Throughput Maximization in Cognitive Radio Network Under Constant Fading using Hierarchical Neural System
Neetu Goyal1, Sanjay Mathur2
1Neetu Goyal, Pursuing Ph.D., Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, India.
2Sanjay Mathur, Professor in the Department of Electronics and Communication Engineering, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2581-2584 | Volume-9 Issue-3, February 2020. | Retrieval Number: C5560029320/2020©BEIESP | DOI: 10.35940/ijeat.C5560.029320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Cognitive radios (CRs) predominantly reuse the spectrum holes to proficiently utilize the scarcely available radio spectrum. In the CRs, the throughput limitation is a major difficulty among the key limitations such as energy consumption, processing resources, cost, and quality of service limitations, that affects a wide range of telecommunication applications nowadays. Moreover, attaining high throughput will overcome the bottleneck of CRs applications. To overcome this emerging throughput limitation issue in the CRs, this paper proposes the best channel prediction algorithm using Multilayer Feed forward Neural Network (MFNN) which tackles the throughput limitations.
Keywords: Cognitive Radios, Constant Fading, Multilayer Feed Forward Neural Network, Channel Prediction, Throughput.