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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".