RadioMaster: Multi-Agent System for Autonomous Radio Signal Generation

2026-06-01

AI Summary

This work addresses the challenge of automatically translating user intent into physical-layer wireless signals for rapid prototyping by proposing the first multi-agent framework dedicated to wireless signal generation. The framework integrates a large language model with domain-specific knowledge retrieval (RadioWiki), I/Q sample synthesis (RadioAgent), and RF simulation-based validation (RadioEmulator), enabling end-to-end generation of real-world wireless signals from natural language through collaborative reasoning and closed-loop verification. The authors introduce RadioBench, a specialized evaluation benchmark, and demonstrate that their approach significantly outperforms existing methods in real hardware deployments, achieving breakthrough improvements in both signal fidelity and configuration feasibility.

Abstract

Translating user intents into physical radio signals represents the critical yet notoriously tedious final step in wireless prototyping, as it requires intricate knowledge of physical layer details and presents immense implementation challenges. Large Language Models (LLMs) and multi-agent systems have revolutionized conventional software engineering, raising the compelling question of whether they can resolve these formidable difficulties. However, our investigations reveal that current models experience significant limitations and fail to accomplish this task when applied to radio signal generation. This performance degradation primarily stems from severe domain ignorance and a fundamental insensitivity to physical hardware constraints. To bridge this gap, we introduce RadioMaster, a fully autonomous multi-agent framework designed to seamlessly translate user input into real-world wireless emissions. RadioMaster operates on three synergistic pillars: RadioWiki for domain-specific knowledge retrieval, RadioAgent for collaborative I/Q sample generation alongside hardware configuration, and RadioEmulator for closed-loop physical layer verification. Furthermore, we construct RadioBench, the first comprehensive benchmark tailored specifically for the radio signal generation domain. Extensive real-world evaluations demonstrate that RadioMaster significantly outperforms state-of-the-art (SOTA) baselines regarding configuration viability and signal fidelity.

Problem

• radio signal generation
• user intent translation
• physical layer constraints
• hardware-aware synthesis
• wireless prototyping

Innovation

• multi-agent system
• radio signal generation
• domain-specific knowledge
• physical layer verification
• autonomous wireless prototyping