Deep Modulation Recognition with Multiple Receive Antennas: An End-to-end Feature Learning Approach

TL;DR

This paper introduces two novel deep neural network architectures for modulation recognition using multiple receive antennas, outperforming existing methods by effectively fusing multi-antenna signals and adapting to varying SNR conditions.

Contribution

The paper proposes two end-to-end deep learning architectures for multi-antenna modulation recognition, including a multi-view CNN and a weight-learning CNN, advancing the state-of-the-art in this area.

Findings

Both architectures outperform existing algorithms.

The weight-learning CNN achieves the best performance.

Effective multi-antenna signal fusion improves recognition accuracy.

Abstract

Modulation recognition using deep neural networks has shown promising advantages over conventional algorithms. However, most existing research focuses on single receive antenna. In this paper, two end-to-end feature learning deep architectures are introduced for modulation recognition with multiple receive antennas. The first is based on multi-view convolutional neural network by treating signals from different receive antennas as different views of a 3D object and designing the location and operation of view-pooling layer that are suitable for feature fusion of multi-antenna signals. Considering that the instantaneous SNRs could be different among receive antennas in wireless communications, we further propose weight-learning convolutional neural network which uses a weight-learning module to automatically learn the weights for feature combing of different receive antennas to perform end-to-end feature learning of multi-antenna signals. Results show that both end-to-end feature learning deep architectures outperform the existing algorithm, and the proposed weight-learning convolutional neural network achieves the best performance.