IEEE Access Special Section Editorial: Advances in Statistical Channel Modeling for Future Wireless Communications Networks
Bibliographic record
Abstract
Wireless communication technology, including both radio and optical frequencies, has become a critical aspect of modern life. The accurate description and understanding of wireless signals are of paramount importance. Statistical channel modeling, which provides an accurate characterization of the propagation channel, is essential for the system design and performance analysis of different applications. Recently, various types of new wireless communication systems have emerged, such as device-to-device (D2D), millimeter-wave (mmWave) and terahertz, massive multiple-input multiple-output (MIMO), and unmanned aerial vehicle (UAV) systems. However, traditional and well-established fading models, such as Rayleigh, Rician, and Nakagami-$m$, may not accurately model the random fluctuations of the received signal. There is a strong, credible body of evidence suggesting that the complex electromagnetic propagation phenomena encountered in new wireless systems should be taken into account by general and unifying physically based channel models.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.022 | 0.014 |
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".