Bibliographic record
Abstract
The Bell-Labs layered space-time (BLAST) architecture is a simple and efficient multi-antenna coding structure that can achieve high spectral efficiency (Foschini, G. and Gans, M., Wireless Personal Commun., vol.6, p.311-35, 1998). Many BLAST detectors require more receiver antennas than transmitter antennas. We propose a novel turbo-processing BLAST detector based on a group detection strategy that can operate in systems with fewer receiver antennas than transmitter antennas. A maximum a posteriori (MAP) decision is made using a group of transmitted symbols and the remaining signal contribution is treated as interference. The interference is characterized as a non-zero mean colored noise source that is whitened before a decision is made. The proposed detector, the group MAP (GMAP) detector, is a generalization of both the MAP detector and the turbo-processing minimum mean squared error (MMSE) detector (Sellathurai, M. and Haykin, S., 2002; Abe, T. and Matsumoto, T., 2001). A novel grouping algorithm is proposed for the GMAP detector. Simulation is used to compare the GMAP detector with the MAP detector and MMSE detector.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".