Multiaccess interference suppression in orthogonal space-time clock coded MIMO systems by adaptive projected subgradient method

IEEE Transactions on Signal Processing

Volumn 56, Issue 3, 2008, Pages 1028-1042

  Cavalcante, Renato L G ^a   Yamada, Isao ^a  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

Adaptive filters; Adaptive projected subgradient method (APSM); Interference suppression; Multiple input multiple output (MIMO) systems

Indexed keywords

ADAPTIVE FILTERS; CHANNEL STATE INFORMATION; COMPUTER SIMULATION; CONVERGENCE OF NUMERICAL METHODS; DIGITAL COMMUNICATION SYSTEMS; INTERFERENCE SUPPRESSION; MIMO SYSTEMS; MULTIPLE ACCESS INTERFERENCE;

ADAPTIVE PROJECTED SUBGRADIENT METHOD; MATRIX INVERSION; ORTHOGONAL SPACE-TIME CLOCK CODED;

SPACE TIME ADAPTIVE PROCESSING;

EID: 40749135005     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSP.2007.907896     Document Type: Article

References (33)

1. S. M. Alamouti, "A simple transmit diversity technique for wireless communications," IEEE J. Sel. Areas Commun., vol. 16, no. 8, pp. 1451-1458, Oct. 1998.
2. S. Shahbazpanahi, M. Beheshti, A. B. Gershman, M. Gharavi-Alkhansari, and K. M. Wong, "Minimum variance linear receivers for multiaccess MIMO wireless systems with space-time block coding," IEEE Trans. Signal Process., vol. 52, no. 12, pp. 3306-3313, Dec. 2004.
3. E. G. Larsson and P. Stoica, Space-Time Block Coding for Wireless Communications. Cambridge, U.K.: Cambridge Univ. Press, 2003.
4. A. F. Naguib, N. Seshadri, and A. Calderbank, "Increasing data rate over wireless channels," IEEE Signal Process. Mag., vol. 17, no. 3, pp. 76-92, May 2000.
5. Y. Rong, S. Shahbazpanahi, and A. B. Gershman, "Robust linear receivers for space-time block coded multiaccess MIMO systems with imperfect channel state information," IEEE Trans. Signal Process., vol. 53, no. 8, pp. 3081-3090, Aug. 2005.
6. Y. Rong, S. A.Vorobyov, and A. B. Gershman, "Robust linear receivers for multiaccess space-time block-coded MIMO systems: A probabilistically constrained approach," IEEE J. Sel. Areas Commun., vol. 24, no. 8, pp. 1560-1569, Aug. 2006.
7. J. G. Proakis, Digital. Communications, 3rd ed. New York: McGraw-Hill, 1995.
8. M. L. Honig, S. L. Miller, M. J. Shensa, and L. B. Milstein, "Performance of adaptive linear interference suppression in the presence of dynamic fading," IEEE Trans. Commun., vol. 49, no. 4, pp. 635-645, Apr. 2001.
9. G. V. Moustakides, "Constrained adaptive linear multiuser detection schemes," J. VLSI Signal Process. (Special Issue on Multiuser Detection), vol. 39, no. 1-3, pp. 293-309, 2002.
10. M. Yukawa, R. L. G. Cavalcante, and I. Yamada, "Efficient blind MAI suppression in DS/CDMA systems by embedded constraint parallel projection techniques," IEICE Trans. Fundam., vol. E88-A, no. 8, pp. 2062-2071, Aug. 2005.
11. R. L. G. Cavalcante, M. Yukawa, and I. Yamada, "Set-theoretic DS/CDMA receivers for fading channels by adaptive projected subgradient method," in Proc. IEEE GLOBECOM, Dec. 2005, vol. 4, pp. 2270-2275.
12. R. L. G. Cavalcante and I. Yamada, "Steady-state analysis of constrained normalized adaptive filters for MAI reduction by energy conservation arguments," Signal Process., vol. 88, no. 2, pp. 326-338, Feb. 2008.
13. I. Yamada and N. Ogura, "Adaptive projected subgradient method for asymptotic minimization of sequence of nonnegative convex functions," Numer. Funct. Anal. Optim., vol. 25, no. 7/8, pp. 593-617, 2004.
14. B. T. Polyak, "Minimization of unsmooth functionals," U.S.S.R. Comput. Math. Phys., vol. 9, pp. 14-29, 1969.
15. Y. Censor and S. A. Zenios, Parallel Optimization: Theory, Algorithm, and Optimization. New York: Oxford Univ. Press, 1997.
16. P. L. Combettes, "Convex set theoretic image recovery by extrapolated iterations of parallel subgradient projections," IEEE Trans. Image Process., vol. 6, no. 4, pp. 493-506, Apr. 1997.
17. G. Pierra, "Decomposition through formalization in a product space," Math. Program., vol. 28, pp. 96-115, 1984.
18. P. L. Combettes and J. Luo, "An adaptive level set method for nondifferentiable constrained image recovery," IEEE Trans. Image Process., vol. 11, no. 11, pp. 1295-1304, Nov. 2002.
19. I. Yamada, K. Slavakis, and K. Yamada, "An efficient robust adaptive filtering algorithm based on parallel subgradient projection techniques," IEEE Trans. Signal Process., vol. 50, no. 5, pp. 1091-1101, May 2002.
20. R. L. G. Cavalcante and I.Yamada, "Multiaccess interference reduction in OSTBC-MIMO systems by adaptive projected subgradient method," in Proc. Int. Conf. Acoust. Speech Signal Process., Apr. 2007, vol. 3, pp. 1253-1256.
21. M. Gharavi-Alkhansari and A. B. Gershman, "Constellation space invariance of orthogonal space-time block codes," IEEE Trans. Inf. Theory, vol. 51, no. 1, pp. 331-334, Jan. 2005.
22. D. G. Luenberger, Optimization by Vector Space Methods. New York: Wiley, 1969.
23. M. Honig, U. Madhow, and S. Verdu, "Blind adaptive multiuser detection," IEEE Trans. Inf. Theory, vol. 41, no. 4, pp. 944-960, Jul. 1995.
24. S. C. Park and J. F. Doherty, "Generalized projection algorithm for blind interference suppression in DS/CDMA communications," IEEE Trans. Circuits Syst. II, vol. 44, no. 6, pp. 453-460, Jun. 1997.
25. H. Stark and Y. Yang, Vector Space Projections - A Numerical Approach to Signal and Image Processing, Neural Nets, and Optics. New York: Wiley, 1998.
26. A. H. Sayed, Fundamentals of Adaptive Filtering. Hoboken, NJ: Wiley, 2003.
27. S. Shahbazpanahi, A. B. Gershman, and G. B. Giannakis, "Semiblind multiuser MIMO channel estimation using Capon and MUSIC techniques," IEEE Trans. Signal Process., vol. 54, no. 9, pp. 3581-3591, Sep. 2006.
28. J. Apolinario, M. L. R. Campos, and P. S. R. Diniz, "Convergence analysis of the binormalized data-reusing LMS algorithm," IEEE Trans. Signal Process., vol. 48, no. 11, pp. 3235-3242, Nov. 2000.
29. V. Tarokh, H. Jafarkhani, and A. R. Calderbank, "Space-time block codes from orthogonal designs," IEEE Trans. Inf. Theory, vol. 45, no. 5, pp. 1456-1467, Jul. 1999.
30. T. Claasen and W. Mecklenbrauker, "Comparison of the convergence of two algorithms for adaptive FIR digital filters," IEEE Trans. Acoust., Speech, Signal Process., vol. 29, no. 3, pp. 670-678, Jun. 1981.
31. I. Ekeland and R. Témam, Convex Analysis and Variational Problems Philadelphia, PA: SIAM, 1999.
32. A. Papoulis, Probability, Random Variables, and Stochastic Processes 2nd ed. Singapore: McGraw-Hill, 1987.
33. G. H. Golub and C. F. V. Loan, Matrix Computations, 3rd ed. Baltimore, MD: The Johns Hopkins Univ. Press, 1996.