UAV-Assisted NOMA for Enhancing ISAC: A Deep Reinforcement Learning Solution
Bibliographic record
Abstract
This letter examines a NOMA downlink scenario where the UAV is deployed to concurrently assist in communication and sensing functionalities, empowering ISAC technology. In this regard and with the goal of maximizing the average achievable rate, we formulate an optimization problem to determine the UAV trajectory, and beamforming vectors at the transmitting base station and UAV, while at the same time satisfy the quality of service constraints for communication users and sensing for a moving target in terms of Cramer Rao bound (CRB) metric. The formulated problem showed to be non-convex and hard to be solved because of the high coupling between the variables as well as the randomness in the environment due to the channels variations and the mobility of the target. For that reason, we adopted a reinforcement learning algorithm, namely, deep deterministic policy gradient (DDPG) approach to deal with the aforementioned problems. Numerical results proved the superiority of the presented model over traditional UAV trajectory benchmarks and the ability to gain knowledge from the environment.
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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".