Performance analysis of lattice-reduction algorithms for a novel LR-compatible K-Best MIMO detector
Bibliographic record
Abstract
Lattice Reduction (LR) has been proposed as a method to enhance the performance of MIMO detectors such as ZF, MMSE and V-BLAST. Until recently, the application of LR to the superior K-Best tree-search detection algorithm was not practical due to the significant increase in complexity of K-Best as a result of the distortion of tree symmetry caused by LR. However, in our recently published work we developed an innovative K-Best algorithm to accommodate tree-asymmetry with no additional complexity. In this work, we build on this result and perform a detailed analysis of the effect of various LR algorithms on the performance of LR-aided K-Best. We show that LLL and Seysen provide equivalent performance enhancement, however, LLL displays a lower computational complexity and thus is more suitable for LR-aided K-Best. In this work we also show that the application of LR to K-Best allows a large reduction of the K value while maintaining its near-ML performance. For 64-QAM MIMO detection, this leads to about 70% reduction in the complexity of the K-Best detector.
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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 | 0.001 | 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".