AI-Programmable Wireless Connectivity: Challenges and Research Directions Toward Interactive and Immersive Industry
It addresses the problem of enabling interactive and immersive industry applications through improved wireless connectivity, but it is incremental as it builds on prior high-level concepts by focusing on integration challenges.
This vision paper tackles the challenge of integrating traditional signal processing with AI to create energy-efficient, programmable, and scalable wireless connectivity for 6G and beyond, emphasizing the use of compact AI models like Tiny and Real-time ML to meet constraints on computing resources, adaptability, and reliability.
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.