Multi-objective resource allocation in interference-limited M2M communication networks
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Bibliographic record
Abstract
In this paper, we propose a multi-objective optimisation-based solution to the problem of resource allocation in interference-limited machine to machine (M2M) communication. We consider machine type communication devices (MTCDs) in a clustered network structure, where they are divided into clusters and the devices belonging to a cluster communicate to cluster head (or controller). The cluster head aggregates the traffic and relays from MTCDs to eNB and vice versa. We maximise the number of admitted MTCD controllers and throughput with least interference caused to conventional primary users. We formulate the problem as a mixed-integer nonlinear problem with multiple objectives and apply meshed adaptive direct search (MADS) algorithm which gives guaranteed convergence. Simulation results show the effects of varying different parameters on cumulative throughput and the number of admitted MTCD controllers.
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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