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_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 pf8nhq8qb o")
_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 \")
_ep101pv-battle-through-the-heavens-5-ep-99-101-fights-break-sphere-5-sub-indo-preview.webp "After three years of not seeing each other,Xiao Yan finally met Xun'er at Jia Nan Academy. After that,they became closer and established the Stone Gate (Pan Gate). In order to continue to improve his strength and avenge him on the Misty Cloud Sect,he risked continuing to go deeper into the Qi Refining Pagoda to devour the Fallen Heart Flame.<br/><br/><br/><br/><br/><br/><br/><br/><br/>#btth<br/>#btth5<br/>#btthseason5<br/>#battlethroughtheheavens<br/>#battlethroughtheheaven100<br/>#btth5100<br/>#battlethroughtheheaven101<br/>#btth5101")
[Ep101PV] Battle through the heavens 5 Ep 99 - 101 (Fights Break Sphere 5) sub indo (斗破苍穹年番) PREVIEW
⏲ 0:54 👁 360K
_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")
_session-19-introduction-to-ric-radio-intelligent-controller-in-open-ran-and-use-cases.webp "Introduction:<br/>In this session, we'll introduce the RAN Intelligent Controller (RIC) and explore its role in enhancing network capabilities. We'll also discuss two examples highlighting the use of RIC in Open RAN scenarios.<br/><br/>Introduction to RIC:<br/>The RAN Intelligent Controller (RIC) is a key component in Open RAN architecture, providing intelligent control and optimization capabilities to the RAN. RIC can be classified into Near Real-Time RIC (NRT-RIC) and Non-Real-Time RIC (Non-RT-RIC), each serving specific functions within the network.<br/><br/>Example 1: RAN Slice for Enterprise Customer:<br/>We'll illustrate how NRT-RIC and Non-RT-RIC can facilitate the creation of RAN slices to cater to enterprise customers. For instance, consider an enterprise customer who has subscribed to services guaranteeing 50Mbps throughput for their users using various XAPPs (e.g., XRAN, XHSS, etc.). NRT-RIC can dynamically allocate resources and prioritize traffic in near real-time to meet the throughput requirements of these XAPPs, ensuring a reliable and high-performance connection for enterprise users. On the other hand, Non-RT-RIC can perform more complex and resource-intensive optimization tasks that do not require immediate action, such as long-term network planning and policy configuration.<br/><br/>Example 2: Power Control using RIC Apps (RApps):<br/>We'll discuss another example focusing on power control using RIC Apps (RApps). RIC can leverage RApps to manage power usage in the RAN, optimizing energy consumption without compromising network performance. For instance, RIC can dynamically adjust transmit power levels based on traffic load and coverage requirements, leading to more efficient power utilization across the network.<br/><br/>Conclusion:<br/>RIC plays a crucial role in enabling dynamic and intelligent control of the RAN, offering significant benefits in terms of performance optimization, resource allocation, and energy efficiency. These examples demonstrate the practical applications of NRT-RIC and Non-RT-RIC in addressing specific network requirements and enhancing overall network performance.<br/><br/>RIC, NRT-RIC, Non-RT-RIC, RAN Slice, Enterprise Customer, Throughput, XAPPs, Power Control, RApps, Optimization, Resource Allocation, Energy Efficiency<br/><br/>Subscribe to \")
_ep-2-ep-2-sub-indo-124-124-the-reformation-siblings.webp "<br/>")
_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 \")
_session-15-explore-unsupervised-learning-amp-reinforcement-learning-for-network-efficiency-in-openran.webp "Hello and welcome to Session 15 of our Open RAN series! In this session, we'll delve into the exciting realms of unsupervised and reinforcement learning, exploring their roles in Open RAN and the challenges associated with supervised learning and labelled data.<br/><br/>Overview:<br/>Challenges with Supervised Learning and Labelled Data<br/>Understanding Unsupervised Learning<br/>Reinforcement Learning: A Deep Dive<br/><br/><br/>Challenges with Supervised Learning and Labelled Data:<br/>While supervised learning is powerful, it comes with its challenges. One major hurdle is the need for large amounts of labelled data, which may not always be available or practical to obtain in Open RAN environments. Additionally, supervised learning may struggle with highly variable or noisy data, making it less effective in certain scenarios.<br/><br/>Understanding Unsupervised Learning:<br/>Unsupervised learning is a type of machine learning where the model learns patterns from unlabelled data. This approach is invaluable in Open RAN, where data may be vast and complex. Unsupervised learning techniques, such as clustering, enable Open RAN systems to group similar data points together, providing insights into network behaviour without the need for predefined labels. Clustering, for example, can help identify patterns in network traffic, which can be used to optimize resource allocation and improve overall network performance.<br/><br/>Reinforcement Learning:<br/>Reinforcement learning is a dynamic approach where an agent learns to make decisions by interacting with an environment. In the context of Open RAN, reinforcement learning can be used to optimize network parameters and resource allocation. For example, an agent could learn to adjust transmission power or scheduling algorithms based on real-time network conditions, leading to improved efficiency and performance.<br/><br/><br/>Join us as we explore the world of unsupervised and reinforcement learning and their potential to transform Open RAN. Don't forget to subscribe to our channel for more insightful content, and share your thoughts in the comments below!<br/><br/>Subscribe to \")
Session 15: Explore Unsupervised Learning & Reinforcement Learning for Network Efficiency in openRAN
⏲ 3:54 ✓ 03-Jun-2024
_1000018607.webp "1000018607 from pf8nhq8qb o")
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