Resource Allocation for Satellite QKD Networks with Atmospheric Forecast
Bibliographic record
Abstract
Quantum Key Distribution (QKD) is a foundational technology for future secure communications, and several QKD networks have been already deployed and tested around the world using optical fibers. However, these networks cannot scale in size due to the inefficiency of fiber QKD networks with increasing distances, making satellite networks a major candidate for long-distance QKD networks. In satellite QKD networks, satellites and ground stations can act as trusted relays, distributing keys between satellite-ground station pairs to serve requests among ground stations. Satellite QKD networks face fundamental challenges due the time-varying nature of the connection between ground stations and satellites, caused by both the satellite’s orbital movement and fluctuating atmospheric attenuation. Thus, it is necessary to design novel schemes to dynamically allocate resources for satellite QKD networks that adapt to evolving network conditions in different time intervals. In this work, we investigate the problem of resource allocation in satellite QKD networks taking into account the changing key generation rates, calculated according to evolving weather conditions and satellite visibility. We first model the achievable key rate of connections between satellite and ground stations under different weather conditions, which is used as an input for optimization. We formulate a Mixed-Integer Linear Programming (MILP) model to allocate resources in satellite QKD networks, which decides both link assignments (i.e., deciding which ground to connect to for satellites) and the appropriate routing path for the trusted relay. In addition, the MILP models multiple timeslots and considers keys stored in the quantum key pool (QKP), allowing keys generated during low-load periods to be used later during high-load periods. Moreover, we propose to decide the link configuration with heuristic algorithms and then utilize ILP to decide the appropriate routing path for the trusted relay, which significantly reduces the execution time. The numerical results show that incorporating link configuration within the ILP achieves up to 20% more total served keys compared to heuristicbased baseline approaches, but with an execution time up to 700x longer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".