Data Rate Utility Analysis for Uplink Two-Hop Internet of Things Networks
Bibliographic record
Abstract
We study the fundamental problem of spectrum allocation and device association in uplink two-hop Internet of Things (IoT) networks under two spectrum allocation schemes: 1) orthogonal spectrum partition (OSP) and 2) full spectrum reuse (FSR). We propose a novel analytical model to estimate the uplink data rate utility function, which takes into account power control fractional and spatial density of aggregators. We then compute the optimal aggregator association bias (for the FSR scheme) and the optimal joint spectrum partition ratio and optimal aggregator association bias (for the OSP scheme) using constraint gradient ascent optimization. Using the above obtained optimal values and the proposed model, we compare the performance of the optimized OSP and FSR schemes with the benchmark maximum-SIR-based association scheme and the minimum-distance association scheme in terms of the cumulative distribution function of device uplink data rate. By optimizing key network parameters, namely the spectrum partition ratio and aggregator association bias, we mitigate interference and enhance the mean uplink per-device data rate for both FSR and OSP. To the best of our knowledge, this paper is the first that proposes an analytical model to estimate the log utility of the uplink data rate of two-hop IoT networks.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".