Quasi-Maximum-Likelihood Detector Based on Geometrical Diversification Greedy Intensification

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.
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https://hal-supelec.archives-ouvertes.fr/hal-00376949
Contributor : Myriam Andrieux <>
Submitted on : Monday, April 20, 2009 - 3:41:35 PM
Last modification on : Thursday, October 17, 2019 - 12:34:55 PM

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  • HAL Id : hal-00376949, version 1

Citation

Amor Nafkha, Emmanuel Boutillon, Christian Roland. Quasi-Maximum-Likelihood Detector Based on Geometrical Diversification Greedy Intensification. IEEE Transactions on Communications, Institute of Electrical and Electronics Engineers, 2009, pp.4. ⟨hal-00376949⟩

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