Feature Engineering for Wireless Communications and Networking: Concepts, Methodologies, and Applications
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912502662561792 |
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| author | Wang, Jiacheng Zhao, Changyuan Xiong, Zehui Xiang, Tao Niyato, Dusit Wang, Xianbin Mao, Shiwen Kim, Dong In |
| author_facet | Wang, Jiacheng Zhao, Changyuan Xiong, Zehui Xiang, Tao Niyato, Dusit Wang, Xianbin Mao, Shiwen Kim, Dong In |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19837 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Feature Engineering for Wireless Communications and Networking: Concepts, Methodologies, and Applications Wang, Jiacheng Zhao, Changyuan Xiong, Zehui Xiang, Tao Niyato, Dusit Wang, Xianbin Mao, Shiwen Kim, Dong In Signal Processing 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. |
| title | Feature Engineering for Wireless Communications and Networking: Concepts, Methodologies, and Applications |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2507.19837 |