Deep Reinforcement Learning-Assisted NOMA Age-Optimal Power Allocation for S-IoT Network
Bibliographic record
Abstract
In this paper, we consider a satellite-based Internet of Things (S-IoT) network under shadowed-Rician fading channels, where a satellite transmits timely status updates to multiple user equipments (UEs) with non-orthogonal multiple access (NOMA). In each transmission, the satellite needs to allocate limited power to the status updates for UEs in an appropriate way to guarantee the freshness of updates, characterized by age of information (AoI). To minimize the average AoI of S-IoT network, we formulate a power-constrained optimization problem and then reformulate it as a Markov decision process (MDP). Considering the non-convexity of the optimization problem and the high dimensionality of the multiuser MDP with large state and action spaces, we propose a deep reinforcement learning-assisted age-optimal power allocation (DRAP) scheme to solve the problem and obtain an optimal power allocation policy. Furthermore, a double-network deep reinforcement learning structure is designed to enhance the training effectiveness for our optimization problem. Finally, simulation results show that our proposed DRAP scheme outperforms the benchmark schemes.
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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 | 0.000 | 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) | 0.002 | 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".