ABSTRACT
The cognitive radio network (CRN) is one of the major technologies expected to be part of the 5G communication technology because it promises a higher data rate and improved spectral efficiency. The main rationale behind the CRN is the fact that the electromagnetic spectrum assigned to each country is restricted, and there is an essential need to optimize the utilization of the available spectrum. The CRN provides better utilization of the available spectrum via reusing the unutilized frequency band called the “spectrum hole”, which was assigned to a licensed user or so-called primary user (PU) by assigning the available spectrum to a non-licensed user in need, the so-called secondary users (SU). The key step in the CRN functionality is the process of detecting the spectrum hole and this is termed as spectrum sensing (SS). In this work, we propose optimum beamforming design using the genetic algorithm with multi-parent crossover (GA-MPC), and cross-entropy (CE) to improve the performance of the energy detector-based spectrum sensing method.
