Reconfigurable RAN Slicing for Ultra-Dense LEO Satellite Networks via DRL
Why this work is in the frame
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Bibliographic record
Abstract
Ultra-dense low earth orbit (LEO) satellite network (UD-LSN) is an emerging architecture in the sixth-generation communication system. Network slicing technology can build multiple virtual logical networks for services provided by UD-LSNs on the common physical network. The spatiotemporal variabilities of service requirements and available satellite resources make it necessary to perform reconfigurable resource slicing in UD-LSNs. In this paper, we present a reconfigurable radio access network (RAN) slicing architecture based on grouping and clustering in UD-LSNs. Time is separated into several slicing windows, each further separated into multiple time slots. We take into account the features of the rate-constrained and delay-constrained slices and formulate an optimization problem aiming at maximizing the long-term slicing revenue that involves resource utilization, the service level agreement satisfaction ratio (SSR), and reconfiguration revenues. The problem is tackled by a two-tier deep reinforcement learning (DRL)-based reconfigurable satellite RAN resource slicing and user access (TDRL-RSUA) algorithm. We decouple the original problem into the RAN resource slicing subproblem in slicing windows and user access subproblem at time slots. Specifically, the resource slicing subproblem is solved with the multi-discrete mask Proximal Policy Optimization (MDMPPO) algorithm, while the user access subproblem is solved with the many-to-one matching algorithm. Simulation results demonstrate that our TDRL-RSUA algorithm can improve resource utilization by more than 30% in comparison to the non-reconfigurable resource slicing strategy and achieves higher slicing revenue and SSR.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it