AI-enabled wireless communications have attracted tremendous research interest in recent years, particularly with the rise of novel paradigms such as low-altitude integrated sensing and communication (ISAC) networks. Within these systems, feature engineering plays a pivotal role by transforming raw wireless data into structured representations suitable for AI models. Hence, this paper offers a comprehensive investigation of feature engineering techniques in AI-driven wireless communications. Specifically, we begin with a detailed analysis of fundamental principles and methodologies of feature engineering. Next, we present its applications in wireless communication systems, with special emphasis on ISAC networks. Finally, we introduce a generative AI-based framework, which can reconstruct signal feature spectrum under malicious attacks in low-altitude ISAC networks. The case study shows that it can effectively reconstruct the signal spectrum, achieving an average structural similarity index improvement of 4%, thereby supporting downstream sensing and communication applications.
@article{arxiv.2507.19837,
title = {Feature Engineering for Wireless Communications and Networking: Concepts, Methodologies, and Applications},
author = {Jiacheng Wang and Changyuan Zhao and Zehui Xiong and Tao Xiang and Dusit Niyato and Xianbin Wang and Shiwen Mao and Dong In Kim},
journal= {arXiv preprint arXiv:2507.19837},
year = {2025}
}