A Case for Agentic Transceivers in Physical AI-RF Circuits and Systems
Conference
Madanayake, A, Karunanayake, K, Hacker, R et al. (2025). A Case for Agentic Transceivers in Physical AI-RF Circuits and Systems
. 10.1109/MAPCON65020.2025.11426198
Madanayake, A, Karunanayake, K, Hacker, R et al. (2025). A Case for Agentic Transceivers in Physical AI-RF Circuits and Systems
. 10.1109/MAPCON65020.2025.11426198
This concept paper proposes an architecture that integrates agentic AI models with digital twin representations for each subsystem component in an RF transceiver. The digital twins, constructed using machine learning techniques such as deep learning and reinforcement learning, are paired with specialized, autonomous AI agent(s) capable of independent reasoning, planning, and adaptive action. Agentic AI models go beyond traditional rule-based/symbolic AI to autonomously select optimal strategies, synthesize data from diverse sources, and interact with both virtual and real-world environments. Ideas for simulations and experimental validations are proposed for future work.
