp. 3916-3922, 2010. [7] L. Zhang, W. Liu, and R. J. Langley, “Adaptive beamforming with real-valued coefficients based on uniform linear arrays,” IEEE Trans. Antennas Propag., vol. 59, pp. 1047-1053, 2011. [8] L. Wang, and R. C. de Lamare, “Constrained adaptive filtering algorithms based on conjugate gradient techniques for beamforming,” IET Signal Process., vol. 4, pp. 686-697, 2010. [9] S. Chen, T. B. Cook, and L. C. Anderson, “A comparative study of two blind FIR equalizers,” Digit. Signal Prog., vol. 14, pp. 18-36, 2004. [10] W. Chung, “Soft decision approaches in adaptive blind decision feedback equalizers,” Electron. Lett., vol. 44, pp. 1328-1329, 2008. [11] F. D’ Agostini, S. Carboni, M. C. F. De Castro, F. C. C. De Castro, and D. von B. M. Trindade, “Adaptive concurrent equalization applied to multicarrier OFDM systems,” IEEE Trans. Broadcast., vol. 54, pp. 441-447, 2008. [12] S. Chen, “Low complexity concurrent constant modulus algorithm and soft decision directed scheme for blind equalization,” IEE Proc.-Vis. Image Signal Process., vol. 150, pp. 312-320, 2003. [13] M. T. M. Silva, M. D. Miranda, and R. Soares, “Concurrent algorithm for blind adaptation of DFE,” Electron. Lett., vol. 41, pp. 63-64, 2005. [14] B. Lin, R. He, X. Wang, and B. Wang, “Excess MSE analysis of the concurrent constant modulus algorithm and soft decision-directed scheme for blind equalization,” IET Signal Process., vol. 2, pp. 147-155, 2008. [15] S. Chen and L. Hanzo, “Fast converging semi-blind space-time equalisation for dispersive QAM MIMO systems,” IEEE Trans. Wirel. Commun., vol. 8, pp. 3969-3974, 2009. [16] C. P. Fan, C. H. Fang, H. J. Hu, and W. N. Hsu, “Design and analyses of a fast feed-forward blind equalizer with two-stage generalized multilevel modulus and soft decision-directed scheme for high-order QAM cable downstream receivers,” IEEE Trans. Consumer Electronics, vol. 56, pp. 2132-2140, 2010. [17] N. Xie, H. Hu, and H. Wang, “A new hybrid blind equalization algorithm with steady-state performance analysis,” Digit. Signal Prog., vol. 22, pp. 233-237, 2012. [18] R. Mitra, S. Singh, and A. Mishra, “Improved multi-stage clustering-based blind equalization,” IET Commun., vol. 5, pp. 1255-1261, 2011. [19] Yao-Jen Chang, Kai-Jiun Yang, Teng-Chieh Yang, “Fast converging CMA-aided maximum a-posteriori algorithm with a conjugate symmetric constraint for adaptive blind beamforming,” IEEE Signal Processing (ICSP) 11th Con, vol. 1 , pp.276-280, 2012 [20] De Castro, F.C.C., De Castro, M.C.F., and Arantes, D.S.: “Concurrent blind deconvolution for channel equalization”. Proc. ICC’2001, Helsinki, Finland, 11–15, vol. 2, pp. 366–371 June 2001 [21] Karaoguz, J., and Ardalan, S.H.: “A soft decision-directed blind equalization algorithm applied to equalization of mobile communication channels”. Proc. ICC’92, Chicago, USA, 1992, vol. 3, pp. 343.4.1–343.4.5 [22] Jiunn-Yann Chen, Yao-Jen Chang, Chia-Lu Ho, “Interference and Noise Cancellation Based on Complex/Real-Valued Blind Maximum-A-Posteriori Probability Algorithms with Symmetric Constraint”. AIT 7th con, pp.7, Chaoyang University of Technology, Taichung city, Taiwan, April 2013 [23] Jian Yang, Feng Yang, Yu Zhao, “Fast Adaptive Blind Beamforming Algorithm for Antenna Array in CDMA Systems”, IEEE Vehicular Technology Society, vol. 55,pp. 549 – 558, March 2006
指導教授	賀佳律	審核日期	2015-7-13
推文	facebook plurk twitter funp google live udn HD myshare reddit netvibes friend youpush delicious baidu
網路書籤	Google bookmarks del.icio.us hemidemi myshare

博碩士論文 102523046 詳細資訊