Reduced Complexity Ordering in Subspace MIMO Detection Algorithms
Bibliographic record
Abstract
In this work, we propose a low-complexity near-optimal detection scheme for multiple-input multiple-output (MIMO) systems. Our proposed detector is based on techniques such as the minimum mean square error generalized decision feedback equalization (MMSE-GDFE) and conditional detection (or subspace detection). One column at a time is selected in order to perform the conditional detection for one transmit symbol through an exhaustive search over the constellation points and the remaining symbols are detected by a suboptimal detector. The QR decomposition (QRD) of the V-BLAST ordered channel matrix along with a column selection strategy is considered which preserves the relative ordering of the remaining submatrix columns and its upper triangular structure. We define two parameters which introduce a trade-off between the computational complexity of the preprocessing and detection stages of the detector. By increasing the number of optimally ordered columns of submatrices, the search size over the constellation points can be reduced. Consequently, by selecting appropriate values for the parameters, a near-optimal error performance can be achieved while the computational complexity is shifted from the detection stage to the preprocessing stage which is desirable for quasi-static channels where the channel is constant over a large block of symbols.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".