Wrasse: Riding Millimetre Radio Waves at Gigabit-per-second Speeds
The number of mobile-connected devices is rapidly increasing and bandwidth-intensive applications with very-low latency requirements, such as ultra-high definition television (4K video), remote medical services and smart transportation systems, are expected to dominate the Internet traffic demand by 2020. To support such services, future 5th generation mobile (5G) networks will harness millimetre-wave radio frequencies.
This project seeks to explore the potential of wireless networks that operate in millimetre wave bands to achieve Gb/s data-rates. The problems we are currently investigating include 802.11ad performance analysis, resource allocation, and small cell backhauling. We are also interested in heterogeneous systems that combine millimetre-wave technology with traditional Wi-Fi operating in the 2.4 and 5GHz bands.
- R. Li, C. Zhang, P. Cao, P. Patras, J. S. Thompson, "DELMU: A Deep Learning Approach to Maximising the Utility of Virtualised Millimetre-Wave Backhauls", in Proceedings International Conference on Machine Learning for Networking (MLN), Paris, France, November 2018. [PDF]
- N. Facchi, F. Gringoli, P. Patras, "Maximising the Utility of Enterprise Millimetre-Wave Networks", Elsevier Computer Communications, vol. 119, pp. 29–42, Apr. 2018, DOI: 0.1016/j.comcom.2018.01.011. [PDF]
- R. Li, P. Patras, "WiHaul: Max-Min Fair Wireless Backhauling over Multi-Hop Millimetre-Wave Links", in Proceedings of the 3rd ACM Workshop on Hot Topics in Wireless (HotWireless), New York City, NY, USA, Oct. 2016, DOI: 10.1145/2980115.2980133. [PDF] [BibTeX]
- G.H. Sim, R. Li, C. Cano, D. Malone, P. Patras, J. Widmer, "Learning from Experience: Efficient Decentralized Scheduling for 60GHz Mesh Networks", in Proceedings of IEEE 17th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), Lisbon, Portugal, June 2016. [PDF] [BibTeX]
Rui Li (Ph.D.), The University of Edinburgh.
We are collaborating with the University of Brescia, University of Trento, IMDEA Networks Institute, Hamilton Institute Ireland, Inria, and Trinity College Dublin.
This research is partially funded by the University of Edinburgh Development Trust through an Innovative Initiative Grant.