Multi-Task Reinforcement Learning-Based Multiple Access for Dynamic Wireless Networks
Bibliographic record
Abstract
With the rapid development of emergent applications, wireless networks require the provision of high throughput. Meanwhile, wireless scenarios exhibit highly dynamic characteristics, involving frequent changes in the network scale and traffic. To satisfy the high demand for new applications in dynamic wireless scenarios, a novel medium access control (MAC) protocol is required to allow stations to access the channel with high efficiency and adaptability. Based on multi-agent reinforcement learning (MARL), we propose a new MAC protocol, Multi-task Transformer-based Multiple Access (MTMA). Multi-task learning is applied to train a single actor to adapt to multiple wireless environments simultaneously. To improve the scalability, we propose a transformer-based critic network, which can scale to different wireless scenarios. Moreover, a novel network called “Generalization for N (Gen-N)” network is proposed to enhance the generalization ability. We conduct simulation experiments to demonstrate that MTMA: 1) achieves over 95% of upper bound of throughput while maximizing the fairness performance; 2) outperforms classic MAC protocol and MARL-based baselines in scenarios with saturated and light traffic; 3) can adapt to environmental changes quickly in dynamic scenarios; 4) can generalize to unseen scenarios during training. Finally, the ablation experiments are conducted to evaluate the effectiveness of components used in MTMA.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 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".