A Review paper on Milli-Meter Wave Communications
Bibliographic record
Abstract
From the literature review analysis, it is evident that NYUSIM, METIS, FRFT and Machine learning techniques are better for channel modelling than other techniques. Moreover, these techniques showcase the most optimum performance for one or more parameters, and thus they must be used in combination for an effective channel modelling system.Milli-meter wave communication systems usually work towards improving the throughput of the system. Channel modelling plays a vital role in achieving this task. Modelling channels requires a lot of complex mathematical analysis, and this analysis changes with changes in traffic patterns, number of communicating nodes, actual channel type and many other real-time parameters. Due to these changes, a static channel model is usually insufficient for real- time use cases. Our problem statement is to integrate machine learning into channel modelling, so that the prepared channel model incorporates most of the real-time changes in network parameters.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".