Topology-Transparent Scheduling Based on Reinforcement Learning in Self-Organized Wireless Networks
Why this work is in the frame
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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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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it