发表机构
NVIDIA（英伟达）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在探索大语言模型驱动的进化搜索在无线通信算法自主发现中的应用，通过AITE框架解决复杂通信问题，在两个物理层任务中取得成果，展现了该方法在发现下一代无线通信算法上的潜力。
AI 中文摘要
大语言模型驱动的进化搜索是一种新兴的算法发现范式，已在多个科学领域取得新成果，但在无线通信中的应用尚待探索。为此引入了AI Telco Engineer（AITE）框架，用于为复杂通信问题自主设计算法并权衡性能与复杂度。通过两个物理层问题展示了AITE，在第一个任务中开发的算法性能超越最佳已知解决方案，计算延迟降低3.6倍；第二个任务中发现了首个与先进神经接收器性能相当的可解释算法。这些结果证明了大语言模型驱动的进化搜索在自主发现下一代无线通信算法方面的强大潜力。
英文摘要
Large language model (LLM)-driven evolutionary search is an emerging algorithm-discovery paradigm that has already produced novel results in several scientific fields. Yet its application to wireless communications remains largely unexplored. To bridge this gap, we introduce The AI Telco Engineer (AITE), a framework to autonomously design algorithms for complex communication problems, while navigating performance-complexity tradeoffs. We showcase AITE on two challenging physical-layer problems: designing an equalizer for an orthogonal time-frequency space (OTFS) system, and constructing a receiver algorithm for an orthogonal frequency-division multiplexing (OFDM) system using a custom constellation and operating without pilots. For the first task, AITE develops algorithms that outperform the best-known solutions while reducing computational latency by a factor of 3.6 compared to the strongest baseline. For the second task, it discovers the first explicit, explainable algorithms that achieve performance parity with state-of-the-art neural receivers. These results demonstrate the strong potential of LLM-driven evolutionary search for the autonomous discovery of next-generation wireless communications algorithms.
