Models model language model announce application integration paper

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

arXiv eess.SPby Haris GacaninApril 1, 20261 min read0 views

arXiv:2603.29752v1 Announce Type: new 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

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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.

Comments: 9 pages, 6 figures

Subjects:

Signal Processing (eess.SP); Systems and Control (eess.SY)

Cite as: arXiv:2603.29752 [eess.SP]

(or arXiv:2603.29752v1 [eess.SP] for this version)

https://doi.org/10.48550/arXiv.2603.29752

arXiv-issued DOI via DataCite (pending registration)