Deep reinforcement learning based rate enhancement scheme for RIS assisted mobile users underlaying UAV
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The fifth generation (5G) network enabled communication between devices has emerged as a state-of-the-art technology. In the era of proliferating smart devices and intelligent wireless communication networks, Reflecting Intelligent Surfaces (RIS) and Unpiloted Air Vehicles (UAV) duplet has turn out to be a trustworthy, lucrative and handy solution for various appearing real world communication issues. This article pitches into the downlink UAV communication empowered by RIS, where UAV communicates with Mobile Instruments (MI) via RIS patches installed at a tall tower. Considering the attributes like transmitted power and UAV trajectory, Deep Reinforcement Learning (DRL) based approach is recommended to maximize the overall Sum-rate. In present scenario, DRL technology has popped up as a commanding tool that allows a network to regulate itself in order to deliver optimum solution. In this article, we have proposed a novel viewpoint evolved from Deep Deterministic Policy Gradient (D-DPG) Algorithm specifically Shared Deep Deterministic Policy Gradient (SD-DPG) algorithm for downlink UAV-MI power allocation and trajectory optimization problem. Numerical outcomes manifest that our model, concerned to maximizing sum-rate, outperformed other DRL based method DD-DPG by at least 30% and D-DPG by approximately 3 folds together with optimizing power, phase-shift and UAV trajectory.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it