Traffic offloading techniques in two-tier femtocell networks
Why this work is in the frame
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Bibliographic record
Abstract
Due to the scarcity of the wireless spectrum along with the ever increasing number of cellular wireless users and the associated drastic increase in the data traffic demand, femtocells are envisioned to provide fast, flexible, cost-efficient, and customer driven solutions to offload users from the congested macro access network and enhance the overall system performance. To control offloading and to achieve the required balance of users and traffic served by each network tier, we quantify offloading and discuss different techniques that can be used to offload users from the macro access network to the femto access network, namely, offloading via power control, offloading via femtocell deployment and offloading via biasing. In this paper, we quantify offloading when users connect to the network entity that provides the strongest instantaneous signal power in a Nakagami-m fading environment. To this end, we discuss the merits and drawbacks of each of the offloading techniques.
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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