latency計算的問題,透過圖書和論文來找解法和答案更準確安心。 我們找到下列懶人包和總整理

latency計算的問題,我們搜遍了碩博士論文和台灣出版的書籍,推薦Glisic, Savo G.,Lorenzo, Beatriz寫的 Artificial Intelligence and Quantum Computing for Advanced Wireless Networks 和的 Science and Technologies for Smart Cities: 6th Eai International Conference, Smartcity 360°, Virtual Event, December 2-4, 2020, 都 可以從中找到所需的評價。

另外網站propagation delay計算 - 工商筆記本也說明:2019年5月2日- IC內部影響:這通常IC手冊也會給出相應的IC內部有多少delay的數據提供計算,這部分只要對應PCB的propagation delay去換算長度就可以了。

這兩本書分別來自 和所出版 。

國立中正大學 通訊工程研究所 潘仁義所指導 梁哲豪的 5G NR低延遲迷你時槽資源配置及HARQ處理時程的系統層級模擬 (2020),提出latency計算關鍵因素是什麼,來自於5G New Radio、低延遲、迷你時槽、混合式自動重送請求、系統層級模擬。

最後網站時間敏感網路(TSN)中央控制器簡介- 技術探索則補充:... 高可靠度和低時延通訊(ultra-Reliable Low-Latency Communication,uRLLC)、大 ... 封包所造成的延遲以及鏈路的傳遞延遲(propagation delay),因而能夠搜尋、計算 ...

接下來讓我們看這些論文和書籍都說些什麼吧:

除了latency計算,大家也想知道這些:

Artificial Intelligence and Quantum Computing for Advanced Wireless Networks

為了解決latency計算的問題,作者Glisic, Savo G.,Lorenzo, Beatriz 這樣論述:

ARTIFICIAL INTELLIGENCE AND QUANTUM COMPUTING FOR ADVANCED WIRELESS NETWORKSA comprehensive presentationof the implementation of artificial intelligence and quantum computing technology in large-scale communication networksIncreasingly dense and flexible wireless networks require the use of artif

icial intelligence (AI) for planning network deployment, optimization, and dynamic control. Machine learning algorithms are now often used to predict traffic and network state in order to reserve resources for smooth communication with high reliability and low latency.In Artificial Intelligence and

Quantum Computing for Advanced Wireless Networks, the authors deliver a practical and timely review of AI-based learning algorithms, with several case studies in both Python and R. The book discusses the game-theory-based learning algorithms used in decision making, along with various specific appli

cations in wireless networks, like channel, network state, and traffic prediction. Additional chapters include Fundamentals of ML, Artificial Neural Networks (NN), Explainable and Graph NN, Learning Equilibria and Games, AI Algorithms in Networks, Fundamentals of Quantum Communications, Quantum Chan

nel, Information Theory and Error Correction, Quantum Optimization Theory, and Quantum Internet, to name a few.The authors offer readers an intuitive and accessible path from basic topics on machine learning through advanced concepts and techniques in quantum networks. Readers will benefit from: A t

horough introduction to the fundamentals of machine learning algorithms, including linear and logistic regression, decision trees, random forests, bagging, boosting, and support vector machinesAn exploration of artificial neural networks, including multilayer neural networks, training and backpropag

ation, FIR architecture spatial-temporal representations, quantum ML, quantum information theory, fundamentals of quantum internet, and moreDiscussions of explainable neural networks and XAIExaminations of graph neural networks, including learning algorithms and linear and nonlinear GNNs in both cla

ssical and quantum computing technologyPerfect for network engineers, researchers, and graduate and masters students in computer science and electrical engineering, Artificial Intelligence and Quantum Computing for Advanced Wireless Networks is also an indispensable resource for IT support staff, al

ong with policymakers and regulators who work in technology.

5G NR低延遲迷你時槽資源配置及HARQ處理時程的系統層級模擬

為了解決latency計算的問題,作者梁哲豪 這樣論述:

行動通訊系統約莫十年會換一個世代。下一代的行動通訊旨在提供使用者更好的服務體驗,並且可以在各種不同的場景中應用,如增強行動寬頻(enhanced Mobile Broadband, eMBB)、大規模物聯網通訊(Massive Machine Type Communications, MMTC)、高可靠性低延遲通訊(Ultra-Reliable and Low Latency Communications, URLLC)。因此第五代行動通訊系統新無線電(5th Generation Mobile Communication New Radio, 5G-NR)必須提供這些需求,即時的流量變化、

超高傳輸量、超低延遲等需求。為了達成URLLC的高可靠性低延遲的需求,3GPP (3rd Generation Partnership Project是一個國際認可的通訊研究標準化機構)有制定了許多規範以及新技術。和4G LTE的10 ms延遲時間不同,5G NR在URLLC的延遲時間要求來到1ms以內,因此在NR Release 15新增了mini slot傳輸,以symbol的時間單位來進行傳輸,讓傳送時間縮短達到其延遲的需求。但若是只有 mini slot傳輸,可能還是無法將延遲達到標準的要求,因為當封包傳送失敗需要進行重傳時,在4G LTE時HARQ的時間固定為8個slot(8ms)後

進行重傳,而這時間早已超過5G NR的要求,因此在NR Release 15除了mini slot之外也將HARQ時間改為以需要經過計算才能知道實際的時間,且時間上也將單位改為symbol-level,來讓重傳時的延遲可以達到要求。因此mini slot和symbol-level HARQ在5G NR是非常重要的技術,而本論文將著重在mini slot及symbol-level HARQ的實作,並根據3GPP的標準制定於工研院系統層級模擬器(WiSE System Level Simulator, WiSE SLS)上實作這兩個功能模組。本論文選擇通過系統層級模擬器在Indoor Hotspo

t場景下模擬mini slot上下行傳輸並進行延遲效能評估。本文將實作的模組和TR 38.824及5G-ACIA的延遲模擬結果進行比較,驗證了模組的正確性及在部分場景下可以和結果相同達到延遲可以在1ms以內。而本文貢獻有:(1)新增mini slot傳輸模組、(2)新增symbol-level HARQ模組、(3)驗證模組正確性、(4)驗證在單次傳輸下,FDD及TDD皆可達到1ms的需求。

Science and Technologies for Smart Cities: 6th Eai International Conference, Smartcity 360°, Virtual Event, December 2-4, 2020,

為了解決latency計算的問題,作者 這樣論述:

AI-assisted Solutions for COVID-19 and Biomedical Applications in Smart-Cities.- IoT and AI for COVID-19 in Scalable Smart Cities.- Automated Segmentation of COVID-19 Lesion from Lung CT Images using U-Net Architecture.- COVID-19 Patient Care: A Content-Based Collaborative Filtering Using Intelli

gent Recommendation Systems.- A new Blood Pressure prediction approach using PPG sensors: subject specific evaluation over a long-term period.- 5G network Slicing Technology and its Impact on COVID-19: A Comprehensive Survey.- An Empirical Study of Trilateration and Clustering for Indoor Localizatio

n and Trend Prediction.- Covid-19 Detection on CT Scans using Local Binary Pattern and Deep Learning.- Security and privacy issues associated with Coronavirus diagnosis and prognosis Approach for the development of a system for COVID-19 preliminary test.- Application of Distributed Generation for Re

duction of Power Losses and Voltage Deviation in Electric Distribution System by Using AI Techniques.- Non-Linear Control Applied to a 3d Printed Hand To Beacon or Not?: Speed Based Probabilistic Adaptive Beaconing Approach for Vehicular Ad-Hoc Networks.- Reduce 802.11 Connection Time Using Offloadi

ng and Merging of DHCP layer to MAC layer.- Hybrid Machine Learning Model for Traffic Forecasting.- Labeling News Article’s Subject Using Uncertainty Based Active Learning.- Intelligent Edge Processing in the IoT Era Environment Monitoring Modules with Fire Detection Capability based on IoT Methodol

ogy.- NetButler: Voice-Based Edge/Cloud Virtual Assistant for Home Network Management.- Low-cost LoRa-based IoT Edge Device for Indoor Air Quality Management in Schools.- Technologies for Industrial Internet of Things (IIoT): Guidelines for Edge Computing Adoption in the Industry.- Scalable Approxim

ate Computing Techniques for Latency and Bandwidth Constrained IoT Edge.- Collaborative task processing with Internet of Things (IoT) Clusters.- An Energy Sustainable CPS/IoT Ecosystem.- Inference Performance Comparison of Convolutional Neural Networks on Edge Devices.- Cognitive Computing and Cyber

Physical Systems A non-intrusive IoT-based real-time alert system for elderly people monitoring.- A Smartphone Application Designed to Detect Obstacles for Pedestrians’ Safety.- Automatic Generation of Security Requirements for Privacy-Preserving Blockchain-Based Solutions in the Internet of Things

.- Assessment of Video Games Players and Teams Behaviour via Sensing and Heterogeneous Data Analysis: Deployment at an eSports Tournament.- A feature-fusion transfer learning method as a basis to support automated smartphone recycling in a circular smart city.- Are Neural Networks Really the Holy Gr

ail? A Comparison of Multivariate Calibration for Low-cost Environmental Sensors.- MOBIUS: Smart Mobility Tracking with Smartphone Sensors.- An Attack-resistant Weighted Least Squares Localization Algorithm Based on RSSI.- Promotion as a Tool of Smart Governance in Cities.- Identity Inclusion: A Dig

ital National Identification for All Computer Vision Assisted Approaches to Detect Street Garbage from Citizen Generated Imagery.- Smart Governance in Urban Mobility Process.- 37 A Framework for GIS-enabled Public Electronic Participation in Municipal Solid Waste Management.- A Crowd-sourced Obstacl

e Detection and Navigation App for Visually Impaired An ecosystem approach to the design of sensing systems for bicycles Calibration of Low-cost Particulate Matter Sensors with Elastic Weight Consolidation (EWC) as an Incremental Deep Learning Method.- Person-Flow Estimation with Preserving Privacy

using Multiple 3D People Counters.- Quality and Reliability Metrics for IoT Systems: A Consolidated View.