  Dynamic spectrum access (DSA) is regarded as an effective and efficient
technology to share radio spectrum among different networks. As a secondary
user (SU), a DSA device will face two critical problems: avoiding causing
harmful interference to primary users (PUs), and conducting effective
interference coordination with other secondary users. These two problems become
even more challenging for a distributed DSA network where there is no
centralized controllers for SUs. In this paper, we investigate communication
strategies of a distributive DSA network under the presence of spectrum sensing
errors. To be specific, we apply the powerful machine learning tool, deep
reinforcement learning (DRL), for SUs to learn "appropriate" spectrum access
strategies in a distributed fashion assuming NO knowledge of the underlying
system statistics. Furthermore, a special type of recurrent neural network
(RNN), called the reservoir computing (RC), is utilized to realize DRL by
taking advantage of the underlying temporal correlation of the DSA network.
Using the introduced machine learning-based strategy, SUs could make spectrum
access decisions distributedly relying only on their own current and past
spectrum sensing outcomes. Through extensive experiments, our results suggest
that the RC-based spectrum access strategy can help the SU to significantly
reduce the chances of collision with PUs and other SUs. We also show that our
scheme outperforms the myopic method which assumes the knowledge of system
statistics, and converges faster than the Q-learning method when the number of
channels is large.
