Optimal Resource Allocation in Cellular Networks With H2H/M2M Coexistence
Why this work is in the frame
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Bibliographic record
Abstract
Machine-to-Machine (M2M) communications plays a key role in the evolution of the Internet of Things (IoT). Cellular networks are one of the main technologies to support the deployment of M2M communications. However, the characteristics and Quality-of-Service (QoS) requirements of M2M traffic are different from those of conventional Human-to-Human (H2H) traffic, that makes radio resource allocation a complex task. In this paper, a two-phase optimal resource allocation algorithm is proposed for H2H/M2M coexistence. The first phase performs joint power-resource allocation in order to satisfy the QoS requirements of H2H traffic while considering the delay constraints of delay-sensitive M2M traffic. Then, the second phase focuses on meeting the QoS requirements of M2M traffic. Simulation results are presented which show that the proposed algorithm is able to balance the demands of M2M and H2H traffic, meet their diverse QoS requirements, and ensure fairness for delay-tolerant M2M traffic.
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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 | 0.001 |
| 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