Plug-and-Play AMC: Context Is King in Training-Free, Open-Set Modulation with LLMs

TL;DR

This paper introduces a novel, training-free modulation classification framework that uses large language models and signal processing to classify wireless signals effectively across various noise conditions.

Contribution

It presents a unique method combining signal features with LLMs for one-shot modulation classification without additional training.

Findings

Achieves competitive accuracy across multiple modulation schemes.

Operates effectively in both noiseless and noisy environments.

Reduces need for channel-specific model training.

Abstract

Automatic Modulation Classification (AMC) is critical for efficient spectrum management and robust wireless communications. However, AMC remains challenging due to the complex interplay of signal interference and noise. In this work, we propose an innovative framework that integrates traditional signal processing techniques with Large-Language Models (LLMs) to address AMC. Our approach leverages higher-order statistics and cumulant estimation to convert quantitative signal features into structured natural language prompts. By incorporating exemplar contexts into these prompts, our method exploits the LLM's inherent familiarity with classical signal processing, enabling effective one-shot classification without additional training or preprocessing (e.g., denoising). Experimental evaluations on synthetically generated datasets, spanning both noiseless and noisy conditions, demonstrate that our framework achieves competitive performance across diverse modulation schemes and Signal-to-Noise Ratios (SNRs). Moreover, our approach paves the way for robust foundation models in wireless communications across varying channel conditions, significantly reducing the expense associated with developing channel-specific models. This work lays the foundation for scalable, interpretable, and versatile signal classification systems in next-generation wireless networks. The source code is available at https://github.com/RU-SIT/context-is-king