Groupwise successive interference cancellation for MIMO communication systems
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a modified detection scheme which reduces the performance gap between the V-BLAST MMSE detection algorithm and the maximum likelihood (ML) detection. In V-BLAST detection algorithm error propagation due to unreliable decision feedback severely limits the system performance. Here, we propose a new detection scheme that reduces the destructive effect of error propagation to a great extent. In our proposed detection algorithm, we have used ML detection to jointly estimate two strongest sub-streams of the transmitted data. For this purpose, we find an optimum beam-forming matrix to minimize the power of cumulative noise and the interfering sub-streams. It is shown that the proposed scheme outperforms the conventional V-BLAST MMSE algorithm with moderate increase in computational complexity. Nevertheless, our study shows that the complexity of our proposed algorithm is almost negligible compared to maximum likelihood detection.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it