Ruibo Wang

Juanjuan Ru, Ruibo Wang, Mohamed-Slim Alouini

To mitigate inter-satellite interference and payload limits in LEO mega-constellations, satellite clusters, groups of small cooperative satellites have been proposed to improve performance and reduce interference. The typical configuration divides the cluster into a leader satellite with full processing and control capabilities and multiple simpler follower satellites that assist with coverage and throughput. These clusters enhance coverage and throughput, prompting interest in their performance gains and optimal deployment. Given that the spherical stochastic geometry (SG) model has been proven effective for modeling such structures, we establish a performance evaluation framework based on the SG approach for the leader-follower satellite architecture, enabling an assessment of communication performance under different deployment configurations quantitatively. We derive analytical expressions for the outage probability and average data rate to evaluate the communication performance of the satellite system, along with low-complexity approximations. Numerical results demonstrate the performance advantages of the leader-follower architecture over a single leader satellite and explore optimal deployment configurations for the follower satellites.

Ruibo Wang, Song Zhang, Ping Huang et al.

Accurately reconstructing road surfaces is pivotal for various applications especially in autonomous driving. This paper introduces a position encoding Multi-Layer Perceptrons (MLPs) framework to reconstruct road surfaces, with input as world coordinates x and y, and output as height, color, and semantic information. The effectiveness of this method is demonstrated through its compatibility with a variety of road height sources like vehicle camera poses, LiDAR point clouds, and SFM point clouds, robust to the semantic noise of images like sparse labels and noise semantic prediction, and fast training speed, which indicates a promising application for rendering road surfaces with semantics, particularly in applications demanding visualization of road surface, 4D labeling, and semantic groupings.