Age of Information-Limited Capacity of Uncoordinated Massive Access Using Massive MIMO
Bibliographic record
Abstract
We derive an achievability bound in an uplink setting where N single-antenna devices, of which a random subset of Kausers are active in each transmission period, attempt to update a base-station (BS), equipped with M antennas, with their status packets. Motivated by emerging applications of massive connectivity we consider the asymptotic scenario where both the total number of users and the number of antennas at the BS grow large at a fixed ratio $\zeta = \frac{M}{N}$. Under maximal-ratio combining and perfect channel state information at the receiver, we find that the achievable rate approaches ${\log _2}\left( {1 + \frac{M}{{{K_a}}}} \right)$ in the large system limit. We explore the trade-offs between this achievable rate and the freshness of the status packets using the age of information (AoI) metric. In the limiting regime, we find that the penalty one pays for increasing the data rate is a rise in the minimum AoI obtainable. Finally, we compare recent massive unsourced random access (URA) schemes against the newly established bound.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".