Designing Time-Hopping Ultrawide Bandwidth Receivers for Multiuser Interference Environments
Bibliographic record
Abstract
The multiple-user interference (MUI) in time-hopped impulse-radio ultrawide bandwidth (UWB) systems is impulse-like and poorly approximated by a Gaussian distribution. Therefore, conventional matched filter receiver designs, which are optimal for Gaussian noise, are not fully efficient for UWB applications. Several alternative distributions for approximating the MUI process and the MUI-plus-noise process in UWB systems are motivated and compared. These distributions have in common that they are more impulsive than the Gaussian approximation, with a greater area in the tails of the probability density function (pdf) compared to a Gaussian pdf. The improved MUI and MUI-plus-noise models are utilized to derive new receiver designs for UWB applications, which are shown to be superior to the conventional matched filter receiver. Multipath propagation is abundant in UWB channels and is exploited by a Rake receiver. A Rake receiver uses multiple fingers to comb the multipath rays with a conventional matched filter implemented in each finger. Rake structures utilizing the new receiver designs that are suitable for reception of UWB signals in multipath fading channels are provided. An optimal performance benchmark, based on an accurate theoretical model for the interference that fully explains the features of the MUI pdf, is also presented. Analysis and simulation results are shown for the novel receivers, which demonstrate that the new designs have superior performance compared to the conventional linear receiver when MUI is significant. Several adaptive receivers are shown to always match or exceed the performance of the conventional linear receiver in all MUI-plus-noise environments. Parameter estimation for the new receivers also is discussed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".