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_session-17-deployment-of-machine-learning-models-in-open-ran-networks.webp "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 \")
_big-ass.webp "good girl")
_kristen-madison-bbw-curvy-super-body-plus-size-big-ass-video-best-4k-video-diva-kurves.webp "Beach Bunnies TV💋")
⏲ 1 minute 27 seconds 👁 530.9K
_session-14-introduction-to-supervised-machine-learning-in-open-ran.webp "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 \")
_alger-hirak-scandale-algerie.webp "Luneeck")
_american-bodybuilder-lying-on-big-as-girl-prank-gone-wrong.webp "prank control")
_session-23-multi-access-edge-computing-mec-in-open-ran-oran-explained-for-beginners.webp "Welcome to Session 23! In this video, we'll dive into the world of Multi-Access Edge Computing (MEC) within the framework of Open RAN (ORAN). This beginner-friendly session breaks down the concepts, providing an easy-to-understand overview with real-world examples. We discuss the key benefits, potential challenges, and various use cases of MEC in ORAN. Learn why MEC is crucial for the future of mobile networks and how it can revolutionize connectivity and performance.<br/><br/>Key Concepts :<br/>* Introduction to MEC and ORAN<br/>* Key advantages of MEC in ORAN<br/>* Common challenges and disadvantages<br/>* Practical use cases of MEC in ORAN<br/>* The importance of MEC in modern network architecture<br/><br/><br/>Welcome to Session 23!<br/>Hello everyone, and welcome to Session 23 of our series. Today, we will explore the fascinating topic of Multi-Access Edge Computing (MEC) in the context of Open RAN (ORAN). This session is designed for beginners, so we'll start with the basics and build up to more detailed insights.<br/><br/>Introduction to MEC and ORAN<br/>Multi-Access Edge Computing (MEC) is an innovative technology that brings computation and data storage closer to the end user, significantly enhancing the performance and efficiency of applications and services. Open RAN (ORAN) is a new approach to building mobile networks using open and interoperable interfaces. By combining MEC with ORAN, we create a powerful synergy that transforms how data is processed and transmitted across networks.<br/><br/>Key Advantages of MEC in ORAN<br/>* Reduced Latency: Processing data closer to the source drastically reduces latency, which is critical for applications such as online gaming, video streaming, and virtual reality.<br/>* Improved Network Efficiency: MEC offloads traffic from the core network, resulting in more efficient use of network resources and better overall performance.<br/>* Enhanced Security: Local data processing offers improved security and privacy controls, reducing the risk of data breaches.<br/><br/>Common Challenges and Disadvantages<br/>* Complexity: Implementing MEC in an ORAN environment can be complex, requiring careful planning and integration.<br/>* Cost: Initial deployment costs can be high, but the long-term benefits often justify the investment.<br/><br/>Practical Use Cases of MEC in ORAN<br/>* Smart Cities: MEC enables real-time data processing for traffic management, public safety, and environmental monitoring.<br/>* Healthcare: Supports advanced telemedicine applications by providing low-latency, high-reliability connections for remote surgeries and patient monitoring.<br/>* Industrial IoT: Facilitates real-time analytics and automation in manufacturing, improving efficiency and reducing downtime.<br/><br/>Subscribe to \")
⏲ 4:48 👁 15K
_les-femmes-du-bus-678-extrait-n1.webp "PyramideDistrib")
_session-21-nfv-network-function-virtualization-concept-in-open-ran.webp "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-16-optimizing-open-ran-with-machine-learning-124-concept-overview.webp "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-27-quick-recap-oran-architecture-vnf-nfv-5g-nodes-and-oran-deployment.webp "In this session, we'll provide a quick recap of key concepts in Open RAN (ORAN) and 5G technology. We'll cover the ORAN architecture, Virtualized Network Functions (VNF), Network Functions Virtualization (NFV) Management (NFVM), 5G nodes, and ORAN deployment strategies. This summary will help reinforce your understanding and prepare you for more advanced topics. Join us to revisit these crucial elements and see how they come together to shape the future of telecommunications.<br/><br/>ORAN Architecture:<br/>* Overview: Modular and open architecture promoting interoperability and flexibility.<br/>* Benefits: Encourages innovation by allowing different vendors' components to work together.<br/><br/>Virtualized Network Functions (VNF):<br/>* Software implementations of network functions that run on virtualized infrastructure.<br/>* Advantages: Offers flexibility, scalability, and cost-efficiency in managing network services.<br/><br/>Network Functions Virtualization (NFV) Management (NFVM):<br/>* Role: Manages the lifecycle of VNFs, including deployment, scaling, and orchestration.<br/>* Importance: Ensures efficient and dynamic network operations.<br/><br/>5G Nodes:<br/>* Types: gNodeB (gNB) for 5G NR and ng-eNodeB (ng-eNB) for 4G evolved to support 5G.<br/>* Functionality: Provide radio access and connect users to the 5G core network.<br/><br/>ORAN Deployment:<br/>* Approaches: Includes centralized, distributed, and hybrid deployment models.<br/>* Impact: Enhances network performance, reduces costs, and accelerates deployment times.<br/><br/><br/>Subscribe to \")
_session-25-open-ran-124-new-front-haul-ecpri-mid-haul-and-back-haul-connectivity-and-new-requirement.webp "In this session, we delve into the crucial components of Open RAN: new front haul eCPRI, mid haul, and back haul connectivity. Understanding these elements is essential for optimizing network performance. We'll cover why new front haul eCPRI is needed, explain the roles of mid haul and back haul, and discuss the advancements required in Open RAN to meet the super-fast latency and throughput demands of modern networks. Join us to learn how these connectivity solutions enhance Open RAN deployments.<br/><br/>Welcome to Session 25! Today, we explore the essential parts of Open RAN connectivity: new front haul eCPRI, mid haul, and back haul. Understanding these components is key to enhancing network performance. We’ll discuss:<br/><br/>Why new front haul eCPRI is needed:<br/>* Enhanced Common Public Radio Interface (eCPRI) is an updated version of CPRI, essential for modern high-speed networks.<br/>* Benefits: Offers better bandwidth, reduced latency, and improved scalability compared to traditional CPRI.<br/><br/>What is mid haul:<br/>* Mid haul connects the centralized unit (CU) and distributed unit (DU) within the network.<br/>* Importance: Essential for efficient data transmission between the central and edge components, enabling flexibility in network deployment.<br/><br/>What is back haul:<br/>* Back haul refers to the connections between the distributed unit (DU) and the core network.<br/>* Role: Critical for carrying data from the edge of the network to the core, ensuring seamless communication and data flow.<br/><br/>Why these are needed in Open RAN:<br/>* These connectivity solutions enable the modular and scalable architecture of Open RAN.<br/>* Performance: They are crucial for achieving the desired network performance, including low latency and high throughput.<br/><br/>Advancements required in Open RAN:<br/>* Super-fast latency: To meet the demands of modern applications, Open RAN must continuously evolve to provide ultra-low latency.<br/>* High throughput: Ensuring high data transfer rates is necessary to support the growing data demands of users and applications.<br/><br/>By the end of this session, you'll have a clear understanding of how new front haul eCPRI, mid haul, and back haul connectivity work together to optimize Open RAN deployments, making them ready for the future of telecom networks.<br/><br/><br/>Subscribe to \")
Session 25: Open RAN | New Front Haul eCPRI, Mid Haul and Back Haul Connectivity and new requirement
⏲ 5:5 👁 15K
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