Selection of Design Parameters for Generalized Sphere Decoding Algorithms

DOI: 10.4236/ijcns.2010.32019   PDF   HTML     4,156 Downloads   7,861 Views   Citations


Various efficient generalized sphere decoding (GSD) algorithms have been proposed to approach optimal ML performance for underdetermined linear systems, by transforming the original problem into the full-column-rank one so that standard SD can be fully applied. However, their design parameters are heuristically set based on observation or the possibility of an ill-conditioned transformed matrix can affect their searching efficiency. This paper presents a better transformation to alleviate the ill-conditioned structure and provides a systematic approach to select design parameters for various GSD algorithms in order to high efficiency. Simulation results on the searching performance confirm that the proposed techniques can provide significant improvement.

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P. WANG and T. LE-NGOC, "Selection of Design Parameters for Generalized Sphere Decoding Algorithms," International Journal of Communications, Network and System Sciences, Vol. 3 No. 2, 2010, pp. 126-132. doi: 10.4236/ijcns.2010.32019.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] E. Agrell, T. Eriksson, A. Vardy, and K.Zeger, “Closest point search in lattices,” IEEE Transactions Information Theory, pp. 2201–2214, August 2002.
[2] O. Damen, A. Chkeif. and J. C. Belfiore, “Lattice code decoder for space-time codes,” IEEE Communication Letters, pp. 161–163, May 2000.
[3] H. Vikalo, B. Hassibi, and T. Kailath, “Iterative decoding for MIMO channels via modified sphere decoding,” IEEE Transactions on Wireless Communication, Vol. 3, No. 6, pp. 2299–2311, November 2004.
[4] L. Brunel, “Multiuser detection techniques using maximum likelihood sphere decoding in multicarrier CDMA systems,” IEEE Tranactions on Wireless Communication, Vol. 3, No. 3, pp. 949–957, May 2004.
[5] T. Cui and C. Tellambura, “An efficient generalized sphere decoder for rank-deficient MIMO systems,” IEEE VTC. Fall, 2004.
[6] T. Cui and C. Tellambura, “An efficient generalized sphere decoder for rank-deficient MIMO systems,” IEEE Communication Letters, Vol. 9, No. 5, pp. 423–425, May 2005.
[7] X. Chang and X. Yang, “An efficient regularization approach for underdetermined MIMO system decoding”, IWCMC, Hawaii, USA, August, 2007.
[8] P. Wang and T. Le-Ngoc, “An efficient multi-user detection scheme for overloaded group-orthogonal MC- CDMA systems,” IET Communications, Vol. 2, No. 2, pp.346–352, February 2008.
[9] M. Damen, K. A. Meraim, and J. C. Belfiore, “Generalised sphere decoder for asymmetrical space-time communication architecture,” Electronics Letters, Vol. 36, No. 2, pp. 166–167, January 2000.
[10] P. Dayal and M. K. Varanasi, “A fast generalized sphere decoder for optimum decoding of under-determined MIMO systems,” 41st Annual Allerton Conference on Communication Control and Computer, October 2003.
[11] Z. Yang, C. Liu, and J. He, “A new approach for fast generalized sphere decoding in MIMO systems,” IEEE Signal Processing Letters, Vol. 12, No. 1, pp. 41–44, January 2005.
[12] X. W. Chang and X. H. Yang, “A new fast generalized sphere decoding algorithm for underdetermined sphere decoding algorithm for underdetermined MIMO systems,” 23rd Biennial symposium on Communications, Kingston, ON, Canada, pp. 18–21, June 2006.
[13] P. Wang and T. Le-Ngoc, “A low complexity generalized sphere decoding approach for underdetermined linear communication systems: performance and complexity evaluation,” IEEE Transactions on Communications, Vol. 57, No. 11, pp. 3376–3388, November 2009.
[14] P. Wang and T. Le-Ngoc, “On the expected complexity analysis of a generalized sphere decoding algorithm for underdetermined linear communication systems,” IEEE International Conference Communication (ICC), Glasgow, June 2007.
[15] M. O. Damen, H. E. Gamal, and G. Caire, “On maximum-likelihood detection and the search for the closest lattice point,” IEEE Transactions on Information Theory, Vol. 49, No. 10, pp. 2389–2402, October 2003.
[16] H. Artes, D. Seethaler, and F. Hlawatsch, “Efficient detection algorithms for MIMO channels: a geometrical approach to approximate ML detection,” IEEE Transactions on Signal Processing. Vol. 51, No. 11, pp. 2808–2820, November 2003.
[17] J. Boutros, N. Gresset, L. Brunel, and M. Fossorier, “Soft-input soft-output lattice sphere decoder for linear channels,” IEEE Globecom 2003, pp. 1583–1587, 2003.

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