Learning Constellation Map with Deep CNN for Accurate Modulation Recognition

TL;DR

This paper introduces a deep convolutional neural network that accurately classifies digital modulation schemes from constellation images, outperforming existing models especially in noisy wireless environments.

Contribution

The paper proposes a novel deep CNN architecture with flow-in-flow blocks and skip connections for improved modulation recognition from constellation diagrams.

Findings

Achieves approximately 87% accuracy at 0 dB SNR in Rayleigh fading channels.

Outperforms several state-of-the-art deep learning models in modulation classification.

Demonstrates robustness of the proposed network under challenging wireless conditions.

Abstract

Modulation classification, recognized as the intermediate step between signal detection and demodulation, is widely deployed in several modern wireless communication systems. Although many approaches have been studied in the last decades for identifying the modulation format of an incoming signal, they often reveal the obstacle of learning radio characteristics for most traditional machine learning algorithms. To overcome this drawback, we propose an accurate modulation classification method by exploiting deep learning for being compatible with constellation diagram. Particularly, a convolutional neural network is developed for proficiently learning the most relevant radio characteristics of gray-scale constellation image. The deep network is specified by multiple processing blocks, where several grouped and asymmetric convolutional layers in each block are organized by a flow-in-flow structure for feature enrichment. These blocks are connected via skip-connection to prevent the vanishing gradient problem while effectively preserving the information identify throughout the network. Regarding several intensive simulations on the constellation image dataset of eight digital modulations, the proposed deep network achieves the remarkable classification accuracy of approximately 87% at 0 dB signal-to-noise ratio (SNR) under a multipath Rayleigh fading channel and further outperforms some state-of-the-art deep models of constellation-based modulation classification.