As the digital landscape continues to evolve, networks have undergone a significant transformation from manual, labor-intensive systems to intelligent, autonomous ecosystems. This evolution reflects the growing demand for efficiency, scalability, and reliability in an increasingly connected world. By leveraging cutting-edge technologies like AI and machine learning, modern networks are now capable of self-optimization, predictive maintenance, and real-time adaptability, setting the stage for a smarter, more resilient future.

Driving innovation
with autonomous network solutions

As network complexity grows with the proliferation of IoT devices, 5G, and edge computing, traditional management methods are no longer sufficient. Autonomous networks are essential to address these challenges by enabling real-time decision-making, predictive maintenance, and self-healing capabilities. These networks reduce the need for manual intervention, minimize downtime, and ensure consistent performance. Additionally, autonomous networks are critical for supporting emerging applications like autonomous vehicles, smart cities, and industrial automation, where reliability and low latency are paramount.

Why autonomous
networks are essential

Enhanced efficiency

Autonomous networks reduce the need for manual intervention, enabling faster and more accurate decision-making through
AI-driven automation.

Proactive maintenance

These networks can predict potential failures and take corrective actions before issues occur, minimizing downtime and ensuring continuous service.

Scalability

Autonomous networks dynamically adapt to growing demands, making them ideal for supporting emerging technologies like 5G, IoT, and edge computing.

Cost optimization

By automating routine tasks and optimizing resource allocation, autonomous networks lower operational costs and improve overall ROI.

Improved user experience

With real-time adaptability and self-healing capabilities, autonomous networks deliver consistent performance and reliability, enhancing the end-user experience.

Strengthen energy efficiency

By leveraging data-driven insights and automated management, energy efficiency is enhanced, thereby conserving sustainability value.

Samsung CognitiV NOS:
A key enabler of autonomous networks

CognitiV NOS (Network Operations Suite), Samsung’s advanced automation platform, is poised to transform how operators manage and optimize their networks. CognitiV NOS aggregates uniform data from across the entire network—spanning RAN, Transport, Core, and Cloud—into a federated inventory or data lake. This centralized data collection enhances extensive analytics and automation capabilities.

By seamlessly integrating various automation applications and AI technologies, CognitiV NOS empowers operators to streamline network operations, enhance energy efficiency, reduce costs, and significantly improve overall performance.

Samsung's CognitiV NOS offers a comprehensive suite of functions in every step of the network life-cycle from planning, deployment, operation to optimization to enable a seamless and intelligent network management along with copilot capabilities. The platform currently hosts an abundance of rApps, developed not only by Samsung but also by third-party developers. This collaborative approach aims to enhance the capabilities of CognitiV NOS and better address the specific needs of its users. The number of rApps supported will continue to increase.

Planning Deployment Operation Optimization

Involve designing network architectures and strategies to meet future demands, ensuring scalability and efficiency. Autonomous networks streamline this process by using predictive analytics and AI to optimize resource allocation and reduce manual errors.

Focus on implementing network configurations and updates across systems. Autonomous networks accelerate deployment through scripting and orchestration, minimizing downtime and ensuring consistent rollouts.

Encompass the day-to-day management of network functions. Autonomous networks enhance operational efficiency by enabling real-time monitoring, self-healing mechanisms, and reducing the need for human intervention.

Aim to improve network performance and resource utilization. Autonomous networks play a crucial role by leveraging AI to dynamically adjust configurations and optimize traffic flow based on real-time data.

Simplifying operations
with CognitiV NOS Copilot

CognitiV NOS Copilot is an AI assistant developed using a Large Language Model (LLM) designed to simplify operations across various applications through conversational interactions. By enabling users to manage tasks and workflows via natural language commands, it enhances efficiency and accessibility, making complex operations more intuitive and user-friendly.

Key enablers for AI-powered networks:

Compute

Train and infer in
a sustainable manner

Combining accelrator with
common silicon

Data

Fuel that powers AI

AI-powered analysis, insights, and
prediction by exploiting
aggregated network data

Algorithm

Key enabling models for
AI-powered networks

Aligning AI models with data
characteristics and use cases

The future of networking:
Embracing autonomy and intelligence

The shift toward intelligent autonomous networks represents a paradigm change in how networks are designed and managed. These networks are not just reactive but proactive, capable of learning from data and adapting to new demands. As technology continues to evolve, the role of AI and automation will become even more critical in driving innovation and delivering next-generation connectivity. Samsung’s automation solution is at the forefront of this transformation, providing the tools needed to build smarter, more resilient networks that meet the challenges of the future. Going further, CognitiV NOS will advance to become a more intelligent, intent-driven solution that meets the demands of future applications and services.