Movable-Antenna-Aided Covert ISAC-NOMA Networks: Joint Antenna Positioning and Resource Allocation
Bibliographic record
Abstract
Integrated sensing and communication (ISAC) systems face critical security vulnerabilities when dual-functional waveforms are used for covert operations, as static antenna architectures inherently lack the spatial agility to harmonize communication reliability, covertness, and sensing accuracy. To address this challenge, we propose a novel movable antenna (MA)-assisted covert ISAC framework integrated with non-orthogonal multiple access (NOMA), where a multi-antenna ISAC base station (BS) dynamically serves two types of user-pairs, public pairs demanding high throughput and covert pairs requiring low-probability-of-detection transmissions, through shared communication-and-sensing (C&S) beams. Each user, in a pair, is equipped with a single MA to enable dynamic spatial reconfiguration and obscure covert signals from warden. To achieve these goals, we minimize the Cramér-Rao bound (CRB) for target estimation while satisfying communication rate requirements, covertness constraints, as well as power allocation and successive interference cancellation (SIC) feasibility. The formulated problem involves highly coupled variables of the transmit beamforming vectors, the NOMA power coefficients, and the MA position vectors of the users. The paper solves the resulting non-convex optimization problem using a block coordinate descent (BCD) algorithm that decomposes it into two subproblems: 1) beamforming and power allocation via successive convex approximation (SCA) augmented with the proper penalty correction step, and 2) MA position optimization using gradient-assisted SCA. Extensive simulations demonstrate significant gains and trade-offs over fixed position antennas and orthogonal multiple access benchmarks.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".