A proposed enhanced scheme for the dynamic frequency hopping performance in the IEEE 802.22 standard
Bibliographic record
Abstract
Abstract This paper presents an integrated scheme for the dynamic‐frequency‐hopping (DFH) technique provided in literature for the IEEE 802.22 standard supporting wireless regional area networks (WRANs). The performance of DFH is analyzed thoroughly for various channel models and for a multiple‐input multiple‐output systems. The core of this research is based on the coexistence of digital terrestrial TV broadcasting and the WRANs in the TV white space. The proposed technique aims at protecting the incumbent users from interfering with the cognitive broadband access in the TV spectrum. In order to achieve this, spectrum sensing is performed in the intended working channel in DFH while spectrum monitoring with an energy‐ratio (ER) algorithm is applied during the WRAN data transmission in the working channel. Hence, in the DFH‐ER algorithm, the reappearance of a digital terrestrial TV signal in a band occupied by the WRANs would be detected immediately. This will provide interference free performance for the licensed signal as well as reliable data transmission for the unlicensed ones. Both analyses and simulation results of the proposed DFH‐ER technique compared with the conventional DFH scenario exemplify the enhancement of the WRAN data transmission while protecting the digital terrestrial TV users. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".