Achieving Low-Complexity Maximum-Likelihood Detection for the 3D MIMO Code

Ming Liu (1) , Matthieu Crussière (1) , Maryline Hélard (1) , Jean-François Hélard (1)

1 IETR - Institut d'Électronique et des Technologies du numéRique

The 3D MIMO code is a robust and efficient space-time block code (STBC) for the distributed MIMO broadcasting but suffers from high maximum-likelihood (ML) decoding complexity. In this paper, we first analyze some properties of the 3D MIMO code to show that the 3D MIMO code is fast-decodable. It is proved that the ML decoding performance can be achieved with a complexity of O(M^{4.5}) instead of O(M^8) in quasi static channel with M-ary square QAM modulations. Consequently, we propose a simplified ML decoder exploiting the unique properties of 3D MIMO code. Simulation results show that the proposed simplified ML decoder can achieve much lower processing time latency compared to the classical sphere decoder with Schnorr-Euchner enumeration.

Mots clés: MIMO; space-time codes; maximum-likelihood (ML) decoding; computational complexity

Domaines: Réseaux et télécommunications [cs.NI]

Ming Liu, Matthieu Crussière, Maryline Hélard, Jean-François Hélard. Achieving Low-Complexity Maximum-Likelihood Detection for the 3D MIMO Code. EURASIP Journal on Wireless Communications and Networking, 2014, pp.1-27. ⟨hal-00929761⟩