Unsourced Random Access With Unequal Power Dynamic Compressed Sensing
Bibliographic record
Abstract
In coded compressed sensing (CCS) unsourced random access (URA), the message of each user is subdivided into smaller sub-messages. These are mapped into columns of sensing matrices and concatenated to form the user’s packet. In order to stitch individually reconstructed sub-messages at the receiver, parity-check constraints from prior encoding by an outer code are exploited. The dynamic compressed sensing (DCS) allows to stitch each user’s sub-sequences without the need for prior encoding and the associated redundancy. In this paper, an unequal power DCS is proposed that allocates a specific power distribution to the columns of the sensing matrices. The power distribution is designed via the proposed state evolution (SE) analysis of the receiver processing. Simulation results demonstrate that the proposed unequal power DCS outperforms state-of-the-art URA in terms of energy-per-bit required to support a vast range of active user numbers, from medium to extremely large, unachievable by the current URA systems.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".