  Cognitive radio technology enables improving the utilization efficiency of
the precious and scarce radio spectrum. How to maximize the overall spectrum
efficiency while minimizing the conflicts with primary users is vital to
cognitive radio. The key is to make the right decisions of accessing the
spectrum. Spectrum prediction can be employed to predict the future states of a
spectrum band using previous states of the spectrum band, whereas spectrum
recommendation recommends secondary users a subset of available spectrum bands
based on secondary user's previous experiences of accessing the available
spectrum bands. In this paper, a framework for spectrum decision based on
spectrum prediction and spectrum recommendation is proposed. As a benchmark, a
method based on extreme learning machine (ELM) for single-user spectrum
prediction and a method based on Q-learning for multiple-user spectrum
prediction are proposed. At the stage of spectrum decision, two methods based
on Q-learning andMarkov decision process (MDP), respectively, are also proposed
to enhance the overall performance of spectrum decision. Experimental results
show that the performance of the spectrum decision framework is much better.
