Work

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

Year: 2026

Type: article

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.

Source: ETRI Journal

DOI: 10.4218/etrij.2025-0187

Authors: Huda Adam Sirag Mekki, Hui Yuan, Mohanad M. G. Hassan, Zejia Chen

Institution: Shandong University

Language: English

Topic: Video Coding and Compression Technologies

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funders: National Natural Science Foundation of China, Natural Science Foundation of Shandong Province, Taishan Scholar Project of Shandong Province