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_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 \")
_4g-marion-ft-4g-chapo-slide-shot-by-breezo-production.webp "4G GSavage")
_tutitu-builds-a-playground-fun-toddler-learning-with-easy-toy-building-activities.webp "TuTiTuTV")
⏲ 3 minutes 9 seconds 👁 104.3M
_best-smartwatch-under-3000-fireboltt-4g-pro-smartwatch-review.webp "Best Smartwatch Under 3000 - FireBoltt 4G Pro Smartwatch Review from xzzz 4g")
_webinar-akk-seri-1-kenaikan-iuran-jkn-di-masa-pandemi-wajarkah-bagaimana-solusinya.webp "Sinvesta FKM UNEJ")
⏲ 2 hours 35 minutes 11 seconds 👁 46
_free-fire-with-you.webp "ĤWĤ ĜÃΜÏÑĜ")
_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 \")
_4g-chapo-mr-corleone.webp "4GDaSickest")
_1-7juta-layar-4k-spec-flagship-124-sony-xperia-xz-premium-unboxing-amp-hands-on-2020.webp "Steff Tech")
⏲ 8 minutes 58 seconds 👁 18.3K
_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
_premium-banget-review-sony-xperia-xz-premium-indonesia.webp "LensGadget")
_9234how-to-be-an-39idiot39-police-man-in-9234roblox-mad-city9234.webp "Aldren Yabo")
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