Title :

Robustness to imperfect CSI of power allocation policies in cognitive relay networks

Author(s) :

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

Conference title :

IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2022

City :

Oulu

Country :

Finlande

Start date of the conference :

2022-07-04

English keyword(s) :

Robustness to imperfect CSI
Full-duplex relaying
Cognitive radio
Unsupervised deep learning

HAL domain(s) :

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

English abstract : [en]

In this paper, the aim is to study the robustness against imperfect channel state information (CSI) of the power allocation policies maximizing the constrained and non-convex Shannon rate problem in a relay-aided cognitive ...
Show more >In this paper, the aim is to study the robustness against imperfect channel state information (CSI) of the power allocation policies maximizing the constrained and non-convex Shannon rate problem in a relay-aided cognitive radio network. The primary communication is protected by a Quality of Service (QoS) constraint and the relay only helps the secondary communication by performing complex and non-linear operations. First, we derive the optimal power allocation policies under Compress-and-Forward (CF) relaying under perfect CSI. Second, we investigate the robustness of this solution jointly with that of the deep learning existing solution for Decode-and-Forward (DF), which we exploit here for CF as well. For all these solutions that strongly rely on perfect CSI, our numerical results show that errors in the channel estimations have a damaging effect not only on the secondary rate, but most importantly on the primary QoS degradation, becoming prohibitive for poor quality estimations. Nevertheless, we show that the deep learning solutions can be made robust by adjusting the training process to rely on both perfect and imperfect CSI observations. Indeed, the resulting predictions are capable of meeting the primary QoS constraint at the cost of secondary rate loss, irrespective from the channel estimation quality.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

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

Source :

Harvested from HAL