TY - GEN
T1 - Performance analysis of quasi-maximum-likelihood detector based on semi-definite programming
AU - Kisialiou, Mikalai
AU - Luo, Zhi Quan
PY - 2005/1/1
Y1 - 2005/1/1
N2 - Despite its optimal bit-error-rate (BER) performance, the maximum-likelihood (ML) detection is known to be NP-hard and suffers from high computational complexity. The currently popular suboptimal detectors either achieve a polynomial time complexity at the expense of BER performance degradation (e.g., MMSE Detector), or offer a near ML performance with a complexity that is exponential in the worst case. This paper considers a highly efficient (polynomial worst case complexity) quasi-ML detection method based on Semi-Definite (SDP) relaxation. It is shown that, for a standard vector Rayleigh fading channel, this SDP-based quasi-ML detector achieves, in the high signal-to-noise ratio (SNR) region, a BER which is identical to that of the exact ML detector. In the low SNR region we use the random matrix theory to show that the SDP-based detector serves as a constant factor approximation to the ML detector for large systems.
AB - Despite its optimal bit-error-rate (BER) performance, the maximum-likelihood (ML) detection is known to be NP-hard and suffers from high computational complexity. The currently popular suboptimal detectors either achieve a polynomial time complexity at the expense of BER performance degradation (e.g., MMSE Detector), or offer a near ML performance with a complexity that is exponential in the worst case. This paper considers a highly efficient (polynomial worst case complexity) quasi-ML detection method based on Semi-Definite (SDP) relaxation. It is shown that, for a standard vector Rayleigh fading channel, this SDP-based quasi-ML detector achieves, in the high signal-to-noise ratio (SNR) region, a BER which is identical to that of the exact ML detector. In the low SNR region we use the random matrix theory to show that the SDP-based detector serves as a constant factor approximation to the ML detector for large systems.
UR - http://www.scopus.com/inward/record.url?scp=33646764918&partnerID=8YFLogxK
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U2 - 10.1109/ICASSP.2005.1415739
DO - 10.1109/ICASSP.2005.1415739
M3 - Conference contribution
AN - SCOPUS:33646764918
SN - 0780388747
SN - 9780780388741
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - III433-III436
BT - 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing,ICASSP '05 - Proceedings - Audio and ElectroacousticsSignal Processing for Communication
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
Y2 - 18 March 2005 through 23 March 2005
ER -