Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Error prediction for adaptive modulation and coding in multiple-antenna OFDM systems
Signal Processing - Special section: Advances in signal processing-assisted cross-layer designs
Adaptive modulation and MIMO coding for broadband wireless data networks
IEEE Communications Magazine
A Pragmatic PHY Abstraction Technique for Link Adaptation and MIMO Switching
IEEE Journal on Selected Areas in Communications
Link adaptation based on adaptive modulation and coding for multiple-antenna OFDM system
IEEE Journal on Selected Areas in Communications
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In this paper, we proposed a neural network (NN) framework as a machine learning technique for link adaptation based on adaptive modulation and coding in 802.11n MIMO-OFDM wireless system to predict the best modulation and coding scheme (MCS) index under packet error rate (PER) constraints. Our approach is compared with the k-nearest neighbour (k-NN) algorithm in frequency selective wireless channels. Simulation results validate the implementation of proposed neural network framework in frequency selective channels, and show that the neural network technique outperforms k-NN algorithm especially in terms of PER when low MCS index selection which provide higher communication reliability is exploited.