1,275 research outputs found
Achieving Low-Complexity Maximum-Likelihood Detection for the 3D MIMO Code
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
Reduced-complexity maximum-likelihood decoding for 3D MIMO code
The 3D MIMO code is a robust and efficient space-time coding scheme for the
distributed MIMO broadcasting. However, it suffers from the high computational
complexity if the optimal maximum-likelihood (ML) decoding is used. In this
paper we first investigate the unique properties of the 3D MIMO code and
consequently propose a simplified decoding algorithm without sacrificing the ML
optimality. Analysis shows that the decoding complexity is reduced from O(M^8)
to O(M^{4.5}) in quasi-static channels when M-ary square QAM constellation is
used. Moreover, we propose an efficient implementation of the simplified ML
decoder which achieves a much lower decoding time delay compared to the
classical sphere decoder with Schnorr-Euchner enumeration.Comment: IEEE Wireless Communications and Networking Conference (WCNC 2013),
Shanghai : China (2013
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