Intelligent and Proactive Optimisation for Service-centric Wireless Networks

Horizon Europe MSCA SE Project funded by European Commission (Contract Number: 101086219)

Project Summary

Reliable and efficient wireless connectivity is a necessity today for most businesses and industries in a broad range of sectors. These include education, finance, healthcare, transport, utilities, logistics, mining and manufacturing. As service demands increase, current wireless networks will face a variety of challenges that will be almost impossible to effectively manage. Funded by the Marie Skłodowska-Curie Actions programme, the IPOSEE project proposes a multidisciplinary approach to automating the optimisation of wireless service-oriented networks. This will involve developing AI algorithms that can anticipate shifts in service demand and intervene to enhance networks. In addition, it will involve streamlining access networks and providing network service with improved performance and reliability at lower costs.

Project description at CORDIS

Coordinator: UPPSALA UNIVERSITET, Sweden

Time Duration: March 2023 to February 2027

Participants:

• WINGS ICT SOLUTIONS INFORMATION AND COMMUNICATION TECHNOLOGIES SA, Greece

• THE UNIVERSITY OF SHEFFIELD, UK

• RANPLAN WIRELESS NETWORK DESIGN LTD, UK

• CHONGQING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS, China

IPOSEE LinkedIn

Technical Work Packages:

  • WP1: develops probabilistic deep-learning algorithms to provide fine-grained forecasts of traffic patterns with uncertainty quantification and context knowledge for individual/aggregated services. The probabilistic traffic forecasts and their posterior distribution obtained in WP1 will be used for the AP-IRS-environment joint optimisation in WP2 and will be coupled with RAN uncertainties from WP2 and fed to WP3 to drive the RAN optimisation applications and cascade uncertainty estimation and regret functions. 
  • WP2: jointly optimises the placement and configuration of multi-antenna APs and IRSs and the propagation environment (i.e., building materials and layouts) to cost-effectively meet the traffic patterns predicted by WP1. The jointly optimised AP/IRS deployment and propagation environment will feed into the design of selected RAN optimisation applications (i.e., traffic steering, handover optimisation, and multi-user massive MIMO beamforming optimisation) in WP3. WP2’s analytical and simulation results of optimised RAN performance will feed back to WP1 to fine-tune the spatial-temporal resolution and service context granularity of traffic forecast. 
  • WP3: develops three selected RAN-optimisation applications and an AI-enabled probabilistic optimisation engine as containerised microservices for O-RAN RIC, and develops public APIs to integrate them with other RIC applications. The projected knowledge including uncertainties in the probabilistic traffic forecasts from WP1 and RAN uncertainties from WP2 will be cascaded into the probabilistic optimisation decision processes. The simulation and testbed results of WP3 will be fed back to WP1 and WP2 to refine the probabilistic traffic forecasting models and the AP-IRS-environment joint optimisation algorithms, respectively. 

Events:

  • May 2025: Project workshop in Sheffield

Publications:

  1. Z. Liu, X. Chu, D. López-Pérez and N. Tang, “Deployment Strategy of Intelligent Omni-Surface-Assisted Outdoor-to-Indoor Millimeter-Wave Communications,” in IEEE Transactions on Wireless Communications, vol. 23, no. 12, pp. 19172-19182, 2024. Available at https://eprints.whiterose.ac.uk/id/eprint/219170/1/Zhiyu%20Liu_paper2_FINAL%20VERSION%2B%2B.pdf
  2. Z. Yu, Y. Zhao, T. Deng, L. You, and D. Yuan, “Less Carbon Footprint in Edge Computing by Joint Task Offloading and Energy Sharing,” in IEEE Networking Letters, vol. 5, no. 4, pp. 245-249, December 2023. Available at https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10154013
  3. Y. Gao, H. Hu, J. Zhang, Y. Jin, S. Xu, and X. Chu, “On the Performance of an Integrated Communication and Localization System: An Analytical Framework,” in IEEE Transactions on Vehicular Technology, vol. 73, no. 7, pp. 10845-10849, July 2024. Available at https://eprints.whiterose.ac.uk/id/eprint/208750/1/Yuan Gao_Capacity and localization accuracy.pdf
  4. Z. Yu, Y. Zhao, X. Chu, and D. Yuan, “Online Learning for Intelligent Thermal Management of Interference-coupled and Passively Cooled Base Stations,” in IEEE Transactions on Machine Learning in Communications and Networking, vol.3, pp. 64-79, Dec. 2024. Available at https://backend.orbit.dtu.dk/ws/files/348818127/Learn_to_Stay_Cool.pdf
  5. Z. Yu, Y. Zhao, L. You, and D. Yuan, “Learn to Stay Cool: Online Load Management for Passively Cooled Base Stations,” 2024 IEEE Wireless Communications and Networking Conference (WCNC), Dubai, United Arab Emirates, 2024. Available at https://backend.orbit.dtu.dk/ws/files/348818127/Learn_to_Stay_Cool.pdf