Bibliographic record
Abstract
Precoding and equalization are important techniques used in MIMO systems to improve performance and mitigate interference. While this area is well researched in RF communications systems, little work has been done on precoding and equalization for optical wireless communication (OWC) systems. That is the focus of this chapter. In a MIMO system, precoding is applied at the transmitter to pre-process the signals before transmission, while equalization is performed at the receiver to compensate for channel effects and recover the original signals. Linear precoding techniques like zero forcing (ZF), minimum mean square error (MMSE) or advanced nonlinear precoding methods like maximum likelihood (MLH) or Tomlinson–Harashima precoding (THP) can be employed to map the data streams from multiple antennas to reduce interference among them. At the receiver, linear equalization techniques such as ZF orMMSE equalization can be applied to the received signals to separate the data streams and combat inter-symbol interference (ISI).
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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 | 0.000 | 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 | 0.001 |
| 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".