RapidRider: Efficient WiFi Backscatter with Uncontrolled Ambient Signals
Bibliographic record
Abstract
This paper presents RapidRider, the first WiFi backscatter system that takes uncontrolled OFDM WiFi signals, e.g., 802.11a/g/n, as excitations and efficiently embeds tag data at the single-symbol rate. Such design brings us closer to the dream of pervasive backscatter communication since uncontrolled WiFi signals are everywhere. Specifically, we show that RapidRider can demodulate tag data for each OFDM symbol while previous systems rely on multi-symbol demodulation. Further, we design deinterleaving-twins decoding that enables RapidRider to use any uncontrolled WiFi signals as carriers. We prototype RapidRider using FPGAs, commodity radios, and USRPs. Comprehensive evaluations show that RapidRider's maximum throughput is 3.92x and 1.97x better than FreeRider and MOXcatter. To accommodate cases where there is only one receiver available, we design RapidRider+ that can take productive data and tag data on the same packet. Results demonstrate that it can achieve an aggregated goodput of productive and tag data around 1 Mbps on average.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".