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Hierarchical Tree of Deep Networks (HTDN) for Joint Classification of Linear and Non-Linear Modulations

Hussain Shah, Maqsood orcid logoORCID: 0000-0002-7375-0131, Dang, Xiaoyu and Liu, Mingming orcid logoORCID: 0000-0002-8988-2104 (2024) Hierarchical Tree of Deep Networks (HTDN) for Joint Classification of Linear and Non-Linear Modulations. In: 12th International Conference, Varna, Bulgaria, August 29-31, 2024., August 29-31, 2024., Varna. (In Press)

Efficient modulation classification remains a key challenge in contemporary communication systems research. In this work, we introduce a novel framework rooted in the principle of divide and conquer to address the complexities associated with jointly classifying linear and nonlinear modulation schemes. Our approach decomposes the classification task into multiple binary problems, effectively leveraging a hierarchical tree-based structure that integrates several low-parameterized CNNs. Simulation results validate the efficacy of our proposed method, demonstrating superior classification performance with a notable reduction in complexity compared to conventional single CNN- based approaches.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Uncontrolled Keywords:Deep Networks, CNN, Hierarchical Trees, LowComplexity, Modulation Classification
Subjects:Computer Science > Artificial intelligence
Engineering > Signal processing
Engineering > Telecommunication
DCU Faculties and Centres:UNSPECIFIED
Funders:Insight SFI Research Centre
ID Code:30073
Deposited On:19 Jun 2024 09:52 by Maqsood Hussain Shah . Last Modified 19 Jun 2024 09:52

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