Pulse shaping for cognitive ultra‐wideband communications
Bibliographic record
Abstract
Abstract Cognitive ultra‐wideband (C‐UWB) systems have recently received much attention because the huge bandwidth of ultra‐wideband (UWB) systems can better exploit the advantages of cognitive radio (CR) systems. Dynamic spectrum access (DSA) is a key technique in CR systems to implement dynamic spectrum change and can be easily implemented by changing the transmitted pulse shape in a C‐UWB communication system. In this paper, we propose an orthogonal expansion based pulse shaping method to implement DSA and to compensate for antenna distortion, which uses the orthogonal Hermite functions as the orthogonal basis. In order to eliminate the direct current (DC) component existing in even orthogonal Hermite functions and to reduce the computational complexity, two modified methods and a simplification procedure are also proposed. Our results indicate that the proposed orthogonal expansion based pulse shaping methods have a much lower computational complexity than the semi‐definite programming (SDP) method, while achieving a high power efficiency. Furthermore, we demonstrate that the distortion caused by the antenna effects can also be compensated during the pulse shaping process and a better signal‐to‐noise ratio (SNR) can thus be achieved. Therefore, the proposed method is very suitable for practical application in C‐UWB communications, in which the spectrum environment changes rapidly. Copyright © 2009 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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".