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⏲ Duration: 1:37:56
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✓ Published: 03-Jun-2024
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✓ Published: 03-Jun-2024
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Contagion of Fear (2024) Hindi Dubbed Movie
Contagion of Fear (2024) Hindi Dubbed Movie
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_session-18-machine-learning-use-cases-in-open-ran.webp "Hello and welcome to Session 18 of our Open RAN series! In this session, we'll explore the exciting world of machine learning and its diverse applications in optimizing Open RAN networks. We'll dive into various use cases where machine learning models play a pivotal role in enhancing network performance, improving customer satisfaction, and ensuring network security. Let's delve into the details of how machine learning is transforming Open RAN.<br/><br/><br/>Network Optimization:<br/>Machine learning models can analyse network performance data and optimize resource allocation, improving overall network efficiency and quality of service. These models can dynamically adjust parameters such as bandwidth allocation, frequency allocation, and power control to ensure optimal network performance.<br/><br/>Predictive Decisions:<br/>By analysing historical data, machine learning models can make predictive decisions about network traffic patterns, allowing for proactive management and optimization. This capability enables networks to anticipate and adapt to changing traffic demands, improving user experience and network efficiency.<br/><br/>Network Design:<br/>Machine learning can assist in network design by analysing terrain data, population density, and other factors to optimize the placement of network components for maximum coverage and efficiency. This approach ensures that network resources are deployed in the most effective manner, minimizing costs and maximizing performance.<br/><br/>Customer Satisfaction:<br/>Machine learning models can analyse customer behaviour and feedback to predict and address potential issues, leading to improved customer satisfaction. By understanding customer needs and preferences, networks can tailor their services to meet user expectations, enhancing overall satisfaction and loyalty.<br/><br/>Fraud Detection:<br/>Machine learning can help detect unusual patterns in network usage that may indicate fraudulent activity, enhancing network security. These models can identify anomalies in user behaviour, signalling potential security threats and allowing for timely intervention to mitigate risks.<br/><br/>Traffic Steering:<br/>Machine learning models can analyse network traffic patterns and dynamically steer traffic to optimize resource usage and improve user experience. By intelligently routing traffic based on real-time conditions, networks can reduce congestion and improve overall network performance.<br/><br/>Subscribe to \")
_session-17-deployment-of-machine-learning-models-in-open-ran-networks.webp "Welcome back to our journey through the world of Open RAN and machine learning. In this session, In this session, we'll explore the deployment of machine learning models in Open RAN networks, focusing on practical examples and deployment strategies.<br/><br/>Deployment Example:<br/>Consider a scenario where an Open RAN operator wants to optimize resource allocation by predicting network congestion. They decide to deploy a machine learning model to predict congestion based on historical traffic data and network conditions.<br/><br/>Deployment Steps:<br/><br/>1. Data Collection and Preprocessing:<br/>The operator collects historical traffic data, including throughput, latency, and user traffic patterns.<br/>They preprocess the data to remove outliers and normalize features.<br/><br/>2. Model Development:<br/>Data scientists develop a machine learning model, such as a regression model, to predict congestion based on the collected data.<br/>They use a development environment with libraries like TensorFlow or scikit-learn for model development.<br/><br/>3. Offline Model Training and Validation (Loop 1):<br/>The model is trained on historical data using algorithms like linear regression or decision trees.<br/>Validation is done using a separate dataset to ensure the model's accuracy.<br/><br/>4. Online Model Deployment and Monitoring (Loop 2):<br/>Once validated, the model is deployed in the network's edge servers or cloud infrastructure.<br/>Real-time network data, such as current traffic conditions, is fed into the model for predictions.<br/>Model performance is monitored using metrics like prediction accuracy and latency.<br/><br/>5. Closed-Loop Automation (Loop 3):<br/>The model's predictions are used by the network's orchestration and automation tools to dynamically allocate resources.<br/>For example, if congestion is predicted in a certain area, the network can allocate additional resources or reroute traffic to avoid congestion.<br/><br/>Subscribe to \")
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