AI自主设计无线通信算法，几小时就超越传统方法。
The AI Telco Engineer: Toward Autonomous Discovery of Wireless Communications Algorithms
- 用大模型迭代生成、评估、优化通信算法
- 在3个任务中性能媲美甚至超过传统算法
- 生成结果可解释且可扩展，适合算法研发者
代理型AI正迅速改变研究方式，从原型设计到文献结果复现。本文探索了利用代理型AI自主设计无线通信算法的能力。为此，我们构建了一个专用框架，借助大语言模型（LLMs）迭代生成、评估并优化候选算法。我们在物理层（PHY）和介质访问控制（MAC）层的三个任务上进行评估：无统计信息的信道估计、已知协方差的信道估计以及链路自适应。结果显示，该框架在数小时内生成的算法在性能上与现有基线相当，部分场景更优。此外，与基于神经网络的方法不同，生成的算法具备完全可解释性和可扩展性。本工作标志着向自主发现新型无线通信算法迈出的第一步，我们期待社区在该方向取得更多进展。
原文摘要 · Abstract (English)
Agentic AI is rapidly transforming the way research is conducted, from prototyping ideas to reproducing results found in the literature. In this paper, we explore the ability of agentic AI to autonomously design wireless communication algorithms. To that end, we implement a dedicated framework that leverages large language models (LLMs) to iteratively generate, evaluate, and refine candidate algorithms. We evaluate the framework on three tasks spanning the physical (PHY) and medium access control (MAC) layers: statistics-agnostic channel estimation, channel estimation with known covariance, and link adaptation. Our results show that, in a matter of hours, the framework produces algorithms that are competitive with and, in some cases, outperforming conventional baselines. Moreover, unlike neural network-based approaches, the generated algorithms are fully explainable and extensible. This work represents a first step toward the autonomous discovery of novel wireless communication algorithms, and we look forward to the progress our community makes in this direction.
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