Optimal MISO UWB pre-equalizer design with spectral mask constraints
Bibliographic record
Abstract
In this paper, we propose a novel optimization-based pre-equalization filter (PEF) design for multiple-input single-output (MISO) direct-sequence ultra-wideband (DS-UWB) systems with pre-Rake combining. The key feature in our design is that we explicitly take into account spectral mask constraints which are usually imposed by telecommunications regulation and standardization bodies. This avoids the need for an inefficient power back-off, which is necessary for existing pre-equalizer and pre-Rake designs that are designed solely based on average transmit power constraints. Simulation results confirm that the proposed PEF design leads to significant performance gains over UWB PEF structures without any explicit spectral mask considerations. Furthermore, the use of multiple transmit antennas is shown to provide substantial combining gains compared to single-antenna transmitter structures. We also investigate the impact of certain system and optimization parameters on the performance of the proposed PEF design.
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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 | 0.000 |
| 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) | 0.001 | 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".