The AI Telco Engineer: Toward Autonomous Discovery of Wireless Communications Algorithms

arXiv:2604.19803v1 Announce Type: new Abstract: 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.

Related

• Enhancing Research Idea Generation through Combinatorial Innovation and Multi-Agent Iterative Search Strategies
• MIND: AI Co-Scientist for Material Research
• AstaBench: Rigorous Benchmarking of AI Agents with a Scientific Research Suite
• AI scientists produce results without reasoning scientifically
• SciFi: A Safe, Lightweight, User-Friendly, and Fully Autonomous Agentic AI Workflow for Scientific Applications
• Towards grounded autonomous research: an end-to-end LLM mini research loop on published computational physics

Source: arXiv cs.AI | 2026-04-23