Multi‐Objective Resource Optimization in <scp>UAV</scp>‐Enabled Heterogeneous Cellular Networks Using Serverless Federated Learning and Power‐Domain <scp>NOMA</scp>
Bibliographic record
Abstract
ABSTRACT The integration of unmanned aerial vehicles (UAVs) into cellular networks has emerged as a promising solution to enhance connectivity and service quality in both urban and remote areas. In this paper, we propose a comprehensive framework that combines multi‐agent deep learning with backhaul traffic optimization to effectively manage resources in UAV‐enabled communication networks. By leveraging the capabilities of intelligent reflecting surfaces (IRS) and cell‐free communication strategies, our approach aims to optimize backhaul traffic, ensuring seamless data transmission and improved network throughput. Our methodology involves a dynamic resource allocation mechanism that utilizes multi‐agent deep learning to accurately predict network demands and adaptively allocate resources. The process begins with the collection of real‐time network data, including user demand, traffic patterns, and UAV positions. This data is then fed into a deep learning model, where multiple agents collaboratively analyze and predict future network requirements. Based on the predictions, the resource allocation mechanism dynamically adjusts the distribution of resources, such as bandwidth and power, to meet the anticipated demand. This adaptive strategy enables the network to efficiently handle varying traffic loads, reducing congestion and latency. Furthermore, our backhaul traffic optimization technique focuses on minimizing the energy consumption of UAVs while maximizing their coverage and connectivity. By optimizing the flight paths and altitudes of UAVs, we ensure that they provide optimal coverage with minimal energy expenditure. Additionally, the IRS‐assisted communication further enhances signal quality, reducing the need for high‐power transmissions and thus conserving energy. Our simulations show that our framework improves network throughput, energy efficiency, and reliability. It offers a promising way to manage resources in future UAV‐enabled communication networks.
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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".