簡易檢索 / 詳目顯示

研究生: 朱彥昀
Chu, Yen-Yun
論文名稱: Low Complexity MIMO Detection with Bound-Constraint Semidefinite Relaxation for 16×16 MIMO Communications
應用於16×16通訊系統的低複雜度邊界限制半定放寬之多輸入多輸出檢測法
指導教授: 馬席彬
Ma, Hsi-Pin
口試委員: 蔡佩芸
吳仁銘
楊家驤
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 通訊工程研究所
Communications Engineering
論文出版年: 2012
畢業學年度: 100
語文別: 中文
論文頁數: 72
中文關鍵詞: 多輸入多輸出半定放寬低複雜度偵測器
相關次數: 點閱:2下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 近年來,半定放寬法被廣泛應用到各種研究領域上,而在多輸入多輸出檢測法應用上,當天線數增加、mapper變大的情況下,半定放寬法更被視為比球體解碼演算法要來得高效能且複雜度低,逐漸的,此方法獲得研究者的興趣和注意。邊界限制半定放寬法是一個簡單且低複雜度的偵測法,其搭配特殊的原始-對偶內點法(PD-IPM)可以達到演算法複雜度O(n3.5)。邊界限制半定放寬法最大的特性,在於mapper的大小並不影響所解問題本身的矩陣維度,所以我們利用此特性選擇了邊界限制半定放寬法當作研究的基礎。
    另一方面,由於我們所制定的收發天線數目為16ˣ16,邊界半定放寬法會導致欲解的矩陣維度增大為原來的四倍,過大的矩陣會使得問題複雜難解,因此,我們提出一個修改過的邊界半定放寬法。此法利用下邊界(lower-bound)對於高階正交調幅調變(higher-QAM)影響較小的概念,將下邊界的限制式移除,使得矩陣維度約只剩邊界限制半定放寬法的一半,來降低複雜度。
    我們以PD-IPM演算法解修改過的邊界限制半定放寬的問題。從效能模擬結果發現,在49dB處可以達到10-3的錯誤率,比原來的邊界限制半定放寬法損失1.5dB,不過仍符合我們所制訂的規格(<50dB at 10-3),在運算複雜度上,此論文所提出的方法將比原來的方法省51.21%。此外,我們也針對其矩陣對稱的特性去設計整個針測器的硬體架構。另外,求解線性方程組中,我們利用重複使用同一個架構達到使用率最佳化及高吞吐量。


    1 Introduction . . . . . . . . . . . . . . . 1 1.1 Overview of MIMO Communication Systems . . . . . 1 1.1.1 Multiple-Input Multiple-Output Detection . . . .1 1.1.2 Semidefinite Relaxation . . . . . . . . . .2 1.2 Motivation of the Thesis . . . . . . . . . .3 1.3 Organization of the Thesis . . . . . . . . .4 2 Convex Problem . . . . . . . . . . . . . . 7 2.1 Definition . . . . . . . . . . . . . . .7 2.2 Introduction . . . . . . . . . . . . . .8 3 MIMO Communication Systems . . . . . . . . . .11 3.1 Introduction . . . . . . . . . . . . . .11 3.2 MIMO Receiving Techniques for Spatial Multiplexing ..11 3.2.1 Maximum Likelihood Detection . . . . . . . .11 3.2.2 Sphere Decoding Detection . . . . . . . . .12 3.3 Overview of SDP Forms . . . . . . . . . . .14 3.3.1 Polynomial Inspired Semidefinite Relaxation . . .15 3.3.2 Virtual Antipodal Semidefinite Relaxation . . . 16 3.3.3 Bound-Constrained Semidefinite Relaxation . . . 17 3.3.4 Tightened Bound-Constrained Semidefinite Relaxation . .18 4 PD-IPM Algorithm for Modified BC-SDR Problem . . . .19 4.1 Basic Concepts . . . . . . . . . . . . .19 4.2 Primal-Dual Interior Point Method (PD-IPM) . . . .21 4.3 Modified BC-SDR . . . . . . . . . . . . .22 4.3.1 Pre-processing . . . . . . . . . . . . 23 4.3.2 Matrix Inversion with Cholesky Decomposition . . 24 4.3.3 Direction Search . . . . . . . . . . . 26 4.3.4 Backtracking Line Search . . . . . . . . .28 4.3.5 Data Renew . . . . . . . . . . . . . 31 4.3.6 Extraction . . . . . . . . . . . . . 31 4.4 Computational Complexity Analysis . . . . . . 32 4.5 Simulation Results and Comparisons . . . . . . 33 4.5.1 Accuracy Searching . . . . . . . . . . .34 4.5.2 Performance Analysis . . . . . . . . . . 34 5 Architecture Design . . . . . . . . . . . .39 5.1 System Model . . . . . . . . . . . . . .39 5.1.1 Mathematical Model . . . . . . . . . . .39 5.1.2 SNR Definition . . . . . . . . . . . . 40 5.1.3 Specification . . . . . . . . . . . . .40 5.2 Proposed Architecture . . . . . . . . . .41 5.3 Pre-processing Architecture . . . . . . . . .43 5.4 Matrix Inversion . . . . . . . . . . . . 44 5.4.1 Cholesky Decomposition . . . . . . . . . .45 5.4.2 Triangular Matrix Inversion . . . . . . . . 46 5.4.3 Data Arrangement . . . . . . . . . . . .47 5.4.4 Gauss-Jordan Elimination . . . . . . . . . 48 5.5 Linear Equations Solver . . . . . . . . . . 49 5.6 Direction Search Architecture . . . . . . . . 49 5.7 Data Renew Architecture . . . . . . . . . . 50 5.8 Word-length Determination . . . . . . . . . 50 5.8.1 Word-length Determination Method . . . . . . 50 5.8.2 Word-length in Proposed Architecture . . . . . 51 5.9 Hardware Analysis . . . . . . . . . . . . 52 5.10 Discussion . . . . . . . . . . . . . . 53 6 Conclusions and Future Works . . . . . . . . . 65 6.1 Conclusions . . . . . . . . . . . . . . 65 6.2 Future Works . . . . . . . . . . . . . .67

    [1] L. Ma, K. Dickson, J. McAllister, and J. McCanny,“QR Decomposition-Based Matrix Inversion for High Performance Embedded MIMO Receivers,”IEEE Trans. Signal Process., vol. 59, no. 4, pp. 1858–1867, Apr. 2011.
    [2] W. K. Ma, C. C. Su, J. Jald´en, T. H. Chang, and C. Y. Chi,“The Equivalence of Semidefinite Relaxation MIMO Detectors for Higher-Order QAM,”IEEE J. Sel. Topics in Signal Process., vol. 3, no. 6, pp. 1038-1052, Dec. 2009.
    [3] J. G. Proakis and M. Salehi, Digital Communications, 5th ed. McGraw-Hill, May 2008.
    [4] P. H. Tan and L. K. Rasmussen,“The Application of Semidefinite Programming for Detection in CDMA,”IEEE J. Sel. Areas Comms., vol. 19, no. 8, pp. 1442-1449, Aug. 2001.
    [5] W. K. Ma, P. C. Ching, and Z. Ding, Semidefinite Relaxation Based Multiuser Detection for M-ary PSK Multiuser Systems,”IEEE Trans. Signal Process., vol. 52, no. 10, pp. 2862-2872, Oct. 2004.
    [6] A. Wiesel, Y. C. Eldar, and S. S. Shitz, “Semidefinite Relaxation for Detection of 16-QAM Signaling in MIMO Channels,”IEEE Signal Process. Lett., vol. 12, no. 9, pp. 653-656, Sept. 2005.
    [7] N. D. Sidiropoulos and Z. Q. Luo,“A Semidefinite Relaxation Approach to MIMO Detection for Higher-Order Constellations,” IEEE Signal Process. Lett., vol. 13, no. 9, pp. 525-528, Sept. 2006.
    [8] A.Chockalingham,“Large MIMO Systems:
    Are They Practical?”Dec. 2008. [Online]. Available:
    http://www.qualcomm.com.au/innovation/research/university relations/lectures.html
    [9] B.Daneshrad,“MIMO: The Next Revolution in Wireless Data Communications,”Apr. 2008. [Online]. Available:
    http://rfdesign.com/military defense electronics/radio mimo next revolution/
    [10] S. Boyd and L. Vandenberghe, Convex Optimization. Cambridge Univ. Press, 2004. [Online]. Available: http://www.stanford.edu/∼boyd/cvxbook/
    [11] H. Hindi,“A Tutorial on Convex Optimization II: Duality and Interior Point Methods,”in Proc. American Control Conference ’06, vol. 4, no. 1, Minneapolis, Minnesota, Jun. 2006, pp. 686-696.
    [12] G. D. Golden, C. J. Foschini, R. A. Valenzuela, and P. W. Wolniansky, “Detection Algorithm and Initial Laboratory Results Using V-BLAST Space-Time Communication Architecture,” Electron. Lett., vol. 35, no. 1, pp. 14-16, Jan. 1999.
    [13] J. Jald´en, L. Barbero, B. Ottersten, and J. S. Thompson, “The Error Probability of the Fixed-Complexity Sphere Decoder,”IEEE Trans. Signal Process., vol. 57, no. 7, pp. 2711-2720, Jul. 2009.
    [14] Z. Mao, X. Wang, and X. Wang, “Semidefinite Programming Relaxation Approach for Multiuser Detection of QAM Signals,”IEEE Trans. Wireless Comms., vol. 6, no. 12, pp. 4275-4279, Dec. 2007.
    [15] Y. Yang, C. Zhao, P. Zhou, and W. Xu,“MIMO Detection of 16-QAM Signaling Based on Semidefinite Relaxation,” IEEE Signal Process. Lett., vol. 14, no. 11, pp. 797-800, Nov. 2007.
    [16] C. Helmberg, F. Rendl, R. Vanderbei, and H. Wolkowicz, “An Interior-Point Method for Semidefinite Programming,” SIAM J. Optim., vol. 6, no. 2, pp. 342-361, Sept. 1996.
    [17] W. K. Ma, C. C. Su, J. Jald´en, and C. Y. Chi,“Some Results on 16-QAM MIMO Detection Using Semidefinite Relaxation,”in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., Las Vegas, NV, Apr. 2008, pp. 2673-2676.
    [18] C. C. Su, “MIMO Detection Using Semidefinite Relaxation with Higher-Order QAM,”Master’s thesis, National Tsing Hua University, HsinChu, Taiwan, Aug. 2008.
    [19] N. Karmarkar,“A New Polynomial-Time Algorithm for Linear Programming,” Combinatorica, vol. 4, pp. 373-395, Dec. 1984.
    [20] M. Grant and S. Boyd,“CVX: Matlab Software for Disciplined Convex Programming, Version 1.21,”Apr. 2011. [Online]. Available: http://cvxr.com/cvx
    [21] P. Salmela, A. Happonen, T. Jarvinen, A. Burian, and J. Takala,“DSP Implementation of Cholesky Decomposition,” in Proc. SympoTIC’06., Bratislava, Slovakia, Jun. 2006, pp. 6-9.
    [22] J. Gallier,“The Schur Complement and Symmetric Positive Semidefinite (and Definite) Matrices,”Dec. 2010. [Online].Available:
    ftp://ftp.cis.upenn.edu/pub/cis511/public html/schur-comp.pdf
    [23] E. S. Quintana, G. Quintana, X. Sun, and R. V. D. Geijn,“Efficient Matrix Inversion via Gauss-Jordan Elimination and Its Parallelization,”1998.
    [24] H. Choi and W. Burleson,“Search-Based Wordlength Optimization for VLSI/DSP Synthesis,”in Proc. VLSI Signal Process., VII, 1994, pp. 198-207.
    [25] L. G. Barbero and J. S. Thompson,“Rapid Prototyping of a Fixed-Throughput Sphere Decoder for MIMO Systems,”in Proc. ICC ’06, vol. 7, Jun. 2006, pp. 3082-3087.
    [26] B. Wu and G. Masera,“A Novel VLSI Architecture of Fixed-Complexity Sphere Decoder,”in Proc. DSD ’10, Sept. 2010, pp. 737-744.
    [27] A. P. Yomi and B. F. Cockburn,“Near-Optimal And Efficient MIMO Detectors for 64-QAM Symbols,”in Proc. CCECE ’10, May 2010, pp. 1-6.
    [28] S. G. Haridas and S. G. Ziavras,“FPGA Implementation of a Cholesky Algorithm for a Shared-Memory Multiprocessor Architecture,”Parallel Algorithms and Applications, vol. 19, no. 6, pp. 411-426, Dec. 2004.
    [29] H. T. Wai, W. K. Ma, and A. M. C. So,“Cheap Semidefinite Relaxation MIMO Detection Using Row-by-Row Block Coordinate Descent,” in Proc. IEEE Int. Conf. Acoust., Speech, Signal Process., May 2011, pp. 3256-3259.

    無法下載圖示 全文公開日期 本全文未授權公開 (校內網路)
    全文公開日期 本全文未授權公開 (校外網路)
    全文公開日期 本全文未授權公開 (國家圖書館:臺灣博碩士論文系統)
    QR CODE