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research-article

Dendrogram-based Heterogeneous Learners for Automatic Modulation Classification in DSTBC-OFDM Systems

Authors:

M. BESSEGHIERAuthors Info & Claims

Volume 62, Issue C

https://doi.org/10.1016/j.phycom.2023.102241

Published: 12 April 2024 Publication History

Abstract

Software-Defined Radios (SDRs) have a crucial role in dynamic spectrum sensing and sharing as they can quickly adapt to changes in their environment. Digital Modulation Recognition (DMR) enables this process by giving SDR the ability to recognize the received signal modulation format to make intelligent and autonomous decisions to switch on the optimal modulation scheme. However, with the deployment of the Distributed Space-Time Block Coding Orthogonal Frequency Division Multiplexing (DSTBC-OFDM) signals that support MIMO systems, that provides spatio-temporel multiplexing, new and challenging signal identification problems have emerged.

This paper proposes a robust DMR method for relaying Space-Time Block Coding (STBC) Orthogonal Frequency Division Multiplexing (OFDM) systems. The proposed classifier is built using heterogeneous base classifiers namely, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RFC), based on a Dendrogram or decision tree structure.

The Dendrogram-based Heterogeneous Learners (DHL) classifier were obtained via Ascendant Hierarchical Clustering (AHC) and by employing Higher-Order Moment (HOMs) and Higher-Order Cumulants (HOCs) as features. Moreover, we propose a grid search-based hyperparameter tuning for DHL to produce the best structure and optimize the performance.

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Cited By

Ma HLiao YRen C(2024)Low probability of interception radar overlapping signal modulation recognition based on an improved you-only-look-once version 8 networkEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109150137:PAOnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.109150

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cover image Physical Communication

Physical Communication Volume 62, Issue C

Feb 2024

276 pages

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Elsevier B.V.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 12 April 2024

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Cited By

Ma HLiao YRen C(2024)Low probability of interception radar overlapping signal modulation recognition based on an improved you-only-look-once version 8 networkEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109150137:PAOnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.109150

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