Secure 3D Directional Modulation Using Subarrays Based on Planar Frequency Diverse Array With Nonuniform Frequency Offsets
Bibliographic record
Abstract
Physical-layer security (PLS) is a new paradigm for secure communication without requiring secret key exchange and management. Moreover, PLS with frequency diverse subarray (FDSA) can better control information leakage in the angle-range domain, which mitigates the security weakness of the phased array caused by its lack of range resolution. In this paper, we propose a three-dimensional (3D) directional modulation (DM) using randomized radiation with FDSA for enhanced PLS, employing a planar array. In addition, nonuniform frequency offsets (FOs) are considered as FO configurations (FOCs) for FDSA to concentrate on the mainlobe and suppress the undesired sidelobes in 3D space, where logarithmically increasing FOC (L-FOC), Hamming window-based FOC (H-FOC), and piecewise trigonometric FOC (P-FOC) are introduced. Characterizing the process of selecting the random subsets for randomized radiation, we provide the exact analysis of the secrecy rate of the proposed scheme. Moreover, FOs applied to FDSA and the number of random subsets are optimized with a genetic algorithm (GA)-based optimization strategy. We evaluate the proposed schemes in terms of secrecy rate and vulnerable volume, where the simulation results verify our analysis and show that nonuniform FOCs are a more favorable choice for FDSA compared to uniform FOC (U-FOC).
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 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".