PilotScatter: High-Throughput OFDM Backscatter via Pilot Tones
Bibliographic record
Abstract
Backscatter is an emerging ultra-low-power wireless communication technology for Internet-of-Things. However, as the widely used modulation scheme in OFDM systems, the combination of QAM and WiFi backscatter is not very satisfactory. To deploy QAM in the WiFi backscatter system, we propose PilotScatter, the first OFDM backscatter system supporting both 16-QAM modulation and non-redundant coding. These changes significantly improve the throughput of PilotScatter over previous backscatter systems. The key insight of PilotScatter is the use of pilot tones and differential demodulation. By modifying the phase and amplitude of pilot tones of the carrier signal, PilotScatter can modulate tag data on ambient WiFi with a 16-QAM scheme. At the receiver, PilotScatter uses a differential algorithm to demodulate tag data. It makes PilotScatter achieve 16-QAM demodulation without relying on ambient WiFi data and past symbols. We prototype PilotScatter using FPGAs, commodity radios, and USRPs. Comprehensive evaluations demonstrate that PilotScatter achieves up to 932.22 kbps throughput for WiFi 802.11g, and the backscatter communication range (Tag-to-Rx) is up to 19 m in Line-of-Sight (LoS) and 16 m in Non-Line-of-Sight (NLoS). Compared with the symbol-level backscatter research, PilotScatter has 7.49x and 3.78x goodput gains over MOXcatter and RapidRider, respectively.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".