Physical-Layer Secret Key Generation via CQI-Mapped Spatial Modulation in Multi-Hop Wiretap Ad-Hoc Networks
Why this work is in the frame
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Bibliographic record
Abstract
Providing security guarantee is a critical concern in the ad-hoc networks relying on multi-hop channels, since their flexible topology is vulnerable to security attacks. To enhance the security of a spatial modulation (SM) assisted wireless network, various SM mapping patterns are activated by random channel quality indicator (CQI) patterns over the legitimate link, as a physical-layer secret key. The SM signals are encrypted by random mapping patterns to prevent eavesdroppers from correctly demapping their detections. This secret key is developed for multi-hop wiretap ad-hoc networks, where eavesdroppers might monitor all the transmitting nodes of a legitimate link. We substantially characterise the multi-hop wiretap model with receiver diversity techniques adopted by eavesdroppers. The security performance of the conceived scheme is evaluated in the scenarios where eavesdroppers attempt to detect their received signals using maximal-ratio combining or maximum-gain selection. The achievable data rates of both legitimate and wiretapper links are formulated with the objective of quantifying the secrecy rates for both Gaussian-distributed and finite-alphabet inputs. Illustrative numerical results are provided for the metrics of ergodic secrecy rate and secrecy outage probability, which substantiate the compelling benefits of the physical-layer secret key generation via CQI-mapped SM.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it