Machine Learning-based Channel-Type Identification for IEEE 802.11ac Link Adaptation

We evaluate the performance of machine learning method in identifying the channel type in 802.11ac systems. It is shown that the reference symbols contained in the packet preamble can be used as a good feature for classification. In addition, the time-domain received preamble also serves as a good classification feature with comparable performance, allowing early identification of channel type since the information can be tapped closer to the receiving antenna. We validate our approach in both software and hardware simulation, and classification accuracy of more than 94% is shown to be attainable at moderate Signal to Noise Ratio (SNR). Finally, we also evaluate the application of our proposed algorithm into link adaptation in 802.11ac systems, and show that up to 1.6dB gain can be achieved compared to the case without channel type identification.