arXiv subjects

Xiangtong Wang

Publications and source records attributed to Xiangtong Wang.

10 recordsLinked to original sources

arXiv2026

Co-optimizing Topology and Geometry in Mega-Constellation Networks via Structural Motif-Lattice Paradigm

Mega-constellation networks (MCNs) are currently revolutionizing global internet accessibility by providing ubiquitous connectivity on a planetary scale. While the architectural configuration of MCNs is paramount to achieving high-performance space-based networking, the design process is inherently complex. This complexity stems from the vast system scale and tightly coupled parameters, which culminate in a high-dimensional combinatorial optimization challenge. To address this challenge, we propose the Structural Motif-Lattice (SML) paradigm, a framework that decouples the MCN design space into two independent dimensions: topological connectivity (defining inter-satellite link logic) and geometric layout (defining the spatial distribution of satellites). This decomposition is theoretically justified by the inherent separability of connectivity logic and spatial distribution in MCN architecture, thereby reducing the original high-dimensional problem to a tractable bi-dimensional optimization task. Within the SML paradigm, we formalize the Reliable and Low-latency MCN Design problem and develop the Progressive Motif and Lattice Search (PLAMS) algorithm to find near-optimal MCN configurations. Experiments conducted on major constellations including Starlink, OneWeb, Kuiper, and Telesat demonstrate that PLAMS achieves performance comparable to or better than state-of-the-art methods, yielding substantially enhanced network reliability and significant reductions in average propagation latency.

cs.NI↗

arXiv2026

Monte Carlo Throughput Estimation in Dynamic LEO Satellite Networks

This study introduces a new framework for analyzing capacity dynamics and throughput performance in Low Earth Orbit satellite networks (LSNs). It focuses on addressing critical gaps in existing models, particularly those concerning unreliable ISLs. Our work systematically resolves two inherent deficiencies in prior research: (1) the conflation of network capacity with maximum throughput, the latter being highly dependent on routing policies and thus failing to reflect the intrinsic characteristics of the system; and (2) the overestimation problem in flow network based throughput calculations, which often generate flow paths that are inconsistent with actual traffic paths. To address these issues, we develop the CAP-uLSN (Capacity under unstable LEO satellites networks) model to characterize time-varying network capacity under stochastic ISL availability. Furthermore, we propose a Monte Carlo Throughput Estimation (MCTE) framework that probabilistically evaluates aggregate throughput performance under dynamic traffic patterns and diverse routing schemes. These insights derived from the CAP-uLSN model and MCTE framework, provide theoretical guidance for optimizing routing schemes (e.g., path selection under throughput fluctuations) and designing adaptive billing models (e.g., distance-based pricing) in future LEO satellite networks.

cs.NI↗

arXiv2026

Entropies of compact subsets and supported measures

Let $(X,T)$ be a topological dynamical system and $(\mathcal M(X),T_*)$ be its induced system. For a non-empty compact subset $K\subset X$, we define $\mathcal M(K)$ as the set of Borel probability measures supported on $K$. In this paper, we systematically study the relationship between various entropies of $(T,K)$ and of $(T_*,\mathcal M(K))$. We show that: \begin{equation*} \begin{aligned} & h_{\mathrm{top}}^{\mathrm{UC}}(T,K)>0 \iff h_{\mathrm{top}}^{\mathrm{UC}}(T_*,\mathcal{M}(K))=\infty, \qquad &h_{\mathrm{top}}^{P}(T,K)>0\iff h_{\mathrm{top}}^{P}(T_*,\mathcal{M}(K))>0, \qquad &h_{\mathrm{top}}^{B}(T,K)>0 \implies h_{\mathrm{top}}^{B}(T_*,\mathcal{M}(K))>0 , \end{aligned} \end{equation*} where $h_{\mathrm{top}}^{\mathrm{UC}}(T,K)$, $h_{\mathrm{top}}^{P}(T,K)$, and $h_{\mathrm{top}}^{B}(T,K)$ denote the upper capacity topological entropy, the packing topological entropy, and the Bowen topological entropy of $K$, respectively. Additionally, we present a counterexample involving a non-invariant set, demonstrating that the converse of the third assertion is not valid in general.

math.DS↗

arXiv2026

BlockFlex: A Hybrid Resilient Routing Method based on Robust Virtual Overlay for Mega-constellation Networks

Mega-constellation networks, comprising thousands of interconnected low-Earth-orbit (LEO) satellites, represent a transformative leap in global Internet connectivity. However, their operational promise is constrained by complex dynamics arising from persistent satellite--ground topology changes and intermittent inter-satellite link (ISL) failures. These dynamics simultaneously trigger global control-plane overhead and induce sparse connectivity in the network, collectively degrading both the efficiency and resilience of routing. To address these challenges, this paper proposes BlockFLEX, a hybrid routing architecture built upon a robust virtual overlay for LEO mega-constellation networks. BlockFLEX constructs a robust virtual overlay by clustering satellites into anonymous \emph{blocks}, which masks underlying network dynamics and provides a stable topology view for the routing layer. The architecture further employs a two-tier hybrid routing strategy: convergence-free geographic forwarding operates \emph{between} blocks, while convergence-isolated routing runs \emph{within} each block, thereby localizing control-traffic propagation. Beyond this core routing foundation, BlockFLEX incorporates complementary mechanisms to enhance scalability, resilience, and efficiency. Experimental evaluations on current operational LEO mega-constellation networks demonstrate that, under scenarios with up to $30\%$ random ISL failures, BlockFLEX substantially outperforms state-of-the-art schemes in both routing resilience and efficiency.

cs.NI↗

arXiv2024

Minimal subshifts of prescribed mean dimension over general alphabets

Let $G$ be a countable infinite amenable group, $K$ a finite-dimensional compact metrizable space, and $(K^G,σ)$ the full $G$-shift on $K^G$. For any $r\in [0,{\rm mdim}(K^G,σ))$, we construct a minimal subshift $(X,σ)$ of $(K^G,σ)$ with mdim$(X,σ)=r$. Furthermore, we construct a subshift of $([0,1]^G,σ)$ such that its mean dimension is $1$, and that the set of all attainable values of the mean dimension of its minimal subsystems is exactly the interval $[0,1)$.

math.DS↗

arXiv2024

Ergodic measures in minimal group actions with finite topological sequence entropy

Let $G$ be an infinite discrete countable group and $(X,G)$ be a minimal $G$-system. In this paper, we prove the supremum of topological sequence entropy of $(X,G)$ is not less than $\log(\sum_{μ\in\mathcal{M}^e(X,G)}e^{h_μ^*(X,G)})$. If additionally $G$ is abelian then there is a constant $K\in\mathbb{N}\cup\{\infty\}$ with $\log K\le h_{top}^*(X,G)$ such that $ν(\{y\in H:|π^{-1}(y)|=K\})=1$ where $(H,G)$ is the maximal equicontinuous factor of $(X,G)$, $π:(X,G)\to (H,G)$ is the factor map and $ν$ is the Haar measure of $H$.

math.DS↗

arXiv2024

Space Networking Kit: A Novel Simulation Platform for Emerging LEO Mega-constellations

This paper presents SNK, a novel simulation platform designed to evaluate the network performance of constellation systems for global Internet services. SNK offers realtime communication visualization and supports the simulation of routing between edge node of network. The platform enables the evaluation of routing and network performance metrics such as latency, stretch, network capacity, and throughput under different network structures and density. The effectiveness of SNK is demonstrated through various simulation cases, including the routing between fixed edge stations or mobile edge stations and analysis of space network structures.

cs.NI↗

arXiv2024

Multi-Protocol Location Forwarding (MPLF) for Space Routing

The structure and routing architecture design is critical for achieving low latency and high capacity in future LEO space networks (SNs). Existing studies mainly focus on topologies of space networks, but there is a lack of analysis on constellation structures, which can greatly affect network performance. In addition, some routing architectures are designed for networks with a small number of network nodes such as Iridium while they introduce significant network overhead for high-density networks (i.e., mega-constellation networks containing thousands of satellites). In this paper, we conduct the quantitatively study on the design of network structure and routing architecture in space. The high density, high dynamics, and large scale nature of emerging Space Networks (SNs) pose significant challenges, such as unstable routing paths, low network reachability, high latency, and large jitter. To alleviate the above challenges, we design the structure of space network to maximum the connectivity through wisely adjusting the inter-plane inter satellite link. We further propose Multi-Protocol Location Forwarding (MPLF), a distributed routing architecture, targeting at minimizing the propagation latency with a distributed, convergence-free routing paradigm, while keeping routing stable and maximum the path diversity. Comprehensive experiments are conducted on a customized platform \textit{Space Networking Kits} (SNK) which demonstrate that our solution can outperform existing related schemes by about 14\% reduction of propagation latency and 66\% reduction of hops-count on average, while sustaining a high path diversity with only $O(1)$ time complexity.

cs.NI↗

arXiv2023

Investigating Inter-Satellite Link Spanning Patterns on Networking Performance in Mega-constellations

Low Earth orbit (LEO) mega-constellations rely on inter-satellite links (ISLs) to provide global connectivity. We note that in addition to the general constellation parameters, the ISL spanning patterns are also greatly influence the final network structure and thus the network performance. In this work, we formulate the ISL spanning patterns, apply different patterns to mega-constellation and generate multiple structures. Then, we delve into the performance estimation of these networks, specifically evaluating network capacity, throughput, latency, and routing path stretch. The experimental findings provide insights into the optimal network structure under diverse conditions, showcasing superior performance when compared to alternative network configurations.

cs.NI↗

arXiv2022

Exploring the Impacts from Datasets to Monocular Depth Estimation (MDE) Models with MineNavi

Current computer vision tasks based on deep learning require a huge amount of data with annotations for model training or testing, especially in some dense estimation tasks, such as optical flow segmentation and depth estimation. In practice, manual labeling for dense estimation tasks is very difficult or even impossible, and the scenes of the dataset are often restricted to a small range, which dramatically limits the development of the community. To overcome this deficiency, we propose a synthetic dataset generation method to obtain the expandable dataset without burdensome manual workforce. By this method, we construct a dataset called MineNavi containing video footages from first-perspective-view of the aircraft matched with accurate ground truth for depth estimation in aircraft navigation application. We also provide quantitative experiments to prove that pre-training via our MineNavi dataset can improve the performance of depth estimation model and speed up the convergence of the model on real scene data. Since the synthetic dataset has a similar effect to the real-world dataset in the training process of deep model, we also provide additional experiments with monocular depth estimation method to demonstrate the impact of various factors in our dataset such as lighting conditions and motion mode.

cs.CV↗