Frequency-Domain Turbo Equalization and Multiuser Detection for DS-UWB Systems
Bibliographic record
Abstract
One of the major challenges in direct sequence-ultra wideband (DS-UWB) receiver design is intersymbol interference (ISI). Several equalization schemes to eliminate ISI in DS-UWB systems have been proposed in the literature. It was shown that frequency-domain (FD) equalization techniques can offer better trade off between performance and complexity compared to time- domain equalization schemes for DS-UWB systems on highly dispersive channels. In this paper, we derive low-complexity FD minimum mean square error turbo equalization schemes for single-user binary phase shift keying (BPSK) and quaternary bi-orthogonal keying (4BOK) DS-UWB systems. For multiuser DS-UWB systems, we combine FD turbo equalization schemes with soft interference cancelation to obtain multiuser FD turbo detectors. The bit error rate performance gain due to turbo detection is shown to be significant, particularly for multiuser DS-UWB systems.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".