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


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
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
ISSN (Print)1550-2252


Conference96th IEEE Vehicular Technology Conference, VTC 2022-Fall 2022
Country/TerritoryUnited Kingdom

Bibliographical note

Publisher Copyright:
© 2022 IEEE.


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
İstanbul Medipol Üniversitesi
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu5200030


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


    Dive into the research topics of 'Identification of Distorted RF Components via Deep Multi-Task Learning'. Together they form a unique fingerprint.

    Cite this