User-in-the-loop for hethetnets with backhaul capacity constraints
Bibliographic record
Abstract
A popular method to model heterogeneous networks is the use of two independent homogeneous Poisson point processes to locate UEs and BSs with unlimited backhaul capacity. Despite the analytical tractability, this approach is far from accurate. First, the distribution of UEs in real scenarios is neither homogeneous nor independent of BSs. Besides, the assumption of unlimited capacity for backhaul connections is optimistic, especially in the future 5G HetNets with small cells. In this article, we propose a novel modeling approach for heterogeneous networks with heterogeneous spatial traffic distribution (HetHetNets). Specifically, in the proposed model, a particular ratio of UEs are collocated with the BSs while the rest of UEs are independently and homogeneously distributed in the network. Moreover, the proposed model presumes backhaul connections with constrained capacity. We study the impact of this more realistic network modeling on the effectiveness of the spatial user-in-the-loop (UIL) schemes in HetHetNets. Spatial UIL assumes that (some) UEs can be influenced by the operator to move in the network. Finally, we propose a new objective for the UIL mechanism that takes into account the impact of the BS loads and the backhaul capacities on the network performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".