Integrated Sensing, Communication, and Computation With Adaptive DNN Splitting in Multi-UAV Networks
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".