Efficient Chaotic Spreading Codes for DS-UWB Communication System
Bibliographic record
Abstract
Ultra wideband (UWB) technology is characterized by transmitting extremely short duration radio pulses. To improve its multiple access (MA) capability, UWB technology can be combined with traditional spread spectrum (SS) techniques. Existing spreading codes are only optimal in additive white Gaussian noise (AWGN) and their performance degrades in multipath fading and narrow band interference. In this paper, we propose the use of spreading codes obtained using a novel design methodology based on genetic programming (GP) and DNA computation for DS-UWB communications. The spreading codes obtained by this novel design methodology performs better than traditional spreading codes in both AWGN and multipath fading. In addition, spreading codes with desired spectral characteristics could be designed to minimize the mutual interference between the DS-UWB and co-existing narrowband systems. The proposed design methodology is attractive for spreading code in terms of performance and flexibility to design spreading codes for a specific design criteria
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".