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

D
Docker
大猫的无限游戏
大猫的无限游戏
Jina AI
Jina AI
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园 - 司徒正美
美团技术团队
雷峰网
雷峰网
阮一峰的网络日志
阮一峰的网络日志
WordPress大学
WordPress大学
T
Tailwind CSS Blog
U
Unit 42
C
Check Point Blog
S
SegmentFault 最新的问题
Martin Fowler
Martin Fowler
Stack Overflow Blog
Stack Overflow Blog
云风的 BLOG
云风的 BLOG
L
LangChain 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
腾讯CDC
罗磊的独立博客
小众软件
小众软件
Recent Announcements
Recent Announcements
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
D
DataBreaches.Net

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
Medium Access Control for Dynamic Spectrum Sharing in Cog...
Le Thanh Tan · 2016-01-20 · via cs.IT updates on arXiv.org

The proliferation of wireless services and applications over the past decade has led to the rapidly increasing demand in wireless spectrum. Hence, we have been facing a critical spectrum shortage problem even though several measurements have indicated that most licensed radio spectrum is very underutilized. These facts have motivated the development of dynamic spectrum access (DSA) and cognitive radio techniques to enhance the efficiency and flexibility of spectrum utilization. In this dissertation, we investigate design, analysis, and optimization issues for joint spectrum sensing and cognitive medium access control (CMAC) protocol engineering for cognitive radio networks (CRNs). The joint spectrum sensing and CMAC design is considered under the interweave spectrum sharing paradigm and different communications settings. Our research has resulted in four major research contributions, namely, the CMAC protocol design with parallel spectrum sensing, the CMAC protocol with sequential sensing, the CMAC protocol with cooperative sensing and the asynchronous Full-Duplex cognitive MAC. We develop various analytical models for throughput performance analysis of our proposed CMAC protocol designs. Based on these analytical models, we develop different efficient algorithms to configure the CMAC protocol including channel allocation, sensing time, transmit power, contention window to maximize the total throughput of the secondary network. Furthermore, extensive numerical results are presented to gain further insights and to evaluate the performance of our CMAC protocol designs. Both the numerical and simulation results confirm that our proposed CMAC protocols can achieve efficient spectrum utilization and significant performance gains compared to existing and unoptimized designs.