Optimized cell ordering for multiuser macrodiversity detection with the conditional metric merge algorithm
Bibliographic record
Abstract
We consider maximum likelihood (ML) multiuser detection (MUD) in microdiversity. Unlike microdiversity, where diversity antennas are collocated, microdiversity employs widely spaced antennas. The sets of users seen by different antennas are in general different, but may be overlapping. From a computational perspective, the microdiversity ML-MUD problem is poorly structured, and risks becoming exponentially, complex in the total number of users. The conditional metric merge (CMM), a recently developed algorithm, dramatically reduces the computation load by exploiting the partial overlaps of user sets, without sacrificing the ML optimality of decisions. However, the CMM computation load still depends on the order of processing the antennas. This paper therefore presents a "meta-algorithm" to determine a sequence of processing antennas in CMM that has the lowest, or almost lowest, computation load. Even in configurations of a few cells, the sequence in which we process the antennas has a great impact on the required calculation, and the improvement gained by the algorithm is important. As with the original CMM algorithm, the ordering algorithm is applicable to both wideband and narrowband systems.
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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 | 0.001 |
| 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".