Delay Laxity-Based Scheduling with Double-Deep Q-Learning for Time-Critical Applications
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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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".