Automatic Modulation Classification Using Dense Memory Fusion Network

Li, Anping and Huang, Juanjuan and Yang, Xu and Zhang, Xiaofei and Wei, Meiying (2020) Automatic Modulation Classification Using Dense Memory Fusion Network. In: Mobimedia 2020, 27-28 August 2020, Cyberspace.

[img]
Preview
Text
eai.27-8-2020.2294984.pdf - Published Version

Download (777kB) | Preview

Abstract

Automatic modulation classification (AMC), as a key technology of cognitive radio (CR), aims to identify the modulation format of the received signal. In this paper, we propose a novel dense memory fusion neural network(DMFN) based AMC method where grid constellation matrix (GCM) extracted from the received signals with low computational complexity are utilized as the input of DMFN. In DMFN, densnet with densely connected structures is designed to extract high representative feature of GCMs, the unit of long short-term memory (LSTM) and fully connected layer are used to make classification decisions. Extensive simulations demonstrate that DMFN yields significant performance gain and takes higher robustness comparing with other methods. In addition, DMFN based AMC scheme achieves 90$\%$ classification accuracy at 4dB when the symbol length is set as 512, which illustrates its outstanding performance.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: semi-supervised face classification graph learning self-representation model low-rank constraint
Subjects: Q Science > QA Mathematics > QA76 Computer software
Depositing User: EAI Editor I.
Date Deposited: 04 Feb 2021 14:20
Last Modified: 04 Feb 2021 14:20
URI: https://eprints.eudl.eu/id/eprint/859

Actions (login required)

View Item View Item