Spectrum Sensing using Enhanced Restricted Boltzmann Machine for Cognitive Radio Network
Amit Kumar, Pushpendra Ratnam Verma, Sajal Swapnil, Rakesh Ranjan · International Journal of Innovative Technology and Exploring Engineering · 2020
Cognitive radio network (CRN) came in to existence as a promising solution to tackle issues due to scarcity of spectrum. Spectrum sensing plays an important role for maximizing the spectrum utilization where spectrum of the primary users (PU) is sensed by the secondary user (SU) at particular time and space. Researchers have presented machine learning techniques for spectrum sensing, though, challenges exists for the improvement in the throughput, energy efficiency, detection probability and delivery ratio. In this paper, an enhanced restricted Boltzmann machine (ERBM) is presented for spectrum sensing based on RBM. Particle Swarm Optimization (PSO) is incorporated for enhancing the performance of spectrum sensing and computation of optimal momentum coefficient of RBM. Simulation results shows that the performance of the proposed spectrum sensing technique is comparable to the existing techniques in terms of throughput, energy efficiency and detection probability and delivery ratio.