Changhan Ge

Kyoungjun Park, Zhiyuan He, Cheng Luo et al.

Low Earth Orbit (LEO) satellite communication is a promising approach to providing Internet connectivity to users in many remote areas. As videos are likely to account for most traffic in the LEO satellite network, as in the rest of the Internet, this work introduces a novel video-aware mobility management framework tailored for LEO satellite networks. Utilizing simulation models alongside real-world datasets, we show the importance of handoff strategy and throughput prediction algorithms in single-user and multi-user video streaming scenarios. Motivated by these observations, we propose a set of novel algorithms that can jointly choose the satellite and video bitrate to optimize the Quality of Experience (QoE). We first develop Model Predictive Control (MPC) and Reinforcement Learning (RL) based algorithms for a single user, and then extend them to accommodate multiple competing users that may share the same satellite. We introduce centralized training and distributed inference for our RL design, enabling a distributed policy informed by a global perspective. We demonstrate the effectiveness of our proposed models using trace-driven simulation and testbed experiments. We share our code and data with the research community.

Kyoungjun Park, Yifan Yang, Changhan Ge et al.

Modeling radio frequency (RF) signal propagation is essential for understanding the environment, as RF signals offer valuable insights beyond the capabilities of RGB cameras, which are limited by the visible-light spectrum, lens coverage, and occlusions. It is also useful for supporting wireless diagnosis, deployment, and optimization. However, accurately predicting RF signals in complex environments remains a challenge due to interactions with obstacles such as absorption and reflection. We introduce Diffusion^2, a diffusion-based approach that uses 3D point clouds to model the propagation of RF signals across a wide range of frequencies, from Wi-Fi to millimeter waves. To effectively capture RF-related features from 3D data, we present the RF-3D Encoder, which encapsulates the complexities of 3D geometry along with signal-specific details. These features undergo multi-scale embedding to simulate the actual RF signal dissemination process. Our evaluation, based on synthetic and real-world measurements, demonstrates that Diffusion^2 accurately estimates the behavior of RF signals in various frequency bands and environmental conditions, with an error margin of just 1.9 dB and 27x faster than existing methods, marking a significant advancement in the field. Refer to https://rfvision-project.github.io/ for more information.