Sustainable Systems

About this pillar

Sustainable Systems aims to drive innovation in sustainability within next generation and 6G networks, addressing multiple dimensions of environmental impact. The following projects contribute to this mission:

Towards Carbon-Neutrality for 6G: This project evaluates the feasibility of carbon-neutral 6G networks through innovative approaches in energy harvesting, renewable energy integration, and energy/carbon efficient operations. By leveraging cutting-edge technologies, we aim to establish a pathway toward networks with minimal environmental footprints, balancing high 6G performance with sustainability.
Integrated Sensing, Communication, and Localisation for 6G and beyond: Integrated Sensing and Communication (ISAC) has the potential to turn the network into a sensor thus saving the materials and resources of placing sensors around our environments. Assessing the integration of advanced technologies like backscatter sensors, intelligent reflecting surfaces, metasurfaces, and MIMO transmission, this project’s goal is to enable a 6G network capable of optimal and seamless data transfer, precision sensing, and optimised/reduced energy use through technological convergence.
Green AutoML for the Edge-Fog-Cloud Continuum: This pillar contributes environmental challenges associated with Artificial Intelligence (AI) in 6G by designing green Automated Machine Learning (AutoML) solutions. The focus is on enhancing the efficiency of AI deployment across the cloud-fog-edge continuum, exploring the potential of energy-efficient hardware, like neuromorphic computing, to reduce carbon footprints. Additionally, this project examines the integration of AutoML with Open Radio Access Network (ORAN) architecture, enabling AI-native and transfer learning capabilities within Radio Access Networks (RAN).

These projects collectively advance the field of sustainable 6G technology by addressing both energy efficiency and resource optimisation across the entire lifecycle of 6G infrastructure. By applying whole-system thinking, Pillar 2 contributes to shaping a sustainable 6G future that aligns with global sustainability goals.

Energy consumption for network coverage

Secret keys for ISAC

Consortium

2.1 Durham and York Teams: Advancing Carbon-Neutral 6G Systems

Durham and York researchers are collaborating to overcome the challenges of creating carbon-neutral 6G systems. They are developing models to better understand and optimise energy use within the network. Instead of relying on traditional energy efficiency metrics, they have selected network-level carbon intensity as a key performance indicator (KPI). Additionally, they are exploring energy harvesting methods and zero-power Reconfigurable Intelligent Surfaces (RIS) to assess their potential in supporting carbon-neutral goals.

2.2 Imperial, Glasgow, and Cranfield Teams: Enhancing Cyber-Security, Localisation Accuracy, and Resource Efficiency for ISAC

Imperial, Glasgow, and Cranfield are focused on improving cyber-security, localisation accuracy, and resource optimisation in Integrated Sensing and Communication (ISAC) systems. They have developed new secret keys for secure ISAC communication and achieved sub-meter level localisation in urban settings through advanced signal processing, addressing the challenges of multi-path interference in GNSS signals. Future work will include designing more energy-efficient waveforms for ISAC to maximise resource efficiency.

2.3 Cranfield and Durham Teams: Building Energy-Efficient, Scalable Machine Learning Frameworks

The Cranfield and Durham teams aim to create energy-efficient machine learning frameworks that remain flexible and scalable. Cranfield is particularly focused on developing scalable over-the-air federated learning methods. Meanwhile, Durham is working on energy-aware task allocation in automated machine learning (AutoML) to minimise the carbon footprint of 6G-based machine learning. The next phase involves orchestrating the machine learning pipeline to achieve an optimal balance between energy efficiency and task accuracy.