Multiclass support vector machines for adaptation in MIMO-OFDM wireless systems

In MIMO-OFDM systems, by matching transmitter parameters such as modulation order and coding rate, link adaptation can increase the throughput significantly. However, creating a tractable mathematical mapping model from environmental variables to transmitter parameters that allows the latter to be optimized in any sense, presents serious challenges due to the large number of variables involved, as well as the complexity required in any model with the ability to accurately capture and explain all factors that affect performance. Machine learning algorithms, which make no mathematical assumptions and use only past observations to model the input-output relationship, have recently been explored for adaptation in MIMO-OFDM systems. In this paper we propose a novel machine learning algorithm based on multi-class support vector machines (SVMs). Our algorithm has considerably smaller operational overhead (including storage requirements) and better performance for link adaptation. With IEEE 802.11n simulations we show that our new algorithm outperforms existing machine-learning based algorithms. Moreover, we show that our algorithm is (asymptotically) consistent, in the sense that as the number of training data used increases, our algorithm obtains the performance-optimal classifier.