Treffer: Pilot Interval Reduction by Deep Learning Based Detectors in Uplink NOMA
Title:
Pilot Interval Reduction by Deep Learning Based Detectors in Uplink NOMA
Authors:
Publisher Information:
arXiv
Publication Year:
2025
Subject Terms:
FOS: Computer and information sciences, Computer Science - Machine Learning, 0203 mechanical engineering, Computer Science - Information Theory, Information Theory (cs.IT), 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, Machine Learning (cs.LG)
Document Type:
Fachzeitschrift
article in journal/newspaper
Language:
unknown
DOI:
10.48550/arxiv.2004.12416
Rights:
OPEN
Accession Number:
edsbas.5CBA65EF
Database:
BASE
Weitere Informationen
Non-Orthogonal Multiple Access (NOMA) has higher spectral efficiency than orthogonal multiple access (OMA) techniques. In uplink communication systems that the channel is not known at the receiver, pilot signals sent from each user in different time intervals have reduced the spectral efficiency of NOMA. In this study, in the uplink communication system, DL-deep learning based detectors which are known to respond to the pilot signals sent from the users at the base station have been researched. It is aimed to maintain the spectral efficiency of NOMA by sending a single pilot from users, thus reducing the time interval in the DL detectors. ; in Turkish language. accepted for IEEE SIU 2020