Topology-Transparent Scheduling Based on Reinforcement Learning in Self-Organized Wireless Networks
Bibliographic record
Abstract
Topology-transparent scheduling policies do not require the maintenance of accurate network topology information and therefore are suitable for highly dynamic scenarios in self-organized wireless networks. However, in topology-transparent scheduling, it is a very challenging problem to make individual nodes efficiently select their transmission slots in a distributed manner. It is desirable for individual nodes, through time slot selection, to avoid collision on the one hand and utilize as many time slots as possible (i.e., minimize the number of redundant slots) on the other. In this paper, learning-based approaches are employed to solve the time slot scheduling problem. Specifically, the proposed method uses a temporal difference learning approach to address the collision issue and use a stochastic gradient descent approach to reduce the number of redundant slots. Unlike previous works, this learning approach is trained through self-play reinforcement learning without incurring communication overhead for the exchange of reservation information, thereby improving the network throughput. Extensive simulation results validate that our proposal can achieve better efficiency than the existing approaches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".