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

博客园 - 三生石上(FineUI控件)
月光博客
月光博客
人人都是产品经理
人人都是产品经理
Google DeepMind News
Google DeepMind News
M
MIT News - Artificial intelligence
Vercel News
Vercel News
MyScale Blog
MyScale Blog
爱范儿
爱范儿
博客园 - 司徒正美
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
IT之家
IT之家
H
Help Net Security
Last Week in AI
Last Week in AI
阮一峰的网络日志
阮一峰的网络日志
酷 壳 – CoolShell
酷 壳 – CoolShell
L
LangChain Blog
罗磊的独立博客
Stack Overflow Blog
Stack Overflow Blog
宝玉的分享
宝玉的分享
博客园 - 聂微东
云风的 BLOG
云风的 BLOG
J
Java Code Geeks
博客园 - 叶小钗
D
Docker

eess.SP updates on arXiv.org

ECG-biometrics-bench: A Unified Framework for Reproducible Benchmarking of ECG Biometrics Physiology-Aware Masked Cross-Modal Reconstruction for Biosignal Representation Learning Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Federated Learning with Hypergradient-based Online Update of Aggregation Weights Soft Graph Diffusion Transformer for MIMO Detection SPLICE: Latent Diffusion over JEPA Embeddings for Conformal Time-Series Inpainting Sequential Inference for Gaussian Processes: A Signal Processing Perspective Statistical Channel Fingerprint Construction for Massive MIMO: A Unified Tensor Learning Framework Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies Cross-Subject Generalization for EEG Decoding: A Survey of Deep Learning Methods Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition Hankel and Toeplitz Rank-1 Decomposition of Arbitrary Matrices with Applications to Signal Direction-of-Arrival Estimation Adaptive Transform Coding for Semantic Compression EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures Sparse Graph Learning from Sparse Data via Fiedler Number Maximization A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management Transfer Learning for Tonal Noise Prediction in VRF Units Using Thermodynamic and Vibration Signals EVT-Based Generative AI for Tail-Aware Channel Estimation Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing BandRouteNet: An Adaptive Band Routing Neural Network for EEG Artifact Removal Phase-Separated Complex Hilbert PCA on Markerless 3D Pose Estimation Data: A Global Phase Network and Its Extension to a Continuous Field on the Body Surface Selective Correlation Based Knowledge Distillation for Ground Reaction Force Estimation Deep Learning-Enabled Dissolved Oxygen Sensing in Biofouling Environments for Ocean Monitoring Speech Enhancement Based on Drifting Models Robust and Clinically Reliable EEG Biomarkers: A Cross Population Framework for Generalizable Parkinson's Disease Detection An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids Explainable AI in Speaker Recognition -- Making Latent Representations Understandable Time-Localized Parametric Decomposition of Respiratory Airflow for Sub-Breath Analysis NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals An Algorithm for On-Sensor Agnostic Detection of Changes in Human Activity for Ultra-Low-Power Applications
Spectrum Sharing Protocols based on Ultra-Narrowband Comm...
Ghaith Hattab, Danijela Cabric · 2018-11-22 · via eess.SP updates on arXiv.org

Ultra-narrowband (UNB) communications is an emerging paradigm that tackles two challenges to realizing massive Internet-of-things (IoT) connectivity over the unlicensed spectrum: the intra-network sharing, i.e., how the spectrum is shared among IoT devices, and the inter-network sharing, i.e., the coexistence of the IoT network with other incumbent networks. Specifically, intra-network sharing is enabled by using extremely narrowband signals to connect a massive number of IoT devices without any prior network synchronization. Further, to enhance robustness to incumbent interference, each IoT packet is sent multiple times, each at a different frequency within a single band. Nevertheless, the interplay between intra-network sharing and inter-technology coexistence at a large scale remains unclear. Thus, in this paper, we develop an analytical framework to model and analyze UNB networks. We use stochastic geometry to derive the probability of successful transmission, identifying the impact of intra- and inter-network interference on the performance. In addition to analyzing the existing single-band access protocols, we present two multiband schemes, where each BS listens to a single band for practical implementation. Different access protocols are further compared in terms of the transmission capacity, i.e., the maximum number of IoT devices a UNB protocol can support in the presence of incumbent networks. Several design insights are gleaned from the derived closed-form expressions, and simulation results are further provided to validate them.