Indoor wireless infrared channel characterization by measurements
Bibliographic record
Abstract
Use of infrared light for indoor wireless communications has received considerable attention recently. We present results obtained from a large set of measurements performed at the University of Ottawa. We investigate impacts of receiver rotation and shadowing on the properties of indoor infrared channels. This paper contains a description of a measurement system developed and used throughout the experiments. Measurement results are used to find and present methods to estimate variations of channel path loss for small changes in the receiver direction, using statistical techniques. This would be useful for generating samples of channel path loss for system performance simulations and modulation analysis. Using the measurement results, it is shown that variations of channel path loss are smooth and a simple curve-fitting algorithm can be used to accurately interpolate intermediate values. It is also shown that for a receiver changing its elevation angle from 0/spl deg/ to 180/spl deg/, five points along the entire path are sufficient to predict variation of channel path loss for the entire rotation range. The results also demonstrate a correlation between the channel delay spread and channel path loss for both diffuse and line-of-sight configurations. A simple formula can provide an estimate of channel delay spread for a known path loss of a given configuration. It is demonstrated that variations of channel path loss for small changes of receiver rotation can be described by a gamma distribution. This enables generation of samples of channel path loss under general conditions for system-level simulation algorithms. Measurements have also been performed to investigate effects of shadowing on indoor infrared channel characteristics. Some important parameters that specify the impact of shadowing on the channel characteristics are included in the measurement plans. Variations of channel path loss due to shadowing and due to these parameters are investigated and detailed results are reported. It is shown that shadowing affects the channel delay spread. For the measured shadowing results, it is shown that channel path loss and delay spread are correlated and their relationship is provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".