Spectrum Sensing using Enhanced Restricted Boltzmann Machine for Cognitive Radio Network
Amit Kumar1, Pushpendra R Verma2, Sajal Swapnil3, Rakesh Ranjan4
1Amit Kumar*, Department of Electronics & Communication Engineering, Himgiri Zee University, Dehradun, India.
2Pushpendra. R. Verma, Department of Electronics & Communication Engineering, Himgiri Zee University, Dehradun, India.
3Sajal Swapnil, Department of Electronics & Communication Engineering, NIT Kurukshetra, Haryana, India.
4Rakesh Ranjan, Vice Chancellor, Himgiri Zee University, Dehradun, India.
Manuscript received on August 11, 2020. | Revised Manuscript received on August 20, 2020. | Manuscript published on September 10, 2020. | PP: 271-278 | Volume-9 Issue-11, September 2020 | Retrieval Number: 100.1/ijitee.K78220991120 | DOI: 10.35940/ijitee.K7822.0991120
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Abstract: 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.
Keywords: Cognitive radio network (CRN), Spectrum sensing, Restricted Boltzmann machine (RBM), Particle swarm optimization (PSO).
Scope of the Article: Artificial Intelligence and Machine Learning