论文标题
启用AI无线通信的数据质量评估框架
A Data Quality Assessment Framework for AI-enabled Wireless Communication
论文作者
论文摘要
使用人工智能（AI）重新设计和增强当前的无线通信系统是未来第六代（6G）无线网络的有前途的途径。启用AI的无线通信的性能在很大程度上取决于无线空气界面数据的质量。尽管针对不同应用程序有多种数据质量评估（DQA）的方法，但没有针对无线空气界面数据设计。在本文中，我们提出了一个DQA框架，以从三个方面：相似性，多样性和完整性来衡量无线空气界限数据的质量。相似性衡量所考虑的数据集的统计分布有多近；多样性衡量了数据集的全面性，而完整性衡量所考虑的数据集在应用程序方案中满足所需的性能指标的程度。所提出的框架可以应用于各种类型的无线空气接口数据，例如通道状态信息（CSI），信号到Interperion-Plus-noise比率（SINR），接收到的参考信号（RSRP）等。系统。
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 scenario. The proposed framework can be applied to various types of wireless air-interface data, such as channel state information (CSI), signal-to-interference-plus-noise ratio (SINR), reference signal received power (RSRP), etc. For simplicity, the validity of our proposed DQA framework is corroborated by applying it to CSI data and using similarity and diversity metrics to improve CSI compression and recovery in Massive MIMO systems.
