Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Haris Gacanin

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.

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

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.

Paper Structure

This paper contains 20 sections, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. An example of future mobile AI application
  3. Technology vision
  4. Goals of this paper
  5. Technology Challenges of Mobile AI Applications
  6. Robust and adaptive communication in real time
  7. Training in Real Time
  8. Device Communications Efficiency
  9. Computing efficiency
  10. Research Directions for Realization of Intelligent Radio Fabric
  11. AI-programmable connectivity
  12. On-device embedded computing
  13. Learning in real-time
  14. Augmenting Physical Context with Network Intelligence
  15. Data control in real time
  16. ...and 5 more sections

Figures (6)

  • Figure 1: 6G performance requirements for interactive mobile AI applications.
  • Figure 2: Intelligent radio fabric for connected intelligence as an interactive application enabler.
  • Figure 3: Distributed AI across non-homogeneous devices.
  • Figure 4: Trade-off between spectrum and energy efficiency leading to AI-based short-range connectivity.
  • Figure 5: Technology components for building IRF and related research directions.
  • ...and 1 more figures