A Deep Neural Network Model for Hybrid Spectrum Sensing in Cognitive Radio
| Autor: | Mohamad Chaitou, Ali Mansour, Abbass Nasser, Koffi Clément Yao, Hussein Charara |
|---|---|
| Přispěvatelé: | American University of Beirut [Beyrouth] (AUB), Lebanese University [Beirut] (LU), Equipe Security, Intelligence and Integrity of Information (Lab-STICC_SI3), Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne), Université de Brest (UBO) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Spectrum sensing
Artificial neural network Artificial neural networks business.industry Computer science Deep learning Cognitive radio Detector 020206 networking & telecommunications Pattern recognition 02 engineering and technology [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] Computer Science Applications [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Electrical and Electronic Engineering business Energy (signal processing) Communication channel |
| Zdroj: | Wireless Personal Communications Wireless Personal Communications, Springer Verlag, 2021, ⟨10.1007/s11277-020-08013-7⟩ |
| ISSN: | 0929-6212 1572-834X |
| DOI: | 10.1007/s11277-020-08013-7⟩ |
| Popis: | Spectrum sensing (SS) is an essential task of the secondary user (SU) in a cognitive radio system. SS monitors the primary user (PU) activity in order to avoid any collision with SU, as the latter should be silent when PU is active on a given channel. Hybrid SS (HSS) is one of the powerful methods used to monitor PU activity. It consists of using different detectors together to make a final decision on the PU status. In this manuscript, artificial neural networks (ANN) are used to perform HSS. Since our data is composed from the test statistics (TSs) of several detectors, thus it can be modeled as tabular. Fully connected neural networks become the most suitable ANN model. We applied cutting-edge techniques in the field of deep learning in order to get the best possible accurate neural network model in our application. These techniques boil down to: embedding, regularization, batch normalization and smart learning rate selection. With the help TSs related to several detectors, ANN is trained to distinguish between two hypotheses, $$H_0$$ : PU is absent and $$H_1$$ : PU is active. Numerical results show the effectiveness of our proposed ANN-based HSS, as it outperforms the classical ANN-based energy detector and proves its capability to detect PU signal at very low SNR. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink