Ultra wideband technologies coexistence in Nakagami-<i>m</i>fading channels
Bibliographic record
Abstract
The wide spectrum of ultra wideband (UWB) communications makes it inevitable to consider strategies for avoiding and mitigating interference from narrowband wireless systems such as GPS, UMTS, and WLAN, or other UWB wireless technologies. In this paper, we provide a performance analysis of multiband orthogonal frequency division multiplexing (MB-OFDM) UWB in the presence of binary phase-shift keying time-hopping (BPSK-TH) UWB or BPSK-DS UWB interfering transmissions under Nakagami-m fading. In the bit-error rate (BER) analysis, several UWB interferers are considered to affect the MB-OFDM signal. A Gaussian approximation is considered for the UWB interferers and used in the analysis of the BER performance of the MB-OFDM UWB system. The Nakagami-m distribution is applied to characterise the amplitude of the fading channels for both the reference signal and the interference signals. Furthermore, a waveforming technique is considered for mitigating the effect of interference and its efficiency is illustrated in terms of BER improvement. Numerical and simulation results are provided and compared for different coexistence scenarios.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".