Application of ELECTRE to Network Selection in A Hetereogeneous Wireless Network Environment
Bibliographic record
Abstract
Inter-working of existing packet switched wireless access technologies can help make services ubiquitously available. However this means that the services will have to be delivered over a heterogeneous mix of access technologies. There are several technical challenges that have to be overcome in such an environment, with selection of an optimal service delivery network being one of the most important issues. Choosing a nonoptimal network can result in problems such as the use of expensive access types or poor service experience. Multi attribute decision making (MADM) algorithms have been considered in the past to rank the candidate networks in a preference order. While many types of MADM algorithms exist, the decision maker may choose to use a particular type of algorithm to solve a decision problem based on an assessment of the suitability of the algorithm to the problem space. This paper adapts ELECTRE, a type of MADM algorithm that performs pair-wise comparisons amongst the alternatives, to solve the problem of network selection. The algorithm has been modified so that it is able to provide complete ranking of networks even in scenarios where the utility of some attributes is nonmonotonic. The algorithm has been evaluated by applying it to a network selection scenario in a heterogeneous wireless network environment.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".