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Machine-Learning-Based Link Adaptation for Energy-Efficient MIMO-OFDM Systems
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 Title & Authors
Machine-Learning-Based Link Adaptation for Energy-Efficient MIMO-OFDM Systems
Oh, Myeung Suk; Kim, Gibum; Park, Hyuncheol;
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 Abstract
Recent wireless communication trends have emphasized the importance of energy-efficient transmission. In this paper, link adaptation with machine learning mechanism for maximum energy efficiency in multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM) wireless system is considered. For reflecting frequency-selective MIMO-OFDM channels, two-dimensional capacity(2D-CAP) feature space is proposed. In addition, machine-learning-based bit and power adaptation(ML-BPA) algorithm that performs classification-based link adaptation is presented. Simulation results show that 2D-CAP feature space can represent channel conditions accurately and bring noticeable improvement in link adaptation performance. Compared with other feature spaces, including ordered postprocessing signal-to-noise ratio(ordSNR) feature space, 2D-CAP has distinguished advantages in either efficiency performance or computational complexity.
 Keywords
Link Adaptation;Energy Efficiency;Machine Learning;MIMO;OFDM;
 Language
Korean
 Cited by
 References
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