  Selection of hyperparameters in deep neural networks is a challenging problem
due to the wide search space and emergence of various layers with specific
hyperparameters. There exists an absence of consideration for the neural
architecture selection of convolutional neural networks (CNNs) for spectrum
sensing. Here, we develop a method using reinforcement learning and Q-learning
to systematically search and evaluate various architectures for generated
datasets including different signals and channels in the spectrum sensing
problem. We show by extensive simulations that CNN-based detectors proposed by
our developed method outperform several detectors in the literature. For the
most complex dataset, the proposed approach provides 9% enhancement in accuracy
at the cost of higher computational complexity. Furthermore, a novel method
using multi-armed bandit model for selection of the sensing time is proposed to
achieve higher throughput and accuracy while minimizing the consumed energy.
The method dynamically adjusts the sensing time under the time-varying
condition of the channel without prior information. We demonstrate through a
simulated scenario that the proposed method improves the achieved reward by
about 20% compared to the conventional policies. Consequently, this study
effectively manages the selection of important hyperparameters for CNN-based
detectors offering superior performance of cognitive radio network.
