A Data Quality Assessment Framework for AI-enabled Wireless Communication
Hanning Tang, Liusha Yang, Rui Zhou, Jing Liang, Hong Wei, Xuan Wang,, Qingjiang Shi, Zhi-Quan Luo

TL;DR
This paper introduces a novel data quality assessment framework tailored for wireless air-interface data in AI-enabled wireless systems, focusing on similarity, diversity, and completeness to enhance 6G network performance.
Contribution
It presents the first comprehensive DQA framework specifically designed for wireless air-interface data, applicable to various data types and improving AI system reliability.
Findings
Framework effectively measures data quality aspects like similarity, diversity, and completeness.
Application to CSI data demonstrates improved compression and recovery in Massive MIMO systems.
Validates the framework's utility for enhancing wireless communication performance.
Abstract
Using artificial intelligent (AI) to re-design and enhance the current wireless communication system is a promising pathway for the future sixth-generation (6G) wireless network. The performance of AI-enabled wireless communication depends heavily on the quality of wireless air-interface data. Although there are various approaches to data quality assessment (DQA) for different applications, none has been designed for wireless air-interface data. In this paper, we propose a DQA framework to measure the quality of wireless air-interface data from three aspects: similarity, diversity, and completeness. The similarity measures how close the considered datasets are in terms of their statistical distributions; the diversity measures how well-rounded a dataset is, while the completeness measures to what degree the considered dataset satisfies the required performance metrics in an application…
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Taxonomy
TopicsMillimeter-Wave Propagation and Modeling · Advanced MIMO Systems Optimization · Power Line Communications and Noise
MethodsNone
