Enabling beam-scanning antenna technologies for terahertz wireless systems: A review
Bibliographic record
Abstract
Due to the exponentially growing global mobile data of wireless communications evolving from 5 G to 6 G in recent years, research activities of leveraging terahertz (THz) waves to obtain larger channel capacities have shown an ever-increasing pace and reached an unprecedented height than before. Historically, the past few decades have already witnessed much progress in THz generation and detection technologies, which have been recognized for a long time as the bottleneck preventing the THz waves from being tamed by human beings. However, the importance of developing advanced components such as antennas, transmission lines, filters, power amplimers, etc., which constitute the basic building blocks of a THz wireless system, should not be overlooked for the sake of exploiting the THz spectra for future advanced wireless communications, sensing and imaging applications. While producing a scannable highly-directive antenna beam proves to be indispensable in the period of microwaves, the significance of such functionality is more critical in the THz era, considering that THz waves have more intractable challenges such as the severity of free-space propagation losses, the susceptibility to atmospheric environments, and the unavailability of efficient signal sources. This article is structured under this background, which is dedicated to reviewing several enabling beam-scanning antenna concepts, structures, and architectures that have been developed for THz wireless systems. Specifically, we divide these THz beam-scanning solutions into four basic groups based on different mechanisms, i.e., mechanical motion, phased array, frequency beam-scanning, and reconfigurable metasurfaces.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".