LLM-AUG: Robust Wireless Data Augmentation with In-Context Learning in Large Language Models

ArXi:2604.17770v1 Announce Type: new Data scarcity remains a fundamental bottleneck in applying deep learning to wireless communication problems, particularly in scenarios where collecting labeled Radio Frequency (RF) data is expensive, time-consuming, or operationally constrained. This paper proposes LLM-AUG, a data augmentation framework that leverages in-context learning in large language models (LLMs) to generate synthetic