High-Reliability Multi-Agent Q-Learning-Based Scheduling for D2D Microgrid Communications
Bibliographic record
Abstract
This paper proposes a multi-agent Q-learning-based resource allocation algorithm that allows long-term evolution (LTE)-enabled device-to-device (D2D) communication agents to generate the orthogonal transmission schedules outside the network coverage. This algorithm reduces packet drop rates (PDR) in distributed D2D communication networks to meet the quality-of-service requirements of the microgrid communications. The data traffic characteristics of three archetypal smart grid applications, namely demand response, solar, and generation forecasting, and synchrophasor communications, were simulated under seven different traffic congestion scenarios, where the total aggregate throughput of users ranged from 50% to 140% channel utilization. The PDR and latency performance of the proposed algorithm were compared with the existing random self-allocation mechanism introduced under the Third-Generation Partnership Project's LTE Release 12 standard for such scenarios. Our algorithm outperformed the LTE algorithm for all tested scenarios, demonstrating 20%-40% absolute reductions in PDR and 10-20-ms reductions in latency for all microgrid applications. The use of our algorithm in a simulated D2D-enabled demand response application resulted in a hundredfold reduction in power oscillations about the desired power flows.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".