An Online Automatic Modulation Classification Scheme Based on Isolation Distributional Kernel

TL;DR

This paper presents a novel online automatic modulation classification method using Isolation Distributional Kernel, enabling real-time signal classification with high efficiency and outperformance over existing deep learning models.

Contribution

It introduces the first online AMC scheme based on distributional kernels, achieving linear time complexity for real-time applications under dynamic channel conditions.

Findings

Outperforms existing baseline models including deep learning classifiers

Operates efficiently in real-time with linear time complexity

Effective in realistic time-varying channel environments

Abstract

Automatic Modulation Classification (AMC), as a crucial technique in modern non-cooperative communication networks, plays a key role in various civil and military applications. However, existing AMC methods usually are complicated and can work in batch mode only due to their high computational complexity. This paper introduces a new online AMC scheme based on Isolation Distributional Kernel. Our method stands out in two aspects. Firstly, it is the first proposal to represent baseband signals using a distributional kernel. Secondly, it introduces a pioneering AMC technique that works well in online settings under realistic time-varying channel conditions. Through extensive experiments in online settings, we demonstrate the effectiveness of the proposed classifier. Our results indicate that the proposed approach outperforms existing baseline models, including two state-of-the-art deep learning classifiers. Moreover, it distinguishes itself as the first online classifier for AMC with linear time complexity, which marks a significant efficiency boost for real-time applications.