Physical Layer Security of Cognitive Ambient Backscatter Communications for Green Internet-of-Things
Bibliographic record
Abstract
The future sixth generation (6G) wireless communication networks will face the challenges of large-scale connections green communication. To meet these requirements, cognitive ambient backscatter communication (C-AmBC) has been proposed as a new spectrum paradigm for the green Internet-of-Things (IoT) with stringent energy and spectrum constraints, in which the backscatter device (BD) can achieve communications by simultaneously sharing both spectrum and radio-frequency (RF) sources. However, due to the broadcasting nature of wireless communication channels, BD is vulnerable to eavesdropping from unlicensed eavesdroppers. To address this, this paper proposes a framework of C-AmBC networks in the presence of an unlicensed eavesdropper. Specifically, we investigate the reliability and security of the proposed framework by invoking the outage probability (OP) and intercept probability (IP) with analytical derivations. In addition, the asymptotic behaviors are conducted for the OP in the high signal-to-noise ratio (SNR) regime and IP in the high main-to-eavesdropper ratio (MER) regime. Extensive analytical and computer simulated performance evaluation results show that: 1) when the considered system is under high SNR, the OP of the legitimate user and BD tends to be a non-zero fixed constant, indicating that the existence of error floors for the diversity orders; 2) the performance trade-off of reliability and security can be optimized by adjusting various parameters of the considered system; 3) with the increase of MER, the security of the legitimate user increases, while that of BD decreases.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".