Sum Rate Maximization for RIS-assisted UAV-IoT Networks using Sample Efficient SAC Technique
Bibliographic record
Abstract
Deep Reinforcement Learning (DRL) based algorithms have been widely adopted to solve the non-convex optimization problems in Reconfigurable Intelligent Surface (RIS)assisted Unmanned Aerial Vehicle (UAV) systems for establishing uninterrupted wireless connections with the ground Internet of Things (IoT) devices. However, model-free DRL techniques such as Deep Deterministic Policy Gradient (DDPG), Deep Q-learning (DQN) and Double Deep Q-learning (DDQN) suffer from low convergence and poor sample efficiency. Use of off-policy DRL techniques can be an appropriate solution to enhance the sample efficiency and training speed in vulnerable and fast changing environments involving multiple IoTs. In this paper, we use a novel off-policy actor-critic DRL technique called Soft ActorCritic (SAC) to solve the sum rate maximization problem in RIS-assisted UAV-IoT networks in dense urban environment. We perform simulations to compare the results of the proposed sample efficient SAC algorithm with the existing DDPG technique with and without RIS optimization. Our simulations show that SAC with optimized RIS outperforms the model-free DDPG technique in terms of maximizing the sum 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) | 0.002 | 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".