Title :

Unsupervised deep learning to solve power allocation problems in cognitive relay networks

Author(s) :

Benatia, Yacine [Auteur]
Equipes Traitement de l'Information et Systèmes [ETIS - UMR 8051]
Ecole Nationale Supérieure de l'Electronique et de ses Applications [ENSEA]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Nord Europe]
Savard, Anne [Auteur]
Circuits Systèmes Applications des Micro-ondes - IEMN [CSAM - IEMN ]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Nord Europe]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Negrel, Romain [Auteur]
Laboratoire d'Informatique Gaspard-Monge [LIGM]
Université Gustave Eiffel
Belmega, Elena Veronica [Auteur]
Equipes Traitement de l'Information et Systèmes [ETIS - UMR 8051]
Ecole Nationale Supérieure de l'Electronique et de ses Applications [ENSEA]
Laboratoire d'Informatique Gaspard-Monge [LIGM]
Université Gustave Eiffel

Conference title :

IEEE International Conference on Communications Workshops, ICC Workshops 2022

City :

Séoul

Country :

Corée du Sud

Start date of the conference :

2022-05-16

Publication date :

2022

English keyword(s) :

Unsupervised deep learning
Full-duplex relaying
Decode-and-Forward
Cognitive radio

HAL domain(s) :

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

English abstract : [en]

In this paper, an unsupervised deep learning approach is proposed to solve the constrained and non-convex Shannon rate maximization problem in a relay-aided cognitive radio network. This network consists of a primary and ...
Show more >In this paper, an unsupervised deep learning approach is proposed to solve the constrained and non-convex Shannon rate maximization problem in a relay-aided cognitive radio network. This network consists of a primary and a secondary user-destination pair and a secondary full-duplex relay performing Decode-and-Forward. The primary communication is protected by a Quality of Service (QoS) constraint in terms of tolerated Shannon rate degradation. The relaying operation leads to non-convex objective and primary QoS constraint, which makes deep learning approaches relevant and promising. For this, we propose a fully-connected neural network architecture coupled with a custom and communication-tailored loss function to be minimized during training in an unsupervised manner. A major interest of our approach is that the required training data contains only system parameters without the corresponding solutions to the non-convex optimization problem, as opposed to supervised approaches. Our numerical experiments show that our proposed approach has a high generalization capability on unseen data without overfitting. Also, the predicted solution performs close to the brute force one, highlighting the high potential of our unsupervised approach.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

Technologies Emergentes pour l'Internet des Objets

Collections :

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

Source :

Harvested from HAL