Integrated Sensing, Communication, and Computation With Adaptive DNN Splitting in Multi-UAV Networks
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Bibliographic record
Abstract
In this paper, we consider deploying multiple unmanned aerial vehicles (UAVs) to provide integrated sensing, communication, and computation (ISCC) services. During serving communication users, each UAV also senses targets and collaborates with the edge server to run a deep neural network (DNN) model to process the obtained sensing data for target classification. Considering that applying the fixed collaborative computation configurations for the UAVs and edge server cannot adapt to various task latency requirements and dynamic network conditions, we propose to adaptively split the DNN into two parts and execute them on the UAV and the edge server separately to realize flexible collaborative computation. We aim to maximize the average sum rate of users by jointly optimizing the user association, target assignment, DNN splitting, transmit beamforming, computation resource allocation, and UAVs’ locations, subject to the latency and accuracy requirements of sensing tasks. We apply alternating optimization algorithm to solve this complicated non-convex optimization problem. Specifically, the problem is decomposed into four subproblems, and the matching-based method, penalty dual decomposition, and successive convex approximation are leveraged to solve them. Finally, simulation results demonstrate the superiority of the proposed adaptive DNN splitting scheme and the effectiveness of the proposed algorithm.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it