Deep Reinforcement Learning-Assisted NOMA Age-Optimal Power Allocation for S-IoT Network
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 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 it