Identification of Distorted RF Components via Deep Multi-Task Learning

Mehmet Ali Aygul*, Ebubekir Memisoglu, Hakan Ali Cirpan, Huseyin Arslan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

High-quality radio frequency (RF) components are imperative for efficient wireless communication. However, these components can degrade over time and need to be identified so that either they can be replaced or their effects can be compensated. The identification of these components can be done through observation and analysis of constellation diagrams. However, in the presence of multiple distortions, it is very challenging to isolate and identify the RF components responsible for the degradation. This paper highlights the difficulties of distorted RF components' identification and their importance. Furthermore, a deep multi-task learning algorithm is proposed to identify the distorted components in the challenging scenario. Extensive simulations show that the proposed algorithm can automatically detect multiple distorted RF components with high accuracy in different scenarios.

Original languageEnglish
Title of host publication2022 IEEE 96th Vehicular Technology Conference, VTC 2022-Fall 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665454681
DOIs
Publication statusPublished - 2022
Event96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022 - London, United Kingdom
Duration: 26 Sept 202229 Sept 2022

Publication series

NameIEEE Vehicular Technology Conference
Volume2022-September
ISSN (Print)1550-2252

Conference

Conference96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022
Country/TerritoryUnited Kingdom
CityLondon
Period26/09/2229/09/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Funding

This work was supported in part by the Scientific and Technological Research Council of Turkey (TÜB˙TAK) under Grant No. 5200030 with the cooperation of VESTEL and Istanbul Medipol University.

FundersFunder number
VESTEL
İstanbul Medipol Üniversitesi
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu5200030

    Keywords

    • Deep learning
    • distorted RF components identification
    • multi-task learning
    • RF impairments

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