Meeting Stringent QoS Requirements in AAV-Assisted Networks: Resource Allocation and AAVs Positioning
Bibliographic record
Abstract
Providing quick and reliable emergency communication in situations of natural disasters or unforeseen incidents may be crucial. In such situations, traditional communication infrastructure, such as ground-based wireless base stations, may become temporarily damaged or unavailable to support emergency teleoperations. Considered a promising solution, autonomous aerial vehicles (AAVs) can be deployed as flying base stations or relays to provide fast and reliable communication between physicians and remote robots in both uplink and downlink directions, while meeting strict transmission requirements. This paper addresses the joint optimization problem of AAV positioning and resource allocation in AAV-assisted wireless networks to minimize the number of deployed AAVs, all while satisfying stringent transmission quality demands. The formulated problem is a non-convex mixed-integer programming problem, which we prove to be$\mathcal {NP}$-hard. We first develop efficient greedy and metaheuristic genetic algorithms. Then, we propose an efficient centralized deep reinforcement learning solution based on the deep deterministic policy gradient (DDPG), where the agent learns optimal AAV positions and resource allocation. Simulation results demonstrate that the greedy solution closely matches the performance of both the genetic and deep reinforcement learning approaches, with a significant reduction in computational complexity. Furthermore, the results highlight the effectiveness of the deep reinforcement learning solution in minimizing the number of AAVs required to fully satisfy the transmission requirements of all users in both uplink and downlink directions.
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 | 0.001 | 0.004 |
| 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".