Evaluating the RAN in its True Form
A graph is a powerful mathematical model that represents data as an interconnected set of nodes (vertices) and the relationships (edges) between them. They are complex, non-uniform structures with many intricate connections between various localized components.
As it turns out, this is exactly how telecom networks are structured—individual network elements and their connection interfaces orchestrated together to form a dynamic, graph-like system.
AI Models Grounded in Network Context
CNS models the RAN as a complex knowledge graph—a precise mirror of each cell, neighbor relation, and performance indicator, all which represent important components of the live network. With a unique combination of graph neural networks and generative AI techniques, we harness this graph to embed the rich contextual nuances of a wireless network's underlying functionality and structure directly into our deep learning AI models.
With this core technology, CNS leverages context-driven insights to meet a wide range of network needs—supporting immediate operational goals while acting as a bedrock for future use cases.
Why Graph-Based Learning is Key
Optimizing a wireless network isn’t just about adjusting individual cells; every change can affect neighboring cells and the entire cluster. Traditional AI models often miss these complex relationships, leading to oversmoothing that dilutes critical local context. Our graph-based models capture these intricate connections, providing precise, context-rich insights. At CNS, our patent-pending framework is designed to preserve these relationships, enabling adaptive and globally optimized recommendations that traditional models simply can’t match.
Stochastic Modeling with Generative AI
RAN optimization is inherently unpredictable, with conditions constantly shifting. Instead of just finding the best configuration, CNS uses generative AI to understand why certain configurations work, adapting dynamically as the network evolves. This allows our system to generate tailored configurations in real-time, optimizing performance as conditions change.
Hardware Acceleration: Powering RAN Innovation
The future of computing is driven by parallel, high-efficiency processing—essential for AI-driven RAN optimization. While the industry may adopt accelerated systems down the road, CNS is enabling these capabilities today. By harnessing hardware acceleration now, our solutions manage large data volumes, ensure low-latency responses, and dynamically adapt to changing network conditions, setting the stage for seamless integration into future RAN systems.
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