Search arXiv⌕ Search

arXiv · 2609.32621

A Novel Dynamic Ray-Tracing Channel Model for 6G LEO Satellite-to-Ground Communication Systems

Abstract

The sixth generation (6G) communication systems envision a space-air-ground integrated network, making accurate satellite-to-ground (S2G) channel modeling crucial for communication systems design. Currently, the majority of S2G channel models rely on stochastic methods, which cannot provide multipath information based on realistic satellite orbits and environments. In this paper, we propose a novel low Earth orbit (LEO) S2G channel model based on dynamic ray-tracing to overcome the challenges of tracking rays for long-distance propagation by setting a virtual transmission plane (VTP). Moreover, since the line-of-sight propagation mechanism contributes the most power to satellite communications, the VTP is constructed based on the first Fresnel zone to generate parallel rays close to the ground. The realistic satellite orbit and environmental information are used in the proposed LEO S2G ray-tracing channel model to compute channel characteristics. The results show that the proposed LEO S2G ray-tracing channel model can accurately generate the channel characteristics based on the satellite orbits. The channel characteristics of LEO S2G communications computed by the proposed ray-tracing method can well match the channel measurements.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Songjiang Yang, Cheng-Xiang Wang, Yinghua Wang, Jie Huang, Yuyang Zhou, Wei Feng, el-Hadi M. Aggoune. 2026-09-26. A Novel Dynamic Ray-Tracing Channel Model for 6G LEO Satellite-to-Ground Communication Systems. https://arxiv.org/abs/2609.32621

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Harnessing Chaotic Signals for Wireless Information and Power Transfer

Chaotic dynamical systems have attracted considerable attention due to their inherent randomness and high sensitivity to initial conditions, which makes them ideal for secure wireless communications. Beyond security, these same characteristics also make chaotic signals particularly effective for wireless power transfer (WPT) applications. On the other hand, connectivity along with self-sustainability are the two cornerstones of the upcoming sixth generation (6G) standard for radio communications. Consequently, with the massive increase in wireless devices and sensors, the concept of self-sustainable wireless networks is becoming more relevant. The aspect of WPT to the widely spread wireless devices and simultaneous wireless information and power transfer (SWIPT) among these devices will play a crucial role in the 6G communication systems. In this context, it has been experimentally observed that chaotic signals result in better WPT performance as compared to the existing benchmark schemes. Hence, in this paper, we characterize the generalized WPT performance of the multi-dimensional chaotic signals and present the use case of the Lorenz and the Henon chaotic systems. Moreover, we provide a novel differential chaos shift keying (DCSK)-based WPT receiver architecture ideal for enhanced energy harvesting (EH). Furthermore, we propose DCSK-based transmit waveform designs for multi-antenna SWIPT architectures and investigate the impact of the rate-energy trade-off. Our goal is to explore these aspects of the chaotic signals and discuss their relevance in the context of both WPT and SWIPT.

eess.SP↗

Semantic Feature Channel Optimization: A Unified Framework for Analog and Digital Semantic Communications

Semantic communication (SC) aims to improve communication efficiency by transmitting only task-relevant information, termed semantic features (SFs). Most existing SC frameworks achieve this by optimizing the encoder and decoder while treating the channel between them as fixed. In this paper, we introduce a new perspective by explicitly modeling the entire process from the encoder output to the decoder input as an SF channel, resulting in an encoder-SF channel-decoder pipeline. We observe that the SF channel is configurable through transceiver operations, such as power allocation, thereby providing an additional degree of freedom for improving task performance. Inspired by this, we formulate a joint optimization problem for the encoder, SF channel, and decoder under a mutual information constraint between the transmitted and reconstructed SFs. To provide analytical insight, we derive the optimal SF channel in closed form for an analytically tractable setting. Based on this formulation, we develop a unified semantic feature channel optimization framework applicable to both analog and digital SC systems. To realize the SF channel in general communication systems, we further propose a physical-layer calibration strategy that aligns the actual SF channel with the trained one. Simulation results demonstrate that the proposed framework consistently improves task performance across various communication environments.

eess.SP↗

Grey-Box Bayesian Optimization for ISAC in Fluid-Antenna Assisted Air-Ground Network

Fluid antenna systems (FAS) provide additional spatial diversity for integrated sensing and communication (ISAC) through joint port selection and precoding. \rev{However, existing designs commonly assume readily available channel state information, neglect residual self-interference, and combine communication and sensing into a single weighted objective. The resulting channel acquisition overhead, unmodeled residual self-interference, and limited characterization of the Pareto trade-off are critical obstacles to implementing ISAC in fluid-antenna-assisted air-ground networks.} \rev{To address these issues, we first formulate the joint design as a grey-box multi-objective optimization problem. This formulation retains the available analytical system mapping while treating the configuration-dependent channel and interference constituents as unknown, and directly represents the communication-sensing Pareto trade-off without predefined scalarization.} We then propose a tailored grey-box multi-objective Bayesian optimization (G-MOBO) method to solve the resulting high-dimensional problem. Specifically, G-MOBO learns the unknown constituents from performance feedback, propagates their predictive distributions through the known mapping, employs expected hypervolume improvement (EHI) to explore the Pareto frontier, and uses an adaptive trust region (TR) to localize the search. A temporal adaptation strategy is further incorporated to track the drifting Pareto frontier in time-varying environments. \rev{The theoretical analysis characterizes the sample efficiency of grey-box modeling and localized TR design via cumulative hypervolume regret.} Simulations demonstrate faster convergence, improved Pareto-frontier quality, and robust dynamic tracking compared with the considered baselines.

eess.SP↗