Correlated Placement of Small Cell Base Stations: A Coverage Enriched HetNet with Massive MIMO
Bibliographic record
Abstract
Most current stochastic geometric modeling of heterogeneous cellular networks (HetNets) assumes independent deployment of small-cell base stations (SBSs) with respect to macro base stations (MBSs), which leads to limited enhancement in network coverage and capacity. Therefore, in this paper we propose a new HetNet model where the locations of SBSs are correlated with those of the MBSs. We place the SBSs at the vertices of each macrocell, where the macrocells are modeled by a Poisson-Voronoi tesellation with the MBSs as seeds. Theoretical analysis of this deployment scheme is studied using the tools of stochastic geometry. A novel distribution is also derived for the distance between the typical user and its closest SBS. One significant advantage of this deployment scheme is that any given SBS can serve users from multiple macrocells, thus offloading the traffic from the MBSs and also improving network coverage. The performance of the proposed model is evaluated for several network parameters and our results concretely demonstrate the improvement in coverage probability compared to other schemes in the literature.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".