odu lolaa waloo Videos - HiFiMov.co

HiFiMov

Did you mean?

Search Results - Showing 0 - 12 Of 30

Welcome back to our journey through the world of Open RAN and machine learning. In this session, In this session, we'll explore the deployment of machine learning models in Open RAN networks, focusing on practical examples and deployment strategies.<br/><br/>Deployment Example:<br/>Consider a scenario where an Open RAN operator wants to optimize resource allocation by predicting network congestion. They decide to deploy a machine learning model to predict congestion based on historical traffic data and network conditions.<br/><br/>Deployment Steps:<br/><br/>1. Data Collection and Preprocessing:<br/>The operator collects historical traffic data, including throughput, latency, and user traffic patterns.<br/>They preprocess the data to remove outliers and normalize features.<br/><br/>2. Model Development:<br/>Data scientists develop a machine learning model, such as a regression model, to predict congestion based on the collected data.<br/>They use a development environment with libraries like TensorFlow or scikit-learn for model development.<br/><br/>3. Offline Model Training and Validation (Loop 1):<br/>The model is trained on historical data using algorithms like linear regression or decision trees.<br/>Validation is done using a separate dataset to ensure the model's accuracy.<br/><br/>4. Online Model Deployment and Monitoring (Loop 2):<br/>Once validated, the model is deployed in the network's edge servers or cloud infrastructure.<br/>Real-time network data, such as current traffic conditions, is fed into the model for predictions.<br/>Model performance is monitored using metrics like prediction accuracy and latency.<br/><br/>5. Closed-Loop Automation (Loop 3):<br/>The model's predictions are used by the network's orchestration and automation tools to dynamically allocate resources.<br/>For example, if congestion is predicted in a certain area, the network can allocate additional resources or reroute traffic to avoid congestion.<br/><br/>Subscribe to \

Session 17: Deployment of Machine Learning Models in Open RAN Networks

⏲ 4:9 👁 75K

OMN

OMN:Oduu Ammee:- Lola Humnoonni Addaa N:Amaaraa Walloo fi Fantaalletti banan!

⏲ 6 minutes 29 seconds 👁 13.6K

OMN

OMN- Oduu Ammee Waraanni Bilisummaa Oromoo Wallo Kamisee to'ataa jira jedhe.

⏲ 3 minutes 21 seconds 👁 114K

Welcome to Session 14 of our Open RAN series! In this session, we'll introduce supervised machine learning and its application in designing intelligent systems for Open RAN.<br/><br/><br/>Understanding Supervised Machine Learning:<br/>Supervised machine learning is a type of machine learning where the algorithm learns from labeled data. It involves training a model on a dataset that contains input-output pairs, where the input is the data and the output is the corresponding label or target variable. The algorithm learns to map inputs to outputs by finding patterns in the data. In Open RAN, supervised learning can be used for tasks such as predicting network performance based on historical data.<br/><br/>Types of Supervised Machine Learning:<br/>There are two main types of supervised machine learning: classification and regression. In classification, the algorithm learns to categorize data into predefined classes or categories. For example, it can classify network traffic into different application types (e.g., video streaming, web browsing). Regression, on the other hand, involves predicting continuous values or quantities. It is used when the output variable is a real or continuous value, such as predicting the signal strength of a network connection.<br/><br/>Binary and Multi-Class Classification:<br/>Binary classification involves categorizing data into two classes or categories. For example, it can be used to classify network traffic as either malicious or benign. Multi-class classification, on the other hand, involves categorizing data into more than two classes. It can be used to classify network traffic into multiple application types (e.g., video streaming, social media, email).<br/><br/>Regression in Machine Learning:<br/>Regression is a supervised learning technique used for predicting continuous values or quantities. It involves fitting a mathematical model to the data, which can then be used to make predictions. In Open RAN, regression can be used for tasks such as predicting network latency, throughput, or coverage based on various input variables such as network parameters, traffic patterns, and environmental conditions.<br/><br/>Subscribe to \

Session 14: Introduction to Supervised Machine Learning in Open RAN

⏲ 4:28 👁 40K

OAI

Leelloo_Abarraa_DANGASHEE - New Ethiopian Afaan Oromo Music video 2024 #duet#oromomusic

⏲ 28 seconds 👁 1.6K

OBN Oromiyaa [Oromia Broadcasting Network]

⏲ 5 minutes 32 seconds 👁 47.9K

In this session, we'll explore the fundamental concepts of NFV (Network Function Virtualization) in the context of Open RAN. We'll delve into the orchestration of virtualized network functions, the role of NFV Management and Virtualization, and how these elements work together to transform traditional network architectures.<br/><br/>Understanding NFV in Open RAN:<br/><br/>NFV Fundamentals: Delve into the core principles of NFV, where traditional hardware-based network functions are replaced with software-based virtual instances, driving agility and scalability.<br/>Essential Components: Learn about the critical components of NFV architecture, including Virtual Network Functions (VNFs), NFV Infrastructure (NFVI), and the NFV Management and Orchestration (MANO) layer.<br/>Benefits of NFV: Explore how NFV optimizes resource utilization, accelerates service deployment, and reduces operational costs, fostering a more adaptable and responsive network ecosystem.<br/>NFV Applications in Open RAN: Understand the pivotal role of NFV in Open RAN, enabling the virtualization of RAN functions and facilitating the seamless deployment of new services.<br/><br/>Understanding NFV and Orchestration:<br/>NFV is a technology that virtualizes network functions traditionally performed by dedicated hardware. Orchestration is the automated arrangement, coordination, and management of these virtualized network functions to enable efficient network operation.<br/><br/>NFV Management and Virtualization (NFVM):<br/>NFVM is a key component of NFV architecture that manages the lifecycle of virtualized network functions. It handles tasks such as instantiation, monitoring, scaling, and termination of virtualized functions.<br/><br/>Orchestration Function:<br/>Orchestration in NFV involves coordinating the deployment and interconnection of virtualized network functions according to service requirements. It ensures that network resources are allocated efficiently and dynamically based on demand.<br/><br/>Conclusion:<br/>NFV and orchestration play a crucial role in the evolution of Open RAN, enabling operators to build agile, scalable, and cost-effective networks. Understanding these concepts is essential for anyone involved in the design, deployment, or management of modern telecom networks.<br/><br/><br/>Subscribe to \

Session 21 : NFV (Network Function Virtualization) Concept in Open RAN

⏲ 6:31 👁 15K

Fana Television

⏲ 4 minutes 28 seconds 👁 73.2K

africanews

Oromo families cry foul in recent conflict

⏲ 1 minute 30 seconds 👁 131.6K

Hello and welcome to Session 16 of our Open RAN series! Today, we're diving into the fascinating world of machine learning and its impact on Open RAN networks. We'll be focusing on how machine learning can boost Open RAN performance, specifically in predicting throughput based on MCS coding schemes. This is a crucial aspect for optimizing network performance and resource allocation in Open RAN environments.<br/><br/>1. Introduction to Machine Learning in Open RAN:<br/>Machine learning plays a pivotal role in enhancing Open RAN networks by enabling predictive capabilities, particularly in throughput optimization. By leveraging machine learning models, Open RAN can predict throughput based on the Modulation and Coding Scheme (MCS) coding scheme. Throughput prediction is critical for optimizing network performance and efficiently allocating resources, ensuring a seamless user experience.<br/><br/>2. Developing Machine Learning Models for Throughput Prediction:<br/>Developing a machine learning model for throughput prediction in Open RAN requires several key considerations. Firstly, the model needs to be trained on a dataset that includes throughput data and corresponding MCS values. The model should be designed to handle the complex relationships between these variables and predict throughput accurately. Mathematical functions and algorithms such as regression and neural networks are commonly used for this purpose, as they can effectively capture the underlying patterns in the data.<br/><br/>3. Deployment of Machine Learning Models in Open RAN:<br/>The deployment of machine learning models in Open RAN involves several steps. Once the model is trained and validated, it is deployed to the network where it operates in real-time. The model continuously monitors network conditions and predicts throughput based on incoming data. This information is then used to dynamically allocate network resources, optimizing performance and ensuring efficient operation.<br/><br/>4. Training Data Acquisition Process:<br/>Acquiring training data for the machine learning model involves collecting throughput data and corresponding MCS values from the network. This data is then cleaned and formatted to remove any inconsistencies or errors. The cleaned data is used to train the model, ensuring that it can accurately predict throughput in various network conditions. The training data acquisition process is crucial as it directly impacts the accuracy and reliability of the machine learning model.<br/><br/>Subscribe to \

Session 16: Optimizing Open RAN with Machine Learning | concept overview

⏲ 5:55 👁 10K

OMN

OMN ODUUAMAJJII 4, 2024

⏲ 17 minutes 20 seconds 👁 18.1K

OMN

OMN Lolli Walloo irratti baname guyyaa 5ffaaf itti fufe (Jan 25, 2023)

⏲ 3 minutes 3 seconds 👁 4.7K

Pages 1 Of 3

Related Searches

Search Videos

Recent Searches

odu lolaa waloo | ta me | www bangla video com video downloadaou | জামা কুলা কিলি বিডিও | jane ja dil mera to | www ban vs under | konjugation des adjectives | zig amp sharko season 4 youtube | x8mcr1d | kajoi charmi hd images com | native instruments low end strings kontakt | charms for bracelets | tumi amar sona go bangladesh song www | vdm109996048 | bangla move song singh video sany | mutanis noise full album | মেদের ছবি | বাংলা বৌমা শশুর video hot aksha | x8ovbnd | vdm329982402 | indo viral | mojo ontor is onladeshi school girl new 3 ভিডিওি xstar jalsha বোঝেনা সে বোঝেনা নাটকের পাখির xwww bangla à | রো‌বোট ভি‌ডিও | ai meghla dine akla thake nato mon kache jabo kobe pabo | kun kanoner ful go tumi akash mahmud | www dhaka ab com | front kick | জি বাংলা সা রে গা মা পা অদিতি মুন্স | jake maye pie | x8pbmxy | নায়িকা মাহিয়া মাহি ফটোশ্বাসের ছবি নরোম এবং ১১ বছর মেয়েদের কচ | bangla movie mp mohua | apk game 唳唳傕唳唳︵唳多唳唳唳唳む唳唳氞唳︵唳氞 唳唳椸 唳锟斤拷唳︵ 唳唳 | ভুলো ভিডিও | indian bangla film shit | www v i d e o com shraddha kapoor gla story hot gram mosla video gan dowenload sd ald popi all video download naika a boudi comla movie song tomay dekhngla songe 2015 wwbg hot su commagizc w ww images desh vs south africa practise match fatullah 2015 wwritu barna full nakla song pickritygenta | কুকুরের সাথে মানুষের à¦®à¦¾à¦¹à¦¿à¦¯à¦¼à¦¾ à¦®à¦¾à¦¹à¦¿à¦¾à¦‚à¦²à¦¾ à¦¬Ã | shnayem9@gmail comgla doraemon cartoon টুনির মা | kolkata bdeos | anik sahan mp3 song নাইকা পিয়াকা | আংগুর ফল | amare charia re bondhu jain na tomar laiga pora | amar sad moina side | poli dam | x8myfhe | labor and delivery | khuda be na dekhe | foot bol | www bangla natok mir video লেগটা ছবিগির | www koel molik vi |