LE QUY DON
Technical University
VietnameseClear Cookie - decide language by browser settings

MoDANet: Multi-task Deep Network for Joint Automatic Modulation Classification and Direction of Arrival Estimation

Doan, Van-Sang and Huynh-The, Thien and Hoang, Van-Phuc and Nguyen, Duy-Thai (2021) MoDANet: Multi-task Deep Network for Joint Automatic Modulation Classification and Direction of Arrival Estimation. IEEE Communications Letters. p. 1. ISSN 1089-7798

Full text not available from this repository. (Upload)

Abstract

In this letter, a multi-task deep convolutional neural network, namely MoDANet, is proposed to perform modulation classification and DOA estimation simultaneously. In particular, the network architecture is designed with multiple residual modules, which tackle the vanishing gradient problem. The multi-task learning (MTL) efficiency of MoDANet was evaluated with different variants of Y-shaped connection and fine-tuning some hyper-parameters of the deep network. As a result, MoDANet with one shared residual module using more filters, larger filter size, and longer signal length can achieve better performance of modulation classification and DOA estimation, but those might result in higher computational complexity. Therefore, choosing these parameters to attain a good trade-off between accuracy and computational cost is important, especially for resource-constrained devices. The network is investigated with two typical propagation channel models, including Pedestrian A and Vehicular A, to show the effect of those channels on the efficiency of the network. Remarkably, our work is the first DL-based MTL model to handle two unrelated tasks of modulation classification and DOA estimation. © 1997-2012 IEEE.

Item Type: Article
Divisions: Institutes > Institute of System Integration
Identification Number: 10.1109/LCOMM.2021.3132018
Uncontrolled Keywords: Backpropagation; Direction of arrival; Economic and social effects; Efficiency; Linearization; MIMO systems; Modulation; Network architecture, Automatic modulation; Automatic modulation classification; Direction of arrival estimation; DOA estimation; Fine tuning; Hyper-parameter; Modulation classification; Multi tasks; Vanishing gradient; Y-shaped, Deep neural networks
URI: http://eprints.lqdtu.edu.vn/id/eprint/10262

Actions (login required)

View Item
View Item