A <i>Q</i>-Learning Approach for Real-Time NOMA Scheduling of Medical Data in UAV-Aided WBANs
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Bibliographic record
Abstract
Unmanned Aerial Vehicles (UAVs) have emerged as a flexible and cost-effective solution for remote monitoring of the vital signs of patients in large-scale Internet of Medical Things (IoMT) Wireless Body Area Networks (WBANs). This paper deals with the problem of using UAVs for real-time scheduling of the transmission of vital signs in delay-sensitive IoMT WBANs. The main challenge for such a network is to timely and reliably transmit the vital signs of patients to the remote monitoring center without interrupting their daily lifestyles. To achieve this goal, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> -learning-based algorithm to optimize the trajectory of each UAV, as the mobile Base Station (BS), to harvest vital signs of patients in outdoor applications, especially in unreachable areas. In this algorithm, UAVs learn to reach the best 3D position by discovering the network environment step-by-step. It stands for the position in which the covered patients by each UAV have the highest transmission rate, the least delay and energy consumption. Moreover, we employ the Non-Orthogonal Multiple Access (NOMA) technique to simultaneously schedule multiple transmissions by accepting a degree of interference between them in order to enhance the spectrum efficiency of the network. Eventually, the performance of our proposed scheme is evaluated via extensive simulations in terms of throughput, energy consumption, and delay. The simulation results show that our proposed scheme iteratively converges to the benchmark value of the mentioned factors by increasing the information of cluster environment through episodes.
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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 | 0.001 | 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 | 0.001 | 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 it