Delay Laxity-Based Scheduling with Double-Deep Q-Learning for Time-Critical Applications
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Bibliographic record
Abstract
In this paper, we propose a novel delay-aware selective admission and scheduling algorithm for time-critical applications to guarantee the delay requirement of each packet in a single-hop downlink network. We consider a series of priorities among packets. To avoid always starving low-priority packets, we define a delay-laxity concept and introduce a new output gain model as our network utility function. In this context, we formulate a multi-objective optimization problem that minimizes the average queue backlog and maximizes the average network utility under the constraints of guaranteeing per-packet delay and achieving fairness among users. To solve this problem, we model our problem as a Markov Decision Process and propose a Double Deep Q Network-based algorithm to learn the optimal policy. Simulation results show that the proposed algorithm can achieve significant improvements in average delay, delay-outage drop rate, and goodput compared with the existing stochastic schemes. Moreover, the proposed algorithm outperforms the conventional Q-learning algorithm in terms of reward and learning speed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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