Bibliographic record
Abstract
Combined deployment of optical fiber technology and wireless networks has great potential for increasing the capacity and quality of service. By using radio-over-fiber (ROF) technology, the capacity of optical networks can be combined with the flexibility and mobility of wireless access networks without significant cost increment. The radio-over-fiber concept means to transport information over optical-fiber by modulating the light with the radio signal. This article discusses the effects of using fiber in conjunction with wireless local area network IEEE 802.11a standard (WLAN) to distribute RF signals. To achieve high throughput 802.11a LAN uses an orthogonal frequency division multiplexing (OFDM) based multicarrier wideband modulation technique. OFDM is one of the most favored modulation techniques in the WLAN scenario due to its efficient implementation and robustness against multipath and narrowband interference. One of the biggest drawbacks of OFDM is its high peak to average power ratio (PAPR). High PAPR of OFDM makes it unusable in nonlinear systems. In this article we discuss better ways to overcome the PAPR problem of the OFDM signal which will improve its performance in fiber.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".