Identification of Distorted RF Components via Deep Multi-Task Learning

TL;DR

This paper introduces a deep multi-task learning approach to accurately identify multiple distorted RF components in wireless systems, addressing the challenge of isolating specific degradations amidst complex distortions.

Contribution

The paper presents a novel deep multi-task learning algorithm for identifying multiple RF component distortions, improving detection accuracy in complex scenarios.

Findings

High detection accuracy in simulations

Effective identification of multiple distortions

Robust performance across different scenarios

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.