Pre-equalization for MISO DS-UWB systems with pre-Rake combining
Bibliographic record
Abstract
In this paper, we propose two novel pre-equalization schemes for multiple-input single-output (MISO) direct-sequence ultra-wideband (DS-UWB) systems with pre-Rake combining and symbol-by-symbol detection. The first scheme employs one pre-equalization filter (PEF) per transmit antenna, whereas in the second scheme, the simplified PEF (S-PEF) scheme, all transmit antennas share the same PEF. For both schemes the optimum finite impulse response (FIR) and infinite impulse response (IIR) PEFs are calculated based on the minimum mean squared error (MMSE) criterion. Our approach is sufficiently general to include also reduced-complexity versions of pre-Rake combining that employ a limited number of Rake fingers. We show that under certain conditions the S-PEF scheme achieves the same performance as the more complex PEF scheme. We also demonstrate that a single-input multiple-output (SIMO) DS-UWB system with post-Rake combining and MMSE post-equalization is the dual system to the considered MISO DS-UWB system with pre-Rake combining and MMSE pre-equalization. This uplink-downlink duality can be exploited for efficient calculation of the PEFs and for complexity reduction. Our simulation results show that the proposed PEF schemes achieve significant performance gains over pre-Rake combining without equalization, even if only short PEFs are employed.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".