The way to apply machine learning to IoT driven wireless network from channel perspective

Internet of Things (IoT) is one of the targeted application scenarios of fifth generation (5G) wireless communication. IoT brings a large amount of data transported on the network. Considering those data, machine learning (ML) algorithms can be naturally utilized to make network efficiently and reliably. However, how to fully apply ML to IoT driven wireless network is still open. The fundamental reason is that wireless communication pursuits the high capacity and quality facing the challenges from the varying and fading wireless channel. So in this paper, we explore feasible combination for ML and IoT driven wireless network from wireless channel perspective. Firstly, a three-level structure of wireless channel fading features is defined in order to classify the versatile propagation environments. This three-layer structure includes scenario, meter and wavelength levels. Based on this structure, there are different tasks like service prediction and pushing, self-organization networking, self adapting largescale fading modeling and so on, which can be abstracted into problems like regression, classification, clustering, etc. Then, we introduce corresponding ML methods to different levels from channel perspective, which makes their interdisciplinary research promisingly.