Impact of TH‐UWB interference on MB‐OFDM UWB systems: interference modeling and performance analysis
Bibliographic record
Abstract
Abstract In this paper, the coexistence issue between multiband‐orthogonal frequency‐division multiplexing (MB‐OFDM) and time‐hopping ultra‐wideband (TH‐UWB) networks is widely analyzed. For this purpose, to study and model the TH‐UWB interference, an analytical framework which describes key features of the interference distribution is provided. The interference distribution is studied in the context of TH‐UWB's signaling parameters. Our results reveal that the interference distribution highly depends on its time‐hopping parameters. Therefore, choosing proper time‐hopping parameters leads to less destructive interferences. The Generalized Gaussian and the Symmetric‐ α ‐Stable (S α S) distributions are used to model the interference‐plus‐noise signal. The maximum likelihood and a characteristic function‐based regression‐type methods are adopted to estimate parameters of Generalized Gaussian and S α S distributions, respectively. Moreover, the interference channel effects on the impulsive behavior of the TH‐UWB signal is studied. It is shown that impulsive behavior of the faded interference signals highly depends on the channel time‐dispersiveness. Furthermore, an exact performance of a multiband‐orthogonal frequency‐division multiplexing system impaired by a TH‐UWB system is derived. The comparison of the analytical performance, the empirical simulation, and the approximation results show that both approximation methods are valid for low interference‐to‐noise‐ratio, while S α S provides a more accurate approximation for high interference‐to‐noise‐ratio. Copyright © 2015 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".