Online adaptive modulation and coding with support vector machines

Optimizing the performance of adaptive modulation and coding (AMC) in practice has proven challenging. Prior research has struggled to find link quality metrics that are suitable for look-up-tables and simultaneously provide an injective mapping to error rate in wireless links that feature selective channels with hardware nonlinearities and non-Gaussian noise effects. This paper proposes a novel online support vector machine algorithm, compatible with accurate multidimensional link quality metrics, that is able to optimize AMC to the unique (potentially dynamic) hardware characteristics of each wireless device in selective channels. IEEE 802.11n simulations show that our proposed algorithm allows each individual wireless device to optimize the operating point in the rate/reliability tradeoff through frame-by-frame error evaluation. These simulations also show that our algorithm displays identical performance to alternative online AMC algorithms while drastically reducing complexity.

[1]  L. Leyssenne,et al.  Analysis of Temperature Modulation on a SiGe Power Amplifier Non Linearity , 2006, 2006 Bipolar/BiCMOS Circuits and Technology Meeting.

[2]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[3]  Robert W. Heath,et al.  Adaptation in Convolutionally Coded MIMO-OFDM Wireless Systems Through Supervised Learning and SNR Ordering , 2010, IEEE Transactions on Vehicular Technology.

[4]  Andrea J. Goldsmith,et al.  Adaptive coded modulation for fading channels , 1997, Proceedings of ICC'97 - International Conference on Communications.

[5]  Nj Piscataway,et al.  Wireless LAN medium access control (MAC) and physical layer (PHY) specifications , 1996 .

[6]  Constantine Caramanis,et al.  Multiclass support vector machines for adaptation in MIMO-OFDM wireless systems , 2009, 2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[7]  Hsuan-Tien Lin,et al.  A note on Platt’s probabilistic outputs for support vector machines , 2007, Machine Learning.

[8]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[9]  Fei Peng,et al.  Adaptive Modulation and Coding for IEEE 802.11n , 2007, 2007 IEEE Wireless Communications and Networking Conference.

[10]  Yourong Lu,et al.  Channel and Modulation Selection Based on Support Vector Machines for Cognitive Radio , 2006, 2006 International Conference on Wireless Communications, Networking and Mobile Computing.

[11]  John M. Cioffi,et al.  Adaptive modulation and coding (AMC) for bit-interleaved coded OFDM (BIC-OFDM) , 2006, IEEE Trans. Wirel. Commun..

[12]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[13]  Abbas Jamalipour,et al.  Wireless communications , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[14]  Rich Caruana,et al.  Predicting good probabilities with supervised learning , 2005, ICML.

[15]  Inkyu Lee,et al.  Adaptive Bit-Interleaved Coded OFDM With Reduced Feedback Information , 2007, IEEE Transactions on Communications.

[16]  John H. Halton,et al.  Error Rates in Differentially Coherent Phase Systems in Non-Gaussian Noise , 1966 .

[17]  Ryan M. Rifkin,et al.  In Defense of One-Vs-All Classification , 2004, J. Mach. Learn. Res..

[18]  B. Lang,et al.  Efficient optimization of support vector machine learning parameters for unbalanced datasets , 2006 .

[19]  Robert W. Heath,et al.  An online learning framework for link adaptation in wireless networks , 2009, 2009 Information Theory and Applications Workshop.

[20]  Faisal Tariq,et al.  Impact of Nonlinear Power Amplifier on Link Adaptation Algorithm of OFDM Systems , 2007, 2007 IEEE 66th Vehicular Technology Conference.

[21]  Bernard H. Fleury,et al.  Mutual Information Metrics for Fast Link Adaptation in IEEE 802.11n , 2008, 2008 IEEE International Conference on Communications.

[22]  A. M. Abdullah,et al.  Wireless lan medium access control (mac) and physical layer (phy) specifications , 1997 .

[23]  S. Sathiya Keerthi,et al.  Improvements to Platt's SMO Algorithm for SVM Classifier Design , 2001, Neural Computation.