Bit error rate of TH-BPSK UWB receivers in multiuser interference
Bibliographic record
Abstract
It is well known that the Gaussian distribution is not an accurate model for approximating the probability density function (PDF) of the multiple access interference (MAI) in timehopping UWB (TH-UWB) systems. An exact theoretical model which explains the key features of the PDF of the MAI in THUWB systems is discussed. These features, which can be precisely anticipated by the proposed model include impulses, singularities, and the tail behaviour in the distribution of the MAI. The model reveals in quantitative terms why a Gaussian approximation for the MAI in TH-UWB systems is highly imprecise even in an environment with a large number of independent interferers. Based on the model obtained for the PDF of the MAI and exploiting the maximum a posteriori (MAP) receiver design rule, the optimal attainable BER performance of binary TH-UWB receivers in additive white Gaussian noise channels is numerically determined. The performances of some recently proposed UWB receivers are benchmarked against the optimal performance showing that some of them achieve near-optimal performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".