Deep Dyna-Reinforcement Learning Based on Random Access Control in LEO Satellite IoT Networks
Bibliographic record
Abstract
Random access schemes in satellite Internet-of-Things (IoT) networks are being considered a key technology of new-type machine-to-machine (M2M) communications. However, the complicated situations and long-distance transmission can make the current random access schemes not suitable for the satellite IoT networks. The random access problem in the satellite IoT networks is studied in this article. A novel random access scheme for machine-type-communication devices (MTCDs) is proposed, to maximize the efficiency of random access for contention-based and contention-free random access. Under the set of random access opportunities (RAOs) and limited delay, the random access control model is designed via maximizing efficiency of random access. The model-free deep reinforcement learning (DRL) algorithm is proposed to tackle the problem based on the random access model. Subsequently, the deep Dyna-$Q$learning algorithm is introduced to deal with the proposed random access control model. In this proposed scheme, the random access model-free DRL algorithm is developed using simulated experience. The proposed algorithms’ performances are discussed, and simulation results show the desirable performance of the proposed DRL methods on different system parameters.
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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 | 0.003 |
| 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".