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

Last Week in AI
Last Week in AI
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
博客园_首页
雷峰网
雷峰网
IT之家
IT之家
I
InfoQ
酷 壳 – CoolShell
酷 壳 – CoolShell
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
B
Blog
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园 - 【当耐特】
大猫的无限游戏
大猫的无限游戏
博客园 - 聂微东
Hugging Face - Blog
Hugging Face - Blog
A
About on SuperTechFans
月光博客
月光博客
P
Proofpoint News Feed
博客园 - 三生石上(FineUI控件)
J
Java Code Geeks
G
Google Developers Blog
小众软件
小众软件
宝玉的分享
宝玉的分享
Jina AI
Jina AI
V
Visual Studio Blog

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
An Overview of Channel Models for NGSO Satellites
Victor Monzon Baeza, Eva Lagunas, Hayder Al-Hraishawi, Symeon Ch · 2022-09-21 · via eess.SP updates on arXiv.org

Satellite communications industry is currently going through a rapid and profound transformation to adapt to the recent innovations and developments in the realm of non-geostationary orbit (NGSO) satellites. The growing popularity of NGSO systems, with cheap manufacturing and launching costs, has set to revolutionize the internet market. In this context, accurate channel characterization is crucial for the performance optimization and designing efficient NGSO communications, especially considering the dynamic propagation environment. While the Third Generation Partnership Project (3GPP) has provided some guidelines in Release 15, we observed certain divergence on the channel models considered in the literature, each with different assumptions and peculiarities. This paper provides an extensive review of the existing methods proposed for NGSO channel modeling that consider different orbits, frequency bands, user equipment, use-case and scenario peculiarities. The provided review discusses the channel modeling efforts from a contemporary perspective through trade-off analyses, classifications, and highlighting their advantages and pitfalls. The main goal is to provide a comprehensive overview of NGSO channel models to facilitate the selection of the most appropriate channel based on the scenario requirements to be evaluated and/or analysed.