Digital Modulation
Digital modulation, the process of encoding information onto a carrier wave for transmission, is a core element of communication systems, with research focused on improving efficiency and robustness. Current efforts concentrate on developing interference-aware modulation techniques, often employing deep learning models like autoencoders and neural networks, to optimize constellation design and enhance signal processing in non-orthogonal multiple access schemes and federated learning. These advancements are crucial for improving data transmission in diverse applications, from industrial machine monitoring and wireless communication networks to brain-computer interfaces and image processing.
Papers
Classification of Intra-Pulse Modulation of Radar Signals by Feature Fusion Based Convolutional Neural Networks
Fatih Cagatay Akyon, Yasar Kemal Alp, Gokhan Gok, Orhan Arikan
Reinforcement Learning with Brain-Inspired Modulation can Improve Adaptation to Environmental Changes
Eric Chalmers, Artur Luczak