论文标题
通过深度多任务学习识别扭曲的RF组件
Identification of Distorted RF Components via Deep Multi-Task Learning
论文作者
论文摘要
高质量的射频（RF）组件对于有效的无线通信至关重要。但是，这些组件可以随着时间的推移而降低，并且需要确定，以便可以更换它们，否则可以补偿其效果。这些组件的识别可以通过观察和分析星座图来完成。但是，在存在多种扭曲的情况下，隔离和识别导致降解的RF组件非常具有挑战性。本文强调了扭曲的RF组件的识别及其重要性的困难。此外，提出了一种深层多任务学习算法，以确定挑战性的情况下的扭曲组件。广泛的模拟表明，在不同情况下，提出的算法可以自动检测具有高精度的多个变形的RF组件。
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.
