Cooperative Trajectory Planning and Resource Allocation for UAV-Enabled Integrated Sensing and Communication Systems
Bibliographic record
Abstract
The flexibility and controllable mobility of unmanned aerial vehicles (UAVs) render them easier to become aerial platforms carrying out integrated sensing and communication (ISAC) functionality, and the cooperation among multiple UAVs is a promising way to achieve simultaneous multi-static radar sensing and coordinated multiple point (CoMP) transmission, leading to an enhanced ISAC service. However, due to the intrinsically limited resources that UAVs can utilize, it is challenging to achieve performance improvement for dual purposes. Toward this end, in this paper, an orthogonal frequency division multiple access (OFDMA) UAV-enabled ISAC system is investigated, and a joint trajectory planning and resource allocation problem is formulated to minimize the Cramér-Rao lower bounds (CRLB) for target location estimation while guaranteeing the communication quality-of-service (QoS) constraints. The formulated problem is non-convex and difficult to solve in general, and we first decompose the original problem into three sub-problems and then propose the corresponding algorithms to obtain the optimal solutions efficiently. The extensive simulations demonstrate the convergence of the proposed algorithm and the performance improvement on the localization with different communication requirements compared to conventional techniques.
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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 | 0.000 |
| 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".