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

arXiv: https://arxiv.org/abs/2008.06720 · HTML: https://mlsanity.com/paper/2008.06720/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2020.

## Built on by (most cited first, 1 of 1)

- [Signal Processing Based Deep Learning for Blind Symbol Decoding and Modulation Classification](https://mlsanity.com/paper/2106.10543/) (2021)

## Abstract (arXiv)

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.