Deep Learning for Multi-Antenna Modulation Recognition of Radio Signals
This work addresses the limited application of deep learning in multi-antenna modulation recognition, offering a more effective and efficient solution for communication systems.
The paper proposes MAMR-IQ, a deep learning method for multi-antenna modulation recognition that concatenates raw IQ signals from multiple antennas and feeds them into a CNN, outperforming existing methods in accuracy and computational complexity. A data augmentation method exchanging IQ sequences between antennas further improves accuracy in few-shot scenarios.
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.