On the design of bandwidth efficient signalling for indoor wireless optical channels
Bibliographic record
Abstract
Abstract It is well known that indoor wireless optical channels are limited not only in transmitted optical power, but also in signalling bandwidth. This bandwidth constraint arises due to multipath dispersion in indoor settings as well as due to response time limitations of optoelectronic components. This paper presents an overview of theoretical and practical issues in the design of signalling for bandwidth constrained intensity modulated, direct detection wireless optical channels. A brief overview of the salient qualities of the wireless optical channel are presented to highlight the amplitude constraints which arise. A survey of modulation design is then presented which includes the review of a general technique to represent optical intensity modulation in a signal space and to construct optical intensity lattice codes. Results on the channel capacity of indoor wireless optical channels are surveyed and particular emphasis is placed on recently derived asymptotically exact bounds. The use of multiple emitters and receivers in wireless optical channels is also presented and particular emphasis is placed on techniques which exploit spatial dimensions to improve spectral performance. The paper concludes with some remarks regarding the status of the research area and suggestions for future work. Copyright © 2005 John Wiley & Sons, Ltd.
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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 | 0.005 |
| 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 | 0.001 | 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".