Publisher : The Korean Institute of Electromagnetic Engineering and Science
DOI : 10.5515/KJKIEES.2016.27.5.407
Title & Authors
Machine-Learning-Based Link Adaptation for Energy-Efficient MIMO-OFDM Systems Oh, Myeung Suk; Kim, Gibum; Park, Hyuncheol;
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.
Link Adaptation;Energy Efficiency;Machine Learning;MIMO;OFDM;
T. L. Jensen, S. Kant, J. Wehinger, and B. H. Fleury, "Fast link adaptation for MIMO OFDM", IEEE Trans. Veh. Tech., vol. 59, pp. 3766-3778, Oct. 2010.
C. Shin, H. Kim, K. J. Kim, and H. Park, "High-throughput low-complexity link adaptation for MIMO BICOFDM systems", IEEE Trans. Commun., vol. 59, pp. 1078-1088, Apr. 2011.
G. P. Fettweis, E. Zimmermann, "ICT energy consumption-trens and challenges", Proc. WPMC SIGCOMM, p. 6, Sep. 2008.
G. Miao, N. Himayat, and G. Y. Li, "Energy-efficient link adaptation in frequency-selective channels", IEEE Trans. Commun., vol. 58, pp. 545-554, Feb. 2010.
E. Eraslan, B. Daneshrad, "Practical energy efficient link adaptation for MIMO-OFDM systems", IEEE 2012 WCNC, pp. 280-485, 2012.
L. Chen, Y. Yang, X. Chen, and G. Wei, "Energy-efficient link adaptation on Rayleigh fading channel for OSTBC MIMO system with imperfect CSIT", IEEE Trans. on Veh. Tech., vol. 62, no. 4, pp. 1577-1585, May 2013.
X. Ge, X. Huang, Y. Wang, M. Chen, Q. Li, T. Han, and C. -X. Wang, "Energy-efficiency optimization for MIMOOFDM mobile multimedia communication systems with QoS constraints", IEEE Trans. Veh. Tech., vol. 63, no. 5, pp. 2127-2138, June 2014.
B. Razavi, RF Microelectronics, New Prentice Hall Inc., 1998.
R. C. Daniels, C. M. Caramanis, and R. W. Heath, "Adaptation in convolutionally coded MIMO-OFDM wireless systems through supervised learning and SNR ordering", IEEE Trans. Veh. Tech., vol. 59, pp. 114-126, Jan. 2010.
S. Yun, C. M. Caramanis, "Reinforcement learning for link adaptation in MIMO-OFDM wireless systems", Proc. IEEE GLOBECOM, pp. 1-5, Dec. 2010.
N. Mastronarde, M. Schaar, "Fast reinforcement learning for energy-efficient wireless communication", IEEE Trans. Signal Processing, vol. 59, no. 12, pp. 6262-6266, Dec. 2011.
Y. -S. Choi, S. Alamouti, "A pragmatic PHY abstraction technique for link adaptation and MIMO switching", IEEE J. Sel. Areas Commun., vol. 26, no. 6, pp. 960-971, Aug. 2008.
Y. Wu, S. Verdu, "The impact of constellation cardinality on Gaussian channel capacity", Proc. Allerton Conf. Commun., Control & Computing, Sep. 2010.
S. T. Chung, A. J. Goldsmith, "Degrees of freedom in adaptive modulation: a unified view", IEEE Trans. Commun., vol. 49, pp. 1561-1571, Sep. 2001.
T. M. Cover, P. E. Hart, "Nearest neighbor pattern classification", IEEE Trans. Inf. Theory., vol. 13. pp. 21-27, Jan. 1967.
IEEE 802.11n Working Group, part 11 standard edition Wireless LAN Medium Access Control(MAC) and Physical Layer(PHY) Specifications - Draft 5.0: Enhancements for Higher Throughput, 2007.