Multiuser detection based MAC design for Ad Hoc networks
Why this work is in the frame
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Bibliographic record
Abstract
Recent technological advances in code division multiple access (CDMA) with multiuser detection (MUD) allow to consider this technology for future wireless Ad Hoc networks. Due to the fundamentally different physical layer architecture, application of MUD in Ad Hoc networks requires novel approaches for medium access control (MAC) and scheduling mechanisms in order to take advantage of the new features. This paper proposes a new MAC and scheduling paradigm which addresses three design issues: distributed dynamic code assignment that avoids code collision, distributed scheduling scheme that provides fairness among contending nodes, and organization of the data transmission based on multiuser detection. Simulation model is used to verify the performance gains from the increased spectrum reuse due to multiuser reception and from the reduced interference due to multiuser detection. This is done by comparisons with existing MAC paradigms, such as CSMA/CA and parallel CDMA.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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