Jinsung Choi

Deutsche Telekom is making a strong push for 5G mmWave technology in the industrial sector. The telco has successfully trialled mmWave frequencies for industrial applications and are now actively targeting industrial customers. This is a strategic move for a few reasons: 🔘 Ultra-Fast Speeds & Low Latency: mmWave offers significantly higher speeds and lower latency compared to traditional mid-band 5G, crucial for real-time industrial applications like robotics and automation. 🔘 Enhanced Network Capacity: mmWave can handle a much larger number of devices and data streams, ideal for dense industrial environments with numerous connected machines and sensors. 🔘 Unlocking New Applications: The high bandwidth and low latency of mmWave can enable entirely new industrial applications that wouldn't be possible with traditional 5G. Deutsche Telekom's focus on mmWave for industry is a clear sign of the growing importance of this version of the 5G which has been widely adopted in the USA, Middle East and Asia but has not been adopted in Europe. What are your thoughts on the use of 5G mmWave in industrial settings?  Is this the future of industrial connectivity? #5GmmWave #IndustrialIoT #DeutscheTelekom #Telecoms #CCSI #TelecomsInsights

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Enhancing Multi-Vendor Interoperability with the 3GPP Network Resource Model (NRM) ◼ Introduction to the NRM The Network Resource Model (NRM) is a critical framework developed by 3GPP to support the management and orchestration of 5G networks. The NRM represents the manageable aspects of these networks through an information model, defined according to specifications from various 3GPP working groups and the requirements of the 5G management architecture and operations. At its core, the NRM describes network resources as discrete entities within an object-oriented environment, managed through abstract entities known as Information Object Classes (IOCs). These IOCs, along with their attributes and relationships, are essential for effective network management. The NRM is specified in three stages: - Requirements Level (Stage 1): Defines conceptual use cases and requirements for network resources. - Information Service (IS) Level (Stage 2): Provides a technology-independent specification focusing on semantics between management consumers and producers. - Solution Set (SS) Level (Stage 3): Maps IS definitions into technology-specific solution sets, ensuring adaptability without altering core definitions. ◼ Facilitating Multi-Vendor Interoperability - Separation of Stages 2 and 3: Ensures new technologies can be integrated without changing existing specifications. - Protocol Technology Independence: Decouples definitions from specific protocols, allowing flexibility while maintaining interoperability. - Umbrella Information Model (UIM): Harmonizes various domain-specific models. - Standardized Naming and Containment: Ensures different implementations can recognize and interact with each other correctly, crucial for multi-vendor environments. ◼ Integration with O-RAN: The NRM's standardized approach and the flexibility of the YANG solution set facilitate seamless integration with O-RAN architectures. This integration supports the goals of O-RAN by enabling interoperable, multi-vendor deployments that can be managed and orchestrated using a common framework. ◼ Optimizing O-RAN Configuration Management with YANG Solutions - YANG Solution Set: Enables detailed configuration and control of network elements, designed for reuse by projects like ONAP and O-RAN. - Configuration Management: Standardizes the configuration, monitoring, and optimization of network resources, ensuring efficient operation in multi-vendor environments. - Future-Proofing: Ensures the management framework can evolve with new technological developments, accommodating emerging technologies and innovations in 5G. The 3GPP NRM provides a robust framework for multi-vendor interoperability, leveraging YANG solution sets for efficient O-RAN configuration management. This ensures flexibility, scalability, and future readiness in managing 5G networks. #ORAN #OpenRAN #NRM #3GPP #NMS #OAM #IRP #YANG #ONAP #MultiVendor #Interoperability #ORANSMO

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Check on the most viewed session of the ONE Summit by István Kispál about how to orchestrate Nokia CNFs using Nephio . Intent based network management realized with open source.

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The Role of AI/ML in Capacity and Coverage Optimization Service providers are striving to maximize the value of their 5G mid-band spectrum. Low-mid-band 4G/5G Dual Connectivity and 5G Carrier Aggregation including low-band FDD/high-band TDD enhance 5G by combining low-band’s broad coverage with mid-band’s capacity, ensuring reliable and extensive network coverage. Moreover, the support for advanced MIMO such as mMIMO technology boosts network efficiency and manages multiple connections effectively. Coordinating multiple frequency bands and integrating them seamlessly into a cohesive network demands sophisticated algorithms and precise synchronization to ensure optimal performance and user experience. This is a typical capacity-coverage optimization (CCO) issue. The coverage and capacity challenge is extensive, and addressing it effectively requires examining at least eight specific sub-problems. Here’s how AI/ML technologies can be leveraged to tackle each of these issues: ◼ Dynamic Spectrum Allocation: AI/ML models, particularly Reinforcement Learning (RL), enhance spectrum use for better coverage and capacity by learning optimal allocation strategies through feedback from network environments. Methods like Deep Q-Networks (DQN) combine Q-learning with deep neural networks to handle large state spaces. ◼ Carrier Aggregation Optimization: Supervised learning models, such as Random Forest and Gradient Boosting Machines (GBM), predict optimal carrier combinations based on network data, leveraging multiple frequency bands for improved coverage and capacity. ◼ Predictive Traffic Management: Time series analysis and forecasting models like ARIMA and LSTM predict traffic patterns to proactively allocate resources, maintaining coverage and preventing congestion. ◼ Interference Mitigation: Unsupervised learning including K-means clustering and Independent Component Analysis (ICA), identify and mitigate interference, reducing signal degradation to improve coverage and capacity. ◼ Load Balancing: RL algorithms, such as Q-learning and Policy Gradient Methods, dynamically distribute traffic loads to maintain consistent coverage and maximize capacity. ◼ mMIMO and Beamforming: Deep learning models, including optimize beamforming and MIMO configurations, enhancing signal directionality for better coverage and higher capacity. ◼ Network Performance Optimization: Multi-Agent Systems and RL, like MADDPG, manage various network components in a coordinated manner to ensure continuous coverage and capacity optimization. ◼ Energy Efficiency: Combining heuristic optimization algorithms and RL, such as Particle Swarm Optimization (PSO) and Proximal Policy Optimization (PPO), reduces energy consumption while maintaining performance, ensuring sustainable network operations. #ORAN #OpenRAN #Capacity #Coverage #AIML #ORANRIC #CarrierAggregation #Midband #TDDFDD #TDDUplinkCoverage

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Introducing O-RAN Y1 Near Real-Time RIC open interface The Y1, O-RAN ALLIANCE's new open interface, introduces a dynamic pathway for enhanced communication and analytics between the near-real-time RAN Intelligent Controller (near-RT RIC) and both internal and external systems. This interface is not just a technical addition; it’s a strategic enabler that broadens the scope of O-RAN capabilities towards more adaptive, intelligent, and integrated network operations. ◼ Y1 Open Interface The Y1 interface in the O-RAN architecture facilitates the sharing of Radio Analytics Information (RAI) from the near-RT RIC to various authorized consumers. These consumers can range from internal components like non-RT RICs and SMO, to external third-party applications. The fundamental role of the Y1 interface is to allow for the subscription and consumption of RAN analytics data, which are vital for enhancing network performance and user experiences. This interface supports robust mutual authentication and authorization protocols, ensuring that only authorized entities can access the sensitive and critical data being transmitted. The Y1 interface is designed to be flexible, supporting consumers both within the PLMN trusted domain, such as NWDAF and multi-RAT dynamic spectrum sharing applications, and outside it through a secure exposure function. ◼ Key Benefits of Y1 - Enhanced Network Intelligence and Optimization The Y1 interface enables the near-RT RIC to transmit vital RAN insights such as QoE and other operational metrics to different network components. This transmission of data supports more informed decision-making processes and facilitates network adjustments in real-time, optimizing both network performance and resource allocation. - Expanded Ecosystem and Multi-Vendor Interoperability By standardizing the way that RAN analytics are shared, the Y1 interface encourages a broader ecosystem of applications and services to integrate seamlessly with O-RAN networks. - Scalability and Flexibility The Y1 interface is designed to be scalable, handling varying levels of data traffic and accommodating growing network demands without degradation in performance. This scalability ensures that as networks evolve and expand, the Y1 interface can continue to provide critical data efficiently and reliably. ◼ V2X: An Use Case of Y1 V2X encompasses various communication modes, all of which require highly reliable and real-time data exchange. In emergency scenarios, timely and accurate data transmission is crucial. The Y1 interface supports the delivery of critical information from sensors, enhancing situational awareness and aiding in quicker, more effective response efforts. The ongoing development and adoption of the Y1 interface will be a critical area of focus as O-RAN transitions towards being more open and intelligent. Architecture Diagram credit: Rimedo Labs #ORAN #OpenRAN #ORANRIC #ORANnearRTRIC #Y1openinterface #AIML #AIORAN

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Navigating the AI Wave: Opportunities in AI for RAN Markets ◼ What is AI-for-RAN RANs are critical components of Telco mobile communication systems, responsible for connecting end-user devices to the core network. AI-for-RAN refers to the application of AI and ML techniques in radio access networks (RANs). AI-for-RAN solutions leverage machine learning, deep learning, and other emerging AI technologies such as GenAI and even LLMs to improve network efficiency, increase capacity, reduce latency, and enhance RAN operations, and most importantly, the overall user experience. ◼ L1 Accelerator and CPU and GPU Chip Markets in AI RAN: Programmable L1 accelerators, CPUs (x86 or arm), and GPUs will play crucial roles in implementing AI algorithms within RANs, particularly, in the DUs and CUs. Programmable L1 accelerators are specialized hardware components designed to accelerate the processing of conventional PHY and AI workloads with their embedded tensor-core based NPU. CPUs and GPUs also contribute to AI for RAN processing within RANs, with CPUs handling general-purpose tasks such as Layer 2/3 protocols and GPUs specializing in parallel processing tasks suited for deep learning algorithms. The market for these components in the context of AI-for-RAN is likely to see growth as demand for AI-enabled RAN optimization increases. ◼ O-RAN RIC Market Opportunities (RIC Platform, Computing, RIC Apps): O-RAN RIC provides a framework for intelligent, software-defined control and management of RAN resources. The RIC platform encompasses both hardware and software components necessary for RAN intelligence, including computing infrastructure and management software. Market opportunities in this space include the development and deployment of RIC platforms, computing hardware optimized for RIC applications, and the creation of RIC apps that leverage AI and machine learning for advanced RAN optimization, such as dynamic spectrum allocation, traffic steering and interference mitigation. ◼ SMO and RAN Automation Market (AIOps, Analytics, SMO, etc.): SMO (Service Management and Orchestration) and RAN automation solutions utilize AI inferencing, analytics, and other advanced techniques to automate network management tasks, optimize resource allocation at a higher level, and improve network resiliency. Market opportunities in this segment include the development of AI-based analytics platforms for network monitoring, predictive maintenance, and anomaly detection. AI inferencing capabilities are essential for processing data collected from RANs, identifying patterns, and making intelligent decisions to optimize network performance. As operators seek to automate and streamline RAN operations, the demand for AI-driven SMO and RAN automation solutions is expected to grow, presenting significant market opportunities for vendors and service providers. #AIforRAN #ORAN #ORANRIC #ORANSMO #AIforRANMarket #GPU #AIML #GenAI #LLM #RIC #SMO #OCloud #RANAutomation #RIC #SMO

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Once market leader BSNL, now not only progressing towards indeginious 4G but also strongly working towards indeginious 5G…BSNL walking past all the odds in the competitive telecom market…. Switch to BSNL…. #BSNL #CONNECTING #INDIA #CONNECTING #HEARTS https://lnkd.in/g2D4XCRv

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Does running your 5G network bring you upside down? ⬇️ Check real examples of how efficiently a 5G network is operating while facing any scenario and improving the accuracy of your testing routines, so you improve network performance and customer satisfaction. 📗 Download the eBook "Evolving Network Testing" to get references from 12 successful use cases of network testing and realize the benefits of 5G! https://bit.ly/3X1ZrKi #NetworkTesting #5G #ToTeNextLevel #NetworkPerformance

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3GPP SA1 stage 1 Workshop on IMT2030 (6G) use cases is in progress in #Rotterdam. TCCA-Critical Communications participates to promote the society need to #trust on mobile communication and have connectivity at all times.

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A very succinct summary of leveraging AI/ML in the automation and optimization of RAN. AirHop Communications is doing that with its AI-powered Apps.

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Download the report on CPaaS API vs Network APIs     This report, written by Dean Bubley at Disruptive Analysis, clears the industry's confusion about CPaaS APIs vs. #NetworkAPIs and explains their roles in unleashing programmable network capabilities.

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The Multifaceted Challenges of Multi-Vendor Open RAN Cloudification Open RAN cloudification introduces complexities across three dimensions: 1. Deployment: Different radio sites have unique requirements for capacity, performance, and hardware. This diversity requires nuanced integration of various components to maintain consistent network performance across all locations. 2. Multivendor Systems Integration: Open RAN systems necessitate managing a mix of technologies from different vendors at each site, which includes COTS servers, CaaS platforms, and RAN software. This setup demands careful coordination to ensure seamless functionality. 3. Life-Cycle Management: Frequent software updates from multiple vendors require coordinated management to maintain system integrity. Adopting cloud-native principles like CI/CD and leveraging AI for automation are essential for efficient lifecycle management. These dimensions highlight the complex interplay of technical, operational, and strategic challenges inherent in the cloudification of RAN. Successfully navigating these challenges requires service providers with profound technical knowledge and system integration capabilities, complemented by innovative management practices to guarantee success. For Telcos, the open RAN cloudification involves several crucial stages, each requiring different kinds of support and expertise from their technology and system integration partners to effectively transition them into a flexible, efficient, and cost-effective network infrastructure. This includes; - Strategic and Tactical Planning: Identifying the right cloud technologies and deployment strategies - Use Case Exploration: Analyzing various use cases to determine how they can enhance network performance and service delivery - PoCs and Trials: Conducting trials to identify technological gaps, assess the maturity of the solutions, and refine operational processes - Multivendor Integration: Components from various vendors are integrated to form a cohesive solution tailored to the Telco’s specific context and network - Trade-off Guidance: Service partners guide Telcos in making crucial decisions between vendor flexibility and pre-integration benefits, balancing system integration costs, performance predictability, and TCO - Cloud-native LCM Practices: Implementing automation and adopting cloud-native practices to manage the high cadence of software updates Hyperscalers have heavily invested in developing AI/ML that can be leveraged to enhance RAN operations. These platforms can analyze vast amounts of network data to optimize traffic flow, predict network failures, and dynamically allocate resources. The integration of these AI capabilities seamlessly into Telco's RAN cloud enables them to deploy AI-enhanced applications without the need for significant investments in separate AI infrastructure, which is often a challenge with traditional NEPs and SIs. #ORAN #CloudRAN #SystemIntegration #Partnership #MultiVendor

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FTTX ODN Solution Introduction PON access system should be an all-optical solution in response to the continuously growing bandwidth requirements in the last-mile (access) domain. PON-based access network follows the trend of delivery of triple-play services to end subscribers, aggregating three types of services over a single delivery platform. This in turns is the next step in the evolution of the core networks to IP-based NGN. In order to build the new generation of optical access networks, this solution draws on the technologies of different maturity extents such as EPON, GPON, Ethernet and xDSL, ranging from local exchange devices of various capacities to serialized user-side devices. It can be deployed in various application scenarios, such as FTTC, FTTB, FTTO and FTTH, enabling operators to deliver services in a simple and straightforward manner, providing cost-effective  high-bandwidth and multi-service support capabilities. This allows operators to become more competitive and adapt services to ever changing customer demands. #ODN #FTTH #FTTx #PON #OLT #ONU

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Navigating the Xn Maze: O-RAN WG5's Blueprint for Multi-Vendor Interoperability The implementation of the 3GPP Xn interface such as X2 in multi-vendor environments presents significant challenges, affecting interoperability and network management and optimization in 5G and beyond. The O-RAN Alliance's Working Group 5 (WG5) is at the forefront of addressing these issues to ensure seamless operation and efficient management in next-generation networks. ◼ Interoperability Challenges Differences in how vendors interpret 3GPP's open specifications can lead to discrepancies in parameter handling and signaling mechanisms. Such divergent implementations result in connectivity problems, handover failures, and performance issues like reduced data throughput and increased latency, impacting user experience adversely. ◼ Network Management Complexity Operating a network with equipment from multiple vendors inherently adds complexity. Network operators face challenges in managing diverse proprietary systems and coordinating network functions like power control and interference management, which are handled differently by each vendor’s system. Additionally, diagnosing and resolving issues becomes more complex in a multi-vendor environment, as pinpointing the source of a problem involves navigating through multiple systems. ◼ O-RAN ALLIANCE WG5's Strategic Approaches - Multi-Vendor Profile Specifications WG5 develops multi-vendor profile specifications that are compliant with 3GPP standards but tailored for open, multi-vendor environments. These specifications aim to minimize variability in how standards are interpreted and applied, ensuring that equipment from different vendors can seamlessly interoperate. This not only reduces integration challenges but also enhances network flexibility and vendor competition. - Ensuring Interoperability A primary role of WG5 is to ensure that hardware and software components from different vendors are not only compliant with industry standards but also capable of working together without compatibility issues. WG5 achieves this through detailed interoperability specifications and guidelines that help standardize the behavior of network components across different implementations. ◼ Support for Testing and Certification To validate that vendor implementations are compliant with O-RAN specifications and capable of interoperating with components from other vendors, WG5 supports comprehensive testing and certification programs. These programs are essential for verifying that different components meet the stringent requirements set out in the multi-vendor profile specifications and function correctly within a multi-vendor ecosystem. WG5's efforts are vital for overcoming the interoperability and management challenges associated with the Xn interface implementation in multi-vendor 5G networks. #ORAN #OpenRAN #Xninterface #Interoperability #MultivendorRAN #MRSS #CoMP #DSS #WG5Specifications

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Check out the latest advancements on 6G radio design and innovation on topics including AI-driven air interface design, advanced MIMO transmissions, joint communication and sensing, integrated terrestrial and non-terrestrial networks, and a lot more from Hexa-X-II! https://lnkd.in/ddaSZXuM #SNSJU #6G #AI

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Networks need to evolve to 5G standalone for multiple reasons: -5G standalone networks offer significantly faster data speeds and lower latency compared to previous network generations. This improved performance enables faster download and upload speeds, better connectivity for devices, and a smoother user experience. -Enhanced Capacity: 5G standalone networks can support a higher number of connected devices simultaneously, making it ideal for the increasing number of IoT devices and smart technologies that require constant connectivity. -Network Slicing: With 5G standalone networks, operators can create virtual network slices tailored to specific services or applications. This enables more efficient use of network resources and allows for customized services based on specific requirements. -Edge Computing: 5G standalone networks facilitate edge computing, bringing computing resources closer to the end-users. This reduces latency and improves application performance for services that require real-time data processing. -Enabling New Use Cases: 5G standalone networks open up opportunities for new use cases such as autonomous vehicles, augmented reality, virtual reality, and remote surgery, which require ultra-reliable low latency communication (URLLC) capabilities. The evolution to 5G standalone networks is crucial to meet the increasing demands for higher data speeds, lower latency, and improved connectivity for a wide range of applications and services.

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The "AI Telco energy savings" paradox AI for Telcos offers amazing efficiencies, such as potentially slicing up to 10%-20 off base station energy consumption. On the other hand, it feeds an ever-growing beast—AI data centers—projected to devour a staggering 25% of US energy by 2030. It's much like turning off the lights to save electricity, only to power a small town with your backyard carnival every night. AI effectiveness in optimizing networks is unmatched, but we must also confront the shadow it cast: a voracious energy appetite that could undermine Telco sustainability goals. https://lnkd.in/gydmwaFy

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Are the projected 5G trends keeping up with reality in 2024? The key 5G trends expected for 2024 according to StartUs Insights were Open RAN (ORAN), Network Slicing, Beamforming, Standalone 5G Infrastructure, and Ultra-Reliable Low Latency Communications (URLLC). After six months, I believe we can conclude that ORAN and Network Slicing have notably expanded, with significant adoption in the US, Europe, and parts of Asia. The ORAN growth is primarily driven by the need for a diverse vendor ecosystem and enhanced competition. I've noticed companies like AT&T planning for 70% of their network traffic to use open-capable platforms by 2026, already starting deployments with Ericsson and Fujitsu. Additionally, Firecell, a French startup, is deploying private 5G solutions using ORAN technology, while Rimedo Labs in Poland supports ORAN scenario experimenting. Network Slicing has been implemented this year in the US and Spain, enabling flexible, efficient network use and improving service delivery across different use cases. Beamforming and Standalone 5G Infrastructure are making steady progress. China and the APAC region are leading in 5G Standalone deployments, with commercial networks now active in Australia, Japan, South Korea, and Singapore. This highlights their ability to overcome technical challenges and high costs. Notably, China has mandated 5G SA capabilities for all devices since early 2020, driving rapid adoption and integration. The region is poised for continued expansion throughout 2024, leveraging cloud-native 5G core technologies to improve service delivery and operational efficiency. Massive Machine-Type Communications (mMTC) and Enhanced Mobile Broadband (eMBB) are prominent in the APAC region due to high IoT adoption. Conversely, economic pressures in the Americas have delayed some 5G applications, while Europe has made significant progress in expanding ORAN, network slicing, and URLLC for industrial applications. One interesting application I noted was SplineAI in India developing 5G-powered Internet of Medical Things (IoMT) applications, leveraging the low latency and high reliability of URLLC for improved patient monitoring and telehealth services. For the remainder of 2024, I believe we can expect further progress in 5G adoption and deployment. ORAN and Network Slicing are set to expand in the US and Europe, enhancing network management flexibility and innovation. Beamforming and Standalone 5G Infrastructure will grow in urban areas despite cost and complexity challenges. I expect URLLC to see increased industrial applications, particularly in healthcare and automotive sectors. Europe and the Americas will focus on scaling standalone 5G and ORAN, while APAC will lead in IoT and urban deployments due to advanced technological infrastructure. #5GTrends2024 #OpenRAN #NetworkSlicing #5GInfrastructure #URLLC #IoTAdoption #TelecomInnovation Link: https://lnkd.in/dywdxwQ8

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Technology-neutral licensing or Technology-neutrality (commonly known as spectrum refarming) is the ability to refarm spectrum used for legacy networks (#2G and #3G) for #4G and #5G services, at a pace driven by market demand. In general terms, technology neutrality refers to the repurposing of spectrum bands to more efficient technologies. Technology neutrality is an important enabler of legacy network sunsets. The initial rollout of 4G in many pioneer markets was based on technology neutrality and spectrum refarming. As of the middle of the last decade, nearly half of 4G deployments globally were running on refarmed 2G and 3G spectrum. Between 2015 and mid-August 2023, a total of 91 networks were shut down, of which 43 were 2G networks and 48 were 3G networks. At least 148 networks will be shut down between Q4 2023 and 2030, based on announced plans from operators.

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