A Novel Base Stations-Mobile Stations Association Policy for Cellular Networks
Bibliographic record
Abstract
We propose a novel base stations (BSs) - mobile stations (MSs) association policy for cellular networks. In this policy, the BS which provides the highest signal-to-interference ratio (SIR) among those located within a predetermined maximum association distance of the MS is selected as the serving BS. This policy encompasses the conventional highest-SIR association as a special case. Application of the new policy in 2- and 3- dimensional single-tier (homogeneous) and 2-dimensional two-tier (heterogeneous) networks is discussed. Coverage probability expressions are derived assuming BSs in each tier are distributed according to an independent homogeneous Poisson point process (PPP). Rayleigh fading and exponential path-loss radio channels are assumed. Analysis is validated by Monte-Carlo simulations. For single-tier networks, two methods are proposed for the selection of the maximum association distance. With such selection, the proposed association policy performs similarly to the highest-SIR association. It is shown that this policy can also be used to manage user offloading to small cells in two-tier heterogeneous networks.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".