惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

MyScale Blog
MyScale Blog
人人都是产品经理
人人都是产品经理
云风的 BLOG
云风的 BLOG
小众软件
小众软件
F
Fortinet All Blogs
爱范儿
爱范儿
WordPress大学
WordPress大学
N
Netflix TechBlog - Medium
Recent Announcements
Recent Announcements
Google DeepMind News
Google DeepMind News
C
Check Point Blog
博客园 - 聂微东
D
Docker
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
aimingoo的专栏
aimingoo的专栏
Vercel News
Vercel News
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
A
About on SuperTechFans
博客园 - 【当耐特】
Microsoft Azure Blog
Microsoft Azure Blog
B
Blog
宝玉的分享
宝玉的分享
Jina AI
Jina AI
H
Hackread – Cybersecurity News, Data Breaches, AI and More

cs.IT updates on arXiv.org

Theoretical Limits of Language Model Alignment $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Expert Routing for Communication-Efficient MoE via Finite Expert Banks Contextual Memory-Enhanced Source Coding for Low-SNR Communications Realizable Bayes-Consistency for General Metric Losses Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy Optimization of CV-QKD Under Practical Constraints Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation Evolving Token Communication with Parametric Memory Network Remote Action Generation: Remote Control with Minimal Communication The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation Linear-Readout Floors and Threshold Recovery in Computation in Superposition Soft Graph Diffusion Transformer for MIMO Detection Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design Exponential families from a single KL identity MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness Diffusion-OAMP for Joint Image Compression and Wireless Transmission Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing
Ergodic Capacity and Optimal Handover in Satellite Mega-C...
[Submitted on 2 Dec 2025 (v1), last revised 11 Sep 2026 (this ve · 2025-12-02 · via cs.IT updates on arXiv.org

View PDF HTML (experimental)

Abstract:Existing analyses of ergodic capacity in satellite mega-constellations often rely on restrictive serving time assumptions or become intractable under realistic handover strategies. This paper develops a framework for characterising the ergodic capacity of low-Earth-orbit (LEO) mega-constellation links under arbitrary handover strategies and serving times. The user--satellite link is modelled as shadowed-Rician fading, and a semi-stochastic satellite channel with persistence is introduced in which visible satellites are drawn from a non-homogeneous binomial point process (NBPP) at each handover and the selected satellite is then propagated using circular orbit dynamics. Under uncoordinated handover decisions, this yields independent serving periods and enables a renewal-theoretic derivation of persistent capacity. This capacity is related to the non-persistent capacity from prior work, and closed-form bounds are provided for efficient evaluation. Optimal handover is then formulated as a non-linear fractional program, yielding an explicit decision rule via a variant of Dinkelbach's algorithm. The results show that a simpler strategy that maximises serving capacity closely approximates the optimum while performing best under SGP4-based orbit prediction and mega-constellation simulation.

Submission history

From: Brendon McBain Dr [view email]
[v1] Tue, 2 Dec 2025 06:14:34 UTC (1,293 KB)
[v2] Wed, 13 May 2026 01:13:35 UTC (1,312 KB)
[v3] Fri, 11 Sep 2026 01:08:11 UTC (1,314 KB)