Agentic Physical-AI for Self-Aware RF Systems

TL;DR

This paper introduces a multi-agent neurosymbolic AI system for intelligent, adaptive control of RF transceivers, demonstrating promising results in modeling RF components like the IF amplifier for future self-aware communication systems.

Contribution

It presents a novel multi-agent neurosymbolic AI framework for RF systems, enabling component-specific control and adaptability, advancing beyond traditional static control methods.

Findings

Successful modeling of the IF amplifier component

Potential extension to all RF components

Foundation for fully intelligent RF systems

Abstract

Intelligent control of RF transceivers adapting to dynamic operational conditions is essential in the modern and future communication systems. We propose a multi-agent neurosymbolic AI system, where AI agents are assigned for circuit components. Agents have an internal model and a corresponding control algorithm as its constituents. Modeling of the IF amplifier shows promising results, where the same approach can be extended to all the components, thus creating a fully intelligent RF system.