Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.17372

Computing All-Pairs Shortest Communication Time Paths for Periodically Varying Satellite Networks

Koushik Sinha·Dibakar Saha·Sai Puppala·Bhabani P. Sinha
View original source ↗Read source document ↗Export metadata ↓

Abstract

We address the problem of designing an efficient algorithm for computing the all-pairs shortest-time paths in time-varying networks, specifically targeting the dynamic topological characteristics of low earth orbit (LEO) satellite constellations. We model such temporal graphs as $G = (V, E_t)$, where $V=\{1, 2,\ldots , n\}$ is the set of $n$ vertices in the graph, and $E_t$ is the set of $M$ time-varying edges considered over a total time window of size $T = Mτ$, $τ$ being the duration of each interval in that time window. For such a network, our algorithm finds the all-pair shortest paths in $O(M^2n^3)$-time. A key advantage of our approach is its computational structure, which mirrors two matrix multiplication steps, first in the forward direction and then in the backward direction. This structure of the algorithm makes it exceptionally suitable for parallelization on SIMD architectures like GPUs, thereby offering a significant speedup over traditional shortest-path algorithms and enabling efficient, network-wide message routing in rapidly changing LEO satellite constellations.

Explore related subjects

cs.NIcs.DC

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Koushik Sinha, Dibakar Saha, Sai Puppala, Bhabani P. Sinha. 2026-10-12. Computing All-Pairs Shortest Communication Time Paths for Periodically Varying Satellite Networks. https://arxiv.org/abs/2610.17372

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

KEEP EXPLORING

Related papers

arXiv2026

Arcturus: A Cloud Overlay Network for Global Accelerator with Enhanced Performance and Stability

Global Accelerator (GA) services play a vital role in ensuring low-latency, high-reliability communication for real-time interactive applications. However, existing GA offerings are tightly bound to specific cloud providers, resulting in high costs, rigid deployment, and limited flexibility, especially for large-scale or budget-sensitive deployments. Arcturus is a cloud-native GA framework that revisits the design of GA systems by leveraging low-cost, heterogeneous cloud resources across multiple providers. Rather than relying on fixed, high-end infrastructure, Arcturus dynamically constructs its acceleration network and balances performance, stability, and resource efficiency. To achieve this, Arcturus introduces a two-plane design: a forwarding plane that builds a proxy network with adaptive control, and a scheduling plane that coordinates load and routing through lightweight, quantitative optimization. Evaluations under millions of RPS show that Arcturus outperforms commercial GA services by up to 1.7X in acceleration performance, reduces cost by 71%, and maintains over 80% resource efficiency--demonstrating efficient use of cloud resources at scale.

cs.NI↗
arXiv2026

How Feasible are Passive Network Attacks on 5G Networks and Beyond? A Survey

Privacy concerns around 5G, the latest generation of mobile networks, are growing, with fears that its deployment may increase exposure to privacy risks. This perception is largely driven by the use of denser deployments of small antenna systems, which enable highly accurate data collection at higher speeds and closer proximity to mobile users. At the same time, 5G's unique radio communication features can make the reproduction of known network attacks more challenging. In particular, passive network attacks, which do not involve direct interaction with the target network and are therefore nearly impossible to detect, remain a pressing concern. Such attacks can reveal sensitive information about users, their devices, and active applications, which may then be exploited through known vulnerabilities or spear-phishing schemes. This survey examines the feasibility of passive network attacks in 5G and beyond (B5G/6G) networks, with emphasis on two major categories: information extraction (system identification, website and application fingerprinting) and geolocation (user identification and position tracking). These attacks are well documented and reproducible in existing wireless and mobile systems, including short-range networks (IEEE 802.11) and, to a lesser extent, LTE. Current evidence suggests that while such attacks remain theoretically possible in 5G, their practical execution is significantly constrained by directional beamforming, high-frequency propagation characteristics, and encryption mechanisms. For B5G and early 6G networks, the lack of public tools and high hardware cost currently renders these attacks infeasible in practice, which highlights a critical gap in our understanding of future network threat models.

cs.NI↗
arXiv2026

A Wireless World Model for AI-Native 6G Networks

Integrating AI into the physical layer is a cornerstone of 6G networks. However, current data-driven approaches struggle to generalize across dynamic environments because they lack an intrinsic understanding of electromagnetic wave propagation. We introduce the Wireless World Model (WWM), a multi-modal foundation framework predicting the spatiotemporal evolution of wireless channels by internalizing the causal relationship between 3D geometry and signal dynamics. Pre-trained on a massive ray-traced multi-modal dataset, WWM overcomes the data authenticity gap, further validated under real-world measurement data. Using a joint-embedding predictive architecture with a multi-modal mixture-of-experts Transformer, WWM fuses channel state information, 3D point clouds, and user trajectories into a unified representation. Across the five key downstream tasks supported by WWM, it achieves remarkable performance in seen environments, unseen generalization scenarios, and real-world measurements, consistently outperforming SOTA uni-modal foundation models and task-specific models. This paves the way for physics-aware 6G intelligence that adapts to the physical world.

cs.NI↗