Physical Layer Security in Full-Duplex Millimeter Wave Communication Systems
Bibliographic record
Abstract
In this article, we investigate physical layer security in full-duplex (FD) millimeter-wave (mmWave) communication system, where an FD receiver generates the jamming signal at the destination to protect the confidential message. Secrecy rate and secrecy throughput are selected as metrics to characterize the secrecy performance. We analyze and derive closed-form expressions for secrecy metrics for both cases with the availability of global and partial channel state information (CSI), respectively. Specifically, we jointly design beamforming at the source and destination to enhance the secrecy performance to the greatest extent possible. With joint optimization of the power allocation ratio, between the source and destination, and the secrecy outage probability constraint, we can further obtain the maximum secrecy rate and secrecy throughput. This study determines that the optimal hybrid beamforming matrix for the jamming signal is a rank-1 matrix. Simulation results illustrate that our designed secrecy transmission scheme with FD jamming receiver outperforms conventional half-duplex (HD) methods even when residual self-interference (SI) is considered. As a result of adequately designing the beamforming of the jamming signal, the secrecy of FD mmWave system is no longer constrained by SI in the multi-antenna case. Furthermore, our results also find that there always exists an optimal pair of the power allocation ratio and secrecy outage probability constraint to maximize the secrecy throughput given a fixed transmit power.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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".