Generalized feedback detection for MIMO systems
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we present a unified detection framework for spatial multiplexing multiple-input multiple-output (MIMO) systems. We propose a generalized feedback detector (GFD) by modifying the classical feedback decoding algorithm for convolutional codes. When the three controlling parameters of the GFD vary, the diversity order of the GFD varies between 1 and N and the SNR gain also varies. Many previous MIMO detectors are special cases of our GFD. The connection between MIMO detectors and tree search algorithms is also established. To reduce redundant computations in the GFD, a shared computation technique is proposed using a tree data structure. The complexity of the GFD varies between those of maximum-likelihood (ML) detection and zero-forcing decision feedback detector (ZF-DFD). Our proposed GFD provides a flexible performance-complexity tradeoff.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 it