English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Radio-frequency (RF) front-end forms a critical part of any radio system,defining its cost as well as communication performance. However, thesecomponents frequently exhibit non-ideal behavior, referred to as impairments,due to the imperfections in the manufacturing/design process. Most of thedesigners rely on simplified closed-form models to estimate these impairments.On the other hand, these models do not holistically or accurately capture theeffects of real-world RF front-end components. Recently, machine learning-basedalgorithms have been proposed to estimate these impairments. However, thesealgorithms are not capable of estimating multiple RF impairments jointly, whichleads to limited estimation accuracy. In this paper, the joint estimation ofmultiple RF impairments by exploiting the relationship between them isproposed. To do this, a deep multi-task learning-based algorithm is designed.Extensive simulation results reveal that the performance of the proposed jointRF impairments estimation algorithm is superior to the conventional individualestimations in terms of mean-square error. Moreover, the proposed algorithmremoves the need of training multiple models for estimating the differentimpairments.

Joint Estimation of Multiple RF Impairments Using Deep Multi-Task Learning