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Mata Kuliah: Seminar 1
Studi Literatur – Review Paper
Oleh :
Muhammad Ibkar Azki Himmawan
(21/490285/PTK/14338)
On Spectrum Sensing, a Machine Learning Method ...
On Spectrum Sensing, a Machine Learning Method for
Cognitive Radio Systems
Youness Arjoune and Naima Kaabouch
2019 : IEEE International Conference on Electro Information
Technology (EIT)
Latar Belakang Permasalahan
Beberapa metode spectrum sensing seperti energy detection,
cyclostationary feature, dan matched filter memiliki beberapa
keterbatasan. Semua metode tersebut memerlukan pengaturan
threshold yang membutuhkan pengetahuan tentang distribusi
noise. Keandalan spectrum sensing masih menjadi isu terbuka
dalam penelitian tentang komunikasi nirkabel.
On Spectrum Sensing, a Machine Learning Method ...
Hipotesis
Penulis mengusulkan metode spectrum sensing berdasarkan teori machine learning
untuk cognitive radio networks dan menyelidiki efisiensi teknik random forest sebagai
teknik spectrum sensing. Efisiensi ini dibandingkan dengan beberapa teknik machine
learning lainnya, seperti SVM dengan kernel yang berbeda, decision tree, KNN, Naive
Bayes, dan regresi logistik.
Pengujian Hipotesis
Untuk mengevaluasi performa dari masing-masing teknik machine learning sebagai
teknik spectrum sensing, penulis dengan menggunakan perhitungan probabilitas
deteksi (Pd), probabilitas kesalahan deteksi (Pm), probabilitas false-alarm (Pfa), dan
akurasi klasifikasi (A).
Hasil dan Kesimpulan
Dari hasil simulasi, terlihat bahwa model spectrum sensing yang diuji dengan
menggunakan teknik random forest lebih unggul dari semua teknik machine learning
lainnya.
Terima Kasih

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Presentasi Review Paper - Mata Kuliah Seminar 1.pptx

  • 1. Mata Kuliah: Seminar 1 Studi Literatur – Review Paper Oleh : Muhammad Ibkar Azki Himmawan (21/490285/PTK/14338)
  • 2. On Spectrum Sensing, a Machine Learning Method ... On Spectrum Sensing, a Machine Learning Method for Cognitive Radio Systems Youness Arjoune and Naima Kaabouch 2019 : IEEE International Conference on Electro Information Technology (EIT) Latar Belakang Permasalahan Beberapa metode spectrum sensing seperti energy detection, cyclostationary feature, dan matched filter memiliki beberapa keterbatasan. Semua metode tersebut memerlukan pengaturan threshold yang membutuhkan pengetahuan tentang distribusi noise. Keandalan spectrum sensing masih menjadi isu terbuka dalam penelitian tentang komunikasi nirkabel.
  • 3. On Spectrum Sensing, a Machine Learning Method ... Hipotesis Penulis mengusulkan metode spectrum sensing berdasarkan teori machine learning untuk cognitive radio networks dan menyelidiki efisiensi teknik random forest sebagai teknik spectrum sensing. Efisiensi ini dibandingkan dengan beberapa teknik machine learning lainnya, seperti SVM dengan kernel yang berbeda, decision tree, KNN, Naive Bayes, dan regresi logistik. Pengujian Hipotesis Untuk mengevaluasi performa dari masing-masing teknik machine learning sebagai teknik spectrum sensing, penulis dengan menggunakan perhitungan probabilitas deteksi (Pd), probabilitas kesalahan deteksi (Pm), probabilitas false-alarm (Pfa), dan akurasi klasifikasi (A). Hasil dan Kesimpulan Dari hasil simulasi, terlihat bahwa model spectrum sensing yang diuji dengan menggunakan teknik random forest lebih unggul dari semua teknik machine learning lainnya.

Editor's Notes

  1. Metode energy detection memiliki performa yang buruk pada nilai SNR yang rendah, metode cyclostationary memerlukan analisis yang kompleks, dan metode matched filter memerlukan pengetahuan sebelumnya terkait isyarat primary user.