Title :
A simple SNR representation method for AMC schemes of MIMO systems with ML detector
Author :
Kim, Jihoon ; Lee, Kyoung-Jae ; Sung, Chang Kyung ; Lee, Inkyu
Author_Institution :
Sch. of Electr. Eng., Korea Univ., Seoul, South Korea
fDate :
10/1/2009 12:00:00 AM
Abstract :
Adaptive modulation and coding (AMC) is a powerful technique to enhance the link performance by adjusting the transmission power, channel coding rates and modulation levels according to channel state information. In order to efficiently utilize the AMC scheme, an accurate signal-to-noise ratio (SNR) value is normally required for determining the AMC level. In this paper, we propose a simple method to represent the SNR values for maximum likelihood (ML) detector in multi-input multi-output (MIMO) systems. By analyzing the relation between the upper bound and the lower bound of the ML detector performance, we introduce an efficient way to determine the SNR for the ML receiver. Based on the proposed SNR representation, an AMC scheme for single antenna systems can be extended to MIMO systems with ML detector. From computer simulations, we confirm that the proposed SNR representation allows us to achieve almost the same system throughput as the optimum AMC systems in frequency selective channels with reduced complexity.
Keywords :
MIMO communication; adaptive codes; adaptive modulation; channel coding; computational complexity; maximum likelihood detection; modulation coding; AMC schemes; MIMO systems; ML detector; SNR representation method; adaptive modulation and coding; channel coding rates; channel state information; complexity reduction; frequency selective channels; maximum likelihood detector; multiple-input multiple-output systems; signal-to-noise ratio; single antenna systems; Channel coding; Channel state information; Computer simulation; Detectors; MIMO; Maximum likelihood detection; Modulation coding; Performance analysis; Signal to noise ratio; Upper bound; Multi-input multi-output (MIMO); adaptive modulation and coding (AMC); maximum likelihood detector (MLD);
Journal_Title :
Communications, IEEE Transactions on
DOI :
10.1109/TCOMM.2009.10.080384