Machine Learning for Physical Layer Link Adaptation in Multiple-Antenna Wireless Networks

Date Added: Sep 2008
Format: PDF

Prototyping and experimentation are key to understanding the operation of wireless systems in practice. In this paper, the authors present an implementation of physical layer link adaptation, or data rate selection, through machine learning on Hydra: an IEEE 802.11n draft standard multihop wireless networking prototype. This implementation highlights both the utility of learning-based link adaptation in practical networks as well as the flexibility of Hydra. To investigate a broad range of wireless research problems (including those mentioned above), they designed Hydra with the primary goals of flexibility and ease of implementation.