A TH-UWB Receiver with Near-MUD Performance for Multiple Access Interference Environments
Bibliographic record
Abstract
The multiple access interference (MAI) in a time-hopping (TH) ultra-wideband (UWB) system is known to be non-Gaussian even when the system has a moderately large number of active users. Therefore, the conventional matched filter (CMF) receiver, the optimal structure for Gaussian noise which maximizes the signal-to-noise ratio (SNR) and minimizes the probability of detection error in the absence of non-Gaussian interference, is not necessarily optimal. We express and prove a stronger claim that even the outputs of the matched filters in the conventional UWB receiver can not provide a sufficient decision statistic for detecting the information bits transmitted by the desired user. Further, a novel TH-UWB receiver is introduced which uses a similar methodology to the optimal multiuser detection (MUD) algorithm for detecting the information bits. However, the complexity of this new receiver is much less than that of the optimal MUD algorithm. It employs only one matched filter instead of a bank of matched filters resulting in a simple and low-cost TH-UWB receiver. Simulation results show that the new receiver outperforms previous single-user TH-UWB receivers and achieves essentially the performance of the CMF receiver operating in a single-user system.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".