RadioMaster: Multi-Agent System for Autonomous Radio Signal Generation
2026-06-01 • Multiagent Systems
Multiagent SystemsArtificial IntelligenceNetworking and Internet Architecture
AI summaryⓘ
The authors address the hard problem of turning user commands into wireless radio signals, which usually requires deep technical knowledge. They found that current large language models don’t work well because they don’t understand radio hardware details. To fix this, the authors created RadioMaster, a system with three parts that work together to generate and verify real radio signals from user input. They also made RadioBench, a testing set for comparing performance in this area. Their tests show RadioMaster produces better and more reliable signals than existing methods.
Radio signalsPhysical layerLarge Language Models (LLMs)Multi-agent systemsI/Q samplesWireless prototypingRadio signal generationHardware constraintsBenchmarkingSignal fidelity
Authors
Jiazhen Lei, Tianze Cao, Yuxin Sha, Sihan Wang, Bingbing Wang, Fengyuan Zhu, Zeming Yang, Xiaohua Tian
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.