Jinsung Choi’s Post

Battle of the Titans: AI Chips Transforming Open vRAN The new era, AI Open vRAN, integrates AI/GenAI to enhance network performance, optimize operations, and provide unprecedented flexibility. As the demand for AI in telecoms grows, a fierce chip war is emerging, with semiconductor companies vying to provide the most advanced and efficient solutions. This shift not only enhances network performance but also reduces operational costs, in particular energy consumption, and improves user experience. ◼ Chip War in AI PCs Recently, the chip war is raging in the AI PC market. For example, Qualcomm's recent announcement of the Snapdragon X Elite SoC highlights their commitment to leading this space. Also, MediaTek has announced a strategic partnership with NVIDIA to develop arm-based AI CPU. These developments indicate that the AI PC market is becoming a new battleground for semiconductor companies, with each striving to outdo the other in terms of performance and efficiency. ◼ Layer 1 Accelerators and CPUs in Open vRAN In the context of AI Open vRAN, two key chip components are essential: - Layer 1 Accelerators: These chips offload specific Layer 1 processing tasks from the CPU, such as Forward Error Correction and channel estimation. They are designed to work with COTS (Commercial Off-the-Shelf) hardware, promoting open interfaces and enhancing flexibility. - CPUs: The CPU remains a critical component, handling higher-level tasks such as MAC, RAN L2/3, RIC, SMO, and other management software and ensuring overall system efficiency. The choice between Arm-based CPUs and traditional x86 CPUs is a significant factor in the chip war. L1 accelerators must balance performance, power consumption, and cost, making them a critical battleground in the AI Open vRAN landscape. Simultaneously, the chip war extends to CPUs in vRAN, with a clear divide between Arm-based and x86 CPUs. Arm-based CPUs, known for their power efficiency and performance, are increasingly being adopted in vRAN deployments. However, x86 CPUs, traditionally dominant in computing, continue to evolve, offering robust performance and compatibility with existing infrastructure. ◼ Edge AI Computing and Open vRAN Synergy Potential The synergy between edge AI computing and open vRAN presents immense potential. Edge AI enables real-time data processing and decision-making closer to the user, reducing latency and improving performance. When combined with open vRAN, this approach can enhance network efficiency and provide a more responsive user experience. Edge AI computing allows for localized data analysis, ensuring that only relevant information is sent to centralized servers, thereby reducing bandwidth requirements and operational costs. This synergy is crucial for the future of telecom networks, as it supports the deployment of more intelligent and adaptive RAN solutions. #ChipWar #L1Accelerator #CPUforvRAN #OpenRAN #CloudRAN #EdgeAI

Generative AI in Open RAN: Pioneering Intelligent Networks As the telecom industry continues to evolve, the fusion of these advanced technologies promises to reshape how networks are designed, operated, and maintained. Applications of Generative AI in Open RAN ◼ Network Optimization and Management: Generative AI models, such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), are adept at creating realistic simulations of network conditions. This capability allows network operators to test and optimize strategies in virtual environments, reducing the need for costly real-world deployments and enabling quicker adaptation to changing conditions. ◼ Enhanced Security Measures: With the increasing threat landscape, security is paramount in network operations. Generative AI can simulate cyber-attacks, enabling the development of robust defense mechanisms. By generating synthetic attack data, these models help improve the training of defensive algorithms, ensuring better preparedness against actual threats. ◼ Improved Spectrum Management: Generative AI can be utilized to mimic radio frequency conditions, aiding in spectrum management—a critical aspect of RAN operations. This application not only helps in optimizing frequency allocation but also ensures compliance with regulatory standards without the need for extensive physical field tests. ◼ Fault Detection and Maintenance: The predictive capabilities of generative AI can revolutionize network maintenance and fault detection. By forecasting potential failures before they occur, these systems allow for proactive maintenance, minimizing downtime and improving overall service quality. ◼ Chatbots using LLMs: LLMs significantly enhance Telco services by providing sophisticated and context-aware interactions. Examples of their applications include: Customer Support: Automated responses to service inquiries, improving customer experience and operational efficiency. Network Fault Detection: Advanced diagnostics to identify and rectify network issues, enhancing network reliability. Configuration: Intelligent recommendations for network settings to optimize performance and user satisfaction. Standards Compliance: Assistance in implementing and adhering to industry standards and regulatory requirements. Challenges and Considerations Issues such as data privacy, model accuracy, and integration complexities with existing infrastructures must be addressed. Moreover, the reliance on accurate and comprehensive training data to inform generative models is critical—the quality of the generated data heavily depends on the quality of the input data. As networks move towards 5G Advanced and 6G, the demand for more intelligent and autonomous networks will drive further integration of AI capabilities. *Source : Presentation by Amparo Canaveras of NVIDIA at Open RAN Summit 2024 Taipei #GenAI #OpenRAN #nVidia #ORANChatBot #GAN #VAE #LLM #Transformer #TelcoNetworkAI

GenAI-Driven Autonomous RAN Management: Reducing OPEX by Improving Network Operations The evolution of open RAN technologies has reached a critical juncture where AI/ML, specifically generative AI (GenAI), can play a pivotal role in autonomous network management. ◼ Overview of the AI-Driven RAN Management System Qualcomm's idea of an AI-driven RAN management system is structured around four key components that integrate seamlessly to automate and optimize RAN operations. These components include two user interfaces and two core AI processing units—each playing a specific role in transforming user inputs into actionable network adjustments. Here's a closer look at each component: ◼ RAN Consultant User Interface (Input) Functionality: This interface serves as the primary interaction point for network operators. Designed to accept natural language inputs, it allows operators to pose questions or commands in a conversational manner. Usage: Operators can input scenarios or queries such as "What happens if network traffic increases by 20%?" or "What is the best way to reduce interference in high-density areas?" ◼ Gen AI (LLM) Core Process: At the heart of the AI-driven system lies a powerful LLM. This model processes the natural language inputs to discern the underlying intent and context, translating them into structured queries that can be analyzed further. Transformation: The LLM acts as a bridge between human queries and machine-processable commands, ensuring that the system accurately understands and responds to operator inputs. ◼ Neural Network (RAN Foresight & Reasoning) Analytical Engine: Following the LLM's processing, the queries are handed over to a neural network designed for deep analytical tasks. This component uses predictive analytics and machine learning to forecast network performance and the implications of potential adjustments. Output: It predicts key performance indicators such as coverage, throughput, and interference levels, and suggests optimal configurations to enhance network efficiency and performance. ◼ RAN Consultant User Interface (Output) Result Display: The final component is the output user interface, which displays the processed information and recommendations generated by the neural network. This interface provides network operators with actionable insights, visualizations of potential outcomes, and direct recommendations for network adjustments. Interaction: Operators receive detailed feedback on their queries, including data-driven predictions and suggested actions to improve network operations. GenAI-driven autonomous open RAN management represents a transformative approach to network operations, harnessing the power of AI to simplify complexities and enhance operational efficiencies. Qualcomm’s idea of the integration of GenAI into RAN management can establish a new standard for automated network operations aimed at reducing costs and improving service delivery. #GenAI #AutonomousRAN #LLM #OPEX #Qualcomm

Commercializing Open RAN: Korea OTIC and Its Impact on the Industry ◼ Establishment of Korea OTIC The Korea OTIC was established in December 2023 by the Ministry of Science and ICT. The center utilizes the advanced facilities of the Telecommunications Technology Association (TTA) in Pangyo and the Electronics and Telecommunications Research Institute (ETRI) in Daejeon. Its primary role is to test the suitability and interoperability of open RAN equipment and issue international certifications, thereby facilitating the early market entry of domestic companies into the open RAN sector. ◼ Government and Industry Collaboration The Ministry of Science and ICT (MSIT) has launched the 'Open RAN Industry Alliance (ORIA)', a public-private consultative body aimed at promoting open RAN. The initiative is part of a broader policy to activate the open RAN ecosystem in South Korea. This alliance has already seen tangible outcomes, such as the successful global interoperability verification events (PlugFests). TTA announced that Korea OTIC would issue three types of international certifications: - Performance of open RAN equipment. - Compatibility with equipment from other manufacturers. - Operational performance within the network environment. The goal is to continuously upgrade OTIC facilities and equipment by 2027, ensuring they can host future PlugFests and meet evolving industry standards. ◼ Government Initiatives for Ecosystem Development To foster an open RAN ecosystem, the South Korean government plan to launch an "open RAN demonstration project" starting next year. This project aims to create initial demand by installing and operating open RAN equipment from domestic companies in large venues like stadiums, exhibition halls, and commercial facilities. The National Information Society Agency (NIA), responsible for the project, plans to provide manufacturers with demonstration experience and telecom companies with opportunities to verify open RAN systems. ◼ SOLiD's Conformance Certification SOLID has made history by obtaining the first 'conformance certification' awarded by Korea OTIC for its self-developed Open RAN Radio Unit (O-RU). This certification is particularly significant as it confirms that SOLID's equipment meets all the required functions of Open RAN technology. To achieve this certification, the equipment must pass 38 rigorous standards set by the O-RAN Technology International Standardization Organization (O-RAN Alliance). ◼ Implications for South Korean SMEs The establishment of Korea OTIC and the subsequent certification signify a broader opportunity for South Korean small and medium-sized enterprises (SMEs). The benefits are; - Reduces costs and logistical challenges for SMEs. - Accelerates the time-to-market for innovative open RAN solutions. - Enhances the global competitiveness of South Korean telecommunications companies. #OTIC #ORAN #Certification #Badging #TTA #ORIA #SOLID #KoreaOTIC #ETRI

Open vRAN with ARM CPU: From Concept to Reality Open vRAN takes this a step further by virtualizing RAN functions, allowing them to run on general-purpose hardware instead of specialized, proprietary equipment. At the heart of this transformation lies the ARM CPU. Known for its power efficiency and performance, ARM architecture has been a cornerstone in the mobile and embedded systems market. Leveraging ARM CPUs for vRAN brings the benefits of energy efficiency, reduced operational costs, and the flexibility to deploy on various hardware platforms. ARM's Neoverse platform has been pivotal in translating the concept of open vRAN into a deployable reality. Designed for high-performance and efficient processing, Neoverse CPUs provide the necessary compute power to handle the demanding workloads of vRAN. These processors are optimized for data-intensive tasks, making them ideal for managing the complexities of modern mobile networks. One of the significant milestones in this journey was the collaborative trial by Nokia and Vodafone, in partnership with ARM and HPE. Conducted at Nokia's Open Cloud RAN Innovation Center in Dallas, this trial successfully showcased an end-to-end Layer 3 (L3) data call using ARM-based general-purpose processors in Nokia's in-line L1 accelerator integrated HPE servers. This demonstration proved the viability of ARM Neoverse-based processors for handling the demanding tasks of vRAN. Moreover, a recent collaboration between NVIDIA and SoftBank Corp. exemplifies the potential of open vRAN with ARM CPUs. At COMPUTEX, NVIDIA and SoftBank announced their partnership to develop a platform for generative AI and 5G/6G applications using the NVIDIA GH200 Grace Hopper™ Superchip. This platform will be deployed in new AI data centers across Japan, setting a precedent for future global deployments of generative AI applications and services. SoftBank's new data centers will utilize the NVIDIA MGX™ reference architecture with Arm Neoverse-based GH200 Superchips. This innovative setup aims to improve performance, scalability, and resource utilization of application workloads while reducing costs and enhancing energy efficiency. The collaboration will enable SoftBank to optimize its RAN and reduce energy consumption by creating interconnected data centers capable of hosting various AI applications. The data centers will handle both AI and 5G workloads, operating at peak capacity with low latency and lower energy costs. SoftBank is exploring 5G applications for autonomous driving, AI factories, augmented and virtual reality, computer vision, and digital twins. All these announcements mark a significant step in demonstrating the viability and flexibility of ARM-based solutions in open vRAN environments, offering network operators more choices in hardware architecture and potential improvements in energy efficiency and performance. #openRAN #vRAN #ARM #SoftBank #nVidia #Nokia

Introduction of ORIA - Korea Open RAN Industry Alliance The Open RAN Industry Alliance (ORIA) is spearheading the transformation of wireless network technology by focusing on the development and deployment of Open RAN solutions. This document outlines ORIA's mission, strategic activities, and organizational structure, highlighting its commitment to enhancing the domestic Open RAN ecosystem and securing global leadership in 5G, 5G Advanced, and 6G technologies. Through strategic market entry initiatives, national R&D and standards development, and collaborative ecosystem building, ORIA aims to position South Korea as a frontrunner in the next generation of wireless communication technologies. ◼ Mission and Structure Open RAN Industry Alliance (ORIA): A mission-oriented organization focused on promoting Open RAN technology. ◼ Primary Goals: Lead the development and deployment of 5G/5G+/6G and Open RAN technologies. Enhance the domestic Open RAN ecosystem and secure global leadership. ◼ Organizational Structure General Assembly: Composed of chair companies, audits, and member companies, holding annual assemblies and special meetings if necessary. Steering Committee: Monthly meetings with chairs of operating committees and subcommittees to review activities, financial statements, and make executive appointments. Expert Committee: Responsible for annual activity reviews, budget plans, and major operational decisions. ◼ Strategic Activities Market Entry and Promotion: Domestic Trials: Support commercialization of Open RAN technologies through public, private, and commercial network trials. Test Beds and Certification: Establish and utilize Open RAN test environments and certification systems to aid product development and market entry. ◼ National R&D and Standards Development: R&D Strategy: Identify and propose key R&D items to the government’s tech roadmap. Standardization: Work on developing domestic standards for Open RAN and engage in international standardization efforts. ◼ Collaboration and Ecosystem Building: PlugFests and Global Engagement: Organize events to test interoperability of Open RAN products and engage in international certification. Support for SMEs: Provide opportunities for SMEs to participate in trials and access global markets. ◼ Key Milestones and Goals 2023-2030 Roadmap: Establish Korea-OTIC, support advanced R&D, and facilitate domestic and international market entry for Open RAN technologies. Next-Generation R&D: Prepare for technological advancements from 5G to 6G, ensuring continued leadership and innovation in the field. ORIA aims to position Korea as a leader in Open RAN by fostering innovation, supporting SMEs, and ensuring interoperability and standardization through collaborative efforts with government and private sectors. #ORAN #OpenRAN #ORIA #Korea #SouthKorea #Ecosystem

Mastering Multi-Tenancy: Key Principles and Challenges in 'AI and RAN' Edge Cloud Multi-tenancy allows multiple tenants to share the same 'AI and RAN' infrastructure while maintaining robust security and performance standards. Key Technical Principles for Multi-Tenancy ◼ Isolation Resource Isolation: Ensure tenants are separated in terms of compute, storage, and network resources using containerization and VMs. This prevents one tenant’s workload from affecting another’s performance or security. Data Isolation: Maintain tenant data privacy and compliance through robust encryption methods and stringent access controls, ensuring data remains separate and protected. ◼ Security Least Privilege: Grant tenants only the access and permissions necessary for their operations to minimize potential damage from security breaches. ◼ Scalability and Efficiency Resource Elasticity: Dynamically scale resources up or down based on tenant requirements for efficient utilization and cost savings. Multi-Tenancy Models: Choose appropriate models (e.g., single-tenant on multi-tenant hardware or virtualized multi-tenancy) based on security and resource needs. ◼ Management and Automation Self-Service Provisioning: Allow tenants to provision and manage their workloads through a self-service portal, reducing operational overhead for the cloud provider. Monitoring and Metering: Accurately monitor tenant resource usage and provide precise metering for billing purposes to ensure fairness and transparency. ◼ Implementation Challenges - Resource Isolation at the Edge: Achieving strong isolation with limited resources at edge locations requires continuous refinement of containerization techniques and lightweight hypervisors. - Data Security at the Edge: Protecting data in distributed edge environments necessitates robust encryption and access control mechanisms due to limited physical security. - Latency-Sensitive Applications: Careful selection and configuration of technologies are crucial to maintaining low-latency performance for critical Telco applications. - Heterogeneity of Edge Deployments: Implementing a consistent multi-tenant environment across diverse hardware and software configurations adds complexity. ◼ Operational Challenges - Standardization and Automation - Monitoring and Troubleshooting - Billing and Metering: Implementing precise billing mechanisms ensures fairness and transparency in resource usage by different tenants. - Regulatory Compliance: Automation tools can streamline compliance assessments and reporting, ensuring adherence to data privacy regulations across various locations. Implementing multi-tenancy in telco edge clouds offers significant benefits in terms of resource efficiency and service scalability. By adhering to key technical principles and addressing associated challenges, telco providers can create robust and secure multi-tenant environments. #MultiTenancy #AIandRAN #Isolation #Security #AIRAN

The Power of Cloud O-RAN, GPU Acceleration, and 'AI and RAN' The integration of O-RAN technology with cloud and AI capabilities marks a significant advancement in modern network infrastructures. This post explores the evolution of traditional RAN into cloud O-RAN, highlighting the benefits of O-RAN standards-driven disaggregation and cloud-native implementations. It further delves into the acceleration of CloudRAN through GPU-aided processing and the optimization of GPU cloud resources, as well as the innovative combination of AI and RAN workloads on the same GPU cloud, showcasing how this synergy enhances network performance, flexibility, and efficiency. ◼ Cloud-native O-RAN implementations: O-RAN components are implemented using cloud-native technologies, which means they are designed to run in cloud environments, leveraging the benefits of cloud infrastructure such as scalability, agility, and efficiency. ◼ Accelerating Cloud O-RAN: GPU-aided acceleration for L1 & L2 RAN layers: To handle the intensive processing needs of the physical (High-PHY) and MAC layers of the O-RAN, GPUs are used. This acceleration enhances performance and efficiency, enabling faster data processing and transmission. ◼ O-RAN in the GPU Cloud: Optimize the GPU Cloud CAPEX: By utilizing the GPU cloud for RAN workloads, organizations can optimize their CAPEX. This optimization comes from better utilization of GPU resources, which can be expensive, ensuring they are used efficiently to maximize return on investment. ◼ AI and O-RAN: AI & RAN on the same GPU cloud: The integration of AI workloads with RAN workloads on the same GPU cloud infrastructure allows for shared resources and better overall utilization. - Optimizing GPU Utilization: Combining RAN and AI workloads on the same GPU cloud can optimize resource use. Dynamic scaling of workloads ensures efficient GPU utilization and improved ROI. - Technical Architecture and Implementation: Functional Blocks: Infrastructure orchestration, AI orchestration, RAN orchestration, and closed-loop control for dynamic resource provisioning. - The overarching orchestrator manages AI and RAN workloads, providing a single pane of glass for infrastructure and workload management across multiple sites. - Dynamic Orchestration Scenarios: Peak Load Scenario: Fully utilizes hardware for RAN workloads. Off-Peak Scenario: Scales down RAN workloads to free up resources for AI workloads. #CloudRAN #CloudORAN #GPUCloud #AIandRAN #DynamicOrchestration #nVidia #AarnaNetworks #RANinGPUCloud #GPUAccelerator #AIonRAN #AINativeRAN #AIML #6GRAN #ORAN

🚀 AI O-RAN: The Future of Telecommunications 🚀 I had the privilege of delivering the keynote at the ORIA Open RAN Symposium 2024 in Incheon, South Korea, on June 12. During my presentation, I shared my perspectives on AI in Open RAN, drawing on the extensive AI RAN-related research spearheaded by the O-RAN ALLIANCE, including GenAI and real-time RIC dApps. Here is the summary. The integration of AI into Radio Access Networks (RAN) through the AI Prism Framework is revolutionizing the telecom industry. Here's how AI is enhancing network performance and efficiency across three key domains: 🔹 Knowledge Transfer: Leveraging large language models (LLMs) to facilitate the transfer of technical knowledge and best practices within the RAN network. 🔹 RAN Operations: Combining traditional ML and GenAI models to optimize network operations, automate routine tasks, and enhance decision-making processes. 🔹 Air Interface Engineering: Utilizing traditional and advanced ML models for traffic prediction, anomaly detection, and capacity planning, ensuring optimal use of the radio spectrum. At the heart of this transformation is the RAN Intelligent Controller (RIC), integrating AI-driven capabilities directly into network operations for real-time analytics, enhanced optimization, and automated functions. AI for RAN Use Cases: 🔸 Natural Language Processing (NLP): Automates task script generation, documentation, and root cause analysis, reducing downtime and improving reliability. 🔸 Pattern Recognition: Analyzes network data to identify usage patterns, predict demands, and recommend optimal configurations, crucial for network optimization and planning. 🔸 GenAI Reinforcement Learning: Used for RAN slice design and energy efficiency optimization, training models offline to determine optimal resource allocation and energy-saving practices. 🔸 AI Agents: Develop self-healing capabilities, monitoring network behavior and autonomously taking corrective actions based on predefined protocols, enhancing reliability and minimizing human intervention. True Real-Time RIC Apps (dApps): 🔹 Real-Time RAN Control: Advanced RIC applications, or dApps, enable dynamic and intelligent network management. These applications leverage the capabilities of the RIC for real-time inference and control, significantly enhancing performance and efficiency. The standardization of dApps for RIC, as approved by nGRG, is a significant step forward, enabling more granular control of RAN resources and rapid adjustments based on real-time data. The development of specialized hardware accelerators combined with AI engines, advanced AI/ML models, and innovative Open RAN applications is driving the telecom transformation, making networks smarter, more flexible, and more resilient. #AI #ORAN #OpenRAN #Telecom #MachineLearning #GenerativeAI #ReinforcementLearning #NetworkOptimization #RIC #dApps #Innovation #FutureTech

The Smart O-Cloud: AI/ML-driven O-RAN Network Automation Inspired by the article 'Report: Smart cloud and the coming paradigm shift' by Stephen Saunders MBE (https://lnkd.in/dxNRk-kZ), I applied the Smart Cloud concept Stephen created to O-RAN O-Cloud with the addition of a closed-control loop layer. New Architecture: Key Layers of Smart O-Cloud Networks ◼ Data Utility: The Data Utility layer consolidates the physical infrastructure and essential network services needed for O-RAN components like O-RUs, O-DUs, and O-CUs. ◼ Cloud or Virtual Networks: This layer provides the foundational infrastructure for advanced operations. It encompasses virtualized network functions and SDN capabilities. ◼ Observability: It provides real-time insights into the network's state, which is essential for proactive management and troubleshooting. ◼ Closed-Loop Control: Integrating real-time feedback from the Observability layer, the Closed-Loop Control layer allows dynamic adjustments to network configurations. This integration ensures that insights from observability directly inform automation processes, creating a responsive and adaptive network environment. ◼ Automation: This layer incorporates advanced automation tools and processes, reducing the need for manual intervention. When combined with closed-loop control, it delivers unparalleled levels of functionality, visibility, and responsiveness, enabling self-healing and self-optimizing networks. ◼ O-RAN Applications and Services: The top layer focuses on value-creating services and applications running over the RAN network, including RAN slicing services. Why This Architecture Enables AI/ML Driven O-RAN Network Automation The Data Utility layer ensures high-capacity, reliable data connectivity, essential for AI/ML models requiring extensive data for learning and accurate predictions. The Cloud or Virtual Networks layer offers a scalable and flexible environment, crucial for deploying AI/ML algorithms that must adapt to varying network demands. The Observability layer provides comprehensive visibility into network performance, which is vital for training AI/ML models and conducting real-time analytics. The Closed-Loop Control layer allows for real-time network adjustments based on feedback, enabling AI/ML models to continuously learn and dynamically optimize operations. The Automation layer, using feedback from closed-loop control, leverages AI/ML to streamline operations, reduce latency, and enhance service quality, resulting in self-healing and self-optimizing networks. The Applications and Services layer benefits from AI/ML-driven efficiencies, focusing on delivering enhanced customer services. The Security and AI pillars ensure secure AI/ML operations across the network, protecting data and applications while enhancing overall network intelligence and driving continuous improvement. #ORAN #OpenRAN #ORANArchitecture #ORANAutomation