Path Loss Modeling of RFID Backscatter Channels With Reconfigurable Intelligent Surface: Experimental Validation
Bibliographic record
Abstract
In the realm of radio frequency identification (RFID) technology, the integration of reconfigurable intelligent surfaces (RISs) has opened up new possibilities for real-time remote data capturing and seamless connectivity. By manipulating the electromagnetic properties of the environment, RIS enables the control of electromagnetic wave propagation and allows for virtual line-of-sight (LOS) in cases where physical LOS is blocked. This has tremendous implications for the future of RFID applications, particularly with the emergence of chipless RFID technology. In this regard, this paper develops free-space path loss models for RIS-assisted RFID wireless communications. The proposed models in this study have taken into account several crucial physical factors, including tag radar cross-section (RCS), the physical properties of the RIS, and the radiative near-field/far-field effects of the RIS. To further validate the theoretical findings, we have conducted experimental measurements using a fabricated RIS. Numerical simulations were also utilized to validate the models and verify our findings. The channel measurements have demonstrated good agreement with the proposed path loss models, further bolstering the potential of RIS-assisted RFID wireless communications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".