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.

翻译：将用户意图转化为物理无线电信号是现代无线原型设计过程中关键且众所周知的繁琐步骤，这需要掌握物理层细节的复杂知识并面临巨大的实现挑战。大语言模型与多智能体系统已彻底革新传统软件工程，引发了一个引人深思的问题：它们能否解决这些严峻困难？然而，我们的研究表明，当前模型存在显著局限性，在应用于无线电信号生成时无法完成此任务。这种性能下降主要源于严重的领域知识缺失和物理硬件约束的基本不敏感性。为弥补这一差距，我们引入RadioMaster——一个完全自主的多智能体框架，旨在无缝地将用户输入转化为真实的无线发射信号。RadioMaster基于三大协同支柱运行：RadioWiki用于特定领域知识检索，RadioAgent用于协同I/Q样本生成与硬件配置，RadioEmulator用于闭环物理层验证。此外，我们构建了RadioBench——首个专为无线电信号生成领域定制的综合性基准。广泛的实际评估表明，RadioMaster在配置可行性和信号保真度方面显著优于现有最优基线方法。