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RadioMaster applies multi-agent systems to automate the complex pipeline of generating verified radio signals from user intent. By decomposing sequential tasks into recoverable stages and using closed-loop physical-layer verification, it solves error propagation issues that plague current LLM-based approaches in wireless prototyping. This work demonstrates a significant leap toward autonomous hardware configuration without relying on hype about general AI capabilities.
RadioMaster: Multi-Agent System for Autonomous Radio Signal Generation
arXiv:2606.01862v2 Announce Type: replace-cross Abstract: Translating user intent into physical radio signals is the last critical step in wireless prototyping. It chains protocol planning, baseband synthesis, and hardware configuration. Large language models and multi-agent systems have reshaped software engineering, raising the question of whether they can solve this problem. Yet current models fail at this task, even when augmented with domain tools. Because the stages run sequentially, an error at any stage propagates downstream, so the end-to-end success rate collapses toward zero even when each stage looks locally competent. We introduce RadioMaster, a fully autonomous multi-agent framework that drives user input to verified emissions transmitted over the air. It rests on three synergistic pillars. RadioWiki grounds generation in domain knowledge to suppress hallucination. RadioAgent decomposes the fragile pipeline into independently executable and locally recoverable stages. RadioEmulator gates deployment behind closed-loop physical-layer verification. We further build RadioBench, the first benchmark for autonomous radio signal generation. Extensive real-world evaluations show that RadioMaster substantially outperforms state-of-the-art baselines in configuration viability and signal fidelity, while reducing configuration time by up to 28x.
Abstract:Translating user intent into physical radio signals is the last critical step in wireless prototyping. It chains protocol planning, baseband synthesis, and hardware configuration. Large language models and multi-agent systems have reshaped software engineering, raising the question of whether they can solve this problem. Yet current models fail at this task, even when augmented with domain tools. Because the stages run sequentially, an error at any stage propagates downstream, so the end-to-end success rate collapses toward zero even when each stage looks locally competent. We introduce RadioMaster, a fully autonomous multi-agent framework that drives user input to verified emissions transmitted over the air. It rests on three synergistic pillars. RadioWiki grounds generation in domain knowledge to suppress hallucination. RadioAgent decomposes the fragile pipeline into independently executable and locally recoverable stages. RadioEmulator gates deployment behind closed-loop physical-layer verification. We further build RadioBench, the first benchmark for autonomous radio signal generation. Extensive real-world evaluations show that RadioMaster substantially outperforms state-of-the-art baselines in configuration viability and signal fidelity, while reducing configuration time by up to 28x.
Multiagent Systems (cs.MA); Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI)
arXiv:2606.01862 [cs.MA]
(or arXiv:2606.01862v2 [cs.MA] for this version)
https://doi.org/10.48550/arXiv.2606.01862 Focus to learn more arXiv-issued DOI via DataCite
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