用大模型自动设计无线通信算法，性能更强且更省算力。
Autonomous Discovery of Wireless Communications Algorithms
- 用大模型驱动进化搜索，自动优化通信算法。
- 比现有最佳方案快3.6倍，性能还更好。
- 首次发现可解释的高性能接收算法，适合研究者参考。
大型语言模型（LLM）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。然而其在无线通信中的应用仍不充分。为此，我们提出AI Telco Engineer（AITE）框架，用于自主设计复杂通信问题的算法，同时权衡性能与复杂度。我们在两个挑战性的物理层问题上验证AITE：为正交时频空间（OTFS）系统设计均衡器，以及在无导频条件下，使用自定义星座设计正交频分复用（OFDM）接收算法。在第一个任务中，AITE所生成算法优于现有最优解，计算延迟相较最强基线降低3.6倍。在第二个任务中，它发现了首个显式、可解释的算法，性能与最先进的神经接收机相当。结果表明，LLM驱动的进化搜索在自主发现下一代无线通信算法方面具有巨大潜力。
原文摘要 · Abstract (English)
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.
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