Dynamic Compressed Sensing Approach for Unsourced Random Access
Bibliographic record
Abstract
In compressed sensing (CS) approach for unsourced random access (URA), each user’s transmitted sequence consists of shorter sub-sequences encoded via sparse regression codes (SPARCs) over several consecutive sub-slots. The approximate message passing (AMP) technique is employed to decode the SPARCs. Stitching together each user’s sub-messages decoded over distinct sub-slots requires an outer parity-check code that adds redundancy bit interconnecting the sub-messages. This paper introduces a novel CS-based URA scheme which is free from the outer code. In the proposed scheme, the encoded sub-sequence of the first sub-slot acts as a temporary user identifier and also customises the sensing matrix used to encode the subsequent sub-messages. The technique allows each user to send several sub-messages (data streams) per sub-slot. Simulation results indicate that the proposed scheme outperforms the existing CS-based algorithms on Gaussian channel. It is also superior than the other state-of-the-art URA schemes when the number of active users exceeds 200. The proposed analysis framework closely predicts the obtained numerical results.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".