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2008
IEEE

A Supervised Learning Approach to Adaptation in Practical MIMO-OFDM Wireless Systems

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A Supervised Learning Approach to Adaptation in Practical MIMO-OFDM Wireless Systems
—MIMO-OFDM wireless systems require adaptive modulation and coding based on channel state information (CSI) to maximize throughput in changing wireless channels. Traditional adaptive modulation and coding attempts to predict the best rate available by estimating the packet error rate for each modulation and coding scheme (MCS) by using CSI, which has shown to be challenging. This paper considers supervised learning with the k-nearest neighbor (k-NN) algorithm as a new framework for adaptive modulation and coding. Practical kNN operation is enabled through feature space dimensionality reduction using subcarrier ordering techniques based on postprocessing SNR. Simulation results of an IEEE 802.11n draftcompatible physical layer in flat and frequency selective wireless channels shows the k-NN with an ordered subcarrier feature space performs near ideal adaptation under packet error rate constraints.
Robert C. Daniels, Constantine Caramanis, Robert W
Added 29 May 2010
Updated 29 May 2010
Type Conference
Year 2008
Where GLOBECOM
Authors Robert C. Daniels, Constantine Caramanis, Robert W. Heath Jr.
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