Deep learning-based robust automatic modulation classification using higher order cumulant features

Name: Nopparuj Suetrong

Organization : Chiang Mai University. Faculty of Engineering

Email : nopparuj_s@cmu.ac.th

Name: Attaphongse Taparugssanagorn

Organization : Asian Institute of Technology. School of Engineering and Technology

Email : attaphongset@ait.asia

Name: Natthanan Promsuk

Organization : Chiang Mai University. Faculty of Engineering

Email : natthanan.p@cmu.ac.th

keyword: Fourth-Order Cumulant

LCSH: Internet of things

; Gated Recurrent Unit

LCSH: Deep learning (Machine learning)

; Long Short-Term Memory

Abstract: The Internet of Things (IoT) represents one of the pivotal technologies in our daily lives. Within these systems, wireless communication plays an indispensable role in connecting IoT devices. Consequently, signal modulation emerges as a technique within wireless communication systems, enabling the transmission of baseband signals at higher frequencies. Given the array of modulation types, modulation classification comes into play to differentiate the modulation type of the received signal. Due to the presence of noise and attenuation, automatic modulation classification (AMC) stands as the primary method for such classification. AMC methods can operate without requiring any prior knowledge regarding the received signal. Multiple research groups have extended the depth of deep learning (DL) models or augmented the neuron count in each layer to enhance performance, albeit at the expense of increased model complexity. To tackle this challenge, we present model that merges the gated recurrent unit (GRU) and long shortterm memory (LSTM) architectures. Furthermore, data preprocessing techniques, specifically Min-Max normalization and fourth-order cumulant (FOC), have been employed to enhance classification accuracy. The outcomes underscore that our proposed model surpasses both LSTM and GRU models. Moreover, the classification accuracy of normalized data outperforms that of data without normalization.

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2023

Modified: 2024-12-20

Issued: 2024-12-20

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BibliograpyCitation : In IEEE Computational Intelligence Society Thailand Chapter, King Mongkut's Institute of Technology Ladkrabang. School of Information Technology and Universitas Gadjah Mada. Department of Electrical Engineering and Information Technology. The 15th International Conference on Information Technology and Electrical Engineering (ICITEE 2023) (pp.143-148) Nonthaburi : IEEE Computational Intelligence Society Thailand Chapter, 2023

eng

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