Unlicensed spectrum splitting between Femtocell and WiFi
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
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.
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 | 0.000 | 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 | 0.000 |
| 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