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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 👁 10K
Murad Wysinger
Word 2 Tha Mutha Pt 54 html PlanetLagu com 3gpp
⏲ 4 minutes 16 seconds
itelcotech
5G Standard & Specification – ITU & 3GPP
⏲ 2 minutes 43 seconds 👁 121
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 \
Session 16: Optimizing Open RAN with Machine Learning | concept overview
⏲ 5:55 👁 10K
Murad Wysinger
Word 2 Tha Mutha Pt 36 html PlanetLagu com 3gpp
⏲ 5 minutes 46 seconds
Uttama Shikshana
PUCCH F1 - Conversion from bits to complex IQ symbol - 3GPP spec Details#5g #3gpp #wireless
⏲ 53 seconds 👁 54
In this session, we delve into the architecture and deployment strategies for Standalone (SA) and Non-Standalone (NSA) 5G, along with Open RAN. Understanding these architectures is vital for optimizing 5G network performance and flexibility. We'll cover:<br/>5G-SA Architecture:<br/>5G-SA operates independently, using a dedicated 5G core network alongside 5G New Radio (NR) infrastructure.<br/>Benefits:<br/>* Enhanced Capabilities: Offers advanced features like network slicing and ultra-reliable low latency communication (URLLC).<br/>* Reduced Latency: Direct communication without relying on 4G, leading to faster response times.<br/>* Greater Scalability: Efficiently supports a massive number of devices, making it ideal for IoT applications.<br/><br/>NSA 5G Architecture:<br/>NSA 5G builds on existing 4G LTE infrastructure, integrating it with 5G NR to provide improved performance while leveraging current investments.<br/>Benefits:<br/>* Quicker Deployment: Faster rollout by utilizing existing 4G networks.<br/>* Cost Efficiency: Lower initial investment as it uses existing infrastructure.<br/>* Smooth Transition: Provides a bridge between current 4G networks and future 5G deployments, ensuring service continuity.<br/><br/>Open RAN Deployment Strategies:<br/><br/>Integration with SA & NSA:<br/>* Support for Flexibility: Open RAN allows for modular and interoperable components, making it adaptable to both SA and NSA 5G architectures.<br/>* Enhanced Interoperability: Facilitates the use of equipment from different vendors, promoting competition and innovation.<br/><br/>Deployment Approaches:<br/>* Centralized Deployment: Centralizes processing functions, beneficial for urban areas with high data demands.<br/>* Distributed Deployment: Distributes processing functions closer to the user, ideal for reducing latency in rural and suburban areas.<br/>* Hybrid Deployment: Combines centralized and distributed approaches to optimize performance based on specific network requirements.<br/><br/>By the end of this session, you'll have a comprehensive understanding of how SA and NSA 5G architectures and Open RAN deployment strategies can revolutionize telecom networks. You'll learn how these technologies work together to deliver superior network performance, flexibility, and scalability, paving the way for the future of telecommunications.<br/><br/>Subscribe to \
Session 26 : 5G SA & 5G-NSA Architecture and Deployment strategy and open RAN
⏲ 4:48 👁 5K
Murad Wysinger
Motion Picture Screen PlanetLagu com 3gpp
⏲ 1 minute 36 seconds 👁 2
Murad Wysinger
Greenray Pt 1 html PlanetLagu com 3gpp
⏲ 5 minutes 41 seconds
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 \
Session 24: Open RAN Deployment Scenarios – Regional Cloud vs. Edge Cloud
⏲ 4:13 👁 5K
Murad Wysinger
Word 2 Tha Mutha Pt 52 html PlanetLagu com 3gpp
⏲ 4 minutes 16 seconds
5G Americas
Becoming 5G Advanced the 3GPP 2025 Roadmap
⏲ 1 minute 31 seconds 👁 2.4K
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