Joint Positioning and Data Communication Provisioning Using 5G Pattern Division Multiple Access (PDMA) Airborne
Bibliographic record
Abstract
Using UAVs (Unmanned Aerial Vehicles) to provide either precise positioning or high speed, low latency data communication service has recently emerged for a new class of 5G applications. However, the joint optimization of data communication and wireless positioning in a UAV airborne network is challenging because each service has different requirements. Communication services demand high throughput, while positioning services require the establishment of multiple connections simultaneously. Pattern Division Multiple Access (PDMA) is an innovative communication technique based on Non-orthogonal Multiple Access (NOMA), which facilitates the sharing of REs (Resource Elements) among multiple users. In this paper, we formulate the joint problem of user-to-UAV association, RE allocation, and transmission power control, aimed to improve the precision of positioning service while meeting the communication constraints. We propose both exact and a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm to solve this non-convex optimization. In addition, to address the problem of interference among users, we propose an attentional Multi-agent Deep Deterministic Policy Gradient (MADDPG) approach. Extensive simulations demonstrated that our proposed algorithm can achieve higher positioning accuracy than state-of-the-art solutions while serving more users.
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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 | 0.000 |
| 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".