Smart Power Control for Quality-Driven Multi-User Video Transmissions: A Deep Reinforcement Learning Approach
Bibliographic record
Abstract
Device-to-device (D2D) communications have been regarded as a promising technology to meet the dramatically increasing video data demand in the 5G network. In this paper, we consider the power control problem in a multi-user video transmission system. Due to the non-convex nature of the optimization problem, it is challenging to obtain an optimal strategy. In addition, many existing solutions require instantaneous channel state information (CSI) for each link, which is hard to obtain in resource-limited wireless networks. We developed a multi-agent deep reinforcement learning-based power control method, where each agent adaptively controls its transmit power based on the observed local states. The proposed method aims to maximize the average quality of received videos of all users while satisfying the quality requirement of each user. After off-line training, the method can be distributedly implemented such that all the users can achieve their target state from any initial state. Compared with conventional optimization based approach, the proposed method is model-free, does not require CSI, and is scalable to large networks.
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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 | 0.001 |
| 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 | 0.001 | 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".