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_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 \")
_juliet-bravo-2024-s01e01-shotgun-american-police-procedural-drama-series-2024.webp "Uncut News Bangla")
⏲ 2 hours 13 minutes 43 seconds 👁 3K
_rajkannya-124-124-bengali-movie-124-full-hd-124-uttam-kumar-rina-ghosh.webp "Uttam Kumar Movies")
⏲ 1 hour 45 minutes 23 seconds 👁 384K
_124-wife-is-right-124-new-bangla-short-film-124-naveen-saini-124-preety-sharma-bangla-dubbed-short-movie.webp "#couplescomedy #banglafilm #bengalimovie <br/>Synopsis - Whatever the wife says is ok. No matter what, but she is always right. Her every move & every turn she takes is right. Google can be wrong sometimes but she can’t because who dares to prove her wrong!!! You must have heard that “Misfortunes never come singly” and what better combination one can ask for when we haveMohit & Dipali together. Watch out Mohit & Dipali’s special trip where right turns wrong and wrong turns worse.<br/><br/>Actors : Naveen Saini & Preety Sharma<br/>Directed By : Ashish Bhatia<br/>Written By : Jyoti Malhotra & Ashish Bhatia<br/>Camera: Sonu Sharma<br/>Editing & Vfx : Sonu Sharma & Shweta Gaikwad<br/>Executive Producer : Akhil Nayar<br/>Dubbing : Sanjay Solanki<br/>Online Promotion : Alok Gautam<br/>Casting Director : Lucky Gautam<br/>Location : F3 Studioz<br/><br/>Don't forget to like our video and share it with your friends.")
⏲ 12:19 👁 15K
_gunda-the-terrorist-2015-l-full-length-bengali-movie-official-l-bappy-l-achol-l-tiger-media.webp "Tiger Media")
⏲ 2 hours 31 minutes 34 seconds 👁 37.4M
_sangee-124-bengali-full-movie-124-jeet-124-ranjit-mullick-124-priyanka-trivedi-124-shilajit-124-anamika-124kanchan.webp "Echo Bengali Movie")
Sangee | Bengali Full Movie | Jeet | Ranjit Mullick | Priyanka Trivedi | Shilajit | Anamika |Kanchan
⏲ 1 hour 32 minutes 12 seconds 👁 3M
_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 \")
_124-shakti-124-jeet-124-raima-sen-124-amitabha-bhattacharya-124-sonali-124-biplab-124-dulal-124-bibhu-124-sankar.webp "Bengali Movies - Channel B Entertainment")
শক্তি | Shakti | Jeet | Raima Sen | Amitabha Bhattacharya | Sonali | Biplab | Dulal | Bibhu | Sankar
⏲ 2 hours 47 minutes 24 seconds 👁 12.8M
_kaka-no-1-124-full-bengali-movie-124-rituparna-sengupta-ranjit-mallick.webp "Ultra Cinema")
⏲ 2 hours 28 minutes 13 seconds 👁 380.5K
_world-championship-wrestling-0592-0592-90.webp "NWA World Championship Wrestling – 5/5/90<br/>Doom vs. Bob Cook & William Bell<br/>Post match INT – Doom<br/>Ric Flair vs. Lex Luger (JIP – WWW – 5/5/90) (Barry Windham returns to NWA)<br/>INT – The Four Horsemen<br/>Cactus Jack, Kevin Sullivan & Bam Bam Bigelow vs. Rocky King, Paul Drake & John Raynor<br/>Tommy Rich vs. Scorpion Bill Ford<br/>Tommy Rich vs. Outlaw Deaton<br/>LOUISVILLE SLUGGER – Fabulous Freebirds (Rock-n-Roll Express run out and chase the Freebirds off. Cornette is left alone in the ring with the RnR Express, funny segment)")
_124-aparichito-124-uttam-kumar-124-soumitra-chatterjee-124-aparna-sen-124-sandhya-124-utpal-124-classic-hd.webp "Bengali Movies - Channel B Entertainment")
অপরিচিত | Aparichito | Uttam Kumar | Soumitra Chatterjee | Aparna Sen | Sandhya | Utpal | Classic HD
⏲ 1 hour 55 minutes 52 seconds 👁 401.6K
_ek-joban-124-124-dipjol-124-reshi-124-anowara-124-miju-124-sadek-bacchu-124-shakiba-124-bangla-movie.webp "CD Choice")
⏲ 2 hours 30 minutes 47 seconds 👁 8.2M
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