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

TL;DR

This paper discusses integrating AI with traditional signal processing to create energy-efficient, programmable wireless networks for 6G, focusing on system-level challenges and opportunities.

Contribution

It advances the discussion by emphasizing integration challenges and research opportunities at the system level for AI-driven wireless connectivity.

Findings

Highlights the role of compact AI models like Tiny and Real-time ML in wireless systems.

Provides application examples illustrating AI-enhanced wireless connectivity.

Outlines a pathway for efficient, adaptive 6G wireless solutions.

Abstract

This vision paper 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 Model (LLM) in 6G systems, this work advances the discussion by emphasizing integration challenges and research opportunities at the system level. Specifically, this paper 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 and highlight how AI-driven signal processing can support next-generation wireless networks. By combining classical signal processing with lightweight AI methods, this paper outlines a pathway toward efficient and adaptive connectivity solutions for 6G and beyond.