  Powerful spectrum sensing schemes enable cognitive radios (CRs) to find
transmission opportunities in spectral resources allocated exclusively to the
primary users. In this paper, maximizing the average throughput of a secondary
user by optimizing its spectrum sensing time is formulated assuming that a
prior knowledge of the presence and absence probabilities of the primary users
is available. The energy consumed for finding a transmission opportunity is
evaluated and a discussion on the impact of the number of the primary users on
the secondary user throughput and consumed energy is presented. In order to
avoid the challenges associated with the analytical method, as a second
solution, a systematic neural network-based sensing time optimization approach
is also proposed in this paper. The proposed adaptive scheme is able to find
the optimum value of the channel sensing time without any prior knowledge or
assumption about the wireless environment. The structure, performance, and
cooperation of the artificial neural networks used in the proposed method are
disclosed in detail and a set of illustrative simulation results is presented
to validate the analytical results as well as the performance of the proposed
learning-based optimization scheme.
