  Efficient dynamic spectrum access mechanism is crucial for improving the
spectrum utilization. In this paper, we consider the dynamic spectrum access
mechanism design with both complete and incomplete network information. When
the network information is available, we propose an evolutionary spectrum
access mechanism. We use the replicator dynamics to study the dynamics of
channel selections, and show that the mechanism achieves an equilibrium that is
an evolutionarily stable strategy and is also max-min fair. With incomplete
network information, we propose a distributed reinforcement learning mechanism
for dynamic spectrum access. Each secondary user applies the maximum likelihood
estimation method to estimate its expected payoff based on the local
observations, and learns to adjust its mixed strategy for channel selections
adaptively over time. We study the convergence of the learning mechanism based
on the theory of stochastic approximation, and show that it globally converges
to an approximate Nash equilibrium. Numerical results show that the proposed
evolutionary spectrum access and distributed reinforcement learning mechanisms
achieve up to 82% and 70% performance improvement than a random access
mechanism, respectively, and are robust to random perturbations of channel
selections.
