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 k-NN operation is enabled through feature space dimensionality reduction using subcarrier ordering techniques based on post-processing SNR.