Massive device activity detection by approximate message passing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
User activity detection is a central problem in massive device communication scenarios in which an access point needs to detect active devices among large number of potential devices each transmitting sporadically. By exploiting sparsity in user activity, the detection problem can be formulated as a compressed sensing problem, thereby allowing the use of computationally efficient approximate message passing (AMP) algorithm for activity detection. This paper proposes an AMP-based user activity detector that accounts for the statistics of device geographic locations in a cellular network. The proposed scheme is based on a minimum mean squared error (MMSE) denoiser designed for specific wireless channel fading and path-loss distributions. This paper further provides an analytic characterization of the false alarm versus missed detection probabilities using state evolution for AMP. Simulation results show significantly improved detection threshold for the channel-aware denoiser as compared to standard soft threshold based AMP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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