Guided Wireless Technology for Near-Field Communication
Bibliographic record
Abstract
Guided wireless technology is an innovative approach that combines the strengths of guided waves and wireless communication. In traditional wireless systems, signals propagate through the air, where they are vulnerable to interference, attenuation, and jamming. Guided communication, in contrast, confines signals within a physical medium, significantly reducing interference and supporting higher data rates over longer distances. Guided wireless technology harnesses these benefits by creating guided wireless channels and offering a controlled pathway for electromagnetic waves. This work harnesses these benefits by focusing on the modeling of near-field communication through long connected arrays deployed in linear-cell environments. We then derive a circuit model for long array as an infinitely long dipole with multiple periodic feed points before approximating it with a finite array through open circuiting. Through our simulations, we show how the standing wave phenomenon is confirmed by the oscillations in spectral efficiency. We also demonstrate the capability of the LMMSE transmit beamformer in mitigating interference and minimizing the mean square error by adaptively allocating more power to the user experiencing the most severe channel attenuation, resulting in a more balanced variation of achievable rates across users.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".