Safety-Aware Age of Information (S-AoI) for Collision Risk Minimization in Cell-Free mMIMO Platooning Networks
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, fresh Basic Safety Messages (BSM) (e.g., vehicle’s position and speed) are used to control the Connected Automated Vehicles (CAVs) to reduce Time to Collision (TTC) error which leads to decrease in Collision Risk (CR). In contrast to exiting works, a novel Safety-aware Age of Information (S-AoI) metric is proposed that in addition to AoI, takes into account the risk assessment of CAVs to design an efficient transmission protocol for BSMs. We also deploy user-centric Cell-free-massive-MIMO (CFmMIMO) to improve the communication coverage, accessibility, and reliability, where each CAV is served by a cluster of nearby Access Points (APs). Unlike previous works, a two time-scale distributed deterministic policy gradients algorithm is adopted which greatly reduces the signal processing complexity, system load as well as signaling overhead while maintaining the performance. Simulation results show that the proposed framework, i.e, user-centric CFmMIMO technology together with S-AoI metric, can reduce average TTC error between 24%-35% across different lane change probabilities compared to the baseline scenario in which we use small cell mMIMO with AoI metric. Such a reduction in TTC error results in significant decrease (as high as 75%) in CR ratio.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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