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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
Eli the Computer Guy
⏲ 1 hour 2 minutes 39 seconds 👁 4.2K
Apne Songs And Style Se Famous Singer Emaan Fatima Ka Luxury Lifestyle - Lavish Home & VIP Protocol<br/>#EmaanFatima #SingerEmaanFatima #PakistaniSInger #ViralSinger #ViralSongs #Songs #Lifestyle #Interview #Lahore<br/>
⏲ 10:17 👁 115K
- TIA/EIA IS-2000 EV-DO Rev. A n(Backward compatible with EV-DO Rev. 0 and CDMA2000 1X)* n- Receiver diversity antennasn- Data interface ports for 4 Ethernet n- Built-in DHCPn- VoIP call (RJ-11) n- PC and Web-based administration tool (Web GUI) n- Data link protocol : Ethernet, IEEE 802.11b and IEEE 802.11gn- Data transfer rate : 54Mbps(wireless), 100Mbps(wired) n- Switching protocol : Ethernetn- Transport protocol : PPTP, IPSec, TCP/IP, IPX/SPX, NetBeuI/NetBIOSn- Communication mode : Full-duple
⏲ 1 min 81 sec ✓ 16-Apr-2010
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 \
⏲ 6:31 👁 25K
Hip replacement surgery significantly alleviates the daily burden of hip pain for many patients. As such, the rehabilitation process plays a crucial role in the success of the operation, which, if properly managed, mitigates future complications. Rehabilitation directly influences the long-term success of the surgery, making it a vital component of the recovery journey. Pristyn Care understands that each patient is unique, necessitating a tailored rehabilitation plan. With this personalized approach, Pristyn Healthcare is committed to ensuring that every patient receives a customized recovery roadmap.
⏲ 0:25 👁 20K
Dhcp(Dynamic Host Configuration Protocol)n kurulumunu gösteren video .
⏲ 1 min 32 sec ✓ 06-Mar-2009
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 \
⏲ 5:5 👁 15K
Welcome to Session 24! Today, we're diving into the world of Open RAN deployment in regional and edge clouds, focusing on the importance of cloud technology. As telecom moves to the cloud, it's important to understand how Open RAN fits into different cloud setups. Join us to discover the systems that work in these clouds and how they improve network performance and flexibility.<br/><br/>Introduction to Cloudification and Its Importance in Open RAN<br/>Cloudification involves moving traditional telecom functions to cloud-based environments. This process is pivotal for modernizing network infrastructure, offering several advantages:<br/><br/>* Scalability: Easily adjust network resources based on demand.<br/>* Flexibility: Quickly adapt and manage network functions.<br/>* Cost Efficiency: Reduce costs by utilizing shared cloud resources.<br/>* Innovation: Accelerate the deployment of new services and innovations.<br/><br/>Open RAN and Cloudification<br/>Open RAN (Radio Access Network) promotes open and interoperable network components, which allows for more flexible and cost-effective network deployments. Cloudification supports Open RAN by providing the necessary infrastructure for these open interfaces and modular components.<br/><br/>Deployment Scenarios for Open RAN<br/>* Regional Cloud Deployment:<br/>In regional cloud deployments, the baseband functions are hosted in regional data centers, providing a centralized approach.<br/>* Benefits: High resource utilization, simplified management, and enhanced performance due to centralized processing.<br/>* Systems Deployed:<br/>**Centralized Units (CU): Handle high-level processing tasks and control functions.<br/>**Core Network Functions: Manage data, signaling, and service delivery.<br/>Use Cases: Suitable for urban and densely populated areas where high capacity and centralized management are critical.<br/><br/>Edge Cloud Deployment:<br/>In edge cloud deployments, baseband functions are distributed closer to the end-users at edge locations.<br/>* Benefits: Reduced latency and improved user experience by processing data near the source.<br/>* Systems Deployed:<br/>** Distributed Units (DU): Perform real-time processing and lower-layer functions.<br/>** User Plane Functions (UPF): Handle user data traffic locally.<br/>Use Cases: Ideal for suburban and rural areas where low latency and local processing are essential.<br/><br/>Conclusion<br/>As telecom networks continue to evolve, integrating cloudification with Open RAN enables flexible and efficient network deployment. By understanding the deployment scenarios in regional and edge clouds, operators can strategically plan their network infrastructure to maximize performance and adaptability. Stay tuned for more insights into the future of telecom networks in our upcoming sessions.<br/><br/><br/>Subscribe to \
⏲ 4:13 👁 10K
Today I was finally able to get my raspberry pi configured as a wireless access point and DHCP router. nnNow The pi will serve out a network for the devices and run the broker for the mqtt protocol. nnThis is a big deal because it means the light, controller, and pi (and any other IoT devices I add) can all travel as a stand alone group. No more updating sketches just to switch wifi networks!nn#maker #arduino #mqtt #raspberrypi #adafruit #networking
⏲ 1 min 39 sec ✓ 28-Feb-2020
How to create a website using cpanel and wordpress 2024 <br/>Cpanel username and password.<br/>Step 1: Log to your Cpanel Account. ...<br/>Step 2: Locate Softaculous apps installer. ...<br/>Step 3: Choose a protocol and a domain name. ...<br/>Step 4: Fill WordPress site settings detail. ...<br/>Step 4: Choose plugins and a theme. ...<br/>Step 5: Test the Installation.<br/>Links:<br/>Facebook:<br/>https://www.facebook.com/Charlie442735?mibextid=ZbWKwL<br/>Instagram:<br/>https://www.instagram.com/micalfa442735?igsh=MXYwb2tqemx3NGFzag==<br/>Pinterest:<br/>https://pin.it/1SbpFrhQ4<br/>WhatsApp:<br/>https://wa.me/qr/SYXOLJQFBPYVI1<br/>LinkedIn:<br/>https://www.linkedin.com/in/md-arifur-rahman-2981171b4/<br/>Telegram:<br/>t.me/Charlie442735<br/>Youtube: https://www.youtube.com/@ASLMovieStoryExplain<br/><br/>Contact Email: arif.discover@gmail.com
⏲ 15:0 👁 5K
Jeremy Renner refused to return to 'Mission: Impossible' for a third film when he heard there were plans for his character to be killed off.
⏲ 1:17 👁 5K
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