Channel Acquisition

Channel acquisition encompasses methods for efficiently obtaining and utilizing data, whether it's sensor readings, knowledge facts, or user preferences, to improve various systems' performance. Current research focuses on developing efficient algorithms and model architectures, including deep learning networks (e.g., graph neural networks, variational autoencoders), to optimize data acquisition processes, often integrating active learning strategies and leveraging generative AI for data augmentation. This field is crucial for advancing diverse applications, from improving 6G network efficiency and enabling real-time 3D scene understanding to enhancing large language models and advancing medical image analysis.