  Wireless links adapt the data transmission parameters to the dynamic channel
state -- this is called link adaptation. Classical link adaptation relies on
tuning parameters that are challenging to configure for optimal link
performance. Recently, reinforcement learning has been proposed to automate
link adaptation, where the transmission parameters are modeled as discrete arms
of a multi-armed bandit. In this context, we propose a latent learning model
for link adaptation that exploits the correlation between data transmission
parameters. Further, motivated by the recent success of Thompson sampling for
multi-armed bandit problems, we propose a latent Thompson sampling (LTS)
algorithm that quickly learns the optimal parameters for a given channel state.
We extend LTS to fading wireless channels through a tuning-free mechanism that
automatically tracks the channel dynamics. In numerical evaluations with fading
wireless channels, LTS improves the link throughout by up to 100% compared to
the state-of-the-art link adaptation algorithms.
