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研究生:辛建威
研究生(外文):Chien-Wei Hsin
論文名稱:十六正交振幅調變之正交時空區塊碼之盲蔽偵測
論文名稱(外文):Blind Detection of Orthogonal Space-Time Block Codes for 16-QAM Constellations
指導教授:祁忠勇
指導教授(外文):Chong-Yung Chi
學位類別:碩士
校院名稱:國立清華大學
系所名稱:通訊工程研究所
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:2008
畢業學年度:96
語文別:英文
論文頁數:46
中文關鍵詞:MIMOOSTBCML
相關次數:
  • 被引用被引用:0
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The blind maximum-likelihood (ML) detection of orthogonal space-time block codes(OSTBCs) is a computationally challenging optimization problem. Fortunately, for BPSK
and QPSK OSTBCs, it has been shown that the blind ML detection problem can be efficiently and accurately approximated by a semidefinite relaxation (SDR) approach and
optimally solved by sphere decoding [1]. This thesis considers the situation where the 16-QAM signals are employed. Due to the nonconstant modulus nature of 16-QAM signals, the associated blind ML OSTBC detection problem has its objective function exhibiting a Rayleigh quotient structure, which makes the SDR approach and sphere decoding not directly applicable. In this thesis, a linear fractional SDR (LF-SDR) approach is proposed for efficient approximation of the optimum blind ML solution. In fact, LF-SDR is a quasi-convex relaxation problem owing to the associated objective function with a fractional quadratic form. Quasi-convex problems in general may be computationally more complex to handle than convex problems, but we show that the optimum solution of our quasi-convex problem can instead be efficiently obtained by solving a convex problem, namely a semidefinite program (SDP). This LF-SDR approach is developed based on the relaxation technique of bound-constrained SDR (BC-SDR), [13] previously proposed for dealing with the coherent MIMO ML detection problem with 16-QAM. We also apply some other existing 16-QAM SDR techniques, namely polynomial-inspired SDR (PI-SDR) [19], and virtually-antipodal SDR (VA-SDR) [20], to develop the LF-SDR. We prove that the three SDR techniques (BC-SDR, PI-SDR, VA-SDR) achieve the same approximation performance. Since the LF-SDR is an approximate ML detector which is suboptimal, we propose a modified sphere decoder to our fractional quadratic problem to obtain the optimal blind ML solution. Simulation results demonstrate that the proposed LF-SDR based blind ML detector outperforms the norm relaxed blind ML detector and
the blind subspace channel estimator [5], especially in the one-receive-antenna scenario. It is also found that the proposed LF-SDR and modified sphere decoder exhibit very
close symbol error performance; while the former is much more appropriate for large size problem due to its relatively low complexity.
1 INTRODUCTION 1
2 SIGNAL MODEL AND PROBLEM STATEMENT 5
2.1 Signal Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Basic Concepts of Orthogonal Space-Time Block Codes (OSTBCs) . . . . . 7
2.3 Review the Blind ML Detection of OSTBC Problem Using BPSK/QPSK . 8
2.4 Simplification of the 16-QAM Blind ML OSTBC Detection Problem . . . . 12
3 PROPOSED LINEAR FRACTIONAL SDR APPROACH 14
3.1 Homogeneous Reformulation of the Blind ML Problem . . . . . . . . . . . 14
3.2 Linear Fractional Semidefinite Relaxation (LF-SDR) . . . . . . . . . . . . 15
3.3 SDP Reformulation of LF-SDR, and Implications . . . . . . . . . . . . . . 17
3.4 Rank-1 Approximation Methods . . . . . . . . . . . . . . . . . . . . . . . . 19
3.5 Application of Other Existing 16-QAM SDRs to Fractional Quadratic Problem
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.5.1 Linear Fractional Polynomial-Inspired Semidefinite Relaxation (LFPISDR)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
3.5.2 Linear Fractional Virtually Antipodal Semidefinite Relaxation (LFVASDR)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.6 Equivalence of the three LF-SDR approaches . . . . . . . . . . . . . . . . . 23
3.7 Complexity Comparisons of the three LF-SDR approaches . . . . . . . . . 25
4 MODIFIED SPHERE DECODING 26
4.1 Convert Fractional Quadratic Form to Integer Least Square . . . . . . . . . 27
4.2 Modified Sphere Decoding Algorithm . . . . . . . . . . . . . . . . . . . . . 28
5 SIMULATION RESULTS 31
5.1 Performance Comparisons of LF-SDR with Other Suboptimal Blind Detectors 32
5.2 Equivalence between LF-SDR, LF-PISDR, and LF-VASDR . . . . . . . . . 36
5.3 Performance Comparisons of LF-SDR and Modified Sphere Decoder . . . . 38
5.4 Complexity Comparisons of LF-SDR and Modified Sphere Decoder . . . . 40
6 CONCLISIONS 42
[1] W.-K. Ma and B.-N. Vo and T. N. Davidson and P.-C. Ching, ”Blind ML detection
of orthogonal space-time block codes: Efficient high-performance implementations,”
IEEE Trans. Signal Process., vol 54, no. 2, pp 738-751, Feb. 2006.
[2] G. Ganesan and P. Stoica, ”Differential detection based on space-time block codes,”
Wireless Perosnal Commun., Norwell, MA: Kluwer, 2002, pp. 163-180.
[3] V. Tarokh and H. Jafarkhani and A. R. Calderbank, ”Space-time block codes from
orthogonal designs,” IEEE Trans. Inform. Theory, vol 45, no. 5, pp. 1456-1467, July
1999.
[4] E. G. Larsson and P. Stoica and J. Li, ”Orthogonal space-time block codes: Maximum
likelihood detection for unknown channels and unstructured interferences,” IEEE
Trans. Signal Process., vol 51, no 2, pp. 362-372, Feb. 2003.
[5] S. Shahbazpanahi and A.B. Gershman and J.H. Manton, ”Closed-form blind MIMO
channel estimation for orthogonal space-time block codes”, IEEE Trans. Signal
Process., vol. 53, no. 12, pp. 4506-4517, Dec. 2005.
[6] W.-K. Ma, ”Blind ML detection of orthogonal space-time block codes: Identifiability
and code construction,” IEEE Trans. Signal Process., vol. 55, no. 7, pp. 3312-3324,
July 2007.
[7] M. O. Damen and H. E. Gamal and G. Caire, ”On maximum-likelihood detection and
the search for the closest lattice point,” IEEE Trans. Inform. Theory, vol.49, no. 10,
pp. 2389-2402, Oct. 2003.
[8] E. Agrell, T.Eriksson, A. Vardy, and K. Zeger, ”Clsest point searcges in lattices,”
IEEE Trans. Inform. Theory, vol.48, no. 2, pp. 2301-2214, 2002. Oct. 2003.
[9] W.-K. Ma and T. N. Davidson and K. M. Wong and Z.-Q. Luo and P.-C. Ching,
”Quasi-maximum-likelihood multiuser detection using semidefinite relaxation with applications
to synchronous CDMA,” IEEE Trans. Signal Process., vol. 50, no. 4, pp.
912-922, April 2002.
[10] T. Cui and C. Tellambura, ”Efficient blind receiver design for orthogonal space-time
block codes”, IEEE Trans. Wireless Commun., vol. 6, no. 5, pp 1890-1899, May. 2007.
[11] L. Zhou and J.-K. Zhang and K.-M. Wong, ”A novel signaling scheme for blind
unique identification of Alamouti space-time block-coded channel”, IEEE Trans. Signal
Process., vol. 55, no. 6, pp. 2570-2582, June. 2007.
[12] J. Via and I. Santamaria, ”Some results on the blind identifiability of orthogonal
space-time blocks from second order statistics,” in Proc. IEEE ICASSP, vol. 3, Honolulu,
Hawaii, April 15-20, 2007, pp. III-313-III-316.
[13] N. D. Sidiropoulos and Z.-Q. Luo, ”A semidefinte relaxation aproach to MIMO detection
for high-order QAM constellations,” IEEE Signal Process. Lett., vol. 13, no.
9, pp. 525-528, Sept. 2006.
[14] J. F. Sturm, ”Using SeDuMi 1.02, a MATLAB toolbox for optimization over symmetric
cones,” Optimization Methods and Software, vol. 11-12, pp. 625-653, 1999, also
see the website http://sedumi.mcmaster.ca/.
[15] S. Boyd and L. Vandenberghe, ”Convex Optimization,” Cambridge, UK: Cambridge
University Press, 2004.
[16] W.-K. Ma and C.-C. Su and J. Jalden and C.-Y. Chi, ”Some results on 16-QAM
MIMO detection using semidefinite relaxation,” in Proc. IEEE ICASSP, Las Vegas,
Nevada, March 30- April 4, 2008.
[17] C. Helmberg and F. Rendl and R. Vanderbei and H. Wolkowicz, ”An interior-point
method for semidefinite programming,” SIAM J. Opt., vol. 6, no. 2, pp. 342-361, 1996.
[18] M.-T. Le and V.-S. Pham and L. Mai and G. Yoon, ”Efficient Algorithm for Blind
Detection of Orthogonal Space-Time Block Codes,” IEEE Signal Process. Lett., vol.
14, no. 5, pp. 301-304, May 2007.
[19] A. Wiesel and Y. C. Eldar and S. Shamai, ”Semidefinite Relaxation for Detection of
16-QAM Signaling in MIMO Channels,” IEEE Signal Process. Lett., vol. 12, no. 9,
pp. 653-656, Sept. 2005.
[20] Z. Mao and X. Wang and X. Wang, ”Semidefinite programming relaxation approach
for multiuser detection of QAM signals,” IEEE Trans. Wireless Commun., vol. 12, no.
6, pp. 4275-4279, Dec. 2007.
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