Journal Article

Open-set Communication Scene Recognition Based on Metric Learning

Guo, Dengke, Zhang, Xiaoying, Ma, Dongtang, Wang, Yankun, Xiong, Jun

2024

Overview

In this paper, we study the problem of recognizing communication scenes under open set protocols, i.e., making the extracted features of communication scenes sufficiently separable under a suitable metric space. To achieve it, we design a feature fusion model based on a convolutional neural network to extract the features of communication scenes, and we take the real and imaginary parts of the channel frequency response as model inputs to extract the scene features sufficiently. Furthermore, in order to optimize the distribution of scene features in angular space, a Linear-Softmax loss function is designed to restrict the inter-class angles of extracted features to be larger than the intra-class angles by two hyperparameters. Meanwhile, a cosine distance-based communication scene recognition algorithm is designed to complete the communication scene recognition by calculating the cosine distance between features. Finally, it is tested on actual measurement data sets, and the experimental results show that compared with the state-of-the-art loss functions, the proposed Linear-Softmax loss function enables the learning of the maximum angular margin. The Linear-Softmax loss function combined with the proposed communication scenes recognition algorithm can complete the recognition of both closed-set and open-set test data, with a recognition accuracy as high as 97.28% on the closed-set test set and 85.65% on the open-set test set.

• Publisher: IOP Publishing
• Subject: Algorithms / Artificial neural networks / channel frequency response / Communication / communication scene recognition / Completeness / Design optimization / Feature extraction / Frequency response / Machine learning / metric learning / Metric space / Test sets / Trigonometric functions