Autonomous Max-Flow Interplanetary Laser Link Scheduling for Martian Exploration
Bibliographic record
Abstract
New communication technologies like delay and disruption-tolerant networking (DTN) and free-space laser communications are being developed to handle massive planetary telemetry data. However, existing infrastructure cannot meet the scalability and autonomy demands of future space missions, resulting in 95% of data left on the mission planet. Manual scheduling of deep-space laser transmissions by operators is inefficient and error-prone, limiting mission parallelization, while autonomous scheduling, though promising, may limit optimal network routes reducing the network capacity. We hypothesize that autonomously scheduling contact opportunities will enable acquisition, tracking, and pointing (ATP) for free-space lasers and increase the capacity of interplanetary backhaul networks. In this work, we propose the Laser Link Scheduler (LLS), which combines a novel max-flow network capacity model and a decision-making algorithm for contact selection. We define a series of deep-space communication scenarios where data is collected from orbiters around Mars and backhauled to Earth. LLS is evaluated in simulations using contact plans generated based on realistic conditions and orbital dynamics. Our results confirm that autonomous contact scheduling increases network capacity by 1.5-2x, reducing operational costs and enabling highly parallel space exploration missions.
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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".