Perceptive Mobile Networks for Standalone and Cooperative UAV Surveillance
Bibliographic record
Abstract
The next-generation wireless network is perceived to integrate with sensing capability and evolve into the perceptive mobile network (PMN), enabling massive sensing-intensive applications. However, the sensing function will affect the communication performance in cellular networks. To study the sensing and communication performance of PMNs and their interactions, this paper investigates a millimeter-wave PMN with dual-functional base stations (BSs) for simultaneous detection of unauthorized unmanned aerial vehicles (UAVs) and user communication via the unified transmit signal and beamforming. We develop a system-level theoretical framework to investigate the sensing and communication performance of PMNs based on stochastic geometry, which captures the mutual interference and resource contention between the two functions and builds a foundation for the optimization of network configurations. In addition, by leveraging the collaboration of multiple BSs in PMNs, we propose a cooperative sensing strategy combining the monostatic and bistatic sensing processes to enhance the reliability of UAV surveillance. Simulation results verify the effectiveness of the proposed theoretical framework and demonstrate the benefits of cooperative sensing in UAV detection and communication performance, as compared with the standalone sensing by individual BSs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".