Phase Transition Analysis for Covariance Based Massive Random Access with Massive MIMO
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Bibliographic record
Abstract
This paper studies the massive random access problem in which a large number of sporadically active devices wish to communicate to a base-station (BS) equipped with a large number of antennas. The devices are pre-assigned unique pilot sequences for random access. It has been shown previously that the device activity detection problem at the BS can be formulated as a maximum likelihood estimation (MLE) problem, whose solution depends on the sample covariance matrix of the received signal. This paper adopts the MLE formulation, and proposes an approach to analyze the covariance based detection by studying the asymptotic properties of the MLE via its associated Fisher information matrix. This paper proposes a necessary condition on the Fisher information matrix such that the estimation error tends to zero in the massive multiple-input multiple-output (MIMO) regime. A phase transition analysis is carried out based on the necessary condition. This paper also analyzes the distribution of the estimation error for the case with a large but finite number of antennas at the BS. Numerical experiments validate the analysis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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