Joint Airborne Wireless Positioning and Communication Services With Pattern Division Multiple Access (PDMA)
Bibliographic record
Abstract
Unmanned Aerial Vehicles (UAVs) have recently emerged as a key component for various 5G applications, offering either precise positioning or high-speed, low-latency data communication services. However, delivering both wireless positioning and data communication at the same time using UAVs remains challenging due to the different requirements of each service. While data communication demands high throughput, positioning services necessitate the establishment of multiple connections simultaneously, but achieving high throughput for all the simultaneous connections remains challenging. Pattern Division Multiple Access (PDMA), which flexibly shares Resource Elements (REs) among multiple users, presents a promising solution to this issue. In this study, we investigate the joint provisioning of communication and positioning services in a UAV airborne network using PDMA. We propose a comprehensive approach that addresses the joint problem of user-to-UAV association, RE allocation, and transmission power control. Our goal is to enhance the precision of positioning services while satisfying communication constraints. To tackle this problem, we develop both an exact solution and a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm, tailored for this highly complex non-convex optimization problem. Extensive simulations demonstrate that our attentional MADDPG algorithm achieves higher positioning accuracy compared to state-of-the-art solutions and can efficiently address interferences, thereby improving both services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".