All Projects

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

Background The next generation of smart cities will be powered by fleets of autonomous AI agents that continuously monitor, analyse and respond to events occurring throughout the urban environment. These agents may support traffic optimisation, emergency response, public transport coordination, environmental monitoring, digital citizen services and public safety applications. For these systems to operate effectively, … Read more

Background Modern AI systems are evolving from isolated models into networks of collaborating AI agents that work together to achieve complex goals. Examples include autonomous transportation systems, distributed robotics platforms, smart manufacturing environments and large-scale digital assistants. A key challenge is determining how these agents coordinate their behaviour while competing for limited computing resources across … Read more

Background Large Language Models (LLMs) are rapidly becoming the foundation for intelligent assistants, autonomous systems and interactive applications. However, running advanced AI models requires significant computational resources and often introduces latency that can negatively impact user experience. Future applications such as real-time translation, intelligent transport systems, augmented reality assistants and emergency response copilots will require … Read more

Background The future computing infrastructure of cities may not be fixed. Public transport vehicles, autonomous cars and drones are increasingly being viewed as mobile computing platforms capable of providing services wherever demand arises. These mobile edge platforms offer the possibility of bringing computational resources directly to users, supporting applications such as augmented reality, autonomous vehicles, … Read more

Background Many AI systems require significant time, data and computational resources to learn effective behaviours. This creates a major barrier to deploying intelligent infrastructure across different cities, regions and application domains. Transfer Learning offers the possibility of enabling AI systems to reuse knowledge acquired in one environment and rapidly adapt to new situations. Research Challenge … Read more

Background Artificial intelligence is becoming one of the fastest-growing consumers of computational resources worldwide. As AI systems scale, concerns surrounding energy consumption, sustainability and environmental impact are becoming increasingly significant. Future cities will require intelligent infrastructure capable of supporting advanced AI applications while minimising their carbon footprint. Research Challenge As Large Language Models (LLMs), AI … Read more