Merim Dzaferagic

This project investigates the application of agentic AI reasoning to the autonomous control and optimisation of wireless communication networks, leveraging the OCUDU platform in conjunction with an AI-native network controller. The student will extend the existing controller architecture to support closed-loop agentic control, wherein an AI agent autonomously issues configuration commands, observes network responses, and … Read more

1. Background and motivation As mobile networks evolve toward 6G, artificial intelligence is moving from an optional optimisation layer to the core control mechanism of the network itself: AI-native architectures increasingly rely on models embedded directly in the control loop (e.g., forecasting traffic, managing topology, orchestrating resources, and making real-time decisions across base stations and … Read more

1. Background and motivation Modern mobile networks are increasingly managed by AI agents rather than fixed rule-based controllers, for example, agents that read live network telemetry (throughput, latency, energy consumption) and issue configuration commands (transmit power, scheduling weights, cell sleep states) to meet operational goals. This is the paradigm behind O-RAN’s RIC (RAN Intelligent Controller) … Read more

1. Background and motivation “Deploy an AI model for network optimisation” is not a single, fixed cost: a traffic-forecasting model that runs once per minute per cell has wildly different compute requirements from an agentic reasoning loop that must diagnose a fault and propose a remediation within a sub-second RIC control-loop deadline, or from an … Read more