  Cognitive ad-hoc networks allow users to access an unlicensed/shared spectrum
without the need for any coordination via a central controller and are being
envisioned for futuristic ultra-dense wireless networks. The ad-hoc nature of
networks require each user to learn and regularly update various network
parameters such as channel quality and the number of users, and use learned
information to improve the spectrum utilization and minimize collisions. For
such a learning and coordination task, we propose a distributed algorithm based
on a multi-player multi-armed bandit approach and novel signaling scheme. The
proposed algorithm does not need prior knowledge of network parameters (users,
channels) and its ability to detect as well as adapt to the changes in the
network parameters thereby making it suitable for static as well as dynamic
networks. The theoretical analysis and extensive simulation results validate
the superiority of the proposed algorithm over existing state-of-the-art
algorithms.
