Device Activity and Embedded Information Bit Detection Using AMP in\n Massive MIMO
Bibliographic record
Abstract
Future cellular networks will support a massive number of devices as a result\nof emerging technologies such as Internet-of-Things and sensor networks.\nEnhanced by machine type communication (MTC), low-power low-complex devices in\nthe order of billions are projected to receive service from cellular networks.\nContrary to traditional networks which are designed to handle human driven\ntraffic, future networks must cope with MTC based systems that exhibit sparse\ntraffic properties, operate with small packets and contain a large number of\ndevices. Such a system requires smarter control signaling schemes for efficient\nuse of system resources. In this work, we consider a grant-free random access\ncellular network and propose an approach which jointly detects user activity\nand single information bit per packet. The proposed approach is inspired by the\napproximate message passing (AMP) and demonstrates a superior performance\ncompared to the original AMP approach. Furthermore, the numerical analysis\nreveals that the performance of the proposed approach scales with number of\ndevices, which makes it suitable for user detection in cellular networks with\nmassive number of devices.\n
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".