Reducing Phase Cancellation Effect with ASK-PSK Modulated Stamp in Augmented UHF RFID Indoor Localization System
Bibliographic record
Abstract
In this paper, we propose exploiting ASK-PSK modulated stamp in receiving path selection technique to lessen the phase cancellation effect in augmented ultra-high-frequency (UHF) radio frequency identification (RFID) indoor localization system (AURIS). In AURIS, a tag-like semi-passive RFID component (referred as sensatag) can capture backscatter signal of other proximal tags with presence of RF source. According to the principle of backscatter radio link, the received signal at sensatag antenna is the superposition of backscatter signal of tags and continuous carrier wave (CW) from RF source. However, due to phase difference between tag's backscatter signal and RF CW, the modulated backscatter signal could be cancelled. We refer this effect as phase cancellation effect. Exploiting the spatial diversity of dual-antenna's two receiving paths, the likelihood of phase cancellation occurrence could be reduced. But the developed technique with two co-operating paths is not energy efficient. Therefore, this paper proposes to inject a ASK-PSK modulated stamp sequence in the pilot tone of backscatter signal as a signature of phase cancellation for ASK modulated data frame, which could be identified by the receiving sensatag. With the knowledge of occurrence of phase cancellation, sensatag could activate the alternative receiving path. This technique fully exploits the space diversity of dual-antenna, and also reduces the power consumption by reducing one receiving path in operation. We demonstrate the performance of stamp based receiving path selection technique with data obtained from computer simulation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".