Please note: This master’s thesis presentation will take place in DC 2564 and online.
Sun Gyu Park, Master’s candidate
David R. Cheriton School of Computer Science
Supervisor: Professor Raouf Boutaba
Quantum Key Distribution (QKD) is a foundational technology for future secure communications, and several QKD networks have already been deployed and tested around the world using optical fibers. However, these networks cannot scale in size due to the strong exponential decay of signal efficiency in fiber with increasing distances, making satellite networks a major candidate for the global deployment of QKD networks.
Despite the advantages of free-space QKD via satellites, such networks face challenges due to changes in satellite-ground links caused by orbital motion and atmospheric fluctuations. Therefore, resource allocation schemes must account for these time-varying conditions.
In this work, we investigate the problem of resource allocation in satellite QKD networks, taking into account the changing key generation rates per time slot, reflecting the evolving weather conditions and satellite visibility.
We formulate a MILP model to allocate resources in satellite QKD networks, which decides both link assignments (i.e., determining the ground node assignment for each satellite) and the appropriate routing paths for sharing end-to-end keys. In addition, the MILP models multiple time slots and considers keys stored in the QKP, allowing keys generated to be used in later periods.
To improve computational efficiency while approaching the near-optimal total served keys of the MILP model, we propose a two-stage approach, Genetic Algorithm-Cumulative Key Reservoir (GA-CKR). In the first stage, satellite QKD links are assigned using a genetic algorithm (GA)-based heuristic. In the second stage, routing and key management (RKM) are performed using Cumulative Key Reservoir (CKR). The proposed approach achieves solutions consistently within 15% of the MILP result while reducing computation time by several orders of magnitude in the most demanding topology.
To attend this master’s thesis presentation in person, please go to DC 2564. You can also attend virtually on MS Teams.