Some results on 16-QAM MIMO detection using semidefinite relaxation

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2008, Pages 2673-2676

  Ma, Wing Kin ^a,b   Su, Chao Cheng ^a   Jaldén, Joakim ^c   Chi, Chong Yung ^a  

^a NATIONAL TSING HUA UNIVERSITY   (Taiwan)

^b CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^c VIENNA UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

Convex optimization; MIMO detection; Semidefinite relaxation

Indexed keywords

ACOUSTICS; BOOLEAN FUNCTIONS; MATHEMATICAL MODELS; MIM DEVICES; MULTIPLEXING; SIGNAL PROCESSING; SPEECH;

CONVEX OPTIMIZATION; INTERNATIONAL CONFERENCES; MIMO DETECTION; SEMIDEFINITE RELAXATION;

INFORMATION RETRIEVAL;

EID: 51449112345     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2008.4518199     Document Type: Conference Paper

References (13)

1. P. Tan and L. Rasmussen, "The application of semidefinite programming for detection in CDMA," IEEE J. Select. Areas Commun., vol. 19, no. 8, pp. 1442-1449, 2001.
2. W.-K. Ma, T. N. Davidson, K. M. Wong, Z.-Q. Luo, and P. C. Ching, "Quasi-maximum-likelihood multiuser detection using semi-definite relaxation with applications to synchronous CDMA," IEEE Trans. Signal Process., vol. 50, no. 4, pp. 912-922, 2002.
3. 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, 2004.
4. A. Wiesel, 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, 2005.
5. 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, 2006.
6. A. Mobasher, M. Taherzadeh, R. Sotirov, and A. K. Khandani, "A near-maximum-likelihood decoding algorithm for MIMO systems based on semi-definite programming," IEEE Trans. Inform. Theory, vol. 53, no. 11, pp. 3869-3886, 2007.
7. M. O. Damen, 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, 2003.
8. A. D. Murugan, H. E. Gamal, M. O. Damen, and G. Caire, "A unified framework for tree search decoding: rediscovering the sequential decoder," IEEE Trans. Inform. Theory, vol. 52, no. 3, pp. 933-953, 2006.
9. J. Jaldén and B. Ottersten, "On the complexity of sphere decoding in digital communications," IEEE Trans. Signal Processing, vol. 53, no. 4, pp. 1474-1484, 2005.
10. _, "The diversity order of the semidefinite relaxation detector," to appear in IEEE Trans. Inform. Theory, Apr. 2008, also http://arxiv.org.
11. 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.
12. 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, 1996.
13. M. Taherzadeh, A. Mobasher, and A. K. Khandani, "LLL reduction achieves the receive diversity in MIMO decoding," IEEE Trans. Inform. Theory, vol. 53, no. 12, pp. 4801-4805, 2007.