Application of ELECTRE to Network Selection in A Hetereogeneous Wireless Network Environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| 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