Deep Learning for Multi-Antenna Modulation Recognition of Radio Signals

TL;DR

This paper introduces MAMR-IQ, a deep learning approach for multi-antenna modulation recognition that leverages raw IQ signals and data augmentation to improve accuracy and efficiency.

Contribution

It proposes a novel deep learning method for multi-antenna modulation recognition and a data augmentation technique to enhance performance with limited data.

Findings

MAMR-IQ outperforms existing methods in accuracy and complexity.

Data augmentation improves recognition accuracy in few-shot scenarios.

Simulation confirms effectiveness of the proposed approach.

Abstract

Multi-antenna receiving systems have become a prevalent technical solution in communication systems. Meanwhile, deep learning has achieved significant progress in automatic modulation recognition tasks in single-antenna systems. However, the application of deep learning in multi-antenna modulation recognition (MAMR) tasks is still limited. In this paper, we propose an MAMR method namely MAMR-IQ to fully explore the diversity gain of a multi-antenna receiving system, which concatenates the raw received in-phase and quadrature (IQ) signals of multiple antennas and feeds them into a convolutional neural network. Simulation results show that the proposed MAMR-IQ method outperforms two existing deep learning-based MAMR methods which are based on direct voting (DV) and weight average (WA) in terms of both recognition accuracy and computational complexity. To address the problem of limited training data in few-shot scenarios, we further propose a data augmentation method that involves exchanging IQ sequences received by any two antennas to generate augmented samples. Simulation results show that with the proposed augmentation method, the recognition accuracy can be further improved.