Deep learning-based energy-efficient relay precoder design in MIMO-CRNs

This paper investigates the problem of energy efficient relay precoder design in multiple-input multiple-output cognitive relay networks (MIMO-CRNs). This is a non-convex fractional programming problem, which is traditionally solved using computationally expensive optimization methods. In this paper...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Physical communication Jg. 50; S. 101486
Hauptverfasser: Sahu, Deepak, Maurya, Shikha, Bansal, Matadeen, Kumar V., Dinesh
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.02.2022
Schlagworte:
Cognitive relay network
Deep learning
Energy efficiency
MIMO
Relay precoder
Deep learning
Cognitive relay network
Energy efficiency
MIMO
Relay precoder
ISSN:1874-4907, 1876-3219
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:This paper investigates the problem of energy efficient relay precoder design in multiple-input multiple-output cognitive relay networks (MIMO-CRNs). This is a non-convex fractional programming problem, which is traditionally solved using computationally expensive optimization methods. In this paper, we propose a deep learning (DL) based approach to compute an approximate solution. Specifically, a deep neural network (DNN) is employed and trained using offline computed optimal solution. The proposed scheme consists of an offline data generation phase, an offline training phase, and an online deployment phase. The numerical results show that the proposed DNN provides comparable performance at significantly lower computational complexity as compared to the conventional optimization-based algorithm that makes the proposed approach suitable for real-time implementation.
ISSN:1874-4907
1876-3219
DOI:10.1016/j.phycom.2021.101486