Time Minimization for Health Monitoring Systems in Internet of Medical Things via Rate Splitting
Why this work is in the frame
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Bibliographic record
Abstract
We propose an uplink rate splitting (RS) scheme for real-time health monitoring in the Internet of Medical Things (IoMT). To minimize total time cost, we jointly optimize biosensor grouping (BG), decoding order, power allocation, receiver beamforming, and computation resources allocation under the constraints of the transmit power and computation resources. This process results in a discrete nonconvex problem, which we decouple into three independent subproblems: 1) reduce co-channel interference to ease the transmit time cost. We solve this with a low-complexity BG algorithm; 2) optimize decoding order, power allocation, and receiver beamforming to reduce the forwarding time cost. We thus develop an alternating optimization algorithm. Specifically, we propose a decoding order update algorithm to optimize ordering, which can converge to the global optimum. We construct accurate surrogates via a quadratic transform approach and use surrogate optimization to attack other variables; and 3) allocate computation resources to minimize the processing time cost. Here, we derive the optimal solution with closed-form expressions. Simulation results indicate that the proposed overall scheme and algorithms present significant performance gains over several existing benchmarks.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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