Optimizing Energy Efficiency and Latency in UAV-Assisted 6G Networks through Intelligent Routing Algorithms - A Systematic Review
Bibliographic record
Abstract
The advent of 6G networks is transforming wireless communication, with Unmanned Aerial Vehicles (UAVs) playing a key role in expanding connectivity to remote and disaster-affected areas. While offering scalability and flexibility, UAV-assisted networks face challenges such as high energy consumption and latency. Efficient routing is essential to maximize UAV operational time and ensure low-latency data transmission. This review examines recent advancements in intelligent routing algorithms for UAV-assisted 6G networks, focusing on energy efficiency and latency reduction. Approaches using artificial intelligence, machine learning, and optimization techniques including deep reinforcement learning, swarm intelligence, and adaptive routing are explored. These strategies enable real-time path adjustments based on energy levels, environmental factors, and network conditions. Integration with edge computing further enhances responsiveness. The paper identifies current strengths and limitations, highlighting key research gaps in scalability, computational complexity, and real-time feasibility. Promising directions include renewable energy integration, energy-aware protocols, and hybrid optimization models. This review offers valuable insights for advancing UAV-based 6G communication systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".