Optimization Research on UAV Semantic Communication System Based on SVD-MADRL
Bibliographic record
Abstract
The study optimizes the flight trajectory and power of multiple unmanned aerial vehicles with the deep deterministic policy gradient algorithm, and constructs a multi-unmanned aerial vehicle semantic communication optimization model based on singular value decomposition and multi-agent deep reinforcement learning. The results show that the relay system developed in this study is better than traditional algorithms in terms of coverage and flight path, with a coverage rate of up to 90%. Moreover, the energy consumption of the model to complete task transmission is only 2835 joules, and the delay time is only 15 seconds. In addition, compared with traditional algorithms, the semantic communication optimization model constructed in this study performs the best in terms of total reward, data collection efficiency, and accuracy. It has strong stability and convergence ability, with an accuracy close to 1, significantly improving the accuracy and efficiency of unmanned aerial vehicle communication transmission. The effectiveness of the two models designed for research is relatively high, both superior to traditional methods, providing a theoretical basis for optimizing the overall performance of unmanned aerial vehicle communication. It is of great significance in promoting the widespread application of unmanned aerial vehicle technology.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".