Performance analysis of quasi-maximum-likelihood detector based on semi-definite programming

Mikalai Kisialiou, Zhi Quan Luo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

43 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2005 IEEE International Conference on Acoustics, Speech, and Signal Processing,ICASSP '05 - Proceedings - Audio and ElectroacousticsSignal Processing for Communication
PublisherInstitute of Electrical and Electronics Engineers Inc.
PagesIII433-III436
ISBN (Print)0780388747, 9780780388741
DOIs
StatePublished - Jan 1 2005
Event2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Philadelphia, PA, United States
Duration: Mar 18 2005Mar 23 2005

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
VolumeIII
ISSN (Print)1520-6149

Other

Other2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
CountryUnited States
CityPhiladelphia, PA
Period3/18/053/23/05

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