NMformer: A Transformer for Noisy Modulation Classification in Wireless Communication

TL;DR

This paper introduces NMformer, a vision transformer-based model that classifies wireless communication modulation signals directly from constellation diagrams, effectively handling noisy environments without additional denoising steps.

Contribution

The study presents a novel ViT-based model for modulation classification that works with 2-D constellation images and demonstrates improved accuracy in noisy and out-of-distribution scenarios.

Findings

Achieves 4.67% higher accuracy than baseline in high SNR conditions.

Outperforms base classifier on low SNR and unseen data.

Effective across a wide range of SNRs.

Abstract

Modulation classification is a very challenging task since the signals intertwine with various ambient noises. Methods are required that can classify them without adding extra steps like denoising, which introduces computational complexity. In this study, we propose a vision transformer (ViT) based model named NMformer to predict the channel modulation images with different noise levels in wireless communication. Since ViTs are most effective for RGB images, we generated constellation diagrams from the modulated signals. The diagrams provide the information from the signals in a 2-D representation form. We trained NMformer on 106, 800 modulation images to build the base classifier and only used 3, 000 images to fine-tune for specific tasks. Our proposed model has two different kinds of prediction setups: in-distribution and out-of-distribution. Our model achieves 4.67% higher accuracy than the base classifier when finetuned and tested on high signal-to-noise ratios (SNRs) in-distribution classes. Moreover, the fine-tuned low SNR task achieves a higher accuracy than the base classifier. The fine-tuned classifier becomes much more effective than the base classifier by achieving higher accuracy when predicted, even on unseen data from out-of-distribution classes. Extensive experiments show the effectiveness of NMformer for a wide range of SNRs.