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_session-21-nfv-network-function-virtualization-concept-in-open-ran.jpg "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 \")
⏲ 6:31 👁 15K
_sweeping-the-yard-part-2.jpg "Transform your yard into an oasis of peace and beauty<br/>A well-kept yard is a reflection of a well-kept home.<br/>Take a deep breath, feel the breeze and start sweeping. You can!")
⏲ 1:0 👁 15K
_ep-1-ep-1-sub-indo-124-124-the-reformation-siblings.jpg "(Ep 1) 战国妖狐：维新兄妹 Ep 1 Sub Indo | 戦国妖狐-世直し姉弟編 | The Reformation Siblings from reham o karam")
⏲ 23:50 👁 15K
_imran-khan-muslims-ummah-leader124pti-chairman124-former-cricketer.jpg "<br/><br/>1. \")
⏲ 0:59 👁 15K
_french-olympic-surfer-fierro-rides-the-waves-to-victory-in-tahiti.jpg "Vahine Fierro becomes the first French surfer to win the women's Tahiti Pro on the waves of Teahupo'o on 29 May, two months before the Olympics surfing events are held at the French Pacific island. Fierro, 24, was born on the neighbouring island of Huahine and, with this success, marked herself down as one of the favourites for Olympic gold.")
⏲ 1:32 👁 30K
_session-16-optimizing-open-ran-with-machine-learning-124-concept-overview.jpg "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 \")
⏲ 5:55 👁 10K
_islamic-video.jpg "Plz support me")
⏲ 4:36 👁 15K
_harley-davidson-dragster-custom-rs-o-aampt-design.jpg "Introducing RS-O Racing Series! This beautiful bike was created based on our “Dragster RS” frame kit matching “RS” fork and swingarm. Except for 103 screaming egals engine and transmission, which of course may not be missing in such a projectile, all other parts are from our house. The machine rolls on the Thunderbike “Vegas Cut” wheels. In addition, we have her our footpeg Base-Hole, plus the matching grips and of course the “RS” Topper series. <br/><br/>The Milwaukee-typical orange paint has been combined with a classic flag check and shows that the bike feels at home on every dragstrip. The position of the footpegs, of course, we have a compromise between Dragster typical relocation and cruiser-style forwarding received, because the bike is largely moved on the road. The radical lowering also had to be done using the Thunderbike Air-Ride suspension system to ensure good driveability, even off the smooth race tracks.<br/><br/>- Base Thunderbike Dragster RS Framekit<br/>- Engine Harley-Davidson S.E. 103cui<br/>- Fuel System Injection<br/>- Gear Harley-Davidson 5-Speed<br/>- Exhaust Thunderbike Dragpipes<br/>- Air Cleaner Harley-Davidson Screamin’ Eagle<br/>- Master Cylinder Rebuffini<br/>- Footpegs Thunderbike RS<br/>- Front End Thunderbike RS<br/>- Swingarm Thunderbike Single Side<br/>- Suspension Tricky Air<br/>- Rims Thunderbike Vegas Cut 4.5x18 front & 10x18 rear<br/>- Tires Metzeler ME880 130/60-18 vorne & 280/35-18 hinten<br/>- Rear Brake Thunderbike Perimeter<br/>- Brake Discs Thunderbike Vegas<br/>- Painting by Ingo Kruse / Kruse Design<br/><br/>Customized Thunderbike Dragster Frame.<br/>https://www.thunderbike.com<br/><br/>(Source: THUNDERBIKE, Harley-Davidson)<br/>Thanks for watching!<br/><br/>#HarleyDavidsonCustom#HarleyDavidsonRSO#MotorcycleDesign")
⏲ 4:7 👁 10K
_ep-2-ep-2-sub-indo-124-124-the-reformation-siblings.jpg "<br/>")
⏲ 23:40 👁 5K
_before-and-after-islam-shorts.jpg "Allah hu Akbar<br/>Before and after Islam women personality")
⏲ 0:18 👁 5K
_srk-hosp.jpg "shah ruhk khan got heat attackand went to hospital")
⏲ 3:8 👁 5K
_session-20-cloudification-in-open-ran-the-shift-from-traditional-to-oran.jpg "Cloudification in Open RAN refers to the transformation of traditional, hardware-centric radio access networks (RANs) into more flexible, software-driven architectures based on open standards. This session will explore the concept of cloudification in Open RAN and the benefits it offers over traditional RAN deployments.<br/><br/>Key Concepts:<br/><br/>Traditional RAN vs. ORAN:<br/>Traditional RANs are characterized by proprietary hardware and tightly integrated components, limiting flexibility and innovation.<br/>ORAN, on the other hand, emphasizes open interfaces, disaggregation of hardware and software, and virtualization, enabling a more flexible and scalable RAN architecture.<br/><br/>Benefits of Cloudification:<br/>Cloudification enables the virtualization of network functions, allowing operators to deploy and manage RAN functions as software instances on standard IT hardware.<br/>It enhances network flexibility, scalability, and resource utilization, leading to lower operational costs and faster deployment of new services.<br/><br/>Components of Cloudified Open RAN:<br/>Centralized Unit (CU) and Distributed Unit (DU) are virtualized and run on cloud infrastructure, providing centralized and distributed processing capabilities, respectively.<br/>Multi-access Edge Computing (MEC) enables the deployment of applications and services at the edge of the network, closer to end-users, improving latency and user experience.<br/><br/>Use Cases of Cloudification:<br/>Network Slicing: Cloudification enables the creation of network slices tailored to specific use cases, such as ultra-reliable low-latency communications (URLLC) for industrial IoT applications.<br/>Massive MIMO: Cloud-based processing can enhance Massive MIMO performance by enabling efficient coordination between antennas and reducing signal processing complexity.<br/><br/>Conclusion:<br/>Cloudification is a fundamental shift in the architecture of RANs, enabling operators to leverage cloud technologies to build more flexible, efficient, and innovative networks. By adopting cloudification, operators can meet the evolving demands of 5G and future wireless networks.<br/><br/><br/>Subscribe to \")
⏲ 4:41 ✓ 03-Jun-2024
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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 \")
_sweeping-the-yard-part-2.webp "Transform your yard into an oasis of peace and beauty<br/>A well-kept yard is a reflection of a well-kept home.<br/>Take a deep breath, feel the breeze and start sweeping. You can!")
_ep-1-ep-1-sub-indo-124-124-the-reformation-siblings.webp "(Ep 1) 战国妖狐：维新兄妹 Ep 1 Sub Indo | 戦国妖狐-世直し姉弟編 | The Reformation Siblings from reham o karam")
_imran-khan-muslims-ummah-leader124pti-chairman124-former-cricketer.webp "<br/><br/>1. \")
_french-olympic-surfer-fierro-rides-the-waves-to-victory-in-tahiti.webp "Vahine Fierro becomes the first French surfer to win the women's Tahiti Pro on the waves of Teahupo'o on 29 May, two months before the Olympics surfing events are held at the French Pacific island. Fierro, 24, was born on the neighbouring island of Huahine and, with this success, marked herself down as one of the favourites for Olympic gold.")
_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 \")
_islamic-video.webp "Plz support me")
_harley-davidson-dragster-custom-rs-o-aampt-design.webp "Introducing RS-O Racing Series! This beautiful bike was created based on our “Dragster RS” frame kit matching “RS” fork and swingarm. Except for 103 screaming egals engine and transmission, which of course may not be missing in such a projectile, all other parts are from our house. The machine rolls on the Thunderbike “Vegas Cut” wheels. In addition, we have her our footpeg Base-Hole, plus the matching grips and of course the “RS” Topper series. <br/><br/>The Milwaukee-typical orange paint has been combined with a classic flag check and shows that the bike feels at home on every dragstrip. The position of the footpegs, of course, we have a compromise between Dragster typical relocation and cruiser-style forwarding received, because the bike is largely moved on the road. The radical lowering also had to be done using the Thunderbike Air-Ride suspension system to ensure good driveability, even off the smooth race tracks.<br/><br/>- Base Thunderbike Dragster RS Framekit<br/>- Engine Harley-Davidson S.E. 103cui<br/>- Fuel System Injection<br/>- Gear Harley-Davidson 5-Speed<br/>- Exhaust Thunderbike Dragpipes<br/>- Air Cleaner Harley-Davidson Screamin’ Eagle<br/>- Master Cylinder Rebuffini<br/>- Footpegs Thunderbike RS<br/>- Front End Thunderbike RS<br/>- Swingarm Thunderbike Single Side<br/>- Suspension Tricky Air<br/>- Rims Thunderbike Vegas Cut 4.5x18 front & 10x18 rear<br/>- Tires Metzeler ME880 130/60-18 vorne & 280/35-18 hinten<br/>- Rear Brake Thunderbike Perimeter<br/>- Brake Discs Thunderbike Vegas<br/>- Painting by Ingo Kruse / Kruse Design<br/><br/>Customized Thunderbike Dragster Frame.<br/>https://www.thunderbike.com<br/><br/>(Source: THUNDERBIKE, Harley-Davidson)<br/>Thanks for watching!<br/><br/>#HarleyDavidsonCustom#HarleyDavidsonRSO#MotorcycleDesign")
_ep-2-ep-2-sub-indo-124-124-the-reformation-siblings.webp "<br/>")
_before-and-after-islam-shorts.webp "Allah hu Akbar<br/>Before and after Islam women personality")
_srk-hosp.webp "shah ruhk khan got heat attackand went to hospital")
_session-20-cloudification-in-open-ran-the-shift-from-traditional-to-oran.webp "Cloudification in Open RAN refers to the transformation of traditional, hardware-centric radio access networks (RANs) into more flexible, software-driven architectures based on open standards. This session will explore the concept of cloudification in Open RAN and the benefits it offers over traditional RAN deployments.<br/><br/>Key Concepts:<br/><br/>Traditional RAN vs. ORAN:<br/>Traditional RANs are characterized by proprietary hardware and tightly integrated components, limiting flexibility and innovation.<br/>ORAN, on the other hand, emphasizes open interfaces, disaggregation of hardware and software, and virtualization, enabling a more flexible and scalable RAN architecture.<br/><br/>Benefits of Cloudification:<br/>Cloudification enables the virtualization of network functions, allowing operators to deploy and manage RAN functions as software instances on standard IT hardware.<br/>It enhances network flexibility, scalability, and resource utilization, leading to lower operational costs and faster deployment of new services.<br/><br/>Components of Cloudified Open RAN:<br/>Centralized Unit (CU) and Distributed Unit (DU) are virtualized and run on cloud infrastructure, providing centralized and distributed processing capabilities, respectively.<br/>Multi-access Edge Computing (MEC) enables the deployment of applications and services at the edge of the network, closer to end-users, improving latency and user experience.<br/><br/>Use Cases of Cloudification:<br/>Network Slicing: Cloudification enables the creation of network slices tailored to specific use cases, such as ultra-reliable low-latency communications (URLLC) for industrial IoT applications.<br/>Massive MIMO: Cloud-based processing can enhance Massive MIMO performance by enabling efficient coordination between antennas and reducing signal processing complexity.<br/><br/>Conclusion:<br/>Cloudification is a fundamental shift in the architecture of RANs, enabling operators to leverage cloud technologies to build more flexible, efficient, and innovative networks. By adopting cloudification, operators can meet the evolving demands of 5G and future wireless networks.<br/><br/><br/>Subscribe to \")
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