AI_ML for 5G and beyond _by_kanhaiya pptx

5G and AI uses

1. AI/ML for 5G and beyond Presented By: Kanhaiy

2. Applying AI/ML to 5G and beyond This presentation discusses the potential of using artificial intelligence and machine learning in 5G networks to enable efficient orchestration, dynamic provisioning, and optimization. It covers the role of AI/ML in beamforming and network slicing.

3. 5G as an Enabler for AI/ML Integration 5G can be a key enabler to drive the integration of machine learning and artificial intelligence into the network edge.

4. Machine Learning and AI for Beamforming 5G, deployed using mm-wave, has beam-based cell coverage unlike 4G which has sector-based coverage. Machine learning algorithms can assist the 5G cell site to compute a set of candidate beams, originating from the serving or neighboring cell site. ML and AI can assist in finding the best beam by considering parameters such as beam index, signal strength, distance, position, mobility, and channel quality indicator.

5. ML and AI for Network Slicing Network slicing creates multiple dedicated virtual networks using a common physical infrastructure. Embedding ML algorithms and AI into 5G networks can enhance automation and adaptability, enabling efficient orchestration and dynamic provisioning of the network slice. ML and AI can collect real-time information for multidimensional analysis and construct a panoramic data map of each network slice based on factors such as user subscription, QoS, network performance, and events and logs.

6. Advantages of AI/ML in 5G and Beyond Enables efficient orchestration, dynamic provisioning, and optimization of network resources. Enables automation and adaptability of the network, reducing manual intervention. Provides real-time information for effective decision making. Enhancing the security in 5G networks preventing attacks and frauds by recognizing user patterns and tagging certain events to prevent similar attacks in future.

7. Beamforming and Network Slicing in 5G The combination of beamforming and network slicing can maximize the potential of 5G networks. By optimizing beamforming, network slicing can ensure efficient use of available network resources, thereby enhancing network performance.

8. Challenges and Considerations Developing and deploying ML and AI algorithms in 5G networks requires significant investment in terms of time, cost, and expertise. Ensuring data privacy and security is a critical consideration for the development and deployment of ML and AI in 5G networks. Underlearning and overlearning is also one of the most challenging work with AI.

9. Future Directions The integration of AI and ML in 5G networks is expected to bring significant advancements in various applications, including autonomous vehicles, virtual and augmented reality, and smart cities. The evolution of AI and ML in 5G networks will be driven by ongoing research and development, as well as collaborations between industry, academia, and government agencies.

10. 6G the Next Frontier for AI and ML 6G networks will significantly rely on millimeter-wave technologies. 6G networks can reach one to ten TB/s compared to the maximum 20 GB/s for 5G networks. Terahertz and optical wireless bands allow for these increased data rates.

11. AI and Network Management for 6G Using AI can increase spectrum efficiency by three to five times and energy efficiency by ten times compared to 5G. AI can assist in improving network management.

12. Heterogeneous Networks and Increased Connection Density HetNets will be heavily used in 6G networks, allowing for a variety of communication scenarios. Wide bandwidths in high-frequency bands will lead to a significant increase in connection density (by ten to hundred times). Ultra-HST and satellites will allow for mobility at speeds greater than a thousand km/hr.

13. Thank you. 😄