Performance Access of Various Ann Layers in Cognitive Radio Network
Pravin Akula1, S N Lakshmipathiraju2
1Pravin Akula, Professor, Electronics and Communication Engineering, BVC Engineering College, AndhraPradesh, India.
2S N Lakshmipathiraju, Professor, Electronics and Communication Engineering, BVC Engineering College, AndhraPradesh, India.
Manuscript received on 15 September 2019 | Revised Manuscript received on 23 September 2019 | Manuscript Published on 11 October 2019 | PP: 975-978 | Volume-8 Issue-11S September 2019 | Retrieval Number: K118009811S19/2019©BEIESP | DOI: 10.35940/ijitee.K1180.09811S19
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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: In this section, the taxonomy outcomes attained by the signal classification system defined in the preceding section are deliberated. To examine the performance, 400 signals were produced and modulated by means of three modulation systems: BPSK, QPSK and QAM. They are transmitted via AWGN channel with 0dB, 1dB, 5 dB and 10dB noise density. By means of temporal and spectral features with ANN classifier, a sum of 5, 10, 15, 20 and 25 hidden layers are taken for the modulation of the signals. 300 signals are employed for training, and 100 signals are tested with ANN classifier with dissimilar distance measures, Euclidean, city block, cosine and correlation.
Keywords: AWGN, QPSK, QAM, ANN and BPSK
Scope of the Article: Cognitive Radio Networks