Multi-Agent Neurosymbolic AI for Self-Adapting RF Transceivers
Conference
Ratnayake, L, Jayakumar, W, Dabare, D et al. (2026). Multi-Agent Neurosymbolic AI for Self-Adapting RF Transceivers
. Midwest Symposium on Circuits and Systems, 708-712. 10.1109/MWSCAS67364.2026.11680687
Ratnayake, L, Jayakumar, W, Dabare, D et al. (2026). Multi-Agent Neurosymbolic AI for Self-Adapting RF Transceivers
. Midwest Symposium on Circuits and Systems, 708-712. 10.1109/MWSCAS67364.2026.11680687
This paper presents a multi agent neurosymbolic artificial intelligence architecture to dynamically adapt operating parameters of radio frequency (RF) transceivers. The proposed dual branch architecture combines deep learning feature extraction with deterministic symbolic reasoning, leveraging sensed physical parameters such as signal power and spectrum occupancy, to ensure self-adapting and robust hardware safety. Component specific control agents are trained using digital twins and integrated with physical hardware via shared memory space and socket-based inter-process communication for low latency. Experimental results demonstrate accurate modeling and rapid parameter adaptation for error vector magnitude optimization, providing a scalable foundation for intelligent self-adapting RF transceivers.
