Equalizer complexity/performance trade-offs for high data-rate IR-UWB linear receivers in multipath channels
Bibliographic record
Abstract
We investigate bit rates above 500 Mb/s rate for impulse radio ultra-wideband (IR-UWB) communications. UWB channels exhibit rich multipath leading to intersymbol interference (ISI) at these bit rates. Previous investigations studied decision feedback equalizers (DFE) for moderate bit rates (100 Mb/s and lower).We examine the effectiveness of this solution when ISI is more severe, and compare performance and complexity to that of a Viterbi algorithm (VA) equalizer with a limited (suboptimal) number of states. We consider a complete UWB link composed of a pulse transmitter, antennas, true UWB multipath channel measurements, and a linear receiver. We examine equalizer memory requirements for reliable Gb/s IR-UWB transmission for line-of-sight (LOS) channels. Non-line-of-sight (NLOS) channels are also investigated, however at lower speeds. Bit error rate (BER) simulations show that equalization is effective to varying degrees. The trade-offs in complexity vs. performance of the VA versus the DFE are 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.000 |
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".