On the Spatio-Temporal Analysis and Optimization of AoI in Cell-Free IIoT Networks
Bibliographic record
Abstract
Cell-free massive multiple-input multiple-output (mMIMO) architecture is a promising solution for Industrial Internet of Things (IIoT) because it not only provides massive connectivity but also eliminates the traditional cell edges. Considering the heterogeneous traffic and requirements in the industry, in this paper, we propose a device priority-aware resource allocation policy under cell-free mMIMO IIoT networks. Specifically, we design a priority-aware frame structure that can be used to provide differentiated age of information (AoI) guarantees for devices of different priorities and locations. To characterize the proposed policy, we develop a general analysis framework to evaluate the signal-to-interference ratio meta distribution and the average AoI of a generic device. The framework captures multiple main features under wireless IIoT networks, including cell-free mMIMO architecture, frame structure, finite-sized geographic areas, densely deployed devices, device priority, retransmission, and interaction among different transmission links. The analytical framework is validated by simulations. Based on the analysis, we study a mean-variance optimization problem to improve the network average AoI, while guaranteeing the average AoI per device. Numerical results show that the proposed frame structure works effectively in enhancing the AoI performance of cell-free IIoT networks.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".