Throughput and Link Design Choices for Communication over LED Optical Wireless Channels
Bibliographic record
Abstract
The staggering growth in the demand for wireless bandwidth is putting stringent pressure on the Radio Frequency (RF) spectrum; high speed applications such as mobile phones or video streaming are demanding more bandwidth per single user while applications such as Internet of Things (IoT) are introducing more connection nodes to the network.Recently, Optical Wireless Communications (OWCs) using Infra-Red (IR) or visible light has shown great potential not only to support low to mid data rate required by the IoT but also to compete with the existing RF solutions in supporting growing data rate demands.Furthermore, OWC offers some great advantages compared to RF.Among other things, OWC offers license-free wide communication channels.Thanks to the fast growing solid-state Light Emitting Diodes (LEDs), OWC links with several hundreds of Mbps data rates have been already commercialized.To further boost the throughput, several issues need to be addressed.LEDs, particularly those used for Visible Light Communications (VLC), have a limited bandwidth, while above their 3 dB bandwidth, the roll-off is relatively gentle.If the modulation bandwidth would be limited to the 3 dB LED bandwidth, the throughput would be unacceptably constrained.Hence, effective communication systems need to optimize the use of bandwidth significantly above this 3 dB point, by employing techniques such as Orthogonal Frequency Division Multiplexing (OFDM).OFDM fine-tunes the amount of power and constellation as a function of the channel response over different frequencies.Various power and bit loading strategies have been proposed and simulated in literature, but their performance was not captured in expressions.This dissertation derives these for optimal waterfilling, uniform and pre-emphasized power loading for the LED channel, that severely attenuates high frequencies.Uniform power loading fixes the amount of power on each sub-carrier while the signal constellation is determined by the signal quality at the receiver.Preemphasis, on the other hand, fixes the signal constellation on all sub-carriers resulting a flat spectrum for the received signal.We also investigate the influence of practical discrete constellations and verify our new results experimentally.Interestingly, simple uniform loading only falls less than 1∼2% short of the throughi
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".