Feature Engineering for Wireless Communications and Networking: Concepts, Methodologies, and Applications

TL;DR

This paper provides a comprehensive overview of feature engineering techniques in AI-driven wireless communications, emphasizing their importance in systems like ISAC networks and introducing a generative AI framework for signal reconstruction under attacks.

Contribution

It offers a detailed analysis of feature engineering principles and introduces a novel generative AI framework for signal spectrum reconstruction in low-altitude ISAC networks.

Findings

Effective signal spectrum reconstruction with 4% SSIM improvement

Enhanced AI model performance through advanced feature engineering

Application of generative AI in malicious attack scenarios

Abstract

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.