Deep Learning for Cooperative Radio Signal Classification

TL;DR

This paper explores deep learning techniques for cooperative radio signal classification, focusing on decision, signal, and feature fusion methods, with simulation-based performance analysis and discussion of future challenges.

Contribution

It introduces novel deep learning-based cooperative classification methods tailored for multi-node radio networks, enhancing decision accuracy and robustness.

Findings

Cooperative methods outperform individual classification approaches.

Deep learning-based fusion improves classification accuracy.

Simulation results validate the effectiveness of proposed methods.

Abstract

Radio signal classification has a very wide range of applications in cognitive radio networks and electromagnetic spectrum monitoring. In this article, we consider scenarios where multiple nodes in the network participate in cooperative classification. We propose cooperative radio signal classification methods based on deep learning for decision fusion, signal fusion and feature fusion, respectively. We analyze the performance of these methods through simulation experiments. We conclude the article with a discussion of research challenges and open problems.