Bibliographic record
Abstract
The recently approved IEEE 802.15.4a standard defines an ultra-wideband (UWB) based physical layer using concatenated coding with mixed binary phase-shift keying and binary pulse-position modulation (BPSK-BPPM) and direct-sequence spreading with time hopping. The concatenated code consists of an outer Reed-Solomon (RS) and an inner convolutional code, and the coding and modulation are combined such that both coherent and noncoherent receiver architectures are supported. In this paper, the error-rate performance of IEEE 802.15.4a compliant UWB radios is investigated. To this end, semi-analytical expressions for the bit-error rate (BER) and frame-error rate (FER) of the coded UWB system are derived. The presented framework is comprehensive in that (i) different methods for generating reliability information (i.e., decoding metrics), (ii) the effects of suboptimal multipath combining, and (iii) coherent and noncoherent reception methods are included. Furthermore, a particularly suited errors-and-erasures RS decoding scheme is devised. The evaluation of the error-rate expressions together with simulation results for realistic UWB channels show that (i) the error-rate approximations are tight over wide ranges of BER and FER, (ii) symbol-wise metrics are clearly advantageous over bit-wise metrics for decoding of the convolutional code, (iii) combining the 5 to 10 strongest multipath components approaches the performance of full combining within 1-2 dB for residential and 3-5 dB for outdoor UWB environments, and (iv) the simplicity of noncoherent detection comes at loss of more than 10 dB in signal-to-noise ratio compared to coherent detection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".