G-SC-IRSA: Graph-Based Spatially Coupled IRSA for Age-Critical Grant-Free Massive Access
Why this work is in the frame
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Bibliographic record
Abstract
In this article, we focus on a grant-free massive access setup and analyze its Age of Information (AoI), where a large number of user equipments (UEs) are randomly activated and attempt to transmit status update packets to a base station (BS) over a common shared channel. To support this age-critical grant-free massive access, we propose a graph-based spatially coupled irregular repetition slotted ALOHA (G-SC-IRSA) random access protocol, which utilizes the pseudo-random access pattern (PRAP), coupled frames, and sliding window decoder (SWD) to improve the packet loss rate (PLR) and AoI performance. Specifically, we derive the approximate expressions to the normalized Average AoI (AAoI) as a function of the PRAP and system load. Then, we establish the problem of minimizing the AAoI under the G-SC-IRSA protocol. Furthermore, we utilize the density evolution (DE) with a bipartite graph to evaluate the system load threshold of G-SC-IRSA in asymptotic regime, achieve an optimal degree distribution via the differential evolution algorithm, and finally obtain the optimal PRAP with progressive edge-growth algorithm. Simulation results validate the accuracy of our theoretical derivations and show that the G-SC-IRSA can achieve the minimum AAoI with the optimal PRAP and outperforms the existing benchmark schemes in terms of PLR and AAoI.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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