Terahertz Band: The Last Piece of RF Spectrum Puzzle for Communication Systems
Bibliographic record
Abstract
Ultra-high bandwidth, negligible latency and seamless communication are envisioned as milestones that will revolutionize the way by which societies create, distribute and consume information. The remarkable expansion of wireless data traffic has advocated the investigation of suitable regimes in the radio spectrum to satisfy users' escalating requirements and allow the exploitation of massive capacity and massive connectivity. To this end, the Terahertz (THz) frequency band (0.1-10 THz) has received noticeable attention in the research community as an ideal choice for scenarios involving high-speed transmission. As such, in this work, we present an up-to-date review paper to analyze key concepts associated with the THz system architecture. THz generation methods are first addressed by highlighting the recent progress in the devices technology. Moreover, the recently proposed channel models available for propagation at THz band frequencies are introduced. A comprehensive comparison is then presented between the THz wireless communication and its other contenders. In addition, several applications of THz communication are discussed taking into account various scales. Further, we highlight the milestones achieved regarding THz standardization activities. Finally, a future outlook is provided by presenting and envisaging several potential use cases and attempts to guide the deployment of the THz frequency band.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".