An N-Selective MRC Rake Receiver with LMS Adaptive Equalizer for UWB Systems
Bibliographic record
Abstract
This paper proposes a receiver to reduce 181 for high speed UWB transmission. The structure consists of an N-selective maximum ratio combiner (MRC) rake receiver and a least-mean-square (LMS) equalizer. This sub-optimal rake receiver processes only a subset of the available resolved multi-path components. A sliding correlation algorithm is used to obtain parameters for the N strongest paths. The LMS equalizer is designed to assist the receiver to reduce ISI. Benefit of this adaptive scheme is the equalizer does not need to know parameters of the entire multi-path component. A short training period with known information sequence is required to initially adjust the tap weights. The equalizer acts as a linear filter to suppress ISI. Simulation results for N=8, 16, and 32 at different values of SNR show significant improvement in BER. There is only a small trade off in system complexity to obtain such gain
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".