DRL-AdCAR: Adaptive Coding-Aware Routing With Maximum Coding Opportunities and High-Quality via Deep Reinforcement Learning in FANET
Bibliographic record
Abstract
Research on flying ad-hoc networks (FANETs) has become important with the development of unmanned aerial vehicle (UAV) systems. Routing design in FANET is a major challenge due to its inherent characteristics, including dynamic network topology and network self-organization. Coding-aware routing based on network coding improves the performance of the network by selecting paths with more coding opportunities. However, current methods are mostly used in networks with relatively fixed topologies, which are difficult to adapt to the FANET environment. To address these issues, we propose an adaptive coding-aware routing algorithm via deep reinforcement learning (DRL-AdCAR). First, we transform the routing problem into a Markov decision model. Then, coding opportunities, coding gains, and link quality are considered simultaneously in the reward function to avoid the drawbacks of coding-aware routing algorithms that simply aim to increase coding opportunities while greatly affecting other aspects of network performance. In addition, we present an improvement of the deep deterministic policy gradient (DDPG) algorithm for FANET, combining the gated recurrent unit (GRU) and long short-term memory networks (LSTM) algorithms to replace the traditional neural network structure, which ensures prediction accuracy and improves the training efficiency. The experimental results showed that DRL-AdCAR can adaptively select the transmission paths with the most suitable coding opportunities according to environmental changes in FANET, improving coding performance and enhancing the network throughput and packet delivery rate.
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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 | 0.002 |
| 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".