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All Journal Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE)
Daryan Pratama Alifi
Universitas Pendidikan Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Optimized Hybrid CLDNN Architecture with Enhanced Temporal-Spatial Feature Extraction for Robust Automatic Modulation Classification in Cognitive Radio Networks Daryan Pratama Alifi; Hane Yorda Dinata; Galura Muhammad Suranegara; Ichwan Nul Ichsan
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 1 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i1.28935

Abstract

Automatic Modulation Classification (AMC) is a pivotal technology for efficient spectrum management in future cognitive radio networks. While Deep Learning has advanced the field, standard Convolutional Neural Networks (CNN) often struggle to capture long-term temporal dependencies in signals affected by fading. This study proposes an Optimized Hybrid CLDNN architecture that integrates a "Wide-Kernel" CNN (k=7) for enhanced spatial feature extraction and a "High-Capacity" LSTM (100 units) for robust temporal modeling. Experimental validation using the RadioML 2016.10a dataset demonstrates that the proposed optimizations yield significant performance gains. Specifically, the model achieves a classification accuracy of 84.5% at 0 dB SNR, outperforming standard baselines in the critical transition regime. Furthermore, it reaches a peak accuracy of 92.4% at high SNR (+18 dB). A notable finding is the reduction of inter-class confusion between 16-QAM and 64-QAM, where the misclassification rate is suppressed to approximately 15%, confirming the architecture's effectiveness in resolving hierarchical modulation ambiguities in dynamic wireless environments.
Co-Authors Galura Muhammad Suranegara Hane Yorda Dinata Ichwan Nul Ichsan