Bibliographic record
Abstract
Granger Causality analysis originated in the field of econometrics is used as a time series analysis tool based on vector auto-regression, and its phased generalized transfer entropy (TE), which is based on conditional co-information in information theory, has been widely used in data analysis in recent years. In this paper, we forecast the Fifth-Generation (5G) channel based on the Granger causality and transfer entropy, then use the water filling algorithm to allocate power for the forecasted 5G channel. In the first part of the paper, we use the Granger causality test to verify the Granger causality correlation of two random 5G channels and ensure that the two channels can be forecasted using the Transfer Entropy method. In the second part, we use transfer entropy to forecast two channels and verify the accuracy of the forecasted channels using Root Mean Square Error (RMSE) and Cramer-Rao Lower Bound (CRLB). Finally, we use the Inverse Water-Filling (IWF) algorithm to perform the power allocation for the forecasted channels and compare it with the Equal Gain (EG) algorithm. The simulations further validate our theoretical results.
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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 | 0.001 |
| 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 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".