RadioMaster: Multi-Agent System for Autonomous Radio Signal Generation
Fuente:
arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
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| _version_ | 1866911739560329216 |
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| author | Lei, Jiazhen Cao, Tianze Sha, Yuxin Wang, Sihan Wang, Bingbing Zhu, Fengyuan Yang, Zeming Tian, Xiaohua |
| author_facet | Lei, Jiazhen Cao, Tianze Sha, Yuxin Wang, Sihan Wang, Bingbing Zhu, Fengyuan Yang, Zeming Tian, Xiaohua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_01862 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RadioMaster: Multi-Agent System for Autonomous Radio Signal Generation Lei, Jiazhen Cao, Tianze Sha, Yuxin Wang, Sihan Wang, Bingbing Zhu, Fengyuan Yang, Zeming Tian, Xiaohua Multiagent Systems Artificial Intelligence Networking and Internet Architecture 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. |
| title | RadioMaster: Multi-Agent System for Autonomous Radio Signal Generation |
| topic | Multiagent Systems Artificial Intelligence Networking and Internet Architecture |
| url | https://arxiv.org/abs/2606.01862 |