Query Diversity Schemes for Backscatter RFID Communications With Single-Antenna Tags
Bibliographic record
Abstract
Recently unitary query (UTQ) was introduced to exploit the query diversity for the multiple-input multiple output backscatter radio-frequency identification communications. Compared with the conventional uniform query (UFQ), UTQ can shift the multiantenna requirement for high performance from the tag end to the reader end. In this paper, we focus on the specific$L \times 1 \times N$backscatter channel where the tag has a single antenna and the reader has multiple antennas. This channel is of particular interest in practice. We first analytically studied the performance of UTQ by showing an equivalence between the$L \times 1 \times N$backscatter system employing UTQ and the$M \times L \times N$system employing UFQ, and by showing an optimal property of UTQ. Furthermore, we proposed another query diversity scheme, the query antenna selection (QAS), and derived its asymptotic symbol error rate in closed form. QAS achieves the same diversity order as that of UTQ and outperforms UTQ by a few decibels of selection gains, and QAS does not require to code the tag signals to achieve this performance. When compared with UTQ, QAS is not applicable when the tag has multiple antennas while instead it is a query diversity scheme particularly proposed for the backscatter channel with single-antenna tag, requiring less complexity for the tag.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".