A hybrid network selection scheme for heterogeneous wireless access network
Bibliographic record
Abstract
Heterogeneous wireless access network (HWAN), an integration of different radio access technologies (RATs) in an overlapping zone, supports bandwidth hungry application and fulfills the demands for high data rates. In this paper, we explored a novel hybrid scheme for RAT selection in HWAN, a two step process, where both a central controller node (CCN) and user device (UD) are involved in the process of network selection. During the first step UD screens the available list of scanned networks based on received signal strength and user mobility profile. The results for the first step of RAT screening using multiplicative exponential weighting method (MEW) are compared with multi criteria simple additive weighting (SAW) utility function. In our second step the CCN takes multi criteria related to application, terminal and network, and generates a sorted list of the most appropriate RATs based on evaluating MEW utility function. The CCN, then associates users to one (single connection) or more available RATs (multi-homed). Using Matlab based simulations, the process of RATs ranking and association is elaborated by calculating final utilities of different networks. The impact of different crucial criteria on RATs ranking results have been explored. Furthermore, we compared our proposed hybrid approach with the traditional mechanisms. The simulation results show that the decision of our proposed hybrid mechanism is more precise than the existing traditional approaches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".