Visible Light Communication with Input-Dependent Noise: Channel Estimation, Optimal Receiver Design and Performance Analysis
Bibliographic record
Abstract
In this paper, single-input single-output (SISO) visible light communication (VLC) subject to signal-dependent shot noise (SDSN) is investigated. We discuss both channel estimation and data transmission. In the former, we introduce least square (LS) and maximum likelihood (ML) estimators. Moreover, we derive the Cram´er–Rao lower bound (CRLB) of the channel estimation error. With regard to data transmission, we present optimal and sub-optimal receiver designs and discuss their bit error rate (BER) performance. In particular, a closed-form expression of the BER is derived for a sub-optimal receiver using the on-off-keying (OOK) modulation technique. An approximated expression is then derived for the optimal receiver. Our analysis shows that the CRLB performance does not have a linear relation with the SDSN, thermal noise, or the fading channel. On the other hand, the SDSN has a dramatic effect on the channel estimation error bound, and it can degrade the BER performance. Increased performance degradation can also be noticed due to the joint effects of the channel estimation error and SDSN.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".