Quasi-maximum-likelihood detector based on geometrical diversification greedy intensification

Authors:
Amor Nafkha;Emmanuel Boutillon;Christian Roland
Affiliations:
CNRS, IETR, SUPELEC, Cesson-Sévigné, France;Lab-STICC, CNRS, Université de Bretagne Sud, Lorient, France;Lab-STICC, CNRS, Université de Bretagne Sud, Lorient, France
Venue:
IEEE Transactions on Communications
Year:
2009

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Abstract

This letter proposes a quasi optimum maximum likelihood detection technique based on Geometrical Diversification and Greedy Intensification (GDGI). The presented detector scheme is shown to achieve almost optimal performance for all signal-to-noise ratio (SNR) values and a cubic computation complexity in the problem dimension. It possesses a regular structure well suited for hardware implementation. Simulation results show that for a system with a high dimension of n = 60, the loss is approximately 0.35 dB at BER=10-5 compared to an optimal decoding.