RIS-Assisted Wireless Link Signatures for Specific Emitter Identification
Bibliographic record
Abstract
As one of the sensing tasks for integrated sensing and communications (ISAC), location distinction based specific emitter identification (SEI) plays an important role in location based services. In this paper, we propose a reconfigurable intelligent surface (RIS)-assisted SEI system, in which the legitimate emitter installs an RIS to customize the wireless link signature by controlling the ON-OFF state of RIS. Specifically, we consider the worst-case that the legitimate and a suspicious emitter are in the same spatial location. The received signal strength (RSS) of the specific emitter is adopted to analyze the feasibility of the proposed system. Then, we derive the statistical properties of this wireless link signature, and find the interesting insights about the phase-shift matrix configuration and the signal-to-noise-rate (SNR) gain, which showcase the huge potential of the proposed system on the integrated communications and security (ICAS) design in the near future. Afterwards, we derive the optimal detection threshold in the context of the presented metrics. Next, considering the acquisition difficulty of the RSS samples of the suspicious emitter, we use a one-class support vector machine (OC-SVM) to identify the specific emitter. Finally, the actual feasibility of the proposed system is verified via proof-of-concept experiments. The experiment results show that there are 76% and 99% performance improvements for the test statistic based and the OC-SVM based RIS-assisted SEI, 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".