A learning approach in link adaptation for MIMO-OFDM systems

We propose a neural network (NN)-based adaptive modulation and coding (AMC) for link adaptation in MIMO-OFDM systems. The AMC optimizes the best modulation and coding scheme (MCS) under a packet error rate (PER) constraint. In our approach, a NN with a multilayer perceptron (MLP) structure is applied for the AMC and its performance is compared with the k-nearest neighbor (k-NN) algorithm under the frequency-flat (1-tap) and frequency-selective (4-tap) wireless channel conditions. The simulation results show that the NN classifier outperforms the k-NN algorithm, especially in terms of the PER, due to the fact that the MLP guarantees a MCS with a lower data rate by way of the selection of a class label with a lower index number. It has a slightly worse spectral efficiency performance compared to the k-NN. Thus, the MLP approach provides higher communication robustness over the k-NN. It can be concluded from the results that the selection of the AMC classifier depends on a trade-off between the PER and the spectral efficiency, relying on the user's requirements.

Anahtar Kelimeler:

MIMO systems, link adaptation, adaptive systems, neural nets, learning systems

A learning approach in link adaptation for MIMO-OFDM systems

Keywords:

MIMO systems, link adaptation, adaptive systems, neural nets, learning systems,

PDF

___

H. Yigit, A. Kavak, K. Kucuk, “Capacity improvement for TDD-MIMO systems via AR modeling based linear prediction”, Wireless Personal Communications, Vol. 52, pp. 411–418, 2010.
E. Telatar, “Capacity of multi-antenna Gaussian channels”, European Transactions on Telecommunications, Vol. 10, pp. 585–595, 1999.
S. Catreux, V. Erceg, D. Gesbert, R.W. Heath Jr, “Adaptive modulation and MIMO coding for broadband wireless data networks”, IEEE Communications Magazine, Vol. 40, pp. 108–115, 2002.
P.H. Tan, Y. Wu, S. Sun, “Link adaptation based on adaptive modulation and coding for multiple-antenna OFDM system”, IEEE Journal on Selected Areas in Communications, Vol. 26, pp. 1599–1606, 2008.
Y.S. Choi, S.A. Alamouti, “Pragmatic PHY abstraction technique for link adaptation and MIMO switching”, IEEE Journal on Selected Areas in Communications, Vol. 26, pp. 960–971, 2008.
H.T. Nguyen, J.B. Andersen, G.F. Pedersen, “On the performance of link adaptation techniques in MIMO systems”, Wireless Personal Communications, Vol. 42, pp. 543–561, 2007.
T. Javornik, S. Plevel, G. Kandus, “A recursive link adaptation algorithm for MIMO systems”, Proceedings of the 12th IEEE Mediterranean Electrotechnical Conference, pp. 429–432, 2004.
S. Simoens, S. Rouquette-L´ eveil, P. Sartori, Y. Blankenship, B. Classon, “Error prediction for adaptive modulation and coding in multiple-antenna OFDM systems”, Signal Processing, Vol. 86, pp. 1911–1919, 2006.
M. Lampe, T. Giebel, H. Rohling, W. Zirwas, “PER-prediction for PHY mode selection in OFDM communication systems”, Proceedings of the Global Telecommunications Conference, Vol. 1, pp. 25–29, 2003.
M. Sandell, “Link adaptation for MIMO systems using reliability values”, Proceedings of the IEEE Wireless Communications and Networking Conference, Vol. 3, pp. 1608–1613, 2006.
R.C. Daniels, C.M. Caramanis, R.W. Heath Jr, “Adaptation in convolutionally coded MIMO-OFDM wireless systems through supervised learning and SNR ordering”, IEEE Transactions on Vehicular Technology, Vol. 59, pp. 114–126, 2010.
S. Haykin, Neural Networks and Learning Machines, 3rd ed., New York, Prentice Hall, 2009.
E. Alpaydin, Introduction to Machine Learning, Massachusetts, Cambridge, MIT Press, 2004.
X. Wu, V. Kumar, J.R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G.J. McLachlan, A. Ng, B. Liu, P.S. Yu, Z.H. Zhou, M. Steinbach, D.J. Hand, D. Steinberg, “Top 10 algorithms in data mining”, Springer Journal on Knowledge and Information Systems, Vol. 14, pp. 1–37, 2008.
Kavak, H. Yigit, H.M. Ertunc, “Using adaline neural network for performance improvement of smart antennas in TDD wireless communications”, IEEE Transactions on Neural Network, Vol. 16, pp. 1616–1625, 2005.
R.C. Daniels, C.M. Caramanis, R.W. Heath Jr, “A supervised learning approach to adaptation in practical MIMOOFDM wireless systems”, Proceedings of the Global Telecommunications Conference, pp. 1–5, 2008.
H. Yigit, A. Kavak, “Adaptation using neural network in frequency selective MIMO-OFDM systems”, Proceedings of the 5th IEEE International Symposium on Wireless Pervasive Computing, pp. 390–394, 2010.
R. Daniels, R.W. Heath Jr, “Online adaptive modulation and coding with support vector machines”, Proceedings of the IEEE European Wireless Conference, pp. 718–724, 2010.
S. Yun, C. Caramanis, “Multiclass support vector machines for adaptation in MIMO-OFDM wireless systems”, Proceedings of the 47th Annual Allerton Conference on Communication, Control, and Computing, 2009. N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge, Cambridge University Press, 2000.
IEEE 802.11n Working Group, Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications - Draft 5.0: Enhancements for Higher Throughput, Part 11, Standard Edition, 2007.