Environmental factors that lead to the movement and type of obstacles in and around wireless links are well-known to directly affect channel characteristics. However, while mobile users typically have repeatable daily or weekly patterns with common locations being frequently visited, many protocols along the network stack do not attempt to identify when physical locations are revisited. If wireless channels could be recognized as previously visited, the observance of good and bad decisions in that particular context could dramatically improve some network protocols. In this paper, we present a channel recognition framework which uses the geometrical shape of the link-level performance in a particular context across transmission modes and channel qualities. When attempting to recognize a channel condition, the performance of data transmissions is observed and compared against known channel types to detect similar behavior. The matching channel type can be used as an input to the link adaptation training and resulting decision structure. We perform extensive experimentation on controlled repeatable channels as well as in-field channels to show the validity of the classification algorithm.
['Jialin He', 'Hui Liu', 'Pengfei Cui', 'Jonathan Landon', 'Dinesh Rajan', 'Joseph Camp']
Geometry-based channel recognition for context-aware applications