Cluster channel equalization using adaptive sensing and reinforcement learning for UAV communication.

ABSTRACT: Aiming to address the need for dynamic sensing and channel equalization in UAV cluster communication environments, this article introduces an equalization algorithm based on a U-Net model and fuzzy reinforcement Q-learning (U-FRQL-EA). This algorithm is designed to enhance the channel sensing and equalization capabilities of UAV communication systems. Initially, we develop a U-Net-based signal processing algorithm that effectively reduces acoustic noise in UAV communication channels and enables real-time, accurate perception of channel states by automatically learning channel features. Subsequently, we enhance fuzzy reinforcement Q-learning by incorporating a fuzzy neural network to approximate the Q-values and integrating this approach with the allocation strategy of

SUBMITTER: Liu X 

PROVIDER: S-EPMC11784828 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature