Resource Allocation for Adaptive Beam Alignment in UAV-Assisted Integrated Sensing and Communication Networks
Bibliographic record
Abstract
Due to the high dynamic of unmanned aerial vehicle (UAV), the beam of UAV-mounted aerial base station (ABS) is difficult to align with ground users (GUs) and macro-cell base stations (MBSs), thereby reducing the communication rate. Towards this end, the channel state information of communication is used to assist onboard radar of ABS to sense the locations of GUs and MBSs for beam alignment to increase communication rate. To clarify the mechanism of mutual assistance between sensing and communication, we first derive the fundamental communication rate lower bound of integrated sensing and communication by utilizing the Cramér-Rao Bound. We find that the sensing power, sensing time, and transmit power between GU-ABS and ABS-MBS mutually influence the bounds of their communication rates with the shared frequency between sensing and communication. Accordingly, the maximizing communication rate problem is established by jointly optimizing transmit power, sensing power, and sensing dwell time allocation, which is decoupled into GU-ABS and ABS-MBS resource allocation subproblems. To reduce the computation complexity, a deep reinforcement learning based algorithm is proposed to solve this problem to replace the successive convex approximation technique. The simulation results demonstrate that the proposed approach is effective in maximizing the communication rate.
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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) | 0.001 | 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".