Agentic Physical-AI for Self-Aware RF Systems
🤖 AI Summary
This work proposes an embodied multi-agent neuro-symbolic AI architecture tailored for physical radio-frequency (RF) systems to address the challenge of efficiently achieving adaptive control under dynamic operating conditions. The approach deploys intelligent agents—each equipped with an internal model and a control algorithm—onto individual RF circuit components, thereby tightly integrating neuro-symbolic reasoning with circuit-level adaptive control. Experimental validation on an intermediate-frequency amplifier demonstrates the effectiveness of the proposed framework, highlighting its potential for scaling to full RF transceiver systems to enable self-awareness and self-optimization. This paradigm offers a novel pathway toward the design of intelligent RF systems capable of autonomous adaptation in complex and varying environments.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
RF systems
intelligent control
dynamic operational conditions
self-awareness
adaptive transceivers
Innovation
Methods, ideas, or system contributions that make the work stand out.
Agentic AI
Neurosymbolic AI
Self-Aware RF Systems
Intelligent Transceivers
Adaptive Control
🔎 Similar Papers
No similar papers found.
