AI Innovations For 5G Network 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

💡 Application of AIML for the New Radio air interface 3GPP TR 38.843 technical report explores how AI/ML can be integrated into 5G networks to enhance various aspects of the air interface, including: - Channel Estimation: Improving the accuracy and efficiency of channel estimation processes. - Beam Management: Enhancing beamforming and beam-tracking techniques to ensure reliable communication. - Interference Management: Utilizing AI/ML to predict and mitigate interference in the network. - Resource Allocation: Optimizing the allocation of network resources to improve overall performance and efficiency. Specifically for beam management, AI/ML have advantages like: - Deep Learning - adaptively learns the features of the channel in support of reliable beam-management - Parameters of Deep Learning models capture the high-dimensional features of the propagation scenario, such as blockage locations and shapes in support of reliable beam-management - Deep Learning can be utilized to extract the nonlinear features inherent in the angular domain for implementing super-resolution beam-prediction - Deep Learning is capable of modeling complex nonlinear factors for beam management whereas mathematical models usually ignore these factors for simplicity. #AIRAN #AI #5G