Reliable WiFi Backscatter Communication in WiTAG
Bibliographic record
Abstract
WiFi backscatter systems offer the potential to provide low-powered WiFi-compatible communication. This technology is especially promising when coupled with low-power sensors to periodically communicate readings from IoT devices. WiTAG is an extremely attractive approach because it greatly reduces power consumption by avoiding the use of WiFi receivers or signal detectors while ensuring compatibility with existing WiFi infrastructure. WiTAG operates at the MAC layer by corrupting or not corrupting subframes (MPDUs) within a transmitted frame (A-MPDU). For example, corruption of an MPDU signals a 0 and non-corruption signals a 1. Because it eschews the use of receivers and signal detectors WiTAG is unable to sense when frames are being sent by nearby WiFi devices that it relies on for communication. \n \nIn this thesis, we describe the significant challenges that arise when formulating, transmitting, and reliably detecting and decoding messages transmitted from WiTAG. We design a message encoding framework to overcome these challenges. We show that although WiTAG relies on probabilities for overlapping a tag’s message with an A-MPDU it is possible to increase the odds of an overlap, thus increasing message rates. This permits the transmission of highly reliable messages in a relatively short period of time.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".