Vector Precoding with MMSE for the Fast Fading and Quasi-Static Multi-User Broadcast Channel
Bibliographic record
Abstract
Vector precoding is arguably the best form of precoding for the multi-user multiple input multiple output (MIMO) broadcast channel. However, conventional vector precoding schemes are designed to minimize the transmit energy, which is suboptimal in terms of the received signals mean square error (MSE). This paper proposes modifications to vector precoding to overcome this shortcoming. Improvements of about 2 dB are realizable in a fast fading environment. Also, conventional vector precoding schemes usually result in unbalanced levels of interference, resulting in a poor performance for some users. Noting the above, another improvement is proposed by directly minimizing the bit error rate rather than the MSE. This improvement adds another 1 dB of gain, resulting in an overall gain of 3 dB in a quasi static fading environment. These improvements are also applied to Tomlinson-Harashima precoding with similar results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".