  In this work, we propose a new learning framework for optimising transmission
strategies when irregular repetition slotted ALOHA (IRSA) MAC protocol is
considered. We cast the online optimisation of the MAC protocol design as a
multi-arm bandit problem that exploits the IRSA structure in the learning
framework. Our learning algorithm quickly learns the optimal transmission
strategy, leading to higher rate of successfully received packets with respect
to baseline transmission optimizations.
