Reconfigurable RAN Slicing for Ultra-Dense LEO Satellite Networks via DRL
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".