Network selection with imprecise information in heterogeneous all-IP wireless systems
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
Selection of an optimal service delivery network is an important problem to solve in an all IP heterogeneous wireless access network environment. Several network parameters impact the process of network selection in such an environment and ideally their precise values should be known by the decision maker. In reality, however, the exact values for many of the parameters, e.g., those related to quality of service, will not be known. Hence there is a need to develop a network selection mechanism for scenarios when some of the parameter values are less reliable or unavailable. This paper describes a novel and comprehensive network selection approach that combines parameter estimation techniques with fuzzy theory and multi attribute decision making algorithm to perform network selection. In addition the paper proposes a new concept of Confidence Level in the network rankings that leverages additional available information in the final decision process. The proposed techniques provide improved network selection in heterogeneous all IP wireless access 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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