Artificial Intelligence-Programmable Wireless Connectivity: Challenges and Research Directions Toward Interactive and Immersive Industry

IEEE Vehicular Technology Magazine, vol. 21, pp. 78–88

Abstract

This vision article addresses the research challenges of integrating traditional signal processing with artificial intelligence (AI) to enable energy-efficient, programmable, and scalable wireless connectivity infrastructures. While prior studies have primarily focused on high-level concepts, such as the potential role of large language models (LLMs) in 6G systems, this article advances the discussion by emphasizing integration challenges and research opportunities at the system level. Specifically, this article examines the role of compact AI models, including tiny and real-time machine learning (ML), in enhancing wireless connectivity while adhering to strict constraints on computing resources, adaptability, and reliability. Application examples are provided to illustrate practical considerations, highlighting how AI-driven signal processing can support next-generation wireless networks. By combining classical signal processing with lightweight AI methods, this article outlines a pathway toward efficient and adaptive connectivity solutions for 6G and beyond.

Authors 1

1. Haris Gacanin corresponding Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University, Aachen, Germany