Age of Information Minimization for Short-Packet Communications RSMA in Satellite-based IoT
Bibliographic record
Abstract
This paper aims to minimize the age of information (AoI) of downlink rate-splitting multiple access (RSMA) in satellite-based Internet of Things (S-IoT) network over shadowed-Rician fading channels, where a satellite multicasts with multiple user equipments (UEs) by timely transmitting short-packet status updates. First, the expressions for block error rate (BLER) and average AoI (AAoI) are derived in a closed-form for short-packet communications with finite blocklength bound. Then, we formulate an AAoI minimization problem based on the theoretical derivations for the downlink RSMA S-IoT network, and design an age-optimal stationary power allocation (ASPA) scheme to solve the problem by utilizing the particle swarm optimization (PSO) algorithm. We further propose an age-optimal dynamic power allocation (ADPA) scheme based on the Markov decision process (MDP), and solve it by two deep reinforcement learning (DRL) algorithms. Monte Carlo simulations verify the accuracy of our derivations of BLER and AAoI, and also show that our ADPA scheme outperforms the related 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 | 0.003 |
| 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) | 0.001 | 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".