Economic access network selection in heterogeneous wireless networks environment
Why this work is in the frame
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Bibliographic record
Abstract
Today's wireless networks are composed of multiple radio technologies and allow users to choose the convenient radio technologies at certain locations. The operators, for their part, hope to maximize their revenue with efficient usage of their networks. To achieve these benefits from the heterogeneous wireless networks, the access network selection has to be optimized. In this paper we propose an access network selection based on Markov Decision Process and related shadow prices. The approach integrates both the user and operator objectives by using operator and user utilities. The proposed access network selection algorithm is tested in a WIFI-LTE heterogeneous wireless network scenario using OMNeT++ simulator. The results show that the proposed approach provides significantly larger operator revenue and notable increased user throughput when compared to commonly used access networks selection based on the received signal strength.
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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