Amplitude-Coherent Detection for Optical Wireless Communications: Opportunities and Limitations
Bibliographic record
Abstract
The proliferation of ubiquitous computing applications created a multi-dimensional optimization problem that includes several conflicting variables such as spectral efficiency, complexity, power consumption, delay, and error probability. To relax the problem and provide efficient solutions, it was necessary to augment the currently overutilized radio spectrum with new frequency bands such as the optical spectrum, which can be used to off-load some of the traffic of certain applications. Therefore, this paper presents an efficient system design that uses amplitude-coherent (AC) detection to reduce OWC system's complexity, improve its reliability and spectral efficiency. More specifically, we use amplitude shift keying with orthogonal frequency division multiplexing (OFDM) at the transmitter, and AC detection at the receiver. The complexity reduction is achieved by using a low complexity detector, channel estimator, and peak-to-average power ratio (PAPR) reduction scheme. The spectral efficiency is achieved by using real data symbols with discrete cosine transform (DCT), which requires a subcarrier spacing that is 50% of the discrete Fourier transform (DFT), and does not require Hermitian symmetry to generate real-valued OFDM signals. Moreover, the derived channel estimator is blind, and the PAPR reduction scheme does not require a feedback overhead between the transmitter and receiver.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".