Machine Learning-Based Channel Classification and Its Application to IEEE 802.11ad Communications
Ernest Kurniawan, Zhiwei Lin, Sumei Sun · 2017
We study the application of machine learning to channel classification for identifying whether a channel belongs to the Line of Sight (LOS) or Non-Line of Sight (NLOS) classes. The machine learning approach is able to work on multiple features, resulting in a much more accurate pattern identification and classification performance. We show that even in the absence of channel estimation, it is possible to classify the channel using the received preamble sequence with machine learning. This allows quicker classification and it is robust to channel estimation error, which is favorable in the low Signal to Noise Ratio (SNR) regime. The scheme is evaluated for IEEE 802.11ad systems, but the concept is also applicable to other wireless systems in general.