Unlicensed spectrum splitting between Femtocell and WiFi
Bibliographic record
Abstract
Femtocell and WiFi are often presented as opposing technologies. The truth is that both of them play a crucial role in sustaining the continues growth in mobile traffic. In many cases both technologies will eventually be employed in a single box with access via an intelligent mobile device that will automatically select the best option. Deploying Femtocells in WiFi hotspots would let access providers add 3G capacity for users who do not have WiFi on their device and improve their quality of experience during mobility. Partitioning of the spectrum resources carries critical importance for maximizing the total capacity and quality of service (QoS) satisfaction of end users. This paper proposes a fair and QoS-based unlicensed spectrum splitting strategy between WiFi and Femtocell networks. Numerical results show that spectrum splitting under total capacity maximization constraint allows for unfair spectrum allocation, while a more equitable spectrum splitting can be accomplished by taking into account the fairness and QoS constraints.
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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 | 0.001 |
| 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 | 0.001 |
| 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".