CASE: Channel Allocation for optimized Spectral Efficiency using deep neural network in underlay cognitive radios

Channel allocation is a critical aspect to be addressed in underlay cognitive radios, especially when the upcoming 5G communications are based on the concept of cognitive radio. To ensure an efficient spectrum allocation, the paper presents an efficient channel allocation for optimizing the spectral efficiency using deep neural network. The proposed scheme named as CASE intends to evaluate the efficiency by considering the maximization of efficient spectrum allocation and minimizing computation time in an underlay cognitive radio network (CRN). The CASE system model provides an overall improvement in the spectrum access by 98.47%, 95% and 85% in terms of computation time compared to the existing IRAWCS technique and random scheme.