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

MongoDB | Blog
MongoDB | Blog
罗磊的独立博客
美团技术团队
B
Blog
量子位
The Cloudflare Blog
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
aimingoo的专栏
aimingoo的专栏
The GitHub Blog
The GitHub Blog
博客园 - 聂微东
P
Proofpoint News Feed
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
D
DataBreaches.Net
博客园 - 三生石上(FineUI控件)
Y
Y Combinator Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
Vercel News
Vercel News
Blog — PlanetScale
Blog — PlanetScale
云风的 BLOG
云风的 BLOG
Microsoft Azure Blog
Microsoft Azure Blog
有赞技术团队
有赞技术团队
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
阮一峰的网络日志
阮一峰的网络日志
S
SegmentFault 最新的问题

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
(k,m)-connectivity in Mobile Clustered Wireless Networks
Jun Zhao, Xinbing Wang · 2013-05-13 · via cs.IT updates on arXiv.org

This paper has been withdrawn by the author due to a crucial error in the calculation of Equation (28). We propose a novel concept of $(k,m)$-connectivity in mobile clustered wireless networks, in which there are $n$ mobile cluster members and $n^d$ static cluster heads, where $k,m,d$ are all positive constants and $k\leq m$. $(k,m)$-connectivity signifies that in a time period consisting of $m$ time slots, there exist at least $k$ time slots for each cluster member and in any one of these $k$ time slots the cluster member can directly communicate with at least one cluster head. We investigate the critical transmission range of asymptotic $(k,m)$-connectivity when cluster members move according to random walk or i.i.d. mobility model. Under random walk mobility, we propose two general heterogeneous velocity models in which cluster members may move with different velocities. Under both mobility models, we also define weak and strong parameters conditions, resulting in different accuracies of evaluations on the probability that the network is asymptotically $(k,m)$-connected, denoted as $P(\mathcal {C})$ below for simplicity. For both mobilities, under weak parameters condition, we provide bounds on $P(\mathcal {C})$ and derive the critical transmission range for $(k,m)$-connectivity. For random walk mobility with one kind of velocity model and i.i.d. mobility, under strong parameters condition, we present a precise asymptotic probability distribution of $P(\mathcal {C})$ in terms of the transmission radius. Our results offer fundamental insights and theoretical guidelines on design of large-scale wireless networks.