Titre :

Robust DNNs for power allocation problems in cognitive relay networks

Auteur(s) :

Benatia, Yacine [Auteur]
Laboratoire d'Informatique Gaspard-Monge [LIGM]
Centre for Digital Systems [CERI SN - IMT Nord Europe]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Nord Europe]
Université Gustave Eiffel
ESIEE Paris
Equipes Traitement de l'Information et Systèmes [ETIS - UMR 8051]
Savard, Anne [Auteur]
Centre for Digital Systems [CERI SN - IMT Nord Europe]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Nord Europe]
Circuits Systèmes Applications des Micro-ondes - IEMN [CSAM - IEMN ]
Negrel, Romain [Auteur]
ESIEE Paris
Université Gustave Eiffel
Laboratoire d'Informatique Gaspard-Monge [LIGM]
Veronica Belmega, E [Auteur]
Equipes Traitement de l'Information et Systèmes [ETIS - UMR 8051]
Université Gustave Eiffel
Laboratoire d'Informatique Gaspard-Monge [LIGM]

Mot(s)-clé(s) en anglais :

Unsupervised deep learning self-supervised deep learning full-duplex relaying cognitive radio imperfect CSI
Unsupervised deep learning
self-supervised deep learning
full-duplex relaying
cognitive radio
imperfect CSI

Discipline(s) HAL :

Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Réseaux et télécommunications [cs.NI]

Résumé en anglais : [en]

<div><p>In this paper, we investigate deep neural network (DNN)-based power allocation policies maximizing the opportunistic rate of a relay-aided cognitive radio network under a quality of service (QoS) constraint protecting ...
Lire la suite ><div><p>In this paper, we investigate deep neural network (DNN)-based power allocation policies maximizing the opportunistic rate of a relay-aided cognitive radio network under a quality of service (QoS) constraint protecting the primary transmission. The full-duplex relay performs either Decode-and-Forward (DF) or Compress-and-Forward (CF) and assists the opportunistic communication. The considered primary QoS constraint is expressed in terms of the tolerated primary rate degradation compared to the case of no opportunistic interference. In order to cope with imperfect channel state information (CSI) especially regarding the links to/from the primary network, we propose a self-supervised learning approach that skillfully exploits both perfect and imperfect CSI knowledge within the training phase. Since none of the two relaying schemes is optimal in all system setups (e.g., relative position of the different transmitters, receiver and of the relay), we then propose a novel supervised DNN-based relaying scheme selection. Finally, we extend all these results by proposing a self-supervised DNN-based power allocation policy that is able to generalize over system parameters such as the individual power budget, and the allowed level of primary degradation. Our extensive numerical results on synthetic data demonstrate the effectiveness of our proposed deep learning approaches.</p></div>Lire moins >

Langue :

Anglais

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL