AI-Powered Network Traffic Management

The AI-RAN Taking Shape I'm thrilled to announce our latest research contribution that fundamentally transforms how we design, deploy, and test key functionalities of cellular networks. Our new paper "ALLSTaR - Automated LLM-Driven Scheduler Generation and Testing for Intent-Based RAN" represents three major industry firsts: ⚡ First-Ever Automated Scheduler Generation: We've developed LLM agents that automatically convert research papers into functional code, generating 18 different scheduling algorithms directly from academic literature using OCR and AI. No more months of manual implementation in ns-3 or Matlab! Automatically generated schedulers are automatically deployed in a live network as dApps through a CI/CD pipeline - without the need to change a single line of code in the gNodeB implementation (CU or DU);  ⚡ Intent-Based Scheduling: Network operators can now express high-level requirements in natural language ("prioritize users with bursty traffic") and ALLSTaR automatically translates these into optimized scheduling policies according to operator’s intent. ⚡ World's First O-RAN Compliant AI-RAN Testbed: All validation conducted on X5G with AutoRAN, production-grade, multi-vendor 5G infrastructure with GPU acceleration, AI-for-RAN and AI-and-RAN capabilities, demonstrating real-world viability at scale. This work also introduces a methodological paradigm shift: instead of implementing one algorithm at a time, we can now systematically evaluate a vast body of scheduling literature in production-like environments. We're moving from manual, months-long integration processes to automated, intent-driven networks that adapt in real-time. This is the Open RAN and the AI-RAN vision - and a pathway toward 6G that builds on our national strengths and open ecosystem. Full paper: https://lnkd.in/eTNWPNRR Open6G www.open6g.us #ORAN #AIRan #OpenRAN #5G #WirelessResearch #AI #MachineLearning #Telecommunications #Research Our brilliant team: Maxime Elkael Michele Polese Reshma Prasad Stefano Maxenti Office of the Under Secretary of Defense for Research and Engineering NSF AI-EDGE Institute National Telecommunications and Information Administration (NTIA) Qualcomm

NVIDIA and Infosys focus on telecom... The blog post discusses how Infosys leveraged NVIDIA's NeMo Retriever and NIM (Neural Inference Microservices) to enhance the efficiency and accuracy of telecom Network Operations Centers (NOCs) through generative AI. Infosys developed a smart NOC solution that uses AI-powered chatbots for network troubleshooting, reducing downtime, and improving customer service. The solution involved creating a vector database of network-specific documents, optimizing embeddings, and reranking for accurate and fast responses. The implementation of NVIDIA's technology significantly reduced latency by 61% and improved accuracy by 22%, enhancing the overall performance and reliability of the NOC systems. #nvidia #telecom #infosys https://lnkd.in/gp85zTUa

Transforming Network Management: The Power of Marvis In a recent discussion, Shirley Wu Sr. Director of Software Engineering leading the Marvis data engineering team since 2018 and Jessica G., Sr. Consulting Sales Engineer, delved deep into the world of Marvis, @Juniper Networks' #AINative virtual network assistant. Their conversation offers a comprehensive exploration of Marvis, from its evolution to the collaborative efforts across firmware, hardware and customer support teams, and the groundbreaking application of AI and ML in network management. Shirley sheds light on the developmental journey of Marvis, from its initial role of answering simple queries to its advanced capability of proactively identifying and troubleshooting network issues, all thanks to its sophisticated AI and ML capabilities. The discussion also highlights the collaborative synergy across data science, customer support, cloud infrastructure, firmware, and hardware, showcasing the multidisciplinary approach powering the success of Marvis. In my recent blog, I share more on how Marvis harnesses deep learning and natural language processing to help IT teams deploy and operate networks on par with human IT domain experts.  This transformative technology not only reduces support tickets improving customer experience, but accelerates troubleshooting to maximize operational efficiency Through evidence of transformative impact on network management, Marvis stands as a testament to Juniper's commitment to changing the paradigm from just managing network elements to managing the end to end client to cloud user experience. To explore further, read the full article here:  https://juni.pr/3X5nrvM #AIInNetworking #JuniperNetworks #Marvis