Adaptive algorithms for group interference suppression for differential spatial multiplexing
Bibliographic record
Abstract
Recently, we have proposed a novel differential space-time (DST) architecture (Cheung, S.K. and Schober, R., IEEE Wireless Commun. and Networking Conf., WCNC, 2004) which is much more robust against carrier phase variations than corresponding coherent ST schemes (Tarokh, V. and Lo, T.K.Y., 1998; Tarokh et al., 1999; Su, H.-J. and Geraniotis, E., 2002). To achieve a linear decoding complexity, we employ group interference suppression (GIS) and subsequent decision-feedback differential detection (DF-DD) at the receiver. The optimization of the GIS filters requires a generalized eigendecomposition of two correlation matrices. In order to reduce the computational complexity, we devise three efficient adaptive algorithms for generalized eigendecomposition. These algorithms are shown to have excellent convergence properties, and preserve the robustness of the proposed DST scheme against carrier phase variations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".