Yet many CSPs still struggle to meet the demand for intelligent services and quick decisions at the edge. AI workloads continue to rise, data volumes grow, and many applications now need a response in the moment. Still, most networks rely on centralized platforms far from subscribers. This adds delay, increases cost, and limits the value operators can deliver. It also prevents the real-time insight that today’s markets expect.RAN sites contain powerful compute resources, but these remain dedicated to radio functions. They cannot support the broader AI needs emerging across industries. Yet AI models grow more complex and place new strain on central systems. CSPs need a way to distribute intelligence without expanding hardware footprints or creating additional silos.
These pressures expose a structural gap. CSPs need a design that supports AI where it offers the most impact: at the edge, close to subscribers and enterprise environments. They also need a model that improves RAN performance while creating new revenue potential.
The Project Aura (AI + RAN) Catalyst addresses this by merging AI for RAN and AI on RAN in one architecture. It uses shared infrastructure to enhance efficiency, reduce cost, and enable edge-native services. This shifts the RAN from a cost center to a strategic platform for growth.
The solution
The project has developed a system supports two intelligence streams running on shared, high-performance hardware. One stream improves RAN performance through AI-driven power saving, interference cancellation, and link adaptation. The other stream supports edge-native applications such as video analytics, drone orchestration, and location-based services. Both run on a single architecture that consolidates data pipelines, orchestration, and GPU acceleration.
The platform blends OSS and BSS logic with CAMARA APIs, enabling consistent integration with systems. Its hybrid-cloud model allows AI workloads to run beside RAN software without reducing radio performance. This improves hardware utilization and reduces the need for dedicated edge devices.
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