Design and analysis of robust detectors for TH IR-UWB systems with multiuser interference
Bibliographic record
Abstract
In this letter, we design and analyze the performance of single-user-type non-linear detectors that are able to cope with the impulsive nature of multiuser interference (MUI) in timehopping impulse-radio ultra-wideband (TH IR-UWB) systems. We collectively refer to these detectors as "robust" detectors. We first propose two novel detectors and then derive semi-analytical expressions for the bit-error rate (BER) of TH IR-UWB with general robust detection. The evaluation of these expressions greatly facilitates the optimization of detector parameters and provides insight into the effects of MUI. A performance comparison shows that (1) robust detection significantly improves performance over conventional detection in the presence of MUI, (2) the parameters for various parametric robust detectors can be chosen to be constant over many transmission scenarios with only little performance degradation compared to using the optimal parameter value, and (3) the proposed two-term detector, which requires a modest amount of parameter estimation, achieves consistently the best performance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".