Curving THz wireless data links around obstacles
Bibliographic record
Abstract
Abstract A key challenge in millimeter-wave and terahertz wireless networks is blockage of the line-of-sight path between a base station and a user. User and environmental mobility can lead to blockage of highly directional beams by intervening people or objects, yielding link disruptions and poor quality of service. Here, we propose a solution to this problem which leverages the fact that, in such scenarios, users are likely to be located within the electromagnetic near field of the base station, which opens the possibility to engineer wave fronts for link maintenance. We show that curved beams, carrying data at high bit rates, can realize a link by curving around an intervening obstacle. We develop a model to analyze and experimentally evaluate the bandwidth limitations imposed by the use of self accelerating beams. We also demonstrate that such links employ the full aperture of the transmitter, even those portions which have no direct line of sight to the receiver, emphasizing that ray optics fails to capture the behavior of these near-field wave fronts. This approach, which is ideally suited for use at millimeter-wave and terahertz frequencies, opens vast new possibilities for wave front management in directional wireless networks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".