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Machine learning based spectrum sensing techniques in cognitive radio / Ahmed Galal Ahmed Mohammed ; Supervised Reda Elkhoribi , Mahmoud Ahmed Ismail

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Ahmed Galal Ahmed Mohammed , 2015Description: 100 Leaves : charts , photographs; 30cmOther title:
  • استشعار الطيف انترددى بإستخدام آليات انتعلم فى الراديو الإدراكى [Added title page title]
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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Computer and Information - Department of Information Technology Summary: We implement and design 8 digital modulations are: 2ASK, 2FSK, 4ASK, 4FSK, 2PSK, 4PSK, DPSK, and 16QAM. The maximum value of spectral density of normalized centered amplitude and the average value of normalized absolute centered instantaneous phase deviation choose as key features for digital modulation recognizer based on the ANN. We used the Rayleigh fading channel to model signals propagation and corrupted the signals by additive white gaussian noise (AWGN) for testing the algorithm. The simulation results show that the ANN could be recognized the different types of the PUs and corrected classify the signals in its current state of development
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Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.20.01.M.Sc.2015.Ah.M (Browse shelf(Opens below)) Not for loan 01010110067107000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.20.01.M.Sc.2015.Ah.M (Browse shelf(Opens below)) 67107.CD Not for loan 01020110067107000

Thesis (M.Sc.) - Cairo University - Faculty of Computer and Information - Department of Information Technology

We implement and design 8 digital modulations are: 2ASK, 2FSK, 4ASK, 4FSK, 2PSK, 4PSK, DPSK, and 16QAM. The maximum value of spectral density of normalized centered amplitude and the average value of normalized absolute centered instantaneous phase deviation choose as key features for digital modulation recognizer based on the ANN. We used the Rayleigh fading channel to model signals propagation and corrupted the signals by additive white gaussian noise (AWGN) for testing the algorithm. The simulation results show that the ANN could be recognized the different types of the PUs and corrected classify the signals in its current state of development

Issued also as CD

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