A Reinforcement-Learning-Based Access Scheme for Low-Latency and Correlated-Traffic MTC Networks
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Bibliographic record
Abstract
This paper presents an access scheme for machine-type communication (MTC) networks where the base station (BS) is equipped with a massive antenna array and devices have correlated traffic and delay constraints. We formulate an optimization problem to allocate resources and calculate the access probabilities to maximize the throughput with delay constraints. Since the traffic model parameters and event locations are not available to the BS and the throughput with delay constraints is hard to be derived, we propose a reinforcement-learning-based algorithm to solve the problem. Our simulation reveals that our proposed algorithm is superior to a random scheduling baseline both in terms of throughput and delay. More importantly, our proposed algorithm achieves comparable throughput and lower average delay compared to the algorithm that has full information of traffic model parameters and event locations but optimizes throughput without delay constraints.
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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