  An opportunistic spectrum access (OSA) for the infrastructure-less (or
cognitive ad-hoc) network has received significant attention thanks to emerging
paradigms such as the Internet of Things (IoTs) and smart grids. Research in
this area has evolved from the \r{ho}rand algorithm requiring prior knowledge
of the number of active secondary users (SUs) to the musical chair (MC)
algorithm where the number of SUs are unknown and estimated independently at
each SU. These works ignore the number of collisions in the network leading to
wastage of power and bring down the effective life of battery operated SUs. In
this paper, we develop algorithms for OSA that learn faster and incurs fewer
number of collisions i.e. energy efficient. We consider two types of
infrastructure-less decentralized networks: 1) static network where the number
of SUs are fixed but unknown, and 2) dynamic network where SUs can
independently enter or leave the network. We set up the problem as a
multi-player mult-armed bandit and develop two distributed algorithms. The
analysis shows that when all the SUs independently implement the proposed
algorithms, the loss in throughput compared to the optimal throughput, i.e.
regret, is a constant with high probability and significantly outperforms
existing algorithms both in terms of regret and number of collisions. Fewer
collisions make them ideally suitable for battery operated SU terminals. We
validate our claims through exhaustive simulated experiments as well as through
a realistic USRP based experiments in a real radio environment.
