MU-MIMO precoding for VLC with imperfect CSI
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Bibliographic record
Abstract
This paper investigates the performance of different precoding schemes for a multi-user MIMO VLC system with channel estimation errors, an assumption that is commonly neglected in the literature. In particular, dirty paper coding, channel inversion, and block diagonalization, are considered for interference mitigation under imperfect channel state information. The impact of the variation of the beam angles of the transmitters and the field of view (FOV) of the receivers on the system performance is also examined. Simulation results reveal that, dirty paper coding provides the best performance under perfect channel state information (CSI). However, under imperfect CSI, suboptimal linear precoding schemes will give better performance. Furthermore, tuning the transmitting angles and the FOVs can significantly improve the system performance.
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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 | 0.000 | 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