DeepJSCC‐based latent space power control for robust and efficient 3D point cloud transmission

Abstract Transmitting 3D point cloud data through wireless networks is challenging, as it entails balancing the demand for precise reconstruction with energy efficiency and consistent performance under changing channel conditions. These issues stem from the high‐dimensional nature of the data and the dynamic wireless communication environment. To address these challenges, we introduce latent space power control (LSPC), a system that features a novel loss function designed to optimize three main objectives: maintaining reconstruction accuracy, reducing energy consumption, and achieving the target signal‐to‐noise ratio (SNR). To preserve the geometric structure of the data, the system employs PointNet++ and a dynamic graph convolutional neural network for feature extraction during compression and reconstruction. Experimental results show that LSPC provides better reconstruction quality than existing methods and uses power more efficiently across various SNR levels. It also performs reliably under adverse wireless conditions, making it a valuable solution for 3D point cloud communication in applications such as autonomous vehicles, augmented reality, and environmental monitoring.