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

L
LangChain Blog
博客园 - 司徒正美
美团技术团队
Martin Fowler
Martin Fowler
雷峰网
雷峰网
aimingoo的专栏
aimingoo的专栏
博客园 - 三生石上(FineUI控件)
Vercel News
Vercel News
酷 壳 – CoolShell
酷 壳 – CoolShell
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
爱范儿
爱范儿
U
Unit 42
Y
Y Combinator Blog
月光博客
月光博客
Hugging Face - Blog
Hugging Face - Blog
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
有赞技术团队
有赞技术团队
GbyAI
GbyAI
H
Help Net Security
量子位
Last Week in AI
Last Week in AI
博客园_首页
腾讯CDC
小众软件
小众软件

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
On throughput capacity for a class of buffer-limited MANETs
Jia Liu, Min Sheng, Yang Xu, Jiandong Li, Xiaohong Jiang · 2015-09-23 · via cs.IT updates on arXiv.org

Available throughput performance studies for mobile ad hoc networks (MANETs) suffer from two major limitations: they mainly focus on the scaling law study of throughput, while the exact throughput of such networks remains largely unknown; they usually consider the infinite buffer scenarios, which are not applicable to the practical networks with limited buffer. As a step to address these limitations, this paper develops a general framework for the exact throughput capacity study of a class of buffer-limited MANETs with the two-hop relay. We first provide analysis to reveal how the throughput capacity of such a MANET is determined by its relay-buffer blocking probability (RBP). Based on the Embedded Markov Chain Theory and Queuing Theory, a novel theoretical framework is then developed to enable the RBP and closed-form expression for exact throughput capacity to be derived. We further conduct case studies under two typical transmission scheduling schemes to illustrate the applicability of our framework and to explore the corresponding capacity optimization as well as capacity scaling law. Finally, extensive simulation and numerical results are provided to validate the efficiency of our framework and to show the impacts brought by the buffer constraint.