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 (Universitas Muhammadiyah Sumatera Utara, Indonesia, 2026)
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