CNS is pioneering the Radio Access Neural Network (RA-NN)— the intelligence layer that transforms wireless networks into fully adaptive neural networks.
For decades, radio networks have been tuned to perform. The RA-NN reframes optimization as learning — not just about data, but about structure, behavior, and causality.
"What if radio networks could learn to perform optimally instead of just being tuned to perform optimally?"
The result isn't smarter tuning — it's a system that understands how to get better.
The RA-NN transforms RAN configuration into neural network computation, enabling every cell to learn, adapt, and optimize its role within the larger system.
Model the RAN as a dynamic graph with nodes (cells) and edges (connection interfaces), enabling sophisticated attention mapping.
Sparse attention • Dynamic topology • Graph Neural Networks
Apply targetted attention mechanisms to focus computational resources on the most critical network elements and relationships.
Efficient computation • Targeted optimization • Scalable architecture
Use real network performance indicators to compute gradients that guide the learning process toward tangible improvements.
Performance metrics • Objective optimization • Actionable outcomes
Train RL policies that make adaptive configuration decisions across the network in real-time.
Policy networks • Reward shaping • Continuous adaptation
Partner with CNS to pioneer the next generation of adaptive wireless networks. Whether you're a telecom operator, vendor, or 6G researcher, let's explore how RA-NN can revolutionize your network infrastructure.
Optimize your existing networks with AI-driven intelligence and prepare for 6G deployment.
Integrate RA-NN capabilities into your existing infrastructure solutions and stay ahead of the curve.
Collaborate on cutting-edge 6G research and explore the frontiers of wireless AI.
