A QoS-Aware Service-Driven Network Selection for HWNs Based on MARCOS and Utility Functions
Bibliographic record
Abstract
Heterogeneous wireless networks (HWNs) are essential in modern communication systems, as they seamlessly integrate various radio access technologies (RATs). In this context, network selection (NS) emerges as a pivotal element, responsible for selecting the most appropriate network for user equipment (UE) during transitions between RATs. Conventional NS mechanisms, such as the multi-attribute decision-making (MADM) methods, are commonly employed for their fast ranking of RATs, real-time support, and flexibility. However, they suffer from three primary limitations; the rank reversal problem (RRP), overlooking specific user/service requirements while favouring the highest-ranking RAT, and the associated frequent handovers. To address these limitations, in this paper, we first employ one of the most recent and effective MADM approaches, known as the measurement of alternatives and ranking according to the compromise solution (MARCOS), to model and solve the NS problem (MARCOS-NS) for the first time in the literature. We then propose novel sigmoid utility functions to assess the quality of each RAT attribute within the HWNs environment, taking into account user/application requirements. Further, we enhance MARCOS-NS by replacing its original normalization technique with the proposed sigmoid utility functions to overcome its limitation, creating a new MADM approach called MARCOS-Utility. Our results demonstrate the superiority of MARCOS-Utility over conventional MADM approaches as it completely eliminates the RRP, reduces vertical handover occurrences by an average of 33.1%, and achieves a balance between data rate and packet loss ratio for the streaming traffic class.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".