A packet-level model for UWB channel with people shadowing process based on angular spectrum analysis
Bibliographic record
Abstract
Ultra-wideband (UWB) wireless communication technologies have been proposed to support high data rate multimedia services in office or residential environments. Due to the low transmission power of UWB, the shadowing effect by moving people can considerably reduce the received signal quality and thus significantly degrade the quality of service (QoS) of on-going transmissions. An open issue is to build a simple model which captures the temporal variation of UWB channels and the packet error rate (PER) due to the people shadowing effect (PSE), which will be a useful tool for upper layer protocol performance analysis and simulation. This paper presents an analytical study of the PSE and the temporal variation of UWB channels induced by the motion of a person. First, we derive the angular power spectral density (APSD) of the indoor UWB channel impulse response (CIR), and the PSE in terms of signal power attenuation. Second, based on a two-dimensional random walk mobility model, the PER variation due to people shadowing is modeled as a finite-state Markov chain (FSMC). The investigation of APSD provides important insights on the spatial propagation characteristics of UWB signals. The proposed packet-level channel model can be conveniently incorporated into analytical frameworks and simulation tools for evaluating upper-layer protocols of UWB networks.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".