Bibliographic record
Abstract
This paper aims to provide an information theoretical analysis of massive device connectivity scenario in which a large number of devices with sporadic traffic communicate in the uplink to a base-station (BS). In each coherence time interval, the BS needs to identify the active devices, to estimate their channels, and to decode the transmitted messages from the devices. This paper first derives an information theoretic upper bound on the overall transmission rate. We then provide a degree-of-freedom (DoF) analysis that illustrates the cost of device identification for massive connectivity. We show that the optimal number of active devices is strictly less than half of the coherence time slots, and the achievable DoF decreases linearly with the number of active devices when it exceeds the number of receive antennas. This paper further presents a two-phase practical framework in which device identification and channel estimation are performed jointly using compressed sensing techniques in the first phase, with data transmission taking place in the second phase. We outline the opportunities in utilizing compressed sensing results to analyze the performance of the overall framework and to optimize the system parameters.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".