Wi-Fi operates in the open radio spectrum and is vulnerable to interference from neighbouring networks. This can result in packet loss and retransmissions, which greatly reduces network capacity and user experience.
In the DISCO project, we have built a decentralized system for discovering and grouping Wi-Fi networks to allow them to collaborate. The system is designed to create clusters of nodes that interfere each the most, for instance neighbours in an apartment building.
Clustering allows the WiFi networks within the cluster to cooperate on improving the utilization of the frequency spectrum. Within the cluster we can use advanced features to reduce the spectrum congestion, including improved spectrum usage through channel allocation and adjusting the transmission power.
In this master's thesis, we will examine how clustering and collaboration between Wi-Fi networks improve network capacity. The thesis aims to implement the clustering algorithm and look at the increased performance by implementing features to improve the spectrum usage. The work will initially be conducted in ns-3 (a network simulator), but it is also possible to extend the thesis to include practical experiments on real Wi-fi 6/7 hardware.
Learning outcome
Experience
- in formulating, investigating and answering research questions
- conducting, evaluating and interpreting research results
- in-depth understanding of wireless networks and Wi-Fi
- experience running large scale network simulationsÂ
Conditions
We expect that you:
- have been admitted to a master's program in MatNat@UiO
- take this as a long thesis
- are interested in and have some knowledge of C++ programming and network simulation
- include the course IN5060 in the study plan, unless you have already completed a course on classical (non-ML) data analysis