Generic BER Analysis of VLC Channels Impaired by 3D User-Mobility and Imperfect CSI
Bibliographic record
Abstract
Visible light communications (VLC) has emerged as a high-speed, low-cost, and green supplement for the existing radio frequency (RF) based infrastructures. However, the performance of VLC based systems is found to degrade significantly due to detrimental outages caused by non-negligible variations in the VLC channel-gain, that are jointly induced by radial user-mobility and random photodetector-orientation (together designated as 3D mobility in this letter). In addition to the 3D user-mobility mentioned above, the performance of VLC based systems is further limited by imperfect channel-state information (CSI). Such degradations in the VLC-link caused by the aforementioned factors necessitate the quantification of performance-metrics for further benchmarking/receiver-design. In this work, an analytical expression for bit-error rate (BER) is derived for a single LED indoor VLC system considering the radial user-mobility, random receiver orientation, and imperfect CSI altogether. Further, the derived BER expressions are validated using computer-simulations using typical VLC channel models from the literature. A close agreement between the analytical and the simulated BER is observed, which verifies the accuracy of the presented analysis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".