Adaptive Denoising and Equalization of Infrared Wireless CDMA System
Bibliographic record
Abstract
Infrared has abundant, unregulated bandwidth enabling rapid deployment at low cost. However, safety limits on power emission levels (IEC825), large noise due to ambient lighting, and multipath dispersion remain as hurdles in diffused indoor environments. Especially, the high-frequency periodic interference produced by fluorescent lights is a major concern. Spread spectrum techniques enable low-power operation and noise rejection, at the expense of large processing gain. In this paper, we quantify the noise received and propose an adaptive FIR filter to jointly cancel the multipath dispersion and the fluorescent light noise in an infrared CDMA system. From analytical and simulation results, the adaptive filter significantly enhances the noise rejection capability of the CDMA system and tracks well the quasistationary indoor wireless channel. Our results show tenfold improvement in the BER for a given SNR and processing gain due to the adaptive filter. The filter also performs well in the multiuser environment.
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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 | 0.001 | 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".