Optimal admission control policies for heterogeneous wireless networks
Why this work is in the frame
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Bibliographic record
Abstract
In the near future, demand for Heterogeneous Wireless Networking (HWN) is expected to to increase. QoS provisioning in these networks is a challenging issue considering the diversity in wireless networking technologies and the existence of mobile users with different communication requirements. In HWNs with their increased complexity, "the curse of dimensionality" problem makes it impractical to directly apply the decision theoretic optimal control methods that are previously used in homogeneous wireless networks to achieve desired QoS levels. In this paper, optimal call admission control policies for HWNs are considered. A decision theoretic framework for the problem is derived by a dynamic programming formulation. We prove that for a two-tier wireless network architecture, the optimal policy has a two-dimensional threshold-based structure. Further, a novel algorithm called Structured Value Iteration is proposed as a numerically efficient method to determine the optimal policy in terms of its thresholds. Extensive simulation experiments are conducted. The numerical results show that the proposed algorithm is efficient in terms of its time-complexity and in achieving the optimal performance.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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