Deep Reinforcement Learning-based Data Transmission for D2D Communications
Why this work is in the frame
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Bibliographic record
Abstract
Device-to-Device (D2D) communication has gained interest as a promising technology for next generation wireless networks. D2D communication promotes the use of point-to-point communications between users without going through the base stations. In this paper, we aim at maximizing the sum rate of a D2D network, under the assumption of realistic time-varying channels and D2D interference. Specifically, we formulate channels as Finite-State Markov Channels (FSMC). With realistic FSMC, the complexity of the problem is high. Consequently, we propose the use of a centralized Deep Reinforcement Learning (DRL) transmission scheme for D2D communications, where transmission decisions are taken by one agent that has a global knowledge of the D2D network. We compare the DRL-based scheme with other transmission schemes. The results show that it outperforms other approaches in terms of achieved sum rate.
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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