  In this paper, we design distributed spectrum access mechanisms with both
complete and incomplete network information. We propose an evolutionary
spectrum access mechanism with complete network information, and show that the
mechanism achieves an equilibrium that is globally evolutionarily stable. With
incomplete network information, we propose a distributed learning mechanism,
where each user utilizes local observations to estimate the expected throughput
and learns to adjust its spectrum access strategy adaptively over time. We show
that the learning mechanism converges to the same evolutionary equilibrium on
the time average. Numerical results show that the proposed mechanisms are
robust to the perturbations of users' channel selections.
