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

J
Java Code Geeks
Martin Fowler
Martin Fowler
B
Blog RSS Feed
D
DataBreaches.Net
L
LangChain Blog
月光博客
月光博客
S
SegmentFault 最新的问题
阮一峰的网络日志
阮一峰的网络日志
V
Visual Studio Blog
美团技术团队
Jina AI
Jina AI
博客园 - 司徒正美
雷峰网
雷峰网
Last Week in AI
Last Week in AI
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
IT之家
IT之家
博客园 - 三生石上(FineUI控件)
WordPress大学
WordPress大学
小众软件
小众软件
罗磊的独立博客
博客园_首页
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
A
About on SuperTechFans
Engineering at Meta
Engineering at Meta

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
Traffic Allocation for Low-Latency Multi-Hop Networks wit...
Guang Yang, Martin Haenggi, Ming Xiao · 2017-11-23 · via eess.SP updates on arXiv.org

For millimeter-wave (mm-wave) buffer-aided tandem networks consisting of relay nodes and multiple channels per hop, we consider two traffic allocation schemes, namely local allocation and global allocation, and investigate the end-to-end latency of a file transfer. We formulate the problem for generic multi-hop queuing systems and subsequently derive closed-form expressions of the end-to-end latency. We quantify the advantages of the global allocation scheme relative to its local allocation counterpart, and we conduct an asymptotic analysis on the performance gain when the number of channels in each hop increases to infinity. The traffic allocations and the analytical delay performance are validated through simulations. Furthermore, taking a specific two-hop mm-wave network as an example, we derive lower bounds on the average end-to-end latency, where Nakagami-$m$ fading is considered. Numerical results demonstrate that, compared to the local allocation scheme, the advantage of global allocation grows as the number of relay nodes increases, at the expense of higher complexity that linearly increases with the number of relay nodes. It is also demonstrated that a proper deployment of relay nodes in a linear mm-wave network plays an important role in reducing the average end-to-end latency, and the average latency decays as the mm-wave channels become more deterministic. These findings provide insights for designing multi-hop mm-wave networks with low end-to-end latency.